From 7f213593bbafc735bbf899d11b96482b75064edc Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Fri, 16 Jul 2021 13:27:06 +0200 Subject: [PATCH 01/51] Bump to v0.3.0.dev --- pulser/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pulser/_version.py b/pulser/_version.py index 74adaecae..fdf5b786b 100644 --- a/pulser/_version.py +++ b/pulser/_version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.3.0" +__version__ = "0.3.0.dev" From 143fe31980d5baa2537dc43ab4ef265cdf66ed39 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Fri, 6 Aug 2021 12:10:59 +0200 Subject: [PATCH 02/51] Bump to v0.3.1.dev --- pulser/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pulser/_version.py b/pulser/_version.py index 7dae45092..56bbbe025 100644 --- a/pulser/_version.py +++ b/pulser/_version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.3.1" +__version__ = "0.3.1.dev" From 2adb88fda26880eb9930b09596bf9830de82f0e1 Mon Sep 17 00:00:00 2001 From: Seb Grijalva <13460713+sebgrijalva@users.noreply.github.com> Date: Thu, 26 Aug 2021 22:00:45 +0200 Subject: [PATCH 03/51] Adapt dependance of `progress_bar` on being a boolean (#256) * Fix case when `progress_bar kwarg is `False`. * Add test for `progress_bar` not a bool of `None`. * Ignore typing for case `None` --- pulser/simulation/simulation.py | 18 +++++++++++++----- pulser/tests/test_simulation.py | 9 +++++++++ 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index c1aa517d6..f1dc6a4a7 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -650,7 +650,7 @@ def get_hamiltonian(self, time: float) -> qutip.Qobj: # Run Simulation Evolution using Qutip def run( self, - progress_bar: Optional[bool] = None, + progress_bar: Optional[bool] = False, **options: qutip.solver.Options, ) -> SimulationResults: """Simulates the sequence using QuTiP's solvers. @@ -659,8 +659,8 @@ def run( Otherwise will return CoherentResults. Keyword Args: - progress_bar (bool): If True, the progress bar of QuTiP's solver - will be shown. + progress_bar (bool or None): If True, the progress bar of QuTiP's + solver will be shown. If None or False, no text appears. options (qutip.solver.Options): If specified, will override SimConfig solver_options. If no `max_step` value is provided, an automatic one is calculated from the `Sequence`'s schedule @@ -688,6 +688,14 @@ def run( def _run_solver() -> CoherentResults: """Returns CoherentResults: Object containing evolution results.""" + # Decide if progress bar will be fed to QuTiP solver + if progress_bar is True: + p_bar = True + elif (progress_bar is False) or (progress_bar is None): + p_bar = None # type: ignore + else: + raise ValueError("`progress_bar` must be a bool.") + if "dephasing" in self.config.noise: # temporary workaround due to a qutip bug when using mesolve liouvillian = qutip.liouvillian( @@ -697,7 +705,7 @@ def _run_solver() -> CoherentResults: liouvillian, self.initial_state, self._eval_times_array, - progress_bar=progress_bar, + progress_bar=p_bar, options=solv_ops, ) else: @@ -705,7 +713,7 @@ def _run_solver() -> CoherentResults: self._hamiltonian, self.initial_state, self._eval_times_array, - progress_bar=progress_bar, + progress_bar=p_bar, options=solv_ops, ) return CoherentResults( diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index 421a7ec0b..11878b724 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -358,6 +358,15 @@ def test_run(): sim.run() assert sim._seq._measurement == "ground-rydberg" + sim.run(progress_bar=True) + sim.run(progress_bar=False) + sim.run(progress_bar=None) + with pytest.raises( + ValueError, + match="`progress_bar` must be a bool.", + ): + sim.run(progress_bar=1) + sim.set_config(SimConfig("SPAM", eta=0.1)) with pytest.raises( NotImplementedError, From 993c8c08eba60636b0c8897ae01db9427d243fd7 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Tue, 7 Sep 2021 11:36:24 +0200 Subject: [PATCH 04/51] Bump to v0.3.2.dev --- pulser/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pulser/_version.py b/pulser/_version.py index f2d14ad39..fdb001cea 100644 --- a/pulser/_version.py +++ b/pulser/_version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.3.2" +__version__ = "0.3.2.dev" From a2d8fa15dedbb1341ff6a80b1940128c02e24da2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Wed, 8 Sep 2021 15:21:32 +0200 Subject: [PATCH 05/51] Make Pulse a frozen dataclass (#242) --- pulser/pulse.py | 72 ++++++++++++++++++-------------------- pulser/sequence.py | 10 ++++-- pulser/tests/test_pulse.py | 8 ++--- 3 files changed, 45 insertions(+), 45 deletions(-) diff --git a/pulser/pulse.py b/pulser/pulse.py index e8c6e6328..189d8288c 100644 --- a/pulser/pulse.py +++ b/pulser/pulse.py @@ -15,9 +15,10 @@ from __future__ import annotations +from dataclasses import dataclass import functools import itertools -from typing import Any, cast, Union +from typing import Any import matplotlib.pyplot as plt import numpy as np @@ -28,6 +29,7 @@ from pulser.json.utils import obj_to_dict +@dataclass(repr=False, frozen=True) class Pulse: r"""A generic pulse. @@ -54,6 +56,10 @@ class Pulse: for enconding of arbitrary single-qubit gates into a single pulse (see ``Sequence.phase_shift()`` for more information). """ + amplitude: Waveform + detuning: Waveform + phase: float + post_phase_shift: float = 0.0 def __new__(cls, *args, **kwargs): # type: ignore """Creates a Pulse instance or a ParamObj depending on the input.""" @@ -63,44 +69,40 @@ def __new__(cls, *args, **kwargs): # type: ignore else: return object.__new__(cls) - def __init__( - self, - amplitude: Union[Waveform, Parametrized], - detuning: Union[Waveform, Parametrized], - phase: Union[float, Parametrized], - post_phase_shift: Union[float, Parametrized] = 0.0, - ): + def __post_init__(self) -> None: """Initializes a new Pulse.""" if not ( - isinstance(amplitude, Waveform) and isinstance(detuning, Waveform) + isinstance(self.amplitude, Waveform) + and isinstance(self.detuning, Waveform) ): raise TypeError("'amplitude' and 'detuning' have to be waveforms.") - if detuning.duration != amplitude.duration: + if self.detuning.duration != self.amplitude.duration: raise ValueError( "The duration of detuning and amplitude waveforms must match." ) - self.duration = amplitude.duration - if np.any(amplitude.samples < 0): + if np.any(self.amplitude.samples < 0): raise ValueError( "All samples of an amplitude waveform must be " "greater than or equal to zero." ) - self.amplitude = amplitude - self.detuning = detuning - phase = cast(float, phase) - self.phase = float(phase) % (2 * np.pi) - post_phase_shift = cast(float, post_phase_shift) - self.post_phase_shift = float(post_phase_shift) % (2 * np.pi) + + self.__dict__["phase"] %= 2 * np.pi + self.__dict__["post_phase_shift"] %= 2 * np.pi + + @property + def duration(self) -> int: + """The duration of the pulse (in ns).""" + return self.amplitude.duration @classmethod @parametrize def ConstantDetuning( cls, - amplitude: Union[Waveform, Parametrized], - detuning: Union[float, Parametrized], - phase: Union[float, Parametrized], - post_phase_shift: Union[float, Parametrized] = 0.0, + amplitude: Waveform, + detuning: float, + phase: float, + post_phase_shift: float = 0.0, ) -> Pulse: """Creates a Pulse with an amplitude waveform and a constant detuning. @@ -111,19 +113,17 @@ def ConstantDetuning( post_phase_shift (float, default=0.): Optionally lets you add a phase shift (in rads) immediately after the end of the pulse. """ - detuning_wf = ConstantWaveform( - cast(Waveform, amplitude).duration, detuning - ) + detuning_wf = ConstantWaveform(amplitude.duration, detuning) return cls(amplitude, detuning_wf, phase, post_phase_shift) @classmethod @parametrize def ConstantAmplitude( cls, - amplitude: Union[float, Parametrized], - detuning: Union[Waveform, Parametrized], - phase: Union[float, Parametrized], - post_phase_shift: Union[float, Parametrized] = 0.0, + amplitude: float, + detuning: Waveform, + phase: float, + post_phase_shift: float = 0.0, ) -> Pulse: """Pulse with a constant amplitude and a detuning waveform. @@ -134,20 +134,18 @@ def ConstantAmplitude( post_phase_shift (float, default=0.): Optionally lets you add a phase shift (in rads) immediately after the end of the pulse. """ - amplitude_wf = ConstantWaveform( - cast(Waveform, detuning).duration, amplitude - ) + amplitude_wf = ConstantWaveform(detuning.duration, amplitude) return cls(amplitude_wf, detuning, phase, post_phase_shift) @classmethod @parametrize def ConstantPulse( cls, - duration: Union[int, Parametrized], - amplitude: Union[float, Parametrized], - detuning: Union[float, Parametrized], - phase: Union[float, Parametrized], - post_phase_shift: Union[float, Parametrized] = 0.0, + duration: int, + amplitude: float, + detuning: float, + phase: float, + post_phase_shift: float = 0.0, ) -> Pulse: """Pulse with a constant amplitude and a constant detuning. diff --git a/pulser/sequence.py b/pulser/sequence.py index ce228cf6f..d3f0ef86e 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -549,9 +549,13 @@ def add( phase_ref = prs.pop() if phase_ref != 0: - # Has to copy to keep the original pulse intact - pulse = copy.deepcopy(pulse) - pulse.phase = (pulse.phase + phase_ref) % (2 * np.pi) + # Has to recriate the original pulse with a new phase + pulse = Pulse( + pulse.amplitude, + pulse.detuning, + pulse.phase + phase_ref, + post_phase_shift=pulse.post_phase_shift, + ) self._add_to_schedule(channel, _TimeSlot(pulse, ti, tf, last.targets)) diff --git a/pulser/tests/test_pulse.py b/pulser/tests/test_pulse.py index 5aa5072c4..b63793d77 100644 --- a/pulser/tests/test_pulse.py +++ b/pulser/tests/test_pulse.py @@ -26,7 +26,7 @@ pls = Pulse(bwf, bwf, 2 * np.pi) pls2 = Pulse.ConstantPulse(100, 1, -10, -np.pi) -pls3 = Pulse.ConstantAmplitude(1, cwf, 1) +pls3 = Pulse.ConstantAmplitude(1, cwf, -np.pi) pls4 = Pulse.ConstantDetuning(bwf, -10, 0) @@ -47,10 +47,8 @@ def test_creation(): Pulse.ConstantPulse(100, -1, 0, 0) assert pls.phase == 0 - assert pls2.amplitude == pls3.amplitude - assert pls2.detuning == pls3.detuning - assert pls2.phase == np.pi - assert pls3.phase == 1 + assert pls2 == pls3 + assert pls != pls4 assert pls4.detuning != cwf assert pls4.amplitude == pls.amplitude From ead61b6313a45f0dd44b384a305867517f3293aa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Julien=20Br=C3=A9mont?= <81154694+julien-bremont@users.noreply.github.com> Date: Fri, 17 Sep 2021 16:23:43 +0200 Subject: [PATCH 06/51] Add notebook explaining classical shadow estimation (#252) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add notebook for shadow estimation * notebook save * Notebook review changes * remove first cell * math processing error * mathprocessing2 * mp3 * Math processing error * Revert "Math processing error" This reverts commit 5dade695069c9481a333212207146b16a9969ffe. Co-authored-by: loic henriet Co-authored-by: Loïc Henriet --- .../Shadow estimation for VQS.ipynb | 1417 +++++++++++++++++ 1 file changed, 1417 insertions(+) create mode 100644 tutorials/applications/Shadow estimation for VQS.ipynb diff --git a/tutorials/applications/Shadow estimation for VQS.ipynb b/tutorials/applications/Shadow estimation for VQS.ipynb new file mode 100644 index 000000000..b8536511c --- /dev/null +++ b/tutorials/applications/Shadow estimation for VQS.ipynb @@ -0,0 +1,1417 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "d69d787b", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, + { + "cell_type": "markdown", + "id": "9f85fda0", + "metadata": {}, + "source": [ + "# Efficient estimation techniques for VQS" + ] + }, + { + "cell_type": "markdown", + "id": "a780a7b7", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "4b2c1220", + "metadata": {}, + "source": [ + "$\\newcommand{\\ket}[1]{\\left|#1\\right>} \\newcommand{\\bra}[1]{\\left<#1\\right|}$\n", + "This notebook's purpose is to introduce the concept of classical shadow estimation, as well as its use in **VQS** (**V**ariational **Q**uantum **S**imulation). This technique, introduced in [this article by Huang, Kueng and Preskill](https://arxiv.org/abs/2002.08953), is used for efficiently estimating multiple observables, and is extremely powerful in that regard, asymptotically reaching theoretical lower bounds of quantum information theory regarding the number of required samples of a given state for estimation ([see here for details](https://arxiv.org/abs/2101.02464)). \n", + "\n", + "The primary goal of this notebook is to estimate the groundstate energy of the $H_2$ molecule, using a VQS. We will first implement the method of random classical shadows in Python. Then, we'll introduce its derandomized counterpart, which is particularly useful in our setting. We'll finally describe the VQS, and benchmark the estimation methods we introduced for computing the molecule's energy. This notebook draws some inspiration from [this PennyLane Jupyter notebook](https://pennylane.ai/qml/demos/tutorial_classical_shadows.html) on quantum machine learning and classical shadows." + ] + }, + { + "cell_type": "markdown", + "id": "05595e94", + "metadata": {}, + "source": [ + "## Random classical shadows" + ] + }, + { + "cell_type": "markdown", + "id": "f19f70f2", + "metadata": {}, + "source": [ + "### Main ideas and implementation" + ] + }, + { + "cell_type": "markdown", + "id": "1b5acf38", + "metadata": {}, + "source": [ + "Classical shadow estimation relies on the fact that for a particular\n", + "choice of measurement, we can efficiently store snapshots of the state\n", + "that contain enough information to accurately predict linear functions\n", + "of observables.\n", + "\n", + "Let us consider an $n$-qubit quantum state $\\rho$ (prepared by a\n", + "pulse sequence) and apply a random unitary $U$ to the state:\n", + "\n", + "$$\\rho \\to U \\rho U^\\dagger.$$\n", + "\n", + "Next, we measure in the computational basis and obtain a bit string of\n", + "outcomes $|b\\rangle = |0011\\ldots10\\rangle$. If the unitaries $U$ are\n", + "chosen at random from a particular ensemble, then we can store the\n", + "reverse operation $U^\\dagger |b\\rangle\\langle b| U$ efficiently in\n", + "classical memory. We call this a *snapshot* of the state. Moreover, we\n", + "can view the average over these snapshots as a measurement channel:\n", + "\n", + "$$\\mathbb{E}\\left[U^\\dagger |b\\rangle\\langle b| U\\right] = \\mathcal{M}(\\rho).$$\n", + "\n", + "We restrict ourselves to unitary ensembles that define a tomographically complete set of\n", + "measurements (i.e $\\mathcal{M}$ is invertible), therefore :\n", + "\n", + "$$\\rho = \\mathbb{E}\\left[\\mathcal{M}^{-1}\\left(U^\\dagger |b\\rangle\\langle b| U \\right)\\right].$$\n", + "\n", + "If we apply the procedure outlined above $N$ times, then the collection\n", + "of inverted snapshots is what we call the *classical shadow*\n", + "\n", + "$$S(\\rho,N) = \\left\\{\\hat{\\rho}_1= \\mathcal{M}^{-1}\\left(U_1^\\dagger |b_1\\rangle\\langle b_1| U_1 \\right)\n", + ",\\ldots, \\hat{\\rho}_N= \\mathcal{M}^{-1}\\left(U_N^\\dagger |b_N\\rangle\\langle b_N| U_N \\right)\n", + "\\right\\}.$$\n", + "\n", + "Since the shadow approximates $\\rho$, we can now estimate **any**\n", + "observable with the empirical mean:\n", + "\n", + "$$\\langle O \\rangle = \\frac{1}{N}\\sum_i \\text{Tr}{\\hat{\\rho}_i O}.$$\n", + "\n", + "We will be using a median-of-means procedure in practice." + ] + }, + { + "cell_type": "markdown", + "id": "284ded31", + "metadata": {}, + "source": [ + "We start by defining several useful quantities, such as the unitary matrices associated with Pauli measurements : the Hadamard matrix, change of basis from $\\{\\ket{0}, \\ket{1}\\}$ to the eigenbasis of $\\sigma_X$, $\\{\\ket{+}, \\ket{-}\\}$, and its $\\sigma_Y, \\sigma_Z$ counterparts. We will then draw randomly from this tomographically complete set of $3$ unitaries.\n", + "\n", + "Note that we will need $4$ qubits for our VQS problem : we will explain the mapping from the molecule to qubits later." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7388f3c5", + "metadata": {}, + "outputs": [], + "source": [ + "num_qubits = 4\n", + "zero_state = qutip.basis(2, 0).proj()\n", + "one_state = qutip.basis(2, 1).proj()\n", + "hadamard = 1/np.sqrt(2) * qutip.Qobj([[1., 1.], [1., -1.]])\n", + "h_mul_phase = qutip.Qobj(np.array([[1., 1], [1.j, -1.j]])) / np.sqrt(2)\n", + "unitary_ensemble = [hadamard, h_mul_phase, qutip.qeye(2)]\n", + "\n", + "g = qutip.basis(2,1)\n", + "r = qutip.basis(2,0)\n", + "n = r*r.dag()\n", + "\n", + "sx = qutip.sigmax()\n", + "sy = qutip.sigmay()\n", + "sz = qutip.sigmaz()\n", + "\n", + "gggg = qutip.tensor([g, g, g, g])\n", + "ggrr = qutip.tensor([g, g, r, r])" + ] + }, + { + "cell_type": "markdown", + "id": "e34e7ce6", + "metadata": {}, + "source": [ + "We first define a function that spits out a random bitstring sampled from a given density matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "66b045bc", + "metadata": {}, + "outputs": [], + "source": [ + "def measure_bitstring(state):\n", + " \"\"\"Auxiliary function that returns a bitstring according to the measure of a quantum state.\"\"\"\n", + " probs = np.real(state.diag())\n", + " probs /= np.sum(probs)\n", + " x = np.nonzero(np.random.multinomial(1, probs))[0][0]\n", + " bitstring = np.binary_repr(x, num_qubits)\n", + " return bitstring" + ] + }, + { + "cell_type": "markdown", + "id": "0434868f", + "metadata": {}, + "source": [ + "We will need to compute the number of shadows needed given :\n", + "* A list of observables $o_i$\n", + "* Desired precision on expectation values $\\epsilon$ : if $\\tilde{o}_i$ is the estimated expectation value for observable $o_i$, we wish for $|Tr(o_i \\rho) - \\tilde{o}_i| \\leq \\epsilon$\n", + "* Failure probability $\\delta$ : we wish for the above equation to be satisfied with probability $1-\\delta$\n", + "\n", + "Precise formulae are given in [Huang et al.](https://arxiv.org/abs/2002.08953)\n", + "The integer $K$ returned by the function will serve as the number of blocks in our median of means procedure afterwards." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f17762c3", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_shadow_size(delta, epsilon, observables):\n", + " \"\"\"Helper function.\n", + " \n", + " Computes both the number of shadows needed as well as the size of blocks needed \n", + " for the median_of_means method in order to approximate the expectation value of M\n", + " (linear) observables with additive error epsilon and fail probability delta.\n", + " \n", + " Args:\n", + " delta (float): Failure probability.\n", + " epsilon (float): Additive error on expectation values.\n", + " observables (list[qutip.Qobj]): Observables the expectation value of which is to be computed.\n", + " \"\"\"\n", + " M = len(observables)\n", + " K = 2 * np.log(2 * M / delta)\n", + " shadow_norm = (\n", + " lambda op: np.linalg.norm(\n", + " op - np.trace(op) / 2 ** int(np.log2(op.shape[0])), ord=np.inf\n", + " ) ** 2\n", + " )\n", + " # Theoretical number of shadows per cluster in the median of means procedure :\n", + " # N = 34 * max(shadow_norm(o) for o in observables) / epsilon ** 2\n", + " # We use N = 20 here to allow for quick simulation\n", + " N = 20\n", + " return int(np.ceil(N * K)), int(K)" + ] + }, + { + "cell_type": "markdown", + "id": "99057a11", + "metadata": {}, + "source": [ + "Next, we design a function that returns snapshots (bitstrings) of the rotated state as well as the sampled unitaries used to rotate the state $\\rho$." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "44ee0fd1", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_classical_shadow(rho, shadow_size):\n", + " \"\"\"\n", + " Given a state rho, creates a collection of snapshots consisting of a bit string\n", + " and the index of a unitary operation.\n", + "\n", + " Returns:\n", + " Tuple of two numpy arrays. The first array contains measurement outcomes as bitstrings\n", + " while the second array contains the index for the sampled Pauli's (0,1,2=X,Y,Z).\n", + " \"\"\"\n", + " # sample random Pauli measurements uniformly\n", + " unitary_ids = np.random.randint(0, 3, size=(shadow_size, num_qubits))\n", + " outcomes = []\n", + " for ns in range(shadow_size):\n", + " unitmat = qutip.tensor([unitary_ensemble[unitary_ids[ns, i]] for i in range(num_qubits)])\n", + " outcomes.append(measure_bitstring(unitmat.dag() * rho * unitmat))\n", + "\n", + " # combine the computational basis outcomes and the sampled unitaries\n", + " return (outcomes, unitary_ids)" + ] + }, + { + "cell_type": "markdown", + "id": "ae6b66e2", + "metadata": {}, + "source": [ + "We then reconstruct an estimate of the quantum state from the sampled bitstrings, using the inverse quantum channel $\\mathcal{M}^{-1}$ defined above. In the particular case of Pauli measurements, we can actually compute the inverse channel : $$\\mathcal{M}^{-1} = \\otimes_{i=1}^n (3 U_i \\ket{b_i}\\bra{b_i} U^\\dagger_i - \\mathbb{1}_2)$$\n", + "where $i$ runs over all qubits : $\\ket{b_i}$, $b_i \\in \\{0,1\\}$, is the single-bit snapshot of qubit $i$ and $U_i$ is the sampled unitary corresponding to the snapshot, acting on qubit $i$." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7218f3ee", + "metadata": {}, + "outputs": [], + "source": [ + "def snapshot_state(outcome_ns, unitary_ids_ns):\n", + " \"\"\"\n", + " Reconstructs an estimate of a state from a single snapshot in a shadow.\n", + "\n", + " Implements Eq. (S44) from https://arxiv.org/pdf/2002.08953.pdf\n", + "\n", + " Args:\n", + " outcome_ns: Bitstring at ns\n", + " unitary_ids_ns: Rotation applied at ns. \n", + "\n", + " Returns:\n", + " Reconstructed snapshot.\n", + " \"\"\"\n", + " state_list = []\n", + " \n", + " for k in range(num_qubits):\n", + " op = unitary_ensemble[unitary_ids_ns[k]]\n", + " b = zero_state if outcome_ns[k] == '0' else one_state\n", + " state_list.append(3 * op * b * op.dag() - qutip.qeye(2))\n", + " \n", + " return qutip.tensor(state_list)" + ] + }, + { + "cell_type": "markdown", + "id": "54db9c45", + "metadata": {}, + "source": [ + "We finally write a median of means procedure. We feed it an observable, the list of snapshots computed above and the number of blocks needed. It returns the median of the means of the observable acting on the snapshots in each block." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cc3a5349", + "metadata": {}, + "outputs": [], + "source": [ + "def _median_of_means(obs, snap_list, K):\n", + " if K > len(snap_list): # preventing the n_blocks > n_observations\n", + " K = int(np.ceil(len(snap_list) / 2))\n", + " # dividing seq in K random blocks\n", + " indic = np.array((list(range(K)) * int(len(snap_list) / K)))\n", + " np.random.shuffle(indic)\n", + " # computing and saving mean per block\n", + " means = []\n", + " for block in range(K):\n", + " states = [snap_list[i] for i in np.where(indic==block)[0]]\n", + " exp = qutip.expect(obs, states)\n", + " means.append(np.mean(exp))\n", + " return np.median(means)" + ] + }, + { + "cell_type": "markdown", + "id": "f069f2a3", + "metadata": {}, + "source": [ + "### Reconstructing a given quantum state" + ] + }, + { + "cell_type": "markdown", + "id": "f384de9c", + "metadata": {}, + "source": [ + "Let us try out the efficiency of this method. We will reconstruct a given density matrix from classical shadows estimation, and observe the evolution of the trace distance between the original state and its reconstruction according to the number of shadows used." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "62f9bc7e", + "metadata": {}, + "outputs": [], + "source": [ + "def state_reconstruction(snaps):\n", + " return sum(snaps) / len(snaps)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "16811f00", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original density matrix :\n", + "[[0.5+0.j 0.5+0.j 0. +0.j 0. +0.j]\n", + " [0.5+0.j 0.5+0.j 0. +0.j 0. +0.j]\n", + " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]\n", + " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]]\n", + "Shadow reconstruction :\n", + "[[ 0.51+0.j 0.5 -0.01j 0.01+0.01j -0. +0.02j]\n", + " [ 0.5 +0.01j 0.49+0.j -0.01-0.01j -0.01-0.01j]\n", + " [ 0.01-0.01j -0.01+0.01j -0. +0.j -0. +0.j ]\n", + " [-0. -0.02j -0.01+0.01j -0. -0.j -0.01+0.j ]]\n" + ] + } + ], + "source": [ + "num_qubits = 2\n", + "shadow_size = 10000\n", + "rho_1 = (qutip.tensor([qutip.basis(2,0), qutip.basis(2,0)]) + qutip.tensor([qutip.basis(2,0), qutip.basis(2,1)])).proj().unit()\n", + "print(\"Original density matrix :\")\n", + "print(rho_1.full())\n", + "outcomes, unitary_ids = calculate_classical_shadow(rho_1, shadow_size)\n", + "snapshots = [snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)]\n", + "print(\"Shadow reconstruction :\")\n", + "print(np.around(state_reconstruction(snapshots).full(), 2))\n", + "\n", + "dist = np.zeros(5)\n", + "shadow_sizes = [100, 1000, 2000, 5000, 10000]\n", + "for i, shadow_size in enumerate(shadow_sizes):\n", + " outcomes, unitary_ids = calculate_classical_shadow(rho_1, shadow_size)\n", + " snapshots = [snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)]\n", + " dist[i] = tracedist(state_reconstruction(snapshots), rho_1)\n", + "num_qubits = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "85f97f9f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(shadow_sizes, dist)\n", + "plt.xlabel(\"Shadow size\")\n", + "plt.ylabel(r\"$||\\rho - \\hat{\\rho}||_1$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "de522cd5", + "metadata": {}, + "source": [ + "As we can expect, the estimation gets better and better as shadow size gets larger, with about $2$% accuracy at $10000$ shadows. This mostly serves as a reality check, as we will be using classical shadows to estimate observables acting on quantum states, not to reconstruct those states." + ] + }, + { + "cell_type": "markdown", + "id": "ce56aafe", + "metadata": {}, + "source": [ + "## Derandomized Paulis" + ] + }, + { + "cell_type": "markdown", + "id": "cd468f15", + "metadata": {}, + "source": [ + "### Derandomization Algorithm" + ] + }, + { + "cell_type": "markdown", + "id": "2a0f0d8c", + "metadata": {}, + "source": [ + "Randomized classical shadows are useful when dealing with low-weight, general observables. However, suppose, as is the case when estimating the Hamiltonian of the $H_2$ molecule written as a sum of Pauli strings, that we're dealing with Pauli observables of varying weights. In this setting, choosing wisely each Pauli measurement instead of randomly drawing a basis is particularly useful : indeed, say one wants to measure observable $\\sigma_x^1 \\otimes \\sigma_x^2 \\otimes \\dots \\otimes \\sigma_x^n$. Using random rotations in each Pauli $X,Y$ or $Z$ basis and projection in the $Z$ (computational) basis, there is a probability $\\frac{1}{3^n}$ to get each measurement basis right (i.e. rotate the system using the Hadamard matrix). This is extremely unlikely and unefficient as the number of qubits goes up. [Huang et al](https://arxiv.org/abs/2103.07510) outline an interesting greedy algorithm used for choosing suitable measurement bases for the efficient estimation of $L$ $n-$qubit Pauli strings, $\\{O_i\\}$. \n", + "\n", + "Feeding these observables and chosen Pauli measurements {P_i} as input, the algorithm aims at optimizing a certain cost function. This function, labeled $Conf_\\epsilon(O_i, P_j)$ is such that, if $Conf_\\epsilon(O_i, P_j) \\leq \\frac{\\delta}{2}$, then the empirical averages $\\tilde{\\omega_l}$ of each Pauli observable $O_l$ will be $\\epsilon$-close to its true average $Tr(\\rho O_l)$ with probability $1-\\delta$." + ] + }, + { + "cell_type": "markdown", + "id": "23edee89", + "metadata": {}, + "source": [ + "In order to implement this cost function, we first need to design two auxiliary functions. The first one decides if a given Pauli measurement $p$ is compatible with (\"hits\") a Pauli observable $o$. This means that each time $o$ acts non-trivially on a qubit $q_i$ with Pauli matrix $\\sigma \\in \\{\\sigma_X, \\sigma_Y, \\sigma_Z\\}, \\sigma \\neq \\mathbb{1}$, $p$ acts on $q_i$ with $\\sigma$. We denote it by $o \\triangleright p$." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "343399b5", + "metadata": {}, + "outputs": [], + "source": [ + "def hits(p, o, end=-1):\n", + " \"\"\"Determines if measurement p hits observable o\n", + "\n", + " Args:\n", + " p (str): Pauli string in str format (ex \"XYZ\"), measurement\n", + " o (str): same as above, observable (ex \"11ZY\")\n", + " end (int): index before which to check if p hits o\n", + " \"\"\"\n", + " if end != -1:\n", + " o = o[:end]\n", + " for i, x in enumerate(o):\n", + " if not(x == p[i] or x == \"1\"):\n", + " return False\n", + " return True" + ] + }, + { + "cell_type": "markdown", + "id": "585f7865", + "metadata": {}, + "source": [ + "The second function simply computes the number of qubits observable $o$ acts non-trivially upon." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "facd57b1", + "metadata": {}, + "outputs": [], + "source": [ + "def weight(o, start=0):\n", + " o_k = o[start:]\n", + " return len(o_k) - o_k.count(\"1\")" + ] + }, + { + "cell_type": "markdown", + "id": "b1250774", + "metadata": {}, + "source": [ + "We now implement the conditioned cost function using these auxiliary functions. We call it \"conditioned\", since we feed it only the first $m \\times n + k$ single-qubit Pauli measurements, and average over the others, not yet determined ones." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d99ccfcb", + "metadata": {}, + "outputs": [], + "source": [ + "def cond_conf(o, P_sharp):\n", + " \"\"\"Returns the (modified) conditionned expectation value of the cost function depending\n", + " on already chosen Paulis in P_sharp.\n", + " \n", + " Args:\n", + " o (list[str]): list of Pauli strings to be measured\n", + " P_sharp (list[str]): list of already chosen Paulis\n", + " \"\"\"\n", + " # Hyperparameters : see Huang et al. for more details\n", + " eta = 0.9\n", + " nu = 1 - np.exp(-eta / 2)\n", + " L = len(o)\n", + " m = len(P_sharp) - 1 # index of last chosen Pauli string\n", + " k = len(P_sharp[-1]) - 1 # index of last chosen Pauli matrix in mth Pauli string\n", + " result = 0\n", + " for l in range(0, L):\n", + " v = 0\n", + " for m_prime in range(0,m):\n", + " v += (eta / 2) * int(hits(P_sharp[m_prime], o[l]))\n", + " v -= np.log(1 - (nu / 3**(weight(o[l], start=k+1))) * hits(P_sharp[m], o[l], end=k+1))\n", + " result += np.exp(-v)\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "id": "5dae4535", + "metadata": {}, + "source": [ + "Finally, we design a simple greedy algorithm which purpose is to minimize this conditioned cost function, choosing one single-qubit Pauli at a time." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "38712dc5", + "metadata": {}, + "outputs": [], + "source": [ + "def derandomization(M, o):\n", + " \"\"\"Derandomization algorithm returning best Pauli indices according to a greedy algorithm\n", + " that aims at minimizing the cost function above.\n", + " \n", + " Args:\n", + " M (int): number of measurements\n", + " n (int): number of qubits (size of Pauli strings)\n", + " epsilon (float): desired accuracy on observable expectation values\n", + " o (list[str]): list of Pauli strings to be measured\n", + " \"\"\"\n", + " n = len(o[0])\n", + " P_sharp = []\n", + " for m in range(M):\n", + " P_sharp.append(\"\")\n", + " for k in range(n):\n", + " P_sharp_m = P_sharp[m]\n", + " P_sharp[m] += \"X\"\n", + " valmin = cond_conf(o, P_sharp)\n", + " argmin = \"X\"\n", + " for W in [\"Y\", \"Z\"]:\n", + " P_sharp[m] = P_sharp_m + W\n", + " val_W = cond_conf(o, P_sharp)\n", + " if val_W < valmin:\n", + " valmin = val_W\n", + " argmin = W\n", + " P_sharp[m] = P_sharp_m + argmin\n", + " return P_sharp" + ] + }, + { + "cell_type": "markdown", + "id": "ff3e6a1b", + "metadata": {}, + "source": [ + "### Estimating expectation values from Pauli measurements" + ] + }, + { + "cell_type": "markdown", + "id": "6b3c897f", + "metadata": {}, + "source": [ + "Now that we have our Pauli measurements, we proceed differently from randomized classical shadows, where we gave an estimate of the actual quantum channels. Here, we're only interested in the Pauli averages $\\tilde{\\omega}_l$, that we can infer from Pauli measurements $p$ that **hit** observable $o_l$. Indeed, we have the following formula :\n", + "$$\\tilde{\\omega}_{l}=\\frac{1}{h\\left(\\mathbf{o}_{l} ;\\left[\\mathbf{p}_{1}, \\ldots, \\mathbf{p}_{M}\\right]\\right)} \\sum_{m: \\mathbf{o}_{l} \\triangleright \\mathbf{p}_{m}} \\prod_{j: \\mathbf{o}_{l}[j] \\neq I} \\mathbf{q}_{m}[j]$$\n", + "where $h\\left(\\mathbf{o}_{l} ;\\left[\\mathbf{p}_{1}, \\ldots, \\mathbf{p}_{M}\\right]\\right)$ is the number of times a Pauli measurement $p_i$ is such that $o \\triangleright p_i$, and $\\mathbf{q}_m$ is the output of the measurement of Pauli string $p_m$ ($\\mathbf{q}_m \\in \\{\\pm 1\\}^n$)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1fc48ac3", + "metadata": {}, + "outputs": [], + "source": [ + "def _pauli_index(letter):\n", + " if letter == \"X\":\n", + " return 0\n", + " elif letter == \"Y\":\n", + " return 1\n", + " else:\n", + " return 2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5746d37e", + "metadata": {}, + "outputs": [], + "source": [ + "def pauli_string_value(x, sigma):\n", + " \"\"\"Returns the evaluation of a Pauli string sigma in a bitstring state $|x>$,\n", + " assuming the state is already rotated in the needed eigenbases of all single-qubit Paulis.\n", + "\n", + " NB : Faster than using qutip.measure due to not returning the eigenstates...\n", + " \n", + " Args:\n", + " x (str): input bitstring\n", + " sigma (str): input Pauli string to be measured on |x>\n", + " \"\"\"\n", + " outcomes = []\n", + " for i, q in enumerate(x):\n", + " if q == \"0\":\n", + " outcomes.append((sigma[i], 1))\n", + " else:\n", + " outcomes.append((sigma[i], -1))\n", + " return outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "27196d9c", + "metadata": {}, + "outputs": [], + "source": [ + "def classical_shadow_derand(rho, measurements):\n", + " \"\"\"Returns the n-strings of ±1 corresponding to measurements in the input list on state rho.\n", + " \n", + " Args:\n", + " rho (qutip.Qobj): input state as a density matrix \n", + " measurements (list[str]): derandomized measurement bases in which to measure state rho\n", + "\n", + " Returns:\n", + " Tuple of two numpy arrays. The first array contains measurement outcomes as bitstrings\n", + " while the second array contains the index for the derandomized Pauli's (0,1,2=X,Y,Z).\n", + " \"\"\"\n", + " # Fill the unitary ids with derandomized measurements ids\n", + " shadow_size = len(measurements)\n", + " outcomes = []\n", + " for ns in range(shadow_size):\n", + " # multi-qubit change of basis\n", + " unitmat = qutip.tensor([unitary_ensemble[_pauli_index(measurements[ns][i])]for i in range(num_qubits)])\n", + " x = measure_bitstring(unitmat.dag() * rho * unitmat)\n", + " outcomes.append(pauli_string_value(x, measurements[ns]))\n", + " # ±1 strings\n", + " return outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "76ce6b86", + "metadata": {}, + "outputs": [], + "source": [ + "def exp_value(input_pauli, pm_strings):\n", + " \"\"\"Computes an estimation of the expectation value of a given Pauli string given multiple ±1 bitstring\n", + " outcomes.\n", + " \"\"\"\n", + " sum_product, cnt_match = 0, 0\n", + "\n", + " for single_measurement in pm_strings:\n", + " not_match = False\n", + " product = 1\n", + "\n", + " for i, pauli in enumerate(input_pauli):\n", + " if pauli != single_measurement[i][0] and pauli != \"1\":\n", + " not_match = True\n", + " break\n", + " if pauli != \"1\":\n", + " product *= single_measurement[i][1]\n", + " if not_match: continue\n", + "\n", + " sum_product += product\n", + " cnt_match += 1\n", + " if cnt_match == 0:\n", + " return f\"No measurement given for {input_pauli}\"\n", + " return sum_product / cnt_match" + ] + }, + { + "cell_type": "markdown", + "id": "8b1aa92c", + "metadata": {}, + "source": [ + "## Variational Quantum Simulation for the $H_2$ molecule" + ] + }, + { + "cell_type": "markdown", + "id": "3509cba4", + "metadata": {}, + "source": [ + "The main problem with usual variational classical algorithms, the classical counterparts of VQS, is computing the value of the $2^n \\times 2^n$ matrix on the output state vector $\\bra{\\psi}H\\ket{\\psi}$ after each loop of the algorithm, which grows exponentially in the size of the system. The purpose of VQS algorithms is to offer a solution which time complexity only grows polynomially, thanks to reading all the important properties on the quantum state. Therefore, we need accurate and efficient methods to estimate these properties, which we'll present afterwards.\n", + "\n", + "For now, let's focus on what makes a VQS algorithm, specifically for computing the groundstate energy of the $H_2$ molecule." + ] + }, + { + "cell_type": "markdown", + "id": "38b77112", + "metadata": {}, + "source": [ + "### Jordan-Wigner Hamiltonian (cost function)" + ] + }, + { + "cell_type": "markdown", + "id": "7192fa80", + "metadata": {}, + "source": [ + "We need to write the Hamiltonian in a way that's compatible with the formalism of quantum computing. We first second-quantize the Hamiltonian, obtaining an expression in terms of fermionic operators $a, a^\\dagger$. Then, we use the Jordan-Wigner transformation, which maps the fermionic operators to Pauli matrices. We obtain the Hamiltonian below, acting on $4$ qubits, decomposed in terms of the coefficients in front of the Pauli matrices.\n", + "\n", + "[This article by Seeley et al.](https://math.berkeley.edu/~linlin/2018Spring_290/SRL12.pdf) gives us the value of \n", + "$H_{JW}$." + ] + }, + { + "cell_type": "markdown", + "id": "1e17315a", + "metadata": {}, + "source": [ + "$$H_{J W}=-0.81261 \\mathbb{1}+0.171201 \\sigma_{0}^{z}+0.171201 \\sigma_{1}^{z}-0.2227965 \\sigma_{2}^{z}-0.2227965 \\sigma_{3}^{z}\n", + "+0.16862325 \\sigma_{1}^{z} \\sigma_{0}^{z}+0.12054625 \\sigma_{2}^{z} \\sigma_{0}^{z}+0.165868 \\sigma_{2}^{z} \\sigma_{1}^{z}+0.165868 \\sigma_{3}^{z} \\sigma_{0}^{z}\n", + "+0.12054625 \\sigma_{3}^{z} \\sigma_{1}^{z}+0.17434925 \\sigma_{3}^{z} \\sigma_{2}^{z}-0.04532175 \\sigma_{3}^{x} \\sigma_{2}^{x} \\sigma_{1}^{y} \\sigma_{0}^{y}\n", + "+0.04532175 \\sigma_{3}^{x} \\sigma_{2}^{y} \\sigma_{1}^{y} \\sigma_{0}^{x}+0.04532175 \\sigma_{3}^{y} \\sigma_{2}^{x} \\sigma_{1}^{x} \\sigma_{0}^{y}-0.04532175 \\sigma_{3}^{y} \\sigma_{2}^{y} \\sigma_{1}^{x} \\sigma_{0}^{x}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ba1a755e", + "metadata": {}, + "outputs": [], + "source": [ + "def pauli(positions=[], operators=[]):\n", + " op_list = [operators[positions.index(j)] if j in positions else qutip.qeye(2) for j in range(num_qubits)]\n", + " return qutip.tensor(op_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "72ebe271", + "metadata": {}, + "outputs": [], + "source": [ + "coeff_fact = [0.81261,\n", + " 0.171201,\n", + " 0.2227965,\n", + " 0.16862325,\n", + " 0.174349,\n", + " 0.12054625,\n", + " 0.165868,\n", + " 0.04532175]\n", + "\n", + "paulis = [pauli(),\n", + " pauli([0], [sz]) + pauli([1], [sz]),\n", + " pauli([2], [sz]) + pauli([3], [sz]),\n", + " pauli([1, 0], [sz, sz]),\n", + " pauli([3, 2], [sz, sz]),\n", + " pauli([2, 0], [sz, sz]) + pauli([3, 1], [sz, sz]),\n", + " pauli([2, 1], [sz, sz]) + pauli([3, 0], [sz, sz]),\n", + " pauli([3, 2, 1, 0], [sx, sx, sy, sy]) + pauli([3, 2, 1, 0], [sy, sy, sx, sx]),\n", + " pauli([3, 2, 1, 0], [sx, sy, sy, sx]) + pauli([3, 2, 1, 0], [sy, sx, sx, sy])]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "43d3cee0", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# H2 Molecule : 4 qubits in Jordan-Wigner mapping of the Hamiltonian\n", + "a = 10\n", + "reg = Register.from_coordinates([[0, 0], [a, 0], [0.5*a, a*np.sqrt(3)/2], [0.5*a, -a*np.sqrt(3)/2]])\n", + "reg.draw()" + ] + }, + { + "cell_type": "markdown", + "id": "692a4141", + "metadata": {}, + "source": [ + "Let us keep the exact ground-state energy of the molecule for future reference, by diagonalizing it exactly - this is possible for such a small system, however, this quickly becomes an intractable problem for large molecules." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "51f155cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1.8510459284448646\n" + ] + } + ], + "source": [ + "def cost_hamiltonian_JW():\n", + " H = - coeff_fact[0] * paulis[0] \\\n", + " + coeff_fact[1] * paulis[1] \\\n", + " - coeff_fact[2] * paulis[2] \\\n", + " + coeff_fact[3] * paulis[3] \\\n", + " + coeff_fact[4] * paulis[4] \\\n", + " + coeff_fact[5] * paulis[5] \\\n", + " + coeff_fact[6] * paulis[6] \\\n", + " - coeff_fact[7] * paulis[7] \\\n", + " + coeff_fact[7] * paulis[8]\n", + " return H\n", + "\n", + "global H\n", + "H = cost_hamiltonian_JW()\n", + "exact_energy, ground_state = cost_hamiltonian_JW().groundstate()\n", + "print(exact_energy)" + ] + }, + { + "cell_type": "markdown", + "id": "e42dc730", + "metadata": {}, + "source": [ + "### Quantum Loop (VQS)" + ] + }, + { + "cell_type": "markdown", + "id": "99aeffa4", + "metadata": {}, + "source": [ + "Much like in the *Using QAOA to solve a MIS problem* notebook, we will use a mixed classical-quantum approach for minimizing the energy. The quantum part will do the exploration in Hilbert space, according to a certain set of parameters $\\theta_i, \\tau_j$, and the classical part will find the optimal parameters given the value of the energy after each loop. For now, we will ignore sampling problems and simply compute the exact expectation value of $H_{JW}$. See [this article by Xiao Yuan et al.](https://arxiv.org/abs/1812.08767) for details about VQS algorithms." + ] + }, + { + "cell_type": "markdown", + "id": "150c5806", + "metadata": {}, + "source": [ + "Two mixing Hamiltonians are used for the exploration of the solution space :\n", + "$H_1 = \\hbar / 2 \\sum_i \\sigma_i^x + \\sum_{j" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot([quantum_loop(pars, gggg) for pars in loop_ising_results.allvecs], 'k')\n", + "plt.axhline(exact_energy, color='red')" + ] + }, + { + "cell_type": "markdown", + "id": "7b63f7f0", + "metadata": {}, + "source": [ + "Seems like we can cut on calculation time by only allowing $100$ iterations, since we don't get much more accurate afterwards." + ] + }, + { + "cell_type": "markdown", + "id": "c4732f2b", + "metadata": {}, + "source": [ + "## Estimating Jordan-Wigner $H_2$ Hamiltonian with classical shadows" + ] + }, + { + "cell_type": "markdown", + "id": "f6ee64af", + "metadata": {}, + "source": [ + "### Randomized measurements" + ] + }, + { + "cell_type": "markdown", + "id": "178451db", + "metadata": {}, + "source": [ + "We now consider the real-life problem where we don't have access to the exact value $\\bra{\\Psi(\\theta_i, \\tau_j)} H_{JW} \\ket{\\Psi(\\theta_i, \\tau_j)}$. It can be estimated with classical shadows.\n", + "We modify the quantum loop to add classical shadow estimation of the several Pauli strings making up the $H_{JW}$ Hamiltonian : this is the perfect setting to do so, because we have multiple Pauli strings and most of them have low weight." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "754df65c", + "metadata": {}, + "outputs": [], + "source": [ + "def exp_value_JW(exp_values):\n", + " return (- coeff_fact[0] * exp_values[0] \\\n", + " + coeff_fact[1] * exp_values[1] \\\n", + " - coeff_fact[2] * exp_values[2] \\\n", + " + coeff_fact[3] * exp_values[3] \\\n", + " + coeff_fact[4] * exp_values[4] \\\n", + " + coeff_fact[5] * exp_values[5] \\\n", + " + coeff_fact[6] * exp_values[6] \\\n", + " - coeff_fact[7] * exp_values[7] \\\n", + " + coeff_fact[7] * exp_values[8])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ed7176b7", + "metadata": {}, + "outputs": [], + "source": [ + "def quantum_loop_shadows(param, in_state, shadow_size=20, r=reg):\n", + " \"\"\"\n", + " Args:\n", + " param (np.array): time parameters for each mixing Hamiltonian. There are 2p time parameters in param.\n", + " in_state (qubit.Qobj): initial state.\n", + " \"\"\"\n", + " seq = Sequence(r, Chadoq2)\n", + " seq.declare_channel('ch0','rydberg_global')\n", + " middle = len(param)//2\n", + " \n", + " for tau, t in zip(param[middle:], param[:middle]):\n", + " pulse_1 = Pulse.ConstantPulse(tau, 1., 0, 0) \n", + " pulse_2 = Pulse.ConstantPulse(t, 1., 1., 0)\n", + " seq.add(pulse_1, 'ch0')\n", + " seq.add(pulse_2, 'ch0')\n", + " \n", + " seq.measure('ground-rydberg')\n", + " simul = Simulation(seq, sampling_rate=.01)\n", + " simul.initial_state = in_state\n", + " \n", + " # Classical shadow estimation\n", + " # Theoretical shadow size and number of clusters :\n", + " # shadow_size, K = compute_shadow_size(0.1, 0.5, paulis)\n", + " # We use K=4 to allow for quick simulation\n", + " K = 4\n", + " rho = simul.run().get_final_state().proj()\n", + " outcomes, unitary_ids = calculate_classical_shadow(rho, shadow_size)\n", + " snapshots = [snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)]\n", + " meds = [_median_of_means(obs, snapshots, K) for obs in paulis]\n", + " return exp_value_JW(meds)\n", + "\n", + "def loop_JW_shadows(param, in_state, shadow_size=20):\n", + " res = minimize(quantum_loop_shadows, param, method='Nelder-Mead', args=(in_state, shadow_size),\n", + " options={'return_all':True, 'maxiter':100, 'adaptive':True})\n", + " return(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "fe0ebe3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.6202606749999986 -1.8510459284448646\n", + "CPU times: user 30.1 s, sys: 24 ms, total: 30.1 s\n", + "Wall time: 30.1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "loop_results_shadows = loop_JW_shadows(param, gggg, 20)\n", + "print(loop_results_shadows.fun, exact_energy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "57e6d360", + "metadata": {}, + "outputs": [], + "source": [ + "shadow_sizes = [10,20,40,60,80,100]\n", + "energies = []\n", + "for shadow_size in shadow_sizes:\n", + " energies.append(abs(loop_JW_shadows(param, gggg, shadow_size=shadow_size).fun - exact_energy))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9860658b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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w8Rhq0aJZKSK3UwZ3TZ/AJb9+mRv+8Ta//vREzCzsskREJA5a3eJgZge0Qbv7a8CIGGqJy6JZZnaumZWZWbmZ3dDM81PMbLGZLTKzUjM7OYaa5RCOK+7G188Zyb+WVvLAG2vCLkdEROIklmW155pZPlBOMMahhthWx9y3aNYbBGMcjuMoF80ys3TgHuBsoAJ4w8wei6yrsc+zwGPu7mY2FngQGBVD3XIIV5wyhBdXVHHT4+9QMqgHw3p1CbskERGJUSzTMScCw4EZwKvA+0Cr7+MQp0WzJgPl7r7S3WuBmcCUJu+z0933LeeYC2hpxzaSlmbc/qnxZGemMWPmQvbWa/FUEZFkF0tXRTZwBTCVYL2KP7j7BzGcbzzwA6AvwaJZf2vFrIoiILpdvCKyr+l7XWxm7wJPAp8/RD1fjHRllFZVVR1lGbJP767Z3Dp1HEvX7eDnc8rCLkdERGIUy6yKB4BhwHrgVGBh9BiFVojHolnNjcA7qEXB3R9291HARcAtzZ3I3e9z9xJ3LyksLDzKMiTa2aN781/HD+S381fx4nKFMBGRZBbLGIfB7r6/GyAyXuA+4JRWnq/C3X8dQz0QtDD0j9ouBtYd6mB3f9HMhppZT3ffFON7y2F89xPH8NrKzVw/6y3+NeMUCrp0CrskERFphVhaHKojYQEAd18MdIvhfPFYNOsNYLiZDTazLGA6QUvGfmY2zCJzA81sIpAF6EZTbSw7M527L5/A9j11fHP2Yj4cZiIiIskklhaHK4AHzWwuwR0kRxEMaGytCcB/mdl3CQLAvrtHPt7SE7h7vZldBcwB0gnGXSw1sysjz98LXAp8xszqgD3AZa5PsXZxTN+ufOe8Udz4+Dv89bX3+cwJg8IuSUREjpK15jMzsrbEFcCfCMYJHEPQJfBXd99ziNc0uHt6C87dhWBa5xhgTGvWv4i3kpISLy0tDbuMlODufP5Pb/Dye5t5/KqTGdknL+ySRESkCTNb4O4lzT7X2i/bZvaqu59wFMe3KDgkIgWH+Nq0cy/n3jmfgtwsHr3qJLIzk/I/CxGRlHW44BDLGId5ZnZNDK8/gJnNNrMfmtmlZjZSK2amrp5dOvHzaWMp21DNT55aFnY5IiJyFGL5cB4NXG1ma8zsATP7rpm1+gZQxGnRLEkOp43sxRdOHsyfX32fZ5dtCLscERFpoVgWuboQDhqTcBbQ4sGMTc4X70WzJMF989yRvPLeZr4xezH/mnEKvbpmh12SiIgcQSx3jnzOzO4ApgG1BAMjZ8RwvngvmiUJrlNGOr+4fDy7a+u5ftZbNDZqcouISKKLpaviaWAAMBT4PrDVzOab2Vdaeb65ZrbSzJ42s9vN7MfEtmiWJIFhvfL4wQXHMn/FJn7/0qqwyxERkSOI5T4Ol7n7hH0bZnYKcDFQbGY/cfdvH83J3H1iZHXLEQSBoQcxLJolyePyyf2Zt3wjt855lxOGFjCmKJb7iImISFuKpcVhl5ntX47a3ecDJ7r7d2jF2IR4L5olycPM+OklYynI7cTV9y9kd2192CWJiMghxBIcrgT+Zmb3mNkVZvbzqOcyW3G+eC+aJUmke24Wt182jlWbd3HLE++EXY6IiBxCLLMqlpjZZOASYCzBAlOfMLMcYHYrThnvRbMkyZw4tCdf/thQfvXCe5w6vJDzjusbdkkiItLEEVsczOzxyJTLg7h7g7vPcvfvu/ud7r7Z3Xe7+02tqCXei2ZJErr27BGMK+7GDQ+9zbptzd69XEREQtSSrorzgZx9G5GbPRVEbaeZWdc41HIF8Hczu8PMPm9mtxLbolmShDLT07hr+gTqGxq59oFFNGiKpohIQmlJcLAm2+dzYEtAIbAlliIit5c+BZgEvEYwzbMcuCyW80pyGtQzl5umjOHfq7Zw77z3wi5HRESixDIdM1pM60q4e6OZfdbdf0MwSFI6uEsnFjFveRW3z13OiUMLmDCge9gliYgIMX7gR4lHe3JcF82S5GZm/M9FY+jTNZsZMxdRXVMXdkkiIkLLg8PnzOz4yL0WID5Boal4L5olSa5b50zumj6eiq27+eGjS8MuR0REaFlweAH4FvAKsAPIBX5mZjMid4vMj0ch7n6huw8BjgHuADYQLJolHVjJoB5cfeZwHlq4lkcWrg27HBGRDu+IYxzc/QwAMxtCMHhx3+P7BLeFhji0QJjZc8BbwOLIz7+6+95YzyvJ76rTh/HSik1875ElTBzQnQEFOUd+kYiItIkWj3Fw95WRezbc4O5nu3tPYAjwKeBncagl3otmSYrISE/jzunjMYMZDyykvqEx7JJERDqsWGdDrHb32ZH1KWJ1mbtf6u7fc/eLCda7eIPIollxOL8kseLuOfzo4uNY+ME27n52RdjliIh0WPGaVREPcV00S1LPheP6MXVSMb98vpzXV8V06xAREWmlRAoO8V40S1LQjRcey4AeOVwzcyHbd2uKpohIe2tVcDCzU6OmZsaFuy8BJhPM4hhA7ItmSQrq0imDu6ZPYGP1Xr7zyNu465bUIiLtqbUtDs8TfLgftXZcNEtS1Lj++Vx3zgieXLyeWQsqwi5HRKRDaW1waLp+xdH4BO2zaJaksC+dOpQThhRw42NLWVm1M+xyREQ6jEQY4xD3RbMk9aWnGbdfNo6sjDRmzFxEbb2maIqItIdECA7NSdS6JIH07daZn14ylrfXbue2uWVhlyMi0iEk6ge0RrxJi5w7pg//8dEB/GbeSl5asSnsckREUl5YwaE9Fs2SDuL7nxjN0MJcrntwEVt21YZdjohISgsjOLxAOyyaJR1H56x07r58Att21/HN2Ys1RVNEpA21e3Bw9zPcvQcwDPhP4FagO8H6FPOAZe1dkyS/Y/t141vnjeKZZRv4v39/EHY5IiIp64irYx7Cj4CYOpTdfSWwEpi1b5+ZDQJKgImxnFs6ps+dOIgXl1fxP0+8w/GDezC8d17YJYmIpJxWtThEbtAU9ymTcV40SzqYtDTj59PG0aVTBl+7fyE1dQ1hlyQiknISdVaFSKsU5nXi59PG8W5lNT/717thlyMiknIUHCTlnD6qF589cRB/fHk1z7+7MexyRERSioKDpKQbzhvFqD55fGP2W1RV7w27HBGRlHHE4HC4RalEElV2ZjBFs7qmnq/PeovGRk3RFBGJh5a0OJxPEi1KZWbnmlmZmZWb2Q3NPP+fZrY48njFzMaFUae0vRG98/jeBaOZt7yKP76yOuxyRERSQkuCQ9OVMBN2USozSwfuAc4DRgOXm9noJoetAj7m7mOBW4D72rdKaU+f/ugAzjqmNz/757ssXbc97HJERJJevMY4JMpYiclAubuvdPdaYCYwJfoAd3/F3bdGNl8Ditu5RmlHZsatU8eSn5PJ1fcvZE+tpmiKiMQiXh/4idKBXASsidquiOw7lC8A/2zTiiR0PXKzuOOy8azctItbnnwn7HJERJJaS4NDsixK1bRbBQ5Rq5mdThAcvnWI579oZqVmVlpVVRXHEiUMJw3ryRdPHcLf//0B/1pSGXY5IiJJqyXB4QWSZ1GqCqB/1HYxsK7pQWY2FvgdMMXdNzd3Ine/z91L3L2ksLCwTYqV9nX92SM5rqgbNzy0mMrtNWGXIyKSlI4YHJJsUao3gOFmNtjMsoDpwGPRB5jZAOAh4L/cfXkINUpIsjLSuGv6ePbWNXLtA4to0BRNEZGj1uJFruKwKFVz3Qhx5e71ZnYVMAdIB/7g7kvN7MrI8/cCPwAKgF+ZGUC9u5e0dW2SGIYUduGmC4/lm/9YzH0vruTLpw0NuyQRkaRi7vrWdSQlJSVeWloadhkSJ+7OVX9fyJyllfzjyycyrn9+2CWJiCQUM1twqC/VrV1We9+JB8Ty+ia2ufuOOJ5PpFlmxo8vPo6FH2xlxsyFPHH1KXTpFNP/CiIiHUasfy1XE78ZFjcBN8fpXCKH1S0nkzunT2D6fa9y42NL+fk03UBURKQlYg0ONxO/4DAvTucRaZHJg3tw1enDuPu5ck4dUciF4/qFXZKISMKLKTi4+41xqkMkFFefOZyXyjfx3YffZkL/fPr3yDnyi0REOrBEuVW0SCgy0tO4a/oE3OHaBxZR39AYdkkiIglNwUE6vP49cvjRxWMofX8rv3y+POxyREQSmoKDCDBlfBGXTCji7mdXULo6IRZ7FRFJSAoOIhE3TTmW4u45zJi5iO176sIuR0QkISk4iETkZWdy1/TxVO6o4XuPLEE3RxMROZiCg0iUCQO6c+1Zw3n8rXU89ObasMsREUk4Cg4iTXz5tGFMHtyDHzy6hNWbdoVdjohIQlFwEGkiPc2487LxpKcZM2YupE5TNEVE9lNwEGlGv/zO/PTSsbxVsZ075mr1dRGRfRQcRA7h/OP6Mv0j/fn1vPd45b1NYZcjIpIQFBxEDuMHnxzN4IJcrnvgLbbuqg27HBGR0Ck4iBxGTlYGd18+gc279nLDQ4s1RVNEOjwFB5EjGFPUjW9+fBRzlm7g/tfXhF2OiEioFBxEWuALJw/mlOE9ufmJpZRvrA67HBGR0Cg4iLRAWppx27Rx5GRl8LX7F7G3viHskkREQqHgINJCvbpmc+ulY1m2fge3/qss7HJEREKh4CByFM4a3ZvPnDCQ37+0ihfKNoZdjohIu1NwEDlK3zn/GEb07sLXZy1m0869YZcjItKuFBxEjlJ2Zjp3Xz6BHTV1fGPWW5qiKSIdioKDSCuM6tOV755/DM+XVfHnV1aHXY6ISLtRcBBppc+cMJAzRvXix/98l2Xrd4RdjohIu1BwEGklM+N/p46lW+dMrr5/ITV1mqIpIqlPwUEkBgVdOnHbtHGs2LiTHz25LOxyRETanIKDSIxOHVHIFacM5q+vvc/cdzaEXY6ISJtScBCJg69/fCTH9uvKN2e/xYYdNWGXIyLSZhQcROKgU0Y6d02fwJ66Bq57cBGNjZqiKSKpScFBJE6G9erCDz95LC+Xb+a381eGXY6ISJtQcBCJo+kf6c+5x/bhf+eUsbhiW9jliIjEnYKDSByZGT+99Dh6dunEjJmL2LW3PuySRETiSsFBJM7yc7K447LxrN68i5sffyfsckRE4krBQaQNnDC0gK+cNpQHStfw5OL1YZcjIhI3Cg4ibeSas0Ywrn8+335oMWu37Qm7HBGRuFBwEGkjmelp3D19PA2NzrUzF9GgKZoikgJSLjiY2blmVmZm5WZ2QzPPjzKzV81sr5l9PYwapeMYWJDLLReN4fXVW/jV8+VhlyMiErOUCg5mlg7cA5wHjAYuN7PRTQ7bAlwN/Lydy5MO6uIJRUwZ3487n13Bgve3hl2OiEhMUio4AJOBcndf6e61wExgSvQB7r7R3d8A6sIoUDoeM+OWi8bQt1s2M2YuZEeN/tMTkeSVasGhCFgTtV0R2ScSqq7Zmdw1fTzrt9fwg0eWhF2OiEirpVpwsGb2tWpEmpl90cxKzay0qqoqxrJEYNLAHlx9xnAeWbSOhxdWhF2OiEirpFpwqAD6R20XA+tacyJ3v8/dS9y9pLCwMC7FiXz19KF8ZFB3vv/IUt7fvCvsckREjlqqBYc3gOFmNtjMsoDpwGMh1ySyX0Z6GndcNh4zmDFzEXUNjWGXJCJyVFIqOLh7PXAVMAdYBjzo7kvN7EozuxLAzPqYWQVwHfA9M6sws67hVS0dTXH3HH5yyXEsWrONu59dEXY5IiJHJSPsAuLN3Z8Cnmqy796o3ysJujBEQnPB2H7MK6vil8+Xc9Kwnhw/pCDskkREWiSlWhxEksmNFx7LoIJcrn1gEdt3a4qmiCQHBQeRkOR2yuCu6eOpqt7Ltx9ejLtuSS0iiU/BQSREY4vz+frHR/LU25U8WLrmyC8QEQmZgoNIyL54yhBOHFrAjY+9w3tVO8MuR0TksBQcREKWlmbc/qnxdMpM4+r7F1JT1xB2SSIih6TgIJIA+nTL5tZLx7J03Q4++uNn+f4jS3hrzTaNexCRhGP6w3RkJSUlXlpaGnYZ0gG8tnIz97/+Af9aUsne+kaG9+rC1EnFXDyxiF552WGXJyIdhJktcPeSZp9TcDgyBQdpbztq6nhy8Xpmla7hzQ+2kZ5mfGxEIVMnFXPmMb3olJEedokiksIUHGKk4CBheq9qJ/9YUMFDb66lckcN+TmZTBnXj6mT+jOmqCtmza3tJiLSegoOMVJwkETQ0Oi8VL6J2QsqmLO0ktr6Rkb1yWPqpGKmjC+iMK9T2CWKSIpQcIiRgoMkmu2763h88TpmL6hg0ZptZKQZp43sxdRJxZwxqhdZGRr3LCKtp+AQIwUHSWTlG6uZtaCCh99cy8bqvfTIzWLK+H5MnVTMsf26hV2eiCQhBYcYKThIMqhvaGR++SZml1Yw950N1DY0ckzfrkydVMxF4/tR0EVdGSLJqrHRqdq5l4qte1i7bQ9rt+6hYuvu/b83uPPc9afF7f0OFxxSbnVMkY4qIz2N00f24vSRvdi2u5bH31rHrAUV3PLEO/zkqWWcMSroyjh9VC8y09WVIZJIausbqdxeQ8W23ayNCgdrtwWP9dtqqG1oPOA1+TmZFOV3ZnDPXPr3yMHd22WwtFocWkAtDpLMyiqr+cebwayMTTv3UpCbxUUTipg6qZhj+nYNuzyRDmF3bT3rtu1hzdY9BweDrXvYUF1D04/jXnmdKO7emaLuORTld6aoe2eKIz/75XemS6e2++6vrooYKThIKqhraOTF5VXMXlDBM8s2UNfgjCnqytSJxVw4vogeuVlhlyiSlNyd7XvqDuhGaNpisGVX7QGvyUgz+uZnB4EgP+eAUFCU35m++dmh3q9FwSFGCg6SarbuquXRRWuZ/WYFS9buIDPdOOuY3kydVMzHRhSSoa4Mkf2aG1+wtkmXwq7aA9eY6ZyZvj8E7PtZHLXdKy+b9LTEvQeLgkOMFBwklS1bv4PZCyp4ZOFaNu+qpWeXTlwyMejKGNE7L+zyRNpcXUNkfEGTYLBvu7nxBd06Zx4yFBR3z6F7TmZS35xNwSFGCg7SEdQ1NPJCWRWzStfw3LsbqW90xhZ3Y9qkYj45rh/5OerKkOS0p7bhgCDQtCthw44aGpsZXxDdYvBhN0LQrdCW4wsSgYJDjBQcpKPZvHMvjyxax6zSNbxbWU1Wehpnjw66Mk4Z3lNdGZIw3J0de+r3z0Y4KBwcYnxBn27ZB7QQJNL4gkSg4BAjBQfpyJau287sBRU8umgdW3bV0iuvExdPLGLapGKG9VJXhrStxkZn0869VDQ36DDyc+fe+gNek52ZFgkFOQd1IxTld6Z318QeX5AIFBxipOAgEswzf+7djcxeUMHzZRtpaHTG989n6qRiPjm2H91yMsMuUZLQocYX7Pt9XTPjC7pmZ1DcPafZ8QVF+Z3pkZuV1OMLEoGCQ4wUHEQOVFW9l0cXrWVWaQVlG6rJykjj48f2YeqkYk4e1lPf5mS/YHzBoWcjVDYzvqAwr1MzYws+/JmXrZDa1hQcYqTgINI8d2fJ2h3MXrCGR99ax7bddfTpms0lE4u4dFIxQwu7hF2itJOaugZWbNhJ2YZqyip3ULZhJ8srq6ncUXPAcelpRt/o8QVNBh327ZZNdmbHHl+QCBQcYqTgIHJke+sbeHZZ0JXxQtlGGh0mDshnWkl/PjG2L131LTEl1Dc0snrzbsoqqynbUM3yyM/3N+/a33KQlZHG8F5dGNk7jyGFufuDQXF3jS9IFgoOMVJwEDk6G3fU8PDCtcxeUMGKjTvplJHGuWP6MG1Sf04YWqAPjiTg7qzfXrM/IJRVBo/yqp3U1gdjDtIMBhXkMrJPHiN65zGqTx4j+uQxsEeOZt4kOQWHGCk4iLSOu7O4YjuzFqzhsUXr2FFTT79u2VwysZhLJxUzuGdu2CUKwZ1E94eDqFaE6poPZyv07Zb9YTjoncfIPnkM69VF3QopSsEhRgoOIrGrqWvgmWUbmL2ggheXV9Ho8JFB3Zk6qZjzj+urAW/tYE9tAys2VvNu5YfhoKyymo3Ve/cf0zU7g1F9ugatCH0iQaFXnmbNdDAKDjFScBCJr8rtQVfGrAVrWFm1i86Z6Zw3JpiVcfyQAtLUlRGT+oZGVm3atb/14N3KapZvqOb9Lbv3r8DYKSONEb33tR50YWSfrozsnUfvrp00lVEUHGKl4CDSNtydhWu2MXtBBY+/tY7qmnqK8jtz6aRipk4sZkBBTtglJjR3Z+22PSzf8GErwruV1ays2rX/3gdpBoN7BuMQRvbuuj8kDOiRo7EmckgKDjFScBBpezV1DcxZWsnsBRW8VL4Jd5g8uAfTIl0ZuSm+NsCRbNlVy7uVOyJdDDspq9zB8g07D7hrYr9u2fu7GEZGxiEMLdQ4BDl6Cg4xUnAQaV/rt+/hoTeDWRmrNu0iJyud88b0ZVpJMZMH9UjprozdtfUsj9wDYV8Xw7uV1Wza+eE4hPyczP3BYGQkJIzok6cprxI3Cg4xUnAQCYe78+YHWyNdGevZubee/j06c+nEYi6dWEz/HsnblVEXGYfQdKDiB1t27z8mOzMYh7AvJOyb1VCYp3EI0rYUHGKk4CASvj21QVfGrAVreOW9zbjDCUMKmDqpmPOO60NOVmJ2ZTQ2BuMQou+HsHxDNe9V7aSuIfj7m55mUeMQPmxF6K9xCBISBYcYKTiIJJaKrbt5+M21zH6zgvc37yY3K51PjO3L1En9+cig7qF9G9+0c+8BrQf7ZjXsqm3Yf0xRfucDuxh65zG0V26HX8ZZEouCQ4wUHEQSk7vzxuqtzF6whicXr2dXbQMDC3KYOrGYSyYVU5TfuU3ed+feelZEhYN9rQibdtbuP6Z7TiYj++Qxqk/X/TdMGtG7i+5XIUlBwSFGCg4iiW93bT3/fDuYlfHqys2YwUlDezJ1UjEfP7YPnbOO/ht9bX0jKzft3H+75X0DFSu27tl/TOfM9Mgshg/vhTCiTxcKu2gcgiQvBYcYKTiIJJc1W3bzjzcr+MebFazZsocunTK4YGwwK2PigIO7MhobnYqtew5Y2bGscgcrq3ZRH1m5KSPNGFKYe8Btl0f16Upx984pPctDOqYOFRzM7FzgLiAd+J27/7TJ8xZ5/nxgN/BZd3/zcOdUcBBJTo2NzuurtzCrtIKn3l7PnroGBvfM5dKJRXTOygimPG6oZsWGanZHjUMo7t75gDUZRvbJY0jPLmRlaOEm6Rg6THAws3RgOXA2UAG8AVzu7u9EHXM+8DWC4PBR4C53/+jhzqvgIJL8du6t559vr2f2ggr+vWoLAAW5WQet7Diidx5dOvjNpkQOFxxS7f+OyUC5u68EMLOZwBTgnahjpgB/8SAxvWZm+WbW193Xt3+5ItJeunTKYFpJf6aV9Kdyew0Z6UbPLp3CLksk6aRau1sRsCZquyKy72iPEZEU1qdbtkKDSCulWnBoboRS076YlhyDmX3RzErNrLSqqiouxYmIiCS7VAsOFUD/qO1iYF0rjsHd73P3EncvKSwsjHuhIiIiySjVgsMbwHAzG2xmWcB04LEmxzwGfMYCxwPbNb5BRESkZVJqcKS715vZVcAcgumYf3D3pWZ2ZeT5e4GnCGZUlBNMx/xcWPWKiIgkm5QKDgDu/hRBOIjed2/U7w58tb3rEhERSQWp1lUhIiIibUjBQURERFpMwUFERERaTMFBREREWkzBQURERFospRa5aitmVgW8H3YdIesJbAq7iA5C17p96Dq3D13n9hHv6zzQ3Zu9+6GCg7SImZUeaqU0iS9d6/ah69w+dJ3bR3teZ3VViIiISIspOIiIiEiLKThIS90XdgEdiK51+9B1bh+6zu2j3a6zxjiIiIhIi6nFQURERFpMwUEOYmb9zex5M1tmZkvNbEZkfw8zm2tmKyI/u4ddayows3QzW2hmT0S2dZ3jzMzyzWy2mb0b+e/6BF3n+DOzayN/M5aY2f1mlq3rHB9m9gcz22hmS6L2HfLamtm3zazczMrM7OPxrEXBQZpTD1zv7scAxwNfNbPRwA3As+4+HHg2si2xmwEsi9rWdY6/u4B/ufsoYBzB9dZ1jiMzKwKuBkrcfQyQDkxH1zle/gSc22Rfs9c28vd6OnBs5DW/MrP0eBWi4CAHcff17v5m5Pdqgj+yRcAU4M+Rw/4MXBRKgSnEzIqBTwC/i9qt6xxHZtYVOBX4PYC717r7NnSd20IG0NnMMoAcYB26znHh7i8CW5rsPtS1nQLMdPe97r4KKAcmx6sWBQc5LDMbBEwA/g30dvf1EIQLoFeIpaWKO4FvAo1R+3Sd42sIUAX8MdIl9Dszy0XXOa7cfS3wc+ADYD2w3d2fRte5LR3q2hYBa6KOq4jsiwsFBzkkM+sC/AO4xt13hF1PqjGzC4CN7r4g7FpSXAYwEfi1u08AdqHm8riL9K9PAQYD/YBcM/t0uFV1WNbMvrhNoVRwkGaZWSZBaPibuz8U2b3BzPpGnu8LbAyrvhRxEnChma0GZgJnmNn/oescbxVAhbv/O7I9myBI6DrH11nAKnevcvc64CHgRHSd29Khrm0F0D/quGKCbqO4UHCQg5iZEfQHL3P326Oeegz478jv/w082t61pRJ3/7a7F7v7IIKBTM+5+6fRdY4rd68E1pjZyMiuM4F30HWOtw+A480sJ/I35EyC8VG6zm3nUNf2MWC6mXUys8HAcOD1eL2pbgAlBzGzk4H5wNt82Pf+HYJxDg8CAwj+SExz96aDdaQVzOw04OvufoGZFaDrHFdmNp5gAGoWsBL4HMEXJ13nODKzm4DLCGZmLQT+H9AFXeeYmdn9wGkEq2BuAH4IPMIhrq2ZfRf4PMG/i2vc/Z9xq0XBQURERFpKXRUiIiLSYgoOIiIi0mIKDiIiItJiCg4iIiLSYgoOIiIi0mIKDiIdiJl91swWmFm1mW2N3IL59qjnB5mZR+5q2VY1lJrZn9rq/K1lZjea2aaw6xBJdAoOIh2EmX2b4F4Gc4BLgM8Q3DDmwjDrSiC/A+K6/LBIKsoIuwARaTdXAb9x9+9E7Xs8ctOeDs/dKwhu1Ssih6EWB5GOIx+obLrTm78LXI6Z/cbMtptZhZndZGb7/16Y2Sgzm2lma8xst5ktNbNroo+JHDfGzF42sxozW2ZmzbZumNmnzOxtM9sbOeePIkszY2bZkf3/EXX8TyJdKhdG7fuFmb18qH94M8uPrIy5LlLPB2b226jnD+iqMLMXIu/R9PGnqGMGRK7Dlsh1mBN1a2uRlKQWB5GO403ga2b2AfCEu28+zLG3EixyNpVgzYEfAEsJbm8LwRK9ZcDfgGpgPHAT0Bn4CYCZdSboFtkE/EfkuTsJbkG8ZN8bmdk5wAPAX4BvAGOBW4AC4Ep3rzGzN4BTgL9HXnYqUBPZ91jUvsPdVvd2gkWXriUIUP0jrzmUrwBdo7ZHE3RnLI/U3QN4CdgMXAnsJlh18xkzG+Huew5zbpHk5e566KFHB3gQfCCvJFhet5EgCNwMdI06ZlDk+b80ee0iYOYhzmsEX0K+A6yM2v8VoA4ojtp3UuT8f4ra9xrwfJNzfhNo2PdagjCyJPJ7NrAX+CXwWmRffuT4Txzmn38J8LXDPH8jsOkQz3UjCAzPAOmRfbcQhIYeUcd1B7YDXw3737ceerTVQ10VIh2Euy8GjiEYDPkrgg/87wOlZtalyeFPN9l+h2BpXmB/98FNZlZO8CFeB/wIGLyviwGYDCzwYOzAvhpeJmpZZTNLJ1jielaT93uAoCv1hMj2fGB05Fv+8cAu4NfARDPLAU6OHHfIrgqC8PMNM/uKmY04zHEHiHS//A3oBEx394bIU2cBc4EdZpYR+eeuBhYAJS09v0iyUXAQ6UDcfa+7P+7uV7n7aILVC4cDX2hy6LYm27UE3/T3+RnwdeA+4HzgI8D/RJ7bd1wfokJClOh9PYFMgtX+ou3b7hH5+TJBS8XJBN0TL7n7UoJv98dH9i1x96Z1R7uKYDXBHwBlZrbCzKYf5vh9biborrnE3aOna/YkWAmyrsnjdIJuEJGUpOAg0oG5+++BLcCoo3zpNOAX7n6ruz/j7qUEy/dGqwR6NfPa6H2bCD5smx7XO/JzS6TO7cBigoBwKvBi5PmXovbNP1zB7r7N3a929z7AOIJl4v9mZqMP9Rozu5igC+Yr7r6gydNbCMZXfKSZx1cPV4tIMlNwEOkgzOygD3EzKyTov2/6jf9IOhN0Uew7TzrQ9Nv7G8AkM4vu4jiJqJAQafZfQBBEon2KYBzGq1H75hN8mz+BD4PDiwT3XpjEEYJDtEi3zTcI/gY2G5oigeLPwL3u/sdmDnkWOBZY6u6lTR5lLa1FJNloVoVIx/G2mT1KMH5hIzCQoLthN8EH5NGYC3w1MsZhC8E37E5Njvkj8D3gSTO7kSBs3ELQyhDth8AcM/sjMBM4LnLcb6PHRxCEhK8BOwlmiEAQFvbd+fKlwxVsZi8BDxMMknTgCoKxEq8f4iWPEHSFzDSz46P2V7n7e5H3/TTwnJn9AlhL0FLyMYKulPsPV49IslJwEOk4bgamAHcTjB2oBF4BLnP3VUd5rq8B9wL3AHsIgsfDBGMeAHD33Wb28chxM4HVwPUEYYKo456OjDX4HvCfBKHmNoJAEW1fi8Kr7r6vW2QhwYDETe6+9gg1vwp8lmDmSEPktec1CSfRhkd+zmuy/8/AZ919UyRQ/Ai4g2Bmx3qCALP4CLWIJC1zb+7eLyIiIiIH0xgHERERaTEFBxEREWkxBQcRERFpMQUHERERaTEFBxEREWkxBQcRERFpMQUHERERaTEFBxEREWkxBQcRERFpsf8PJ/sz4AOVm7MAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,5))\n", + "plt.xlabel(\"Shadow size\", fontsize=15)\n", + "plt.ylabel(r\"$|\\frac{E - E_{ground}}{E_{ground}}|$\", fontsize=20)\n", + "plt.plot(shadow_sizes, [-e/exact_energy for e in energies])" + ] + }, + { + "cell_type": "markdown", + "id": "beb39526", + "metadata": {}, + "source": [ + "As could be expected, the estimation can be worse than what we got before : we added both randomness and sampling issues to the problem. Raising shadow size will allow more and more precise results. However, it can also be closer to the exact value for the same reasons." + ] + }, + { + "cell_type": "markdown", + "id": "a07ae036", + "metadata": {}, + "source": [ + "### Derandomized measurements" + ] + }, + { + "cell_type": "markdown", + "id": "ec0b65a1", + "metadata": {}, + "source": [ + "Finally, we try out the derandomized measurements method. To implement this one, we need to decompose the Hamiltonian into individual Pauli strings, rather than group them when they share the same leading coefficient as we did before, as it reduced the number of estimations." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "b62cc268", + "metadata": {}, + "outputs": [], + "source": [ + "coeff_non_fact = [-0.81261,\n", + " 0.171201,\n", + " 0.171201,\n", + " -0.2227965,\n", + " -0.2227965,\n", + " 0.16862325,\n", + " 0.174349,\n", + " 0.12054625,\n", + " 0.12054625,\n", + " 0.165868,\n", + " 0.165868,\n", + " -0.04532175,\n", + " -0.04532175,\n", + " 0.04532175,\n", + " 0.04532175]\n", + "\n", + "paulis_str = [\"1111\", \"Z111\", \"1Z11\", \"11Z1\", \"111Z\", \"ZZ11\", \"11ZZ\", \"Z1Z1\", \"1Z1Z\", \"1ZZ1\",\n", + " \"Z11Z\", \"YYXX\", \"XXYY\", \"XYYX\", \"YXXY\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "ba92cf7e", + "metadata": {}, + "outputs": [], + "source": [ + "def exp_value_JW_non_fact(outcomes):\n", + " return sum([c*exp_value(sigma, outcomes) for c, sigma in zip(coeff_non_fact, paulis_str)])" + ] + }, + { + "cell_type": "markdown", + "id": "80458815", + "metadata": {}, + "source": [ + "Then, we ask the derandomization algorithm to return $60$ suitable Pauli measurements regarding our input Pauli observables. $60$ is arbitrary, but is small enough that the algorithm runs quickly and large enough that it gives good results." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7a9c7be6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZZZZ measurements : 18, XXYY measurements : 11, YXXY measurements : 11, XYYX measurements : 10, YYXX measurements : 10 : total = 60 measurements\n" + ] + } + ], + "source": [ + "measurements = derandomization(60, paulis_str)\n", + "print(f\"ZZZZ measurements : {measurements.count('ZZZZ')}, XXYY measurements : {measurements.count('XXYY')}, \" +\n", + " f\"YXXY measurements : {measurements.count('YXXY')}, XYYX measurements : {measurements.count('XYYX')}, \" +\n", + " f\"YYXX measurements : {measurements.count('YYXX')} : total = 60 measurements\")" + ] + }, + { + "cell_type": "markdown", + "id": "4b6bcc43", + "metadata": {}, + "source": [ + "As we can see, since all Pauli observables appearing in the Jordan-Wigner Hamiltonian involving the $Z$-basis never involve another basis, we find that it is always worth it to measure Pauli string $ZZZZ$ rather than $ZZZX$, or $ZYZZ$, etc. This is a sign that our cost function is doing its job !" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "795d8fe7", + "metadata": {}, + "outputs": [], + "source": [ + "def quantum_loop_derand(param, in_state, r=reg):\n", + " \"\"\"\n", + " Args:\n", + " param (np.array): time parameters for each mixing Hamiltonian. There are 2p time parameters in param.\n", + " in_state (qubit.Qobj): initial state.\n", + " \"\"\"\n", + " seq = Sequence(r, Chadoq2)\n", + " seq.declare_channel('ch0','rydberg_global')\n", + " middle = len(param)//2\n", + " \n", + " for tau, t in zip(param[middle:], param[:middle]):\n", + " pulse_1 = Pulse.ConstantPulse(tau, 1., 0, 0) \n", + " pulse_2 = Pulse.ConstantPulse(t, 1., 1., 0)\n", + " seq.add(pulse_1, 'ch0')\n", + " seq.add(pulse_2, 'ch0')\n", + " \n", + " seq.measure('ground-rydberg')\n", + " simul = Simulation(seq, sampling_rate=.01)\n", + " simul.initial_state = in_state\n", + " \n", + " # Classical shadow estimation\n", + " rho = simul.run().get_final_state().proj()\n", + " outcomes = classical_shadow_derand(rho, measurements)\n", + " return exp_value_JW_non_fact(outcomes)\n", + "\n", + "def loop_JW_derand(param, in_state):\n", + " res = minimize(quantum_loop_derand, param, method='Nelder-Mead', args=in_state,\n", + " options={'return_all':True, 'maxiter':150, 'adaptive':True})\n", + " return(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "84675919", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1.8321093136363638 -1.8510459284448646\n", + "CPU times: user 35.1 s, sys: 12 ms, total: 35.1 s\n", + "Wall time: 35.1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "loop_results_shadows = loop_JW_derand(param, gggg)\n", + "print(loop_results_shadows.fun, exact_energy)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "777fdab7", + "metadata": {}, + "outputs": [], + "source": [ + "measurement_sizes = [20,30,40,60,80,100]\n", + "energies_derand = []\n", + "for meas_size in measurement_sizes:\n", + " measurements=derandomization(meas_size, paulis_str)\n", + " energies_derand.append(abs(loop_JW_derand(param, gggg).fun - exact_energy) / abs(exact_energy))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "c5979d01", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,5))\n", + "plt.xlabel(\"Measurement size\", fontsize=15)\n", + "plt.ylabel(r\"$|\\frac{E - E_{ground}}{E_{ground}}|$\", fontsize=20)\n", + "plt.plot(measurement_sizes, energies_derand)" + ] + }, + { + "cell_type": "markdown", + "id": "d2fc69a1", + "metadata": {}, + "source": [ + "We consistently obtain accurate results using this derandomized technique, and we obtain them far quicker than when dealing with randomized classical shadows. For roughly the same number of samples ($\\sim 60$ for each method, be it for shadow size or number of measurements), we experience much less computing time using the derandomized method. This was to be expected : by restricting the observables to Pauli strings, we allow for efficient estimation that can be easily computed in $O(M\\times n)$, as well as remove randomness problematic with higher-weight observables (such as $YYXX$ or $YXXY$).\n", + "\n", + "Note that we obtain $2\\%$ accuracy after about $50$ $Z-$ basis measurements (fluorescence) of the output state, rotated before each sampling in the bases returned by the derandomization algorithm." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1504e02cfd61d94e55dc11936afc1edc4ceb477e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Tue, 21 Sep 2021 10:22:07 +0200 Subject: [PATCH 07/51] Adding support for XY Simulation (#264) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Enable CI tests for the xy-simulation branch (temporarily) * Adding method to set the magnetic field on a Sequence (#200) * Add magnetic field setting to Sequence * Add tests for magnetic field features * Addressing review comments * Fix typo in docstring * Include magnetic field in serialization (#201) * Storing mag field call for serialization * Add tests for serialization with magnetic field * Declare self._mag_field in __init__ * [unitaryHACK] Xy simulation (#198) * add XY simulation * C coef * add xy tests * flake8 * Update pulser/simulation/simulation.py Co-authored-by: Henrique Silvério * typing + suggestions * C_3 on device * suggested changes * add external field * add external field * test external field * typo * add general method build operator * style + fix test * fix typing * style * fix CI * style * property mag_field * _build_operator accept index * Update pulser/simulation/simresults.py Co-authored-by: Henrique Silvério * coeff_ising + meas_basis check * correction new magnetic field * fix err message get_final_state * style * implement build_operator * update build_operator * update notebook * update notebook * style * update build_operator * style * Update pulser/simulation/simresults.py Co-authored-by: Henrique Silvério * Update pulser/simulation/simulation.py Co-authored-by: Henrique Silvério * Update pulser/simulation/simulation.py Co-authored-by: Henrique Silvério * correct style * style * modify build_operator + style * style * corrections NB * Update pulser/tests/test_simulation.py Co-authored-by: Henrique Silvério * Fixing up XY tutorial * Reverting unnecessary change to `Simulation` * Removing CI tests on `xy-simulation` branch * Adapting the XY tutorial to the new changes * Setting supported noise types for different interaction modes * Adaptations to use SPAM on XY * Changing the creation procedure of `qobj_list` * Formatting with black * Finishing unit tests * Fixing unit test randomness * Patching `set_config` mechanism upon failure * Changing where invalid qubits are checked in "build_operator" * SLM mask (#258) * implemented SLM mask * added missing tests on SLM for xy * reverted changes involving wrong slm mask implementation * added support for initial state preparation with slm mask * fixed typo * implementing mask on first pulse only * improved slm mask * adding unit tests for slm mask * completed unit tests for slm mask * fixed bug in drawing slm mask * implemented some corrections * implemented corrections as requested * removed affected_by_slm * fixed bug affecting registers with number of unmasked qubits less than 2 * added default argument masked=False * included state preparation with SLM mask in XY tutorial * better pulse for slm mask * fixed effective size bug * reverted unwanted change Co-authored-by: Mauro D'Arcangelo Co-authored-by: Slimane33 <47427641+Slimane33@users.noreply.github.com> Co-authored-by: Mauro D'Arcangelo <32898410+darcangelomauro@users.noreply.github.com> Co-authored-by: Mauro D'Arcangelo --- pulser/_seq_drawer.py | 19 + pulser/devices/_device_datacls.py | 10 +- pulser/sequence.py | 129 ++++- pulser/simulation/simresults.py | 34 +- pulser/simulation/simulation.py | 333 +++++++++---- pulser/tests/test_devices.py | 2 + pulser/tests/test_sequence.py | 185 +++++++- pulser/tests/test_simresults.py | 59 ++- pulser/tests/test_simulation.py | 284 ++++++++++- .../Spin chain of 3 atoms in XY mode.ipynb | 440 ++++++++++++++++++ 10 files changed, 1380 insertions(+), 115 deletions(-) create mode 100644 tutorials/applications/Spin chain of 3 atoms in XY mode.ipynb diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index 50df3bf35..0a068f554 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -145,6 +145,7 @@ def phase_str(phi: float) -> str: q_box = dict(boxstyle="round", facecolor="orange") ph_box = dict(boxstyle="round", facecolor="ghostwhite") area_ph_box = dict(boxstyle="round", facecolor="ghostwhite", alpha=0.7) + slm_box = dict(boxstyle="round", alpha=0.4, facecolor="grey", hatch="//") fig = plt.figure(constrained_layout=False, figsize=(20, 4.5 * n_channels)) gs = fig.add_gridspec(n_channels, 1, hspace=0.075) @@ -398,6 +399,24 @@ def phase_str(phi: float) -> str: bbox=ph_box, ) + # Draw the SLM mask + if seq._slm_mask_targets and seq._slm_mask_time: + tf_m = seq._slm_mask_time[1] + a.axvspan(0, tf_m, color="black", alpha=0.1, zorder=-100) + b.axvspan(0, tf_m, color="black", alpha=0.1, zorder=-100) + tgt_strs = [str(q) for q in seq._slm_mask_targets] + tgt_txt_x = t[-1] * 0.005 + tgt_txt_y = b.get_ylim()[0] + tgt_str = "\n".join(tgt_strs) + b.text( + tgt_txt_x, + tgt_txt_y, + tgt_str, + fontsize=12, + ha="left", + bbox=slm_box, + ) + if "measurement" in data[ch]: msg = f"Basis: {data[ch]['measurement']}" b.text( diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index f59e62745..c268d5084 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -36,9 +36,13 @@ class Device: max_radial_distance: The furthest away an atom can be from the center of the array (in μm). min_atom_distance: The closest together two atoms can be (in μm). - interaction_coeff: :math:`C_6/\hbar` (in :math:`\mu m^6 / \mu s`), + interaction_coeff: :math:`C_6/\hbar` + (in :math:`\mu m^6 / \mu s`), which sets the van der Waals interaction strength between atoms in - the Rydberg state. + the same Rydberg state. + interaction_coeff_xy: :math:`C_3/\hbar` (in :math:`\mu m^3 / \mu s`), + which sets the van der Waals interaction strength between atoms in + different Rydberg states. """ name: str @@ -47,7 +51,9 @@ class Device: max_radial_distance: int min_atom_distance: int _channels: tuple[tuple[str, Channel], ...] + # Ising interaction coeff interaction_coeff: float = 5008713.0 + interaction_coeff_xy: float = 3700.0 def __post_init__(self) -> None: # Hack to override the docstring of an instance diff --git a/pulser/sequence.py b/pulser/sequence.py index d3f0ef86e..7518eff36 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -182,6 +182,7 @@ def __init__(self, register: Register, device: Device): self._register: Register = register self._device: Device = device self._in_xy: bool = False + self._mag_field: Optional[tuple[float, float, float]] = None self._calls: list[_Call] = [_Call("__init__", (register, device), {})] self._channels: dict[str, Channel] = {} self._schedule: dict[str, list[_TimeSlot]] = {} @@ -198,6 +199,11 @@ def __init__(self, register: Register, device: Device): self._variables: dict[str, Variable] = {} self._to_build_calls: list[_Call] = [] self._building: bool = True + # Marks the sequence as empty until the first pulse is added + self._empty_sequence: bool = True + # SLM mask targets and on/off times + self._slm_mask_targets: set[QubitId] = set() + self._slm_mask_time: list[int] = [] # Initializes all parametrized Sequence related attributes self._reset_parametrized() @@ -222,7 +228,8 @@ def available_channels(self) -> dict[str, Channel]: """Channels still available for declaration.""" # Show all channels if none are declared, otherwise filter depending # on whether the sequence is working on XY mode - if not self._channels: + # If already in XY mode, filter right away + if not self._channels and not self._in_xy: return dict(self._device.channels) else: # MockDevice channels can be declared multiple times @@ -236,6 +243,23 @@ def available_channels(self) -> dict[str, Channel]: and (ch.basis == "XY" if self._in_xy else ch.basis != "XY") } + @property + def magnetic_field(self) -> np.ndarray: + """The magnetic field acting on the array of atoms. + + The magnetic field vector is defined on the reference frame of the + atoms in the Register (with the z-axis coming outside of the plane). + + Note: + Only defined in "XY Mode", the default value being (0, 0, 30) G. + """ + if not self._in_xy: + raise AttributeError( + "The magnetic field is only defined when the " + "sequence is in 'XY Mode'." + ) + return np.array(self._mag_field) + def is_parametrized(self) -> bool: """States whether the sequence is parametrized. @@ -299,6 +323,48 @@ def current_phase_ref( return self._phase_ref[basis][qubit].last_phase + def set_magnetic_field( + self, bx: float = 0.0, by: float = 0.0, bz: float = 30.0 + ) -> None: + """Sets the magnetic field acting on the entire array. + + The magnetic field vector is defined on the reference frame of the + atoms in the Register (with the z-axis coming outside of the plane). + Can only be defined before there are pulses added to the sequence. + + Note: + The magnetic field only work in the "XY Mode". If not already + defined through the declaration of a Microwave channel, calling + this function will enable the "XY Mode". + + Keyword Args: + bx (float): The magnetic field in the x direction (in Gauss). + by (float): The magnetic field in the y direction (in Gauss). + bz (float): The magnetic field in the z direction (in Gauss). + """ + if not self._in_xy: + if self._channels: + raise ValueError( + "The magnetic field can only be set in 'XY " "Mode'." + ) + # No channels declared yet + self._in_xy = True + elif not self._empty_sequence: + # Not all channels are empty + raise ValueError( + "The magnetic field can only be set on an empty " "sequence." + ) + + mag_vector = (bx, by, bz) + if np.linalg.norm(mag_vector) == 0.0: + raise ValueError( + "The magnetic field must have a magnitude greater" " than 0." + ) + self._mag_field = mag_vector + + # No parametrization -> Always stored as a regular call + self._calls.append(_Call("set_magnetic_field", mag_vector, {})) + def declare_channel( self, name: str, @@ -355,8 +421,15 @@ def declare_channel( else: raise ValueError(f"Channel {channel_id} is not available.") + # Remove this check once SLM is available in Ising mode + if self._slm_mask_targets and ch.basis != "XY": + raise NotImplementedError( + "SLM mask is not yet available in Ising mode" + ) + if ch.basis == "XY" and not self._in_xy: self._in_xy = True + self.set_magnetic_field() self._channels[name] = ch self._taken_channels[name] = channel_id self._schedule[name] = [] @@ -479,6 +552,9 @@ def add( if self.is_parametrized(): if not isinstance(pulse, Parametrized): self._validate_pulse(pulse, channel) + # Sequence is marked as non-empty on the first added pulse + if self._empty_sequence: + self._empty_sequence = False return if not isinstance(pulse, Pulse): @@ -568,6 +644,19 @@ def add( pulse.post_phase_shift, *last.targets, basis=basis ) + # Sequence is marked as non-empty on the first added pulse + if self._empty_sequence: + self._empty_sequence = False + + # If the added pulse starts earlier than all previously added pulses, + # update SLM mask initial and final time + if self._slm_mask_targets: + try: + if self._slm_mask_time[0] > ti: + self._slm_mask_time = [ti, tf] + except IndexError: + self._slm_mask_time = [ti, tf] + @_store def target( self, @@ -1044,6 +1133,44 @@ def _reset_parametrized(self) -> None: self._variables = {} self._to_build_calls = [] + @_store + def config_slm_mask(self, qubits: Set[QubitId]) -> None: + """Setup an SLM mask by specifying the qubits it targets.""" + try: + targets = set(qubits) + except TypeError: + raise TypeError("The SLM targets must be castable to set") + + if not targets.issubset(self._qids): + raise ValueError("SLM mask targets must exist in the register") + + if not self._in_xy and self._channels: + raise NotImplementedError("SLM mask can only be added in XY mode") + + if self.is_parametrized(): + return + + if self._slm_mask_targets: + raise ValueError("SLM mask can be configured only once.") + + # If checks have passed, set the SLM mask targets + self._slm_mask_targets = targets + + # Find tentative initial and final time of SLM mask if possible + for channel in self._channels: + # Cycle on slots in schedule until the first pulse is found + for slot in self._schedule[channel]: + if not isinstance(slot.type, Pulse): + continue + ti = slot.ti + tf = slot.tf + if self._slm_mask_time: + if ti < self._slm_mask_time[0]: + self._slm_mask_time = [ti, tf] + else: + self._slm_mask_time = [ti, tf] + break + class _PhaseTracker: """Tracks a phase reference over time.""" diff --git a/pulser/simulation/simresults.py b/pulser/simulation/simresults.py index 12b0bc25d..099b209d2 100644 --- a/pulser/simulation/simresults.py +++ b/pulser/simulation/simresults.py @@ -50,9 +50,10 @@ def __init__( """ self._dim = 3 if basis_name == "all" else 2 self._size = size - if basis_name not in {"ground-rydberg", "digital", "all"}: + if basis_name not in {"ground-rydberg", "digital", "all", "XY"}: raise ValueError( - "`basis_name` must be 'ground-rydberg', 'digital' or 'all'." + "`basis_name` must be 'ground-rydberg', 'digital', 'all' or " + "'XY'." ) self._basis_name = basis_name self._sim_times = sim_times @@ -220,16 +221,16 @@ def _proj_from_bitstring(bitstring: str) -> qutip.Qobj: ) def _meas_projector(self, state_n: int) -> qutip.Qobj: - """Gets the post measurement projector (defaults to the ideal case). + """Gets the post measurement projector. Args: state_n: The measured state (0 or 1). """ - if self._basis_name == "ground-rydberg": - # 0 = |g> = |1>, 1 = |r> = |0> - return qutip.basis(2, 1 - state_n).proj() + if self._basis_name == "digital": + return qutip.basis(2, state_n).proj() - return qutip.basis(2, state_n).proj() + # 0 = |g or d> = |1>; 1 = |r or u> = |0> + return qutip.basis(2, 1 - state_n).proj() class NoisyResults(SimulationResults): @@ -271,8 +272,6 @@ def __init__( 'digital' if given value 'all'. sim_times (np.ndarray): Times at which Simulation object returned the results. - meas_basis (Optional[str]): The basis in which a sampling - measurement is desired. n_measures (int): Number of measurements needed to compute this result when doing the simulation. """ @@ -401,11 +400,16 @@ def __init__( "epsilon_prime". """ super().__init__(size, basis_name, sim_times) - if meas_basis: + if self._basis_name == "all": if meas_basis not in {"ground-rydberg", "digital"}: raise ValueError( "`meas_basis` must be 'ground-rydberg' or 'digital'." ) + else: + if meas_basis != self._basis_name: + raise ValueError( + "`meas_basis` and `basis_name` must have the same value." + ) self._meas_basis = meas_basis self._results = run_output if meas_errors is not None: @@ -437,7 +441,8 @@ def get_state( t (float): Time (µs) at which to return the state. reduce_to_basis (str, default=None): Reduces the full state vector to the given basis ("ground-rydberg" or "digital"), if the - population of the states to be ignored is negligible. + population of the states to be ignored is negligible. Doesn't + apply to XY mode. ignore_global_phase (bool, default=True): If True, changes the final state's global phase such that the largest term (in absolute value) is real. @@ -503,7 +508,8 @@ def get_final_state( Args: reduce_to_basis (str, default=None): Reduces the full state vector to the given basis ("ground-rydberg" or "digital"), if the - population of the states to be ignored is negligible. + population of the states to be ignored is negligible. Doesn't + apply to XY mode. ignore_global_phase (bool, default=True): If True, changes the final state's global phase such that the largest term (in absolute value) is real. @@ -540,6 +546,7 @@ def _calc_weights(self, t_index: int) -> np.ndarray: # State vector ordered with r first for 'ground_rydberg' # e.g. n=2: [rr, rg, gr, gg] -> [11, 10, 01, 00] # Invert the order -> [00, 01, 10, 11] correspondence + # The same applies in XY mode, which is ordered with u first weights = ( probs if self._meas_basis == "digital" else probs[::-1] ) @@ -586,7 +593,8 @@ def _meas_projector(self, state_n: int) -> qutip.Qobj: else self._meas_errors["epsilon_prime"] ) # 'good' is the position of the state that measures to state_n - # Matches for the digital basis, is inverted for ground-rydberg + # Matches for the digital basis, is inverted for ground-rydberg and + # for XY good = state_n if self._basis_name == "digital" else 1 - state_n return ( qutip.basis(2, good).proj() * (1 - err_param) diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index a72c38ae1..6b839825e 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -38,7 +38,10 @@ from pulser.sequence import _TimeSlot -SUPPORTED_BASES = {"ground-rydberg", "digital"} +SUPPORTED_NOISE = { + "ising": {"dephasing", "doppler", "amplitude", "SPAM"}, + "XY": {"SPAM"}, +} class Simulation: @@ -82,17 +85,10 @@ def __init__( raise ValueError("The provided sequence has no declared channels.") if all(sequence._schedule[x][-1].tf == 0 for x in sequence._channels): raise ValueError( - "No instructions given for the channels in the " "sequence." - ) - not_supported = ( - set(ch.basis for ch in sequence._channels.values()) - - SUPPORTED_BASES - ) - if not_supported: - raise NotImplementedError( - "Sequence with unsupported bases: " + "".join(not_supported) + "No instructions given for the channels in the sequence." ) self._seq = sequence + self._interaction = "XY" if self._seq._in_xy else "ising" self._qdict = self._seq.qubit_info self._size = len(self._qdict) self._tot_duration = self._seq.get_duration() @@ -104,7 +100,7 @@ def __init__( ) if int(self._tot_duration * sampling_rate) < 4: raise ValueError( - "`sampling_rate` is too small, less than 4 data " "points." + "`sampling_rate` is too small, less than 4 data points." ) self._sampling_rate = sampling_rate self._qid_index = {qid: i for i, qid in enumerate(self._qdict)} @@ -112,6 +108,7 @@ def __init__( self._times = self._adapt_to_sampling_rate( np.arange(self._tot_duration, dtype=np.double) / 1000 ) + self.evaluation_times = evaluation_times self._bad_atoms: dict[Union[str, int], bool] = {} self._doppler_detune: dict[Union[str, int], float] = {} @@ -123,7 +120,7 @@ def __init__( if self.basis_name in {"digital", "all"}: self._meas_basis = "digital" else: - self._meas_basis = "ground-rydberg" + self._meas_basis = self.basis_name self.initial_state = "all-ground" @property @@ -139,6 +136,13 @@ def set_config(self, cfg: SimConfig) -> None: """ if not isinstance(cfg, SimConfig): raise ValueError(f"Object {cfg} is not a valid `SimConfig`.") + not_supported = set(cfg.noise) - SUPPORTED_NOISE[self._interaction] + if not_supported: + raise NotImplementedError( + f"Interaction mode '{self._interaction}' does not support " + f"simulation of noise types: {', '.join(not_supported)}." + ) + prev_config = self.config if hasattr(self, "_config") else SimConfig() self._config = cfg if not ("SPAM" in self.config.noise and self.config.eta > 0): self._bad_atoms = {qid: False for qid in self._qid_index} @@ -148,9 +152,10 @@ def set_config(self, cfg: SimConfig) -> None: self._construct_hamiltonian() if "dephasing" in self.config.noise: if self.basis_name == "digital" or self.basis_name == "all": + # Go back to previous config + self.set_config(prev_config) raise NotImplementedError( - "Cannot include dephasing noise in" - + " digital- or all-basis." + "Cannot include dephasing noise in digital- or all-basis." ) # Probability of phase (Z) flip: # First order in prob @@ -159,7 +164,7 @@ def set_config(self, cfg: SimConfig) -> None: if prob > 0.1 and n > 1: warnings.warn( "The dephasing model is a first-order approximation in the" - + f" dephasing probability. p = {2*prob} is too large for " + f" dephasing probability. p = {2*prob} is too large for " "realistic results.", stacklevel=2, ) @@ -171,8 +176,8 @@ def set_config(self, cfg: SimConfig) -> None: self._collapse_ops += [ k * ( - self._build_operator("sigma_rr", qid) - - self._build_operator("sigma_gg", qid) + self.build_operator([("sigma_rr", [qid])]) + - self.build_operator([("sigma_gg", [qid])]) ) for qid in self._qid_index ] @@ -192,6 +197,13 @@ def add_config(self, config: SimConfig) -> None: if not isinstance(config, SimConfig): raise ValueError(f"Object {config} is not a valid `SimConfig`") + not_supported = set(config.noise) - SUPPORTED_NOISE[self._interaction] + if not_supported: + raise NotImplementedError( + f"Interaction mode '{self._interaction}' does not support " + f"simulation of noise types: {', '.join(not_supported)}." + ) + old_noise_set = set(self.config.noise) new_noise_set = old_noise_set.union(config.noise) diff_noise_set = new_noise_set - old_noise_set @@ -240,7 +252,10 @@ def initial_state(self, state: Union[str, np.ndarray, qutip.Qobj]) -> None: self._initial_state: qutip.Qobj if isinstance(state, str) and state == "all-ground": self._initial_state = qutip.tensor( - [self.basis["g"] for _ in range(self._size)] + [ + self.basis["d" if self._interaction == "XY" else "g"] + for _ in range(self._size) + ] ) else: state = cast(Union[np.ndarray, qutip.Qobj], state) @@ -361,10 +376,15 @@ def draw( def _extract_samples(self) -> None: """Populates samples dictionary with every pulse in the sequence.""" - self.samples: dict[str, dict[str, dict]] = { - addr: {basis: {} for basis in ["ground-rydberg", "digital"]} - for addr in ["Global", "Local"] - } + self.samples: dict[str, dict[str, dict]] + if self._interaction == "ising": + self.samples = { + addr: {basis: {} for basis in ["ground-rydberg", "digital"]} + for addr in ["Global", "Local"] + } + else: + self.samples = {addr: {"XY": {}} for addr in ["Global", "Local"]} + if not hasattr(self, "operators"): self.operators = deepcopy(self.samples) @@ -417,13 +437,27 @@ def write_samples( if addr == "Global" and ( set(self.config.noise).issubset({"dephasing"}) ): - samples_dict = self.samples["Global"][basis] - if not samples_dict: - samples_dict = prepare_dict() + slm_on = bool(self._seq._slm_mask_targets) for slot in self._seq._schedule[channel]: if isinstance(slot.type, Pulse): - write_samples(slot, samples_dict, True) - self.samples["Global"][basis] = samples_dict + # If SLM is on during slot, populate local samples + if slm_on and self._seq._slm_mask_time[1] > slot.ti: + samples_dict = self.samples["Local"][basis] + for qubit in slot.targets: + if qubit not in samples_dict: + samples_dict[qubit] = prepare_dict() + write_samples( + slot, samples_dict[qubit], True, qubit + ) + self.samples["Local"][basis] = samples_dict + # Otherwise, populate corresponding global + else: + slm_on = False + samples_dict = self.samples["Global"][basis] + if not samples_dict: + samples_dict = prepare_dict() + write_samples(slot, samples_dict, True) + self.samples["Global"][basis] = samples_dict # Any noise : global becomes local for each qubit in the reg # Since coefficients are modified locally by all noises @@ -442,23 +476,63 @@ def write_samples( ) self.samples["Local"][basis] = samples_dict - def _build_operator( - self, op_id: str, *qubit_ids: Union[str, int], global_op: bool = False - ) -> qutip.Qobj: - """Create qutip.Qobj with nontrivial action at *qubit_ids.""" - if global_op: - return sum( - self._build_operator(op_id, q_id) for q_id in self._qdict - ) - if len(set(qubit_ids)) < len(qubit_ids): - raise ValueError("Duplicate atom ids in argument list.") - # List of identity operators, except for op_id where requested: - op_list = [ - self.op_matrix[op_id] - if j in map(self._qid_index.get, qubit_ids) - else self.op_matrix["I"] - for j in range(self._size) - ] + # Apply SLM mask if it was defined + if self._seq._slm_mask_targets and self._seq._slm_mask_time: + tf = self._seq._slm_mask_time[1] + for qubit in self._seq._slm_mask_targets: + for x in ("amp", "det", "phase"): + self.samples["Local"][basis][qubit][x][0:tf] = 0 + + def build_operator(self, operations: Union[list, tuple]) -> qutip.Qobj: + """Creates an operator with non trivial actions on some qubits. + + Takes as argument a list of tuples [(operator_1, qubits_1), + (operator_2, qubits_2)...]. Returns the operator given by the tensor + product of {operator_i applied on qubits_i} and Id on the rest. + (operator, 'global') returns the sum for all $j$ of operator + applied at qubit $j$ and identity elsewhere. + + Example for 4 qubits: [(Z, [1, 2]), (Y, [3])] returns ZZYI + and [(X, 'global')] returns XIII + IXII + IIXI + IIIX + + Args: + operations (list): List of tuples (operator, qubits) + operator can be a qutip.Quobj or a string key for + self.op_matrix qubits is the list on which operator + will be applied. The qubits can be passed as their + index or their label in the register. + + Returns: + qutip.Qobj: the final operator. + """ + op_list = [self.op_matrix["I"] for j in range(self._size)] + + if not isinstance(operations, list): + operations = [operations] + + for operator, qubits in operations: + if qubits == "global": + return sum( + self.build_operator([(operator, [q_id])]) + for q_id in self._qdict + ) + else: + qubits_set = set(qubits) + if len(qubits_set) < len(qubits): + raise ValueError("Duplicate atom ids in argument list.") + if not qubits_set.issubset(self._qdict.keys()): + raise ValueError( + "Invalid qubit names: " + f"{qubits_set - self._qdict.keys()}" + ) + if isinstance(operator, str): + try: + operator = self.op_matrix[operator] + except KeyError: + raise ValueError(f"{operator} is not a valid operator") + for qubit in qubits: + k = self._qid_index[qubit] + op_list[k] = operator return qutip.tensor(op_list) def _adapt_to_sampling_rate(self, full_array: np.ndarray) -> np.ndarray: @@ -491,28 +565,34 @@ def _update_noise(self) -> None: def _build_basis_and_op_matrices(self) -> None: """Determine dimension, basis and projector operators.""" - # No samples => Empty dict entry => False - if ( - not self.samples["Global"]["digital"] - and not self.samples["Local"]["digital"] - ): - self.basis_name = "ground-rydberg" - self.dim = 2 - basis = ["r", "g"] - projectors = ["gr", "rr", "gg"] - elif ( - not self.samples["Global"]["ground-rydberg"] - and not self.samples["Local"]["ground-rydberg"] - ): - self.basis_name = "digital" + if self._interaction == "XY": + self.basis_name = "XY" self.dim = 2 - basis = ["g", "h"] - projectors = ["hg", "hh", "gg"] + basis = ["u", "d"] + projectors = ["uu", "du", "ud", "dd"] else: - self.basis_name = "all" # All three states - self.dim = 3 - basis = ["r", "g", "h"] - projectors = ["gr", "hg", "rr", "gg", "hh"] + # No samples => Empty dict entry => False + if ( + not self.samples["Global"]["digital"] + and not self.samples["Local"]["digital"] + ): + self.basis_name = "ground-rydberg" + self.dim = 2 + basis = ["r", "g"] + projectors = ["gr", "rr", "gg"] + elif ( + not self.samples["Global"]["ground-rydberg"] + and not self.samples["Local"]["ground-rydberg"] + ): + self.basis_name = "digital" + self.dim = 2 + basis = ["g", "h"] + projectors = ["hg", "hh", "gg"] + else: + self.basis_name = "all" # All three states + self.dim = 3 + basis = ["r", "g", "h"] + projectors = ["gr", "hg", "rr", "gg", "hh"] self.basis = {b: qutip.basis(self.dim, i) for i, b in enumerate(basis)} self.op_matrix = {"I": qutip.qeye(self.dim)} @@ -536,20 +616,83 @@ def _construct_hamiltonian(self) -> None: def make_vdw_term() -> qutip.Qobj: """Construct the Van der Waals interaction Term. - For each pair of qubits, calculate the distance between them, then - assign the local operator "sigma_rr" at each pair. The units are - given so that the coefficient includes a 1/hbar factor. + For each pair of qubits, calculate the distance between them, + then assign the local operator "sigma_rr" at each pair. + The units are given so that the coefficient includes a + 1/hbar factor. """ - vdw = 0 * self._build_operator("I") + vdw = cast(qutip.Qobj, 0) # Get every pair without duplicates for q1, q2 in itertools.combinations(self._qdict.keys(), r=2): # no VdW interaction with other qubits for a badly prep. qubit - if not (self._bad_atoms[q1] or self._bad_atoms[q2]): - dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) - U = 0.5 * self._seq._device.interaction_coeff / dist ** 6 - vdw += U * self._build_operator("sigma_rr", q1, q2) + if self._bad_atoms[q1] or self._bad_atoms[q2]: + continue + dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) + U = 0.5 * self._seq._device.interaction_coeff / dist ** 6 + vdw += U * self.build_operator([("sigma_rr", [q1, q2])]) return vdw + def make_xy_term(masked: bool = False) -> qutip.Qobj: + """Construct the XY interaction Term. + + For each pair of qubits, calculate the distance between them, + then assign the local operator "sigma_du * sigma_ud" at each pair. + The units are given so that the coefficient + includes a 1/hbar factor. + """ + # Calculate the total number of good, unmasked qubits + if masked: + effective_size = self._size - sum(self._bad_atoms.values()) + for q in self._seq._slm_mask_targets: + if not self._bad_atoms[q]: + effective_size -= 1 + if effective_size < 2: + return 0 * self.build_operator([("I", "global")]) + + xy = cast(qutip.Qobj, 0) + # Get every pair without duplicates + for q1, q2 in itertools.combinations(self._qdict.keys(), r=2): + if ( + self._bad_atoms[q1] + or self._bad_atoms[q2] + or ( + masked + and ( + q1 in self._seq._slm_mask_targets + or q2 in self._seq._slm_mask_targets + ) + ) + ): + continue + dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) + mag_norm = np.linalg.norm(self._seq.magnetic_field[0:2]) + if mag_norm < 1e-8: + cosine = 0.0 + else: + cosine = ( + np.dot( + (self._qdict[q1] - self._qdict[q2]), + self._seq.magnetic_field[0:2], + ) + / (dist * mag_norm) + ) + U = ( + 0.5 + * self._seq._device.interaction_coeff_xy + * (1 - 3 * cosine ** 2) + / dist ** 3 + ) + xy += U * self.build_operator( + [("sigma_du", [q1]), ("sigma_ud", [q2])] + ) + return xy + + def make_interaction_term(masked: bool = False) -> qutip.Qobj: + if self._interaction == "XY": + return make_xy_term(masked) + else: + return make_vdw_term() + def build_coeffs_ops(basis: str, addr: str) -> list[list]: """Build coefficients and operators for the hamiltonian QobjEvo.""" samples = self.samples[addr][basis] @@ -559,6 +702,8 @@ def build_coeffs_ops(basis: str, addr: str) -> list[list]: op_ids = ["sigma_gr", "sigma_rr"] elif basis == "digital": op_ids = ["sigma_hg", "sigma_gg"] + elif basis == "XY": + op_ids = ["sigma_du", "sigma_dd"] terms = [] if addr == "Global": @@ -570,8 +715,8 @@ def build_coeffs_ops(basis: str, addr: str) -> list[list]: if np.any(coeff != 0): # Build once global operators as they are needed if op_id not in operators: - operators[op_id] = self._build_operator( - op_id, global_op=True + operators[op_id] = self.build_operator( + [(op_id, "global")] ) terms.append( [ @@ -592,8 +737,8 @@ def build_coeffs_ops(basis: str, addr: str) -> list[list]: for coeff, op_id in zip(coeffs, op_ids): if np.any(coeff != 0): if op_id not in operators[q_id]: - operators[q_id][op_id] = self._build_operator( - op_id, q_id + operators[q_id][op_id] = self.build_operator( + [(op_id, [q_id])] ) terms.append( [ @@ -604,12 +749,35 @@ def build_coeffs_ops(basis: str, addr: str) -> list[list]: self.operators[addr][basis] = operators return terms + qobj_list = [] # Time independent term: - if self.basis_name == "digital" or self._size == 1: - qobj_list = [0 * self._build_operator("I")] - else: - # Van der Waals Interaction Terms - qobj_list = [make_vdw_term()] + effective_size = self._size - sum(self._bad_atoms.values()) + if self.basis_name != "digital" and effective_size > 1: + # Build time-dependent or time-independent interaction term based + # on whether an SLM mask was defined or not + if self._seq._slm_mask_time: + # Build an array of binary coefficients for the interaction + # term of unmasked qubits + coeff = np.ones(self._tot_duration) + coeff[0 : self._seq._slm_mask_time[1]] = 0 + # Build the interaction term for unmasked qubits + qobj_list = [ + [ + make_interaction_term(), + self._adapt_to_sampling_rate(coeff), + ] + ] + # Build the interaction term for masked qubits + qobj_list += [ + [ + make_interaction_term(masked=True), + self._adapt_to_sampling_rate( + np.logical_not(coeff).astype(int) + ), + ] + ] + else: + qobj_list = [make_interaction_term()] # Time dependent terms: for addr in self.samples: @@ -617,6 +785,9 @@ def build_coeffs_ops(basis: str, addr: str) -> list[list]: if self.samples[addr][basis]: qobj_list += cast(list, build_coeffs_ops(basis, addr)) + if not qobj_list: # If qobj_list ends up empty + qobj_list = [0 * self.build_operator([("I", "global")])] + ham = qutip.QobjEvo(qobj_list, tlist=self._times) ham = ham + ham.dag() ham.compress() diff --git a/pulser/tests/test_devices.py b/pulser/tests/test_devices.py index ba5ddd91f..0a8f83519 100644 --- a/pulser/tests/test_devices.py +++ b/pulser/tests/test_devices.py @@ -30,6 +30,7 @@ def test_init(): assert dev.max_radial_distance > 10 assert dev.min_atom_distance > 0 assert dev.interaction_coeff > 0 + assert dev.interaction_coeff_xy > 0 assert isinstance(dev.channels, dict) with pytest.raises(FrozenInstanceError): dev.name = "something else" @@ -46,6 +47,7 @@ def test_mock(): assert dev.max_atom_num > 1000 assert dev.min_atom_distance <= 1 assert dev.interaction_coeff == 5008713 + assert dev.interaction_coeff_xy == 3700 names = ["Rydberg", "Raman", "Microwave"] basis = ["ground-rydberg", "digital", "XY"] for ch in dev.channels.values(): diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 4c4e33115..3d5a998f0 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -70,7 +70,7 @@ def test_channel_declaration(): seq2.declare_channel("ch0", "raman_local", initial_target="q1") seq2.declare_channel("ch1", "rydberg_global") seq2.declare_channel("ch2", "rydberg_global") - assert set(seq2.available_channels) == available_channels - {"mw_global"} + assert set(seq2.available_channels) == (available_channels - {"mw_global"}) assert seq2._taken_channels == { "ch0": "raman_local", "ch1": "rydberg_global", @@ -90,6 +90,51 @@ def test_channel_declaration(): seq2.declare_channel("ch3", "rydberg_global") +def test_magnetic_field(): + seq = Sequence(reg, MockDevice) + with pytest.raises( + AttributeError, + match="only defined when the sequence " "is in 'XY Mode'.", + ): + seq.magnetic_field + seq.declare_channel("ch0", "mw_global") # seq in XY mode + # mag field is the default + assert np.all(seq.magnetic_field == np.array((0.0, 0.0, 30.0))) + seq.set_magnetic_field(bx=1.0, by=-1.0, bz=0.5) + assert np.all(seq.magnetic_field == np.array((1.0, -1.0, 0.5))) + with pytest.raises(ValueError, match="magnitude greater than 0"): + seq.set_magnetic_field(bz=0.0) + assert seq._empty_sequence + seq.add(Pulse.ConstantPulse(100, 1, 1, 0), "ch0") + assert not seq._empty_sequence + with pytest.raises(ValueError, match="can only be set on an empty seq"): + seq.set_magnetic_field(1.0, 0.0, 0.0) + + seq2 = Sequence(reg, MockDevice) + seq2.declare_channel("ch0", "rydberg_global") # not in XY mode + with pytest.raises(ValueError, match="can only be set in 'XY Mode'."): + seq2.set_magnetic_field(1.0, 0.0, 0.0) + + seq3 = Sequence(reg, MockDevice) + seq3.set_magnetic_field(1.0, 0.0, 0.0) # sets seq to XY mode + assert set(seq3.available_channels) == {"mw_global"} + seq3.declare_channel("ch0", "mw_global") + # Does not change to default + assert np.all(seq3.magnetic_field == np.array((1.0, 0.0, 0.0))) + var = seq3.declare_variable("var") + # Sequence is marked as non-empty when parametrized too + seq3.add(Pulse.ConstantPulse(100, var, 1, 0), "ch0") + assert seq3.is_parametrized() + with pytest.raises(ValueError, match="can only be set on an empty seq"): + seq3.set_magnetic_field() + + seq3_str = seq3.serialize() + seq3_ = Sequence.deserialize(seq3_str) + assert seq3_._in_xy + assert str(seq3) == str(seq3_) + assert np.all(seq3_.magnetic_field == np.array((1.0, 0.0, 0.0))) + + def test_target(): seq = Sequence(reg, device) seq.declare_channel("ch0", "raman_local", initial_target="q1") @@ -363,3 +408,141 @@ def test_sequence(): assert json.loads(s)["__version__"] == pulser.__version__ seq_ = Sequence.deserialize(s) assert str(seq) == str(seq_) + + +def test_config_slm_mask(): + reg_s = Register({"q0": (0, 0), "q1": (10, 10), "q2": (-10, -10)}) + seq_s = Sequence(reg_s, device) + + with pytest.raises(TypeError, match="must be castable to set"): + seq_s.config_slm_mask(0) + with pytest.raises(TypeError, match="must be castable to set"): + seq_s.config_slm_mask((0)) + with pytest.raises(ValueError, match="exist in the register"): + seq_s.config_slm_mask("q0") + with pytest.raises(ValueError, match="exist in the register"): + seq_s.config_slm_mask(["q3"]) + with pytest.raises(ValueError, match="exist in the register"): + seq_s.config_slm_mask(("q3",)) + with pytest.raises(ValueError, match="exist in the register"): + seq_s.config_slm_mask({"q3"}) + with pytest.raises(ValueError, match="exist in the register"): + seq_s.config_slm_mask([0]) + with pytest.raises(ValueError, match="exist in the register"): + seq_s.config_slm_mask((0,)) + with pytest.raises(ValueError, match="exist in the register"): + seq_s.config_slm_mask({0}) + + targets_s = ["q0", "q2"] + seq_s.config_slm_mask(targets_s) + assert seq_s._slm_mask_targets == {"q0", "q2"} + + with pytest.raises(ValueError, match="configured only once"): + seq_s.config_slm_mask(targets_s) + + reg_i = Register({0: (0, 0), 1: (10, 10), 2: (-10, -10)}) + seq_i = Sequence(reg_i, device) + + with pytest.raises(TypeError, match="must be castable to set"): + seq_i.config_slm_mask(0) + with pytest.raises(TypeError, match="must be castable to set"): + seq_i.config_slm_mask((0)) + with pytest.raises(ValueError, match="exist in the register"): + seq_i.config_slm_mask("q0") + with pytest.raises(ValueError, match="exist in the register"): + seq_i.config_slm_mask([3]) + with pytest.raises(ValueError, match="exist in the register"): + seq_i.config_slm_mask((3,)) + with pytest.raises(ValueError, match="exist in the register"): + seq_i.config_slm_mask({3}) + with pytest.raises(ValueError, match="exist in the register"): + seq_i.config_slm_mask(["0"]) + with pytest.raises(ValueError, match="exist in the register"): + seq_i.config_slm_mask(("0",)) + with pytest.raises(ValueError, match="exist in the register"): + seq_i.config_slm_mask({"0"}) + + targets_i = [0, 2] + seq_i.config_slm_mask(targets_i) + assert seq_i._slm_mask_targets == {0, 2} + + with pytest.raises(ValueError, match="configured only once"): + seq_i.config_slm_mask(targets_i) + + +def test_slm_mask(): + reg = Register({"q0": (0, 0), "q1": (10, 10), "q2": (-10, -10)}) + targets = ["q0", "q2"] + pulse1 = Pulse.ConstantPulse(100, 10, 0, 0) + pulse2 = Pulse.ConstantPulse(200, 10, 0, 0) + + # Try to set mask when Ising was already declared + seq_ising1 = Sequence(reg, MockDevice) + seq_ising1.declare_channel("ch_rg", "rydberg_global") + with pytest.raises( + NotImplementedError, match="SLM mask can only be added in XY mode" + ): + seq_ising1.config_slm_mask(targets) + + # Try to set mask and then declare Ising + seq_ising2 = Sequence(reg, MockDevice) + seq_ising2.config_slm_mask(targets) + with pytest.raises( + NotImplementedError, match="SLM mask is not yet available in Ising" + ): + seq_ising2.declare_channel("ch_rg", "rydberg_global") + + # Set mask when an XY pulse is already in the schedule + seq_xy1 = Sequence(reg, MockDevice) + seq_xy1.declare_channel("ch_xy", "mw_global") + seq_xy1.add(pulse1, "ch_xy") + seq_xy1.config_slm_mask(targets) + assert seq_xy1._slm_mask_time == [0, 100] + + # Set mask and then add an XY pulse to the schedule + seq_xy2 = Sequence(reg, MockDevice) + seq_xy2.config_slm_mask(targets) + seq_xy2.declare_channel("ch_xy", "mw_global") + seq_xy2.add(pulse1, "ch_xy") + assert seq_xy2._slm_mask_time == [0, 100] + + # Check that adding extra pulses does not change SLM mask time + seq_xy2.add(pulse2, "ch_xy") + assert seq_xy2._slm_mask_time == [0, 100] + + # Check that SLM mask time is updated accordingly if a new pulse with + # earlier start is added + seq_xy3 = Sequence(reg, MockDevice) + seq_xy3.declare_channel("ch_xy1", "mw_global") + seq_xy3.config_slm_mask(targets) + seq_xy3.delay(duration=100, channel="ch_xy1") + seq_xy3.add(pulse1, "ch_xy1") + assert seq_xy3._slm_mask_time == [100, 200] + seq_xy3.declare_channel("ch_xy2", "mw_global") + seq_xy3.add(pulse1, "ch_xy2", "no-delay") + assert seq_xy3._slm_mask_time == [0, 100] + + # Same as previous check, but mask is added afterwards + seq_xy4 = Sequence(reg, MockDevice) + seq_xy4.declare_channel("ch_xy1", "mw_global") + seq_xy4.delay(duration=100, channel="ch_xy1") + seq_xy4.add(pulse1, "ch_xy1") + seq_xy4.declare_channel("ch_xy2", "mw_global") + seq_xy4.add(pulse1, "ch_xy2", "no-delay") + seq_xy4.config_slm_mask(targets) + assert seq_xy4._slm_mask_time == [0, 100] + + # Check that paramatrize works with SLM mask + seq_xy5 = Sequence(reg, MockDevice) + seq_xy5.declare_channel("ch", "mw_global") + var = seq_xy5.declare_variable("var") + seq_xy5.add(Pulse.ConstantPulse(200, var, 0, 0), "ch") + assert seq_xy5.is_parametrized() + seq_xy5.config_slm_mask(targets) + seq_xy5_str = seq_xy5.serialize() + seq_xy5_ = Sequence.deserialize(seq_xy5_str) + assert str(seq_xy5) == str(seq_xy5_) + + # Check drawing method + with patch("matplotlib.pyplot.show"): + seq_xy2.draw() diff --git a/pulser/tests/test_simresults.py b/pulser/tests/test_simresults.py index cac330556..0adf2f9a8 100644 --- a/pulser/tests/test_simresults.py +++ b/pulser/tests/test_simresults.py @@ -21,7 +21,7 @@ from qutip.piqs import isdiagonal from pulser import Sequence, Pulse, Register -from pulser.devices import Chadoq2 +from pulser.devices import Chadoq2, MockDevice from pulser.waveforms import BlackmanWaveform from pulser.simulation import Simulation, SimConfig from pulser.simulation.simresults import CoherentResults, NoisyResults @@ -58,7 +58,14 @@ def test_initialization(): with pytest.raises(ValueError, match="`basis_name` must be"): CoherentResults(state, 2, "bad_basis", None, [0]) - with pytest.raises(ValueError, match="`meas_basis` must be"): + with pytest.raises( + ValueError, match="`meas_basis` must be 'ground-rydberg' or 'digital'." + ): + CoherentResults(state, 1, "all", None, "XY") + with pytest.raises( + ValueError, + match="`meas_basis` and `basis_name` must have the same value.", + ): CoherentResults( state, 1, "ground-rydberg", [0], "wrong_measurement_basis" ) @@ -296,3 +303,51 @@ def test_sample_final_state_noisy(): ] ), ).all() + + +def test_results_xy(): + q_dict = { + "A": np.array([0.0, 0.0]), + "B": np.array([0.0, 10.0]), + } + reg = Register(q_dict) + duration = 1000 + pi = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0.0, 0) + seq = Sequence(reg, MockDevice) + + # Declare Channels + seq.declare_channel("ch0", "mw_global") + seq.add(pi, "ch0") + seq.measure("XY") + + sim = Simulation(seq) + results = sim.run() + + ground = qutip.tensor([qutip.basis(2, 1), qutip.basis(2, 1)]) + + assert results._dim == 2 + assert results._size == 2 + assert results._basis_name == "XY" + assert results._meas_basis == "XY" + assert results.states[0] == ground + + with pytest.raises(TypeError, match="Can't reduce a system in"): + results.get_final_state(reduce_to_basis="all") + + with pytest.raises(TypeError, match="Can't reduce a system in"): + results.get_final_state(reduce_to_basis="ground-rydberg") + + with pytest.raises(TypeError, match="Can't reduce a system in"): + results.get_final_state(reduce_to_basis="digital") + + state = results.get_final_state(reduce_to_basis="XY") + + assert np.all( + np.isclose( + np.abs(state.full()), np.abs(results.states[-1].full()), atol=1e-5 + ) + ) + + # Check that measurement projectors are correct + assert results._meas_projector(0) == qutip.basis(2, 1).proj() + assert results._meas_projector(1) == qutip.basis(2, 0).proj() diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index 628c2fe91..8c1dbb5ac 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -169,7 +169,16 @@ def test_building_basis_and_projection_operators(): # Check local operator building method: with pytest.raises(ValueError, match="Duplicate atom"): - sim._build_operator("sigma_gg", "target", "target") + sim.build_operator([("sigma_gg", ["target", "target"])]) + with pytest.raises(ValueError, match="not a valid operator"): + sim.build_operator([("wrong", ["target"])]) + with pytest.raises(ValueError, match="Invalid qubit names: {'wrong'}"): + sim.build_operator([("sigma_gg", ["wrong"])]) + + # Check building operator with one operator + op_standard = sim.build_operator([("sigma_gg", ["target"])]) + op_one = sim.build_operator(("sigma_gg", ["target"])) + assert np.linalg.norm(op_standard - op_one) < 1e-10 # Global ground-rydberg seq2 = Sequence(reg, Chadoq2) @@ -222,23 +231,41 @@ def test_building_basis_and_projection_operators(): == qutip.basis(2, 1) * qutip.basis(2, 0).dag() ) + # Global XY + seq2 = Sequence(reg, MockDevice) + seq2.declare_channel("global", "mw_global") + seq2.add(pi, "global") + sim2 = Simulation(seq2, sampling_rate=0.01) + assert sim2.basis_name == "XY" + assert sim2.dim == 2 + assert sim2.basis == {"u": qutip.basis(2, 0), "d": qutip.basis(2, 1)} + assert ( + sim2.op_matrix["sigma_uu"] + == qutip.basis(2, 0) * qutip.basis(2, 0).dag() + ) + assert ( + sim2.op_matrix["sigma_du"] + == qutip.basis(2, 1) * qutip.basis(2, 0).dag() + ) + assert ( + sim2.op_matrix["sigma_ud"] + == qutip.basis(2, 0) * qutip.basis(2, 1).dag() + ) + def test_empty_sequences(): - seq = Sequence(reg, Chadoq2) + seq = Sequence(reg, MockDevice) with pytest.raises(ValueError, match="no declared channels"): Simulation(seq) + seq.declare_channel("ch0", "mw_global") + with pytest.raises(ValueError, match="No instructions given"): + Simulation(seq) + + seq = Sequence(reg, MockDevice) + seq.declare_channel("test", "rydberg_local", "target") + seq.declare_channel("test2", "rydberg_global") with pytest.raises(ValueError, match="No instructions given"): - seq.declare_channel("test", "rydberg_local", "target") - seq.declare_channel("test2", "rydberg_global") Simulation(seq) - seqMW = Sequence(reg, MockDevice) - with pytest.raises(NotImplementedError): - seqMW.declare_channel("ch0", "mw_global") - seqMW.add( - Pulse.ConstantDetuning(RampWaveform(1500, 0.0, 2.0), 0.0, 0.0), - "ch0", - ) - Simulation(seqMW) def test_get_hamiltonian(): @@ -474,14 +501,13 @@ def test_config(): def test_noise(): sim2 = Simulation( - seq, sampling_rate=0.01, config=SimConfig(noise=("doppler")) + seq, sampling_rate=0.01, config=SimConfig(noise=("SPAM"), eta=0.4) ) sim2.run() with pytest.raises(NotImplementedError, match="Cannot include"): sim2.set_config(SimConfig(noise="dephasing")) - sim2.run() assert sim2.config.spam_dict == { - "eta": 0.005, + "eta": 0.4, "epsilon": 0.01, "epsilon_prime": 0.05, } @@ -562,3 +588,231 @@ def test_cuncurrent_pulses(): ham_no_noise = sim_no_noise.get_hamiltonian(t) ham_with_noise = sim_with_noise.get_hamiltonian(t) assert ham_no_noise[0, 1] == ham_with_noise[0, 1] + + +def test_get_xy_hamiltonian(): + simple_reg = Register.from_coordinates( + [[0, 10], [10, 0], [0, 0]], prefix="atom" + ) + detun = 1.0 + amp = 3.0 + rise = Pulse.ConstantPulse(1500, amp, detun, 0.0) + simple_seq = Sequence(simple_reg, MockDevice) + simple_seq.declare_channel("ch0", "mw_global") + simple_seq.set_magnetic_field(0, 1.0, 0.0) + simple_seq.add(rise, "ch0") + + assert np.isclose(np.linalg.norm(simple_seq.magnetic_field[0:2]), 1) + + simple_sim = Simulation(simple_seq, sampling_rate=0.01) + with pytest.raises( + ValueError, match="less than or equal to the sequence duration" + ): + simple_sim.get_hamiltonian(1650) + with pytest.raises(ValueError, match="greater than or equal to 0"): + simple_sim.get_hamiltonian(-10) + # Constant detuning, so |ud>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def magnetization(j, total_sites):\n", + " prod = [qutip.qeye(2) for _ in range(total_sites)]\n", + " prod[j] = (qutip.sigmaz() + qutip.qeye(2)) / 2\n", + " return qutip.tensor(prod)\n", + "\n", + "magn = magnetization(0, 1)\n", + "plt.figure(figsize=[16, 6])\n", + "results.plot(magn)\n", + "plt.xlabel('Pulse duration (ns)', fontsize='x-large')\n", + "plt.ylabel('Excitation of the atom', fontsize='x-large')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spin chain of 3 atoms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now simulate the free evolution of a spin chain of 3 atoms, starting with 1 excitation in the initial state $|100\\rangle$ as shown in the figure 3 (c) of the [reference](https://arxiv.org/pdf/1408.1055.pdf). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "coords = np.array([[-8., 0], [0, 0], [8., 0]])\n", + "qubits = dict(enumerate(coords))\n", + "\n", + "reg = Register(qubits)\n", + "seq = Sequence(reg, MockDevice)\n", + "seq.declare_channel('ch0', 'mw_global')\n", + "reg.draw()\n", + "\n", + "# State preparation using SLM mask\n", + "masked_qubits = [1, 2]\n", + "seq.config_slm_mask(masked_qubits)\n", + "masked_pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi), 0, 0)\n", + "seq.add(masked_pulse, 'ch0')\n", + "\n", + "# Simulation pulse\n", + "simple_pulse = Pulse.ConstantPulse(7000, 0, 0, 0)\n", + "seq.add(simple_pulse, 'ch0')\n", + "seq.measure(basis='XY')\n", + "\n", + "sim = Simulation(seq, sampling_rate=1)\n", + "results = sim.run(nsteps=5000)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "magn_list = [magnetization(j, 3) for j in range(3)]\n", + "\n", + "expectations = results.expect(magn_list)\n", + "\n", + "plt.figure(figsize=[16, 18])\n", + "plt.subplot(311)\n", + "plt.plot(expectations[0])\n", + "plt.ylabel('Excitation of atom 0', fontsize='x-large')\n", + "plt.xlabel('Time (ns)', fontsize='x-large')\n", + "plt.subplot(312)\n", + "plt.plot(expectations[1])\n", + "plt.ylabel('Excitation of atom 1', fontsize='x-large')\n", + "plt.xlabel('Time (ns)', fontsize='x-large')\n", + "plt.ylim([0, 1])\n", + "plt.subplot(313)\n", + "plt.plot(expectations[2])\n", + "plt.ylabel('Excitation of atom 2', fontsize='x-large')\n", + "plt.xlabel('Time (ns)', fontsize='x-large')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## External field and angular dependency" + ] + }, + { + "attachments": { + "angular_dependency.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An external magnetic field can be added to the experiment, and will modify the hamiltonian. The XY Hamiltonian is then\n", + "\n", + "$$\n", + "H_{XY} = \\frac{1}{2}\\sum_{i\n", + "\"Angular\n", + "\n", + "\n", + "We add an external field along the Y axis, and we put the qubit 2 at the angle such that $\\text{cos}^2(\\theta_{12}) = 1/3$, and the interaction between the qubits 1 and 2 cancels out. This is done by the method `set_magnetic_field`from the `Sequence`.\n", + "\n", + "This is the principle that enables to create [topological phases](https://science.sciencemag.org/content/365/6455/775) on long chain of atoms." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "coords = np.array([[-1., 0], [0, 0], [np.sqrt(2/3), np.sqrt(1/3)]]) * 8.\n", + "qubits = dict(enumerate(coords))\n", + "\n", + "reg = Register(qubits)\n", + "seq = Sequence(reg, MockDevice)\n", + "seq.declare_channel('ch0', 'mw_global')\n", + "seq.set_magnetic_field(0., 1., 0)\n", + "reg.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then simulate again the free evolution from the initial state $|100\\rangle$. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 0.04s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.08s. Est. time left: 00:00:00:00\n", + "30.0%. Run time: 0.12s. Est. time left: 00:00:00:00\n", + "40.0%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "50.0%. Run time: 0.20s. Est. time left: 00:00:00:00\n", + "60.0%. Run time: 0.24s. Est. time left: 00:00:00:00\n", + "70.0%. Run time: 0.28s. Est. time left: 00:00:00:00\n", + "80.0%. Run time: 0.31s. Est. time left: 00:00:00:00\n", + "90.0%. Run time: 0.35s. Est. time left: 00:00:00:00\n", + "Total run time: 0.39s\n" + ] + } + ], + "source": [ + "# State preparation using SLM mask\n", + "masked_qubits = [1, 2]\n", + "seq.config_slm_mask(masked_qubits)\n", + "masked_pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi), 0, 0)\n", + "seq.add(masked_pulse, 'ch0')\n", + "\n", + "# Simulation pulse\n", + "simple_pulse = Pulse.ConstantPulse(7000, 0, 0, 0)\n", + "seq.add(simple_pulse, 'ch0')\n", + "seq.measure(basis='XY')\n", + "\n", + "sim = Simulation(seq, sampling_rate=1)\n", + "results = sim.run(progress_bar=True, nsteps=5000)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "magn_list = [magnetization(j, 3) for j in range(3)]\n", + "\n", + "expectations = results.expect(magn_list)\n", + "\n", + "plt.figure(figsize=[16, 18])\n", + "plt.subplot(311)\n", + "plt.plot(expectations[0])\n", + "plt.ylabel('Excitation of atom 0', fontsize='x-large')\n", + "plt.xlabel('Time ($\\mu$s)', fontsize='x-large')\n", + "plt.ylim([0, 1])\n", + "plt.subplot(312)\n", + "plt.plot(expectations[1])\n", + "plt.ylabel('Excitation of atom 1', fontsize='x-large')\n", + "plt.xlabel('Time ($\\mu$s)', fontsize='x-large')\n", + "plt.ylim([0, 1])\n", + "plt.subplot(313)\n", + "plt.plot(expectations[2])\n", + "plt.ylabel('Excitation of atom 2', fontsize='x-large')\n", + "plt.xlabel('Time ($\\mu$s)', fontsize='x-large')\n", + "plt.ylim([0, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see there that there is almost no excitation in the qubit 2. It still remains some because the interaction between the qubits 0 and 2 is not completely negligible." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0696d4e375c1ab153ba36002f3b24c9423c4ec78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Henriet?= Date: Tue, 5 Oct 2021 07:17:18 +0200 Subject: [PATCH 08/51] XYZ quantum sim tutorial (#269) * First commit: creating notebook * Test * Tempo print statement * Revert print and update tuto accordingly * Finding Fig. 1 results. * Finishing tutorial * Changing name QEK tutorial * reformatting simulation.py * Changing QEK tuto path in docs. * Addressing comments of reviewer 1. * OBC and PBC N_flip * Adding legend. --- docs/source/tutorials/qek.nblink | 2 +- ...ltonians in arrays of Rydberg atoms .ipynb | 718 ++++++++++++++++++ ...l.ipynb => Quantum Evolution Kernel.ipynb} | 0 3 files changed, 719 insertions(+), 1 deletion(-) create mode 100644 tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb rename tutorials/applications/{QuantumEvolutionKernel_Tutorial.ipynb => Quantum Evolution Kernel.ipynb} (100%) diff --git a/docs/source/tutorials/qek.nblink b/docs/source/tutorials/qek.nblink index 63bd4dc40..cdc8ab93e 100644 --- a/docs/source/tutorials/qek.nblink +++ b/docs/source/tutorials/qek.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/applications/QuantumEvolutionKernel_Tutorial.ipynb" + "path": "../../../tutorials/applications/Quantum Evolution Kernel.ipynb" } diff --git a/tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb b/tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb new file mode 100644 index 000000000..bd6fededf --- /dev/null +++ b/tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb @@ -0,0 +1,718 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation of XYZ spin models using Floquet engineering in XY mode" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "matplotlib.rcParams['text.usetex'] = True\n", + "import matplotlib.pyplot as plt\n", + "import qutip\n", + "\n", + "import pulser\n", + "from pulser import Pulse, Sequence, Register\n", + "from pulser.simulation import Simulation\n", + "from pulser.devices import MockDevice, Chadoq2\n", + "from pulser.waveforms import BlackmanWaveform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we will reproduce some results of \"Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms\", P. Scholl, et. al., https://arxiv.org/pdf/2107.14459.pdf." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Floquet Engineering on two atoms\n", + "\n", + "We start by considering the dynamics of two interacting atoms under $H_{XXZ}$. To demonstrate the dynamically tunable aspect of the microwave engineering, we change the Hamiltonian during the evolution of the system. More specifically, we start from $|\\rightarrow \\rightarrow \\rangle_y $, let the atoms evolve under $H_{XX}$ and apply a microwave pulse sequence between $0.9\\mu s$ and $1.2\\mu s$ only.\n", + "\n", + "Let us first define our $\\pm X$ and $\\pm Y$ pulses. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Times are in ns\n", + "t_pulse = 26\n", + "\n", + "X_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, 0)\n", + "Y_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, -np.pi/2)\n", + "mX_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, np.pi)\n", + "mY_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, np.pi/2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also define a function to add the pulses during one cycle." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def Floquet_XXZ_cycles(n_cycles, tau_1, tau_2, t_pulse):\n", + " t_half = t_pulse/2.\n", + " tau_3 = tau_2 \n", + " tc = 4*tau_2 + 2*tau_1\n", + " for _ in range(n_cycles):\n", + " seq.delay(tau_1-t_half, 'MW')\n", + " seq.add(X_pulse, 'MW')\n", + " seq.delay(tau_2-2*t_half, 'MW')\n", + " seq.add(mY_pulse, 'MW')\n", + " seq.delay(2*tau_3-2*t_half, 'MW')\n", + " seq.add(Y_pulse, 'MW')\n", + " seq.delay(tau_2-2*t_half, 'MW')\n", + " seq.add(mX_pulse, 'MW')\n", + " seq.delay(tau_1-t_half, 'MW')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are ready to start building our sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# We take two atoms distant by 10 ums.\n", + "coords = np.array([[0, 0], [10, 0]])\n", + "qubits = dict(enumerate(coords))\n", + "reg = Register(qubits)\n", + "\n", + "seq = Sequence(reg, MockDevice)\n", + "seq.declare_channel('MW', 'mw_global')\n", + "seq.set_magnetic_field(0., 0., 1.)\n", + "\n", + "tc = 300\n", + "seq.delay(3 * tc, 'MW')\n", + "Floquet_XXZ_cycles(4, tc/6., tc/6., t_pulse)\n", + "seq.delay(6 * tc, 'MW') \n", + "\n", + "# Here are our evaluation times\n", + "t_list= []\n", + "for p in range(13):\n", + " t_list.append(tc/1000.*p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's draw the sequence, to see that the microwave engineering only happens between $900 ns$ and $2100 ns$, which corresponds to $H_{XX} \\to H_{XXX}$. During that period, the total y-magnetization $\\langle \\sigma^y_1 + \\sigma^y_2 \\rangle$ is expected to be frozen, as this quantity commutes with $H_{XXX}$." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seq.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "sim = Simulation(seq, sampling_rate=1.0, config=None, evaluation_times=t_list)\n", + "psi_y = (qutip.basis(2, 0)+1j*qutip.basis(2, 1)).unit()\n", + "sim.initial_state = qutip.tensor(psi_y, psi_y)\n", + "res = sim.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sy = qutip.sigmay()\n", + "Id = qutip.qeye(2)\n", + "Sigma_y = (qutip.tensor(sy, Id)+qutip.tensor(Id, sy))/2.\n", + "Sigma_y_res = res.expect([Sigma_y])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "\n", + "# Showing the Hamiltonian engineering period.\n", + "line1 = 0.9\n", + "line2 = 2.1 \n", + "plt.axvspan(line1, line2, alpha=.1, color='grey')\n", + "plt.text(1., 0.5, r\"$H_{XX} \\to H_{XXX}$\", fontsize=14)\n", + "\n", + "plt.plot(sim._eval_times_array, Sigma_y_res[0], 'o')\n", + "plt.xlabel(r\"Time [$\\mu$ s]\", fontsize=16)\n", + "plt.ylabel(fr'$ \\langle \\sigma_1^y + \\sigma_2^y \\rangle$', fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Domain-wall dynamics\n", + "\n", + "Now, we will look at the dynamics of the system under $H_{XX2Z}$ when starting in a Domain-Wall (DW) state $|\\psi_0\\rangle = |\\uparrow \\uparrow \\uparrow \\uparrow \\uparrow \\downarrow \\downarrow \\downarrow \\downarrow \\downarrow\\rangle $, for two distinct geometries : open boundary conditions (OBC) and periodic boundary conditions (PBC). In the case of $H_{XX2Z}$, only 2 pulses per Floquet cycle are required, as the $X$ and $-X$ pulses cancel out." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def Floquet_XX2Z_cycles(n_cycles, t_pulse):\n", + " t_half = t_pulse/2.\n", + " tau_3 = tau_2 = tc/4.\n", + " for _ in range(n_cycles):\n", + " seq.delay(tau_2-t_half, 'MW')\n", + " seq.add(mY_pulse, 'MW')\n", + " seq.delay(2*tau_3-2*t_half, 'MW')\n", + " seq.add(Y_pulse, 'MW')\n", + " seq.delay(tau_2-t_half, 'MW')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "N_at = 10\n", + "# Number of Floquet cycles \n", + "N_cycles = 20 \n", + "# In the following, we will take 1000 projective measurements of the system at the final time.\n", + "N_samples = 1000 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also create the initial state of the system using QuTiP." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Creation of the initial DW state\n", + "initial_DW_state=[]\n", + "for m in range(N_at):\n", + " if m" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "img = ax.imshow(magnetizations_obc, cmap=plt.get_cmap('RdBu'))\n", + "plt.title('OBC',fontsize=16)\n", + "ax.set_xlabel('Cycle',fontsize=16)\n", + "ax.set_ylabel('Atom number',fontsize=16)\n", + "cbar = fig.colorbar(img, shrink=0.7)\n", + "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)\n", + "\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "img = ax.imshow(magnetizations_pbc, cmap=plt.get_cmap('RdBu'))\n", + "plt.title('PBC',fontsize=16)\n", + "ax.set_xlabel('Cycle',fontsize=16)\n", + "ax.set_ylabel('Atom number',fontsize=16)\n", + "cbar = fig.colorbar(img, shrink=0.7)\n", + "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the magnetization profiles look rather different for OBC and PBC. It seems that the initial DW melts in the case of PBC. In fact, the decrease of $|\\langle \\sigma^z_j \\rangle|$ for all sites is due to a delocalization of the DW along the circle. This delocalization can be more apparent when looking at correlations. More specifically, we see on the plot below that the number of spin flips between consecutive atoms along the circle, $\\langle N_{flip} \\rangle=1/2\\sum_j(1-\\langle \\sigma_j^z \\sigma_{j+1}^z\\rangle)$, remains quite low during the dynamics for both OBC (red) and PBC (blue), while it should tend to $N_{at}/2=5$ for randomly distributed spins. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "plt.title(r'Evolution of $\\langle N_{flip} \\rangle$ in time for OBC (red) and PBC (blue).', fontsize=16)\n", + "ax.set_xlabel('Cycle',fontsize=16)\n", + "ax.set_ylabel(r'$\\langle N_{flip} \\rangle$',fontsize=14)\n", + "ax.plot(correl_pbc,'--o',color='blue')\n", + "ax.plot(correl_obc,'--o',color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To investigate even more this delocalization effect, let's consider a smaller region of only 3 spins prepared in $|\\uparrow \\rangle$. The delocalization timescale will then be shorter, and we will see it more clearly happening in the system" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Creation of the initial DW state with only 3 spins up.\n", + "initial_DW_state=[]\n", + "for m in range(N_at):\n", + " if m < 3:\n", + " initial_DW_state.append(qutip.basis(2, 0))\n", + " else:\n", + " initial_DW_state.append(qutip.basis(2, 1))\n", + " \n", + "initial_DW_state = qutip.tensor(initial_DW_state)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "N_cycles=26\n", + "\n", + "magnetizations_pbc = np.zeros((N_at, N_cycles), dtype=float)\n", + "samples_evol = []\n", + "for m in range(N_cycles):\n", + " seq = Sequence(reg, MockDevice)\n", + " seq.set_magnetic_field(0., 0., 1.)\n", + " seq.declare_channel('MW', 'mw_global')\n", + " seq.set_magnetic_field(0., 0., 1.)\n", + " seq.add(X_pulse, 'MW')\n", + " Floquet_XX2Z_cycles(m, t_pulse)\n", + " seq.add(mX_pulse, 'MW')\n", + " sim = Simulation(seq)\n", + " sim.initial_state = initial_DW_state\n", + " res = sim.run()\n", + " samples = res.sample_final_state(N_samples)\n", + " samples_evol.append(samples)\n", + " correl = 0.\n", + " for key, value in samples.items():\n", + " for j in range(N_at):\n", + " magnetizations_pbc[j][m] += (2*float(key[j])-1)*value/N_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "img = ax.imshow(magnetizations_pbc, cmap=plt.get_cmap('RdBu'))\n", + "ax.set_xlabel('Cycle', fontsize=16)\n", + "ax.set_ylabel('Atom number', fontsize=16)\n", + "cbar = fig.colorbar(img)\n", + "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see above that the magnetization profile tends to average. But if we look at the histogram of sampled states in time, we will remark that domain-wall configurations are dominant (in red in the histograms below). As time increases, the delocalization mechanism populates more and more domain-wall states distinct from the initial state." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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AgGVt+AmdiIjwMzocwJVZAAAAuiOZBQAAoDuSWQAAALojmQUAAKA7klkAAAC6I5kFAACgO5JZAAAAuiOZBQAAoDtNJLOZ+fmVx/cz815mPlizTwAAALRr9WQ2M+9FxPnw+H5ERCnlyZU2AAAA+J5Vk9nMPI+IF1ee+vDKv19ExJ3FOwUAAEDz1r4ye15KuZrMnl1rf/f6Apn5IDOfZubTly9fVu0cAAAAbVotmc3Me5dfJ77idUTc2rZcKeVhKeVuKeXu7du3q/UPAACAdr214rZfDffEnkXEeWbeiYhv4rurs+cR8XidrgEAANCy1a7MllKeD1dmb8WQwJZSvoqLxPZeRJxtuHILAAAAq16ZjYiLrw1HxMMr//5ieCiRBQAAYKO1C0ABAADAwSSzAAAAdEcyCwAAQHckswAAAHRHMgsAAEB3Vq9mDHCwzM3Pl7JsPwAAWI0rswAAAHRHMgsAAEB3JLMAAAB0RzILAABAdySzAAAAdEcyCwAAQHckswAAAHRHMgsAAEB3JLMAAAB0RzILAABAdySzAAAAdEcyCwAAQHckswAAAHRHMgsAAEB33lq7Ayctc/PzpSzbDwAAgM64MgsAAEB3JLMAAAB0RzILAABAdySzAAAAdEcyCwAAQHckswAAAHRHMgsAAEB3/M4sAJPkht/MLn4vGwCozJVZAAAAuuPKLAAAdGLTt2EifCOG07RaMpuZZxFxPvz3YSnls+H5+xHxOiLOSykP1+ofAAAA7Vrza8Y/j4i7pZSvIiIy88GQyEYp5cnw3L0V+wdARZm58T8AgH2slsyWUh5eufJ6HhEvIuLD4f8x/P/OGn0DAACgbasXgMrM84h4NVyNPbvW/O6G1z/IzKeZ+fTly5dLdBEAAIDGrJ7MRsT9Usonw+PXEXFr24uHK7p3Syl3b9++Xb1zAAAAtGfVZDYz75dSvhge34mIb+K7q7PnEfF4pa4BAADQsNWS2aG40+eZ+Swzn0XEraEY1PnQdnZZCAoAAACuWu2neYZE9f0Nz38xPJTIAgAAsFEL98wCAADAQSSzAAAAdEcyCwAAQHdWu2cWAOhDZm58vpSycE8A4DuuzAIAANAdV2YBAI6Eq+ht2bQ/7AuYjyuzAAAAdEcyCwAAQHckswAAAHTHPbMA4T4zAIDeSGYBYAJ/CAGAdfiaMQAAAN2RzAIAANAdySwAAADdcc8sAHDy3PsM0B9XZgEAAOiOK7PUdcNfusNfugGARrgyD31yZRYAAIDuuDILe/AXW46BcQxwupwDOEauzAIAANAdV2YbNfqvZ5XuUfXXPJjfpuPKMQXOOTDWKRw7pxAj+3NlFgAAgO64MgsrWeMvi/6aCX1wrALAbq7MAgAA0B1XZivz1/Xljb0P0b46DvYjAMBpcGUWAACA7rgy26HWrjy11h84ZadwPJ5CjIynSng/7KvjYE5mTa7MAgAA0B1XZmGirX+RrPS7v6P709A6AQB64HNQu1yZBQAAoDuuzJ6Ylu5PaeV3Vlv9q1pPfe1FrTG39L7q6djx1+z5eU+Xt8bccWjbHP3pxanHv4ZjOQe09NnqWD6TrK25ZDYz70fE64g4L6U8XLk7AAAANKipZHZIZKOU8iQzH2TmvVLKk7X7dSpa+kvOtr9WtfZXvjXU2Fdj39cp+2rsVYmlTRlzLcWxTWvHVY3xeFN7zfG4xly29NW+pZe7adkWx2pPx1WNtpu2WfOKXq39sfT8sIZejqslt7nWPN/acdWy1u6Z/TAiXgyPX0TEnRX7AgAAQKOaujIbEWfX/v3u9Rdk5oOIeDD88/9l5v+s3akZ/SQi/jli419G3rRtqIC713IzrvNN+7a2BftTo+1Ne60YF4p/1Tha21cNjKvJcZxCjI309dRjXDWOhcbxm3bHVfNtb9o7j3GvOKbE2MBYnSWOU4+xgzha8y9vbCmlNPNfRHweEfeGx/ci4vO1+zRzfE97aGutP2I8/jhOIcbW+iPG046xtf6I8fjjOIUYW+uPGE87xqnL9vJfa18z/ia+uzp7HhGP1+sKAAAArWoqmS2lfBUR55l5LyLOiuJPAAAAbNDaPbNRSvlieHiMiey2nxpqqW2NbYpxXNsa2xTjuLY1tinG+dvW2KY4xrWtsU1xjGtbY5viGNe2xjbFOK5t6rJdyOE70wAAANCNpr5mDAAAAPto7mvGxy4zf1xK+fOutsz8y7io6PxuRPwhIp6XUv77rvVuW25sW43tTVlvT8v1vD/2HY+1tjl3jMeyr6bEsa0/S/e11ngURztta7yvS4//Nfozdnut7asaMe5aZ0ufEdY4J9fo6ymcy8Zur+Z6x7SNmct64GvGC8vMX5dSfrWtLTN/HRGvIuJFRLyOiwrPH0ZE2bbs8HDjcmPbamzvpnXus96x/VljuW37cduyNfozdp1rxLHGGO9lX02Jo6X3fFcc2/rZ0vx4CnFMib+l46rG+K91Llv6WG1tnq+1j2sccz2dk3uZV1o7l00ZxzXiWHou64FktpLM/F1E/Ou4GDBvno6In0bE1ze1lVLezczfllJ+sWGd/xARt7as98mW5d4Z2Tb79kopf73j/dm23rH9WXS5IcZt+7GZ/bFtnXuMxxpx3Hh8TIhx13jsZV9NiaOZ93xH25TxuPT8eApxHMtxNfv4r3gu29af2Y/VlfZVjRjXOOZ6Oid3Ma80eC4btb2JY27s8TF6Lrv+fE8ks5Vk5tsR8aCU8vfXnv+7iPj1TW2llP84DPL/HRHP4+KvKLci4k5E/GTbsnHxV5ablhvbNvv2hhi3vT81+rPocnvsx1Hv3YQ4Rq1zpTgWHeOd7aspcTTzno993xqcH08hjlFtDR5Xs4//iueyRY/VBuf52fdxxWOup3NyF/NKg+eyUdurGEeVuSw6JpmtKDPfLqX86dC2of1nEfFRRLwdF39heVy++17/tvVeLnd2Zbmv91jntuVm394B650z/kWX2yOOZvbHAePx0PWO3Y9jx8ao93Sm9Y4dq7Ntb2J/as4rs71vE2Os0dc14ziLZfZHrfG49HE1+/ifsuyE/kwd4zfF0dI8P/s+3iPGmp9Jas0dm+Lofl6ZGMein5EqzvOLHjs9e2vtDhy5n2bmxhutNw20/P7N26+GZeLK/2PXssNB+fWmth3r3LbctjhGbW+P92DbsmP7s/RysSOOlvbHjeucuN5R43hHf2q8p6PXu6Nt9jh2tG1tH/ue19j/O9q2jscJy9bo6+JxrLA/qozHCW2j9nGl8b+1P9uWndCfUefOXXG0NM9PiHHrZ6ux59YJ26wyd0z4rDf758dKbbvat70/s8+dO963Kp/nd8RR4/NTt1yZrSQn3PQ9LPuHiPj9octu6UuMWefw8OA4xmxvn2Un9mex5Xbtx20xLr0/xqzzgPXOGmON7U3ZV3u0zRrHrhjHzh3b+jpxXM36vu25rxZ7z9eIY6X9Mft4nLjNg/dx5Xl+1LJz92d4ONtnh33WO3abjcZYY1zNPnfsEcdinx9rtM0wz68+rqZ+np8Yx2zjuBeS2Upy5E3fZb4bxjMuBu+hBQyqL1emFc3Y9yb9VePfYz+2FMchxbpmWW9sH8e1C5nNua+aaSvTimYsOq4qxtjM/qgYRzP7o2KMixbkinrz/LZ9dfV8PbY/c8bR0jw/+3INxjjluBobR43Pjz3NHXN8tnrTFOsVdJ19zBUFoNgkR970Xda58X3R5cq0ohnNxLFtuT32Y0tx3NjW4L6afXsT91UzbRPnjpb21ZQYm9kfFeNoZn90tq/WmOe7OF83OM+fQoyj2ibGUWM81opx6YJks79vPZ2TiwJQ3CTXKQyyrW3pghpzFM3YtM2WCjhM2Y/N7I9t65zYn5YKUey7r+bcZk9FM2oWHZvtPZ3pvRvbn1biqHE8thZjjb7OPv4nbrO189Va83xPMZ7FAeOj4jxfYy5fNI4D5o5NcSz9uWPU+zZTfxY5B/RMAai6XsXIIgXblo3liyaM7cu2G+2rFLGoEceE5XYt29L+2LbOXXGOirFSX0fvq0rbXLptV/vY93zUcpXe013to+aOGsvViiOWL5C2Row1+jp2HB/F+XqPOGqMq6ZibOxzx5TjavaxvEIcW2Nc+nPAhH085XPgqGOu0r7qliuzleSEIk7blh1e+ioWurl923Jj+3JT29S+1ohj7HJ77scm9seuvuSWAid79Gexvs69vZm2uVhb5bljtuN8ynu6xnHVWhxD26zHY6MxNjE/Hsv5es84mhhXtWLc1FZrvbXOHbv21ZSxvGQcu2Lc1JeZ+lPjXDblc+DBx1yt+XrTOnshma0k6xWAaqnYwpSCO2MLQN243hhfUKO1AgaL7o9t65wYx9JFGtYoqHHqRYX2PR5neU8bjHGNOLo45irGWGN+GHVe6ex8XetzR08xdv+5Y4/jamxfW/v8tPQ5efZ9vNAcuHd/tq2zKADFJlmvANTZmPVWWm7UOosCBmsUlBi1zolxLNrXiftq7Hhspq3BuaNKkYrGYlwjjhptPcW49Px4LOfrWnH0FGP3nzv22Fdj+9ra56elz8mzvzcNHldb+xodk8xWlPWL0Wxqu3G9Y/tToy8V+1ojjjUKGCy6Pw6IY9t7MPZ9na2vY/t5wDa3xTG2P3PH0UVhjLHv6ZRlK8XYWoGTmu9NrfG4dF9nO68cEMemtqXP1/vG0cK+qhXjWp87ZjsHVOzronHsEePS/Zn9vZmpr2cx39yxdb7ulQJQdb2KcTd9jy6qsmlwZ93CGGP7UqXIUY04dvRlW9sahUpqjI2tcU7oT42+jurn0H7jNiuNjypxTNjm2DG3bV+NWueOfrZWAGiN+WHs+Ni23KgYp4zHGvtxx3Kzn1d2xdHY+XrrOXnbNpceV7ViXPpzx451jp47avS10jw3Jcal39ca+3h0f2rMHXvM111yZbaSnLkoxOU6b2rf1TY8nK0/U/qyK8axfa0Rxx592dhWK8Ya+2NXHNvG8pLjo8b+2Gc81uhPrTj2mHcWG3Nj1zkm/n2WrRjjGvPDweNj6eWmzB17xL/tHDDr+N/Wdkzn6xr7qrUYN7VN7c8ebbOfA8Z+vtzW1tp5LusVnlxsH085J9f6PHfoOnshma0kR970XXbf+P71nm1vVhvLFMY4tGjG2EIMtW/un6XQwB77sZn9sa0vZb5iE82O4z3G4+zjY0LbrjiWLrgzdl9tW+eu8bjmcXVIjK3ND7UL7hw6Hsf2tbWiaz2er7+3vYr7auy4qhXj0p87Wiv0t3ThydZi7GlfLXreLaW8e32ZnkhmK8mRN32X3Te+L30D/+zFBPaIcWxfa8QxansVY1y0uENZp9jEouN44jE3tj+14li62MSiBV4aPK5GbW+lOBZdbuLcMTb+RduO5XxdcV+1FuPSnztmb1tpnj+WGHuKY9HzblEAiptkvRvfx7bVKFIxqu2Avh703u1oW/R9qxjjomNjj/egRn+m7o+D3reV+lMrjl6OnVHHxpRlK8d40D5eKY5FixFV7GszBdn2iKOL8/Ue78HS46pWsa6lP3fMfg6o2J+a782UGA9atqX9WHF/zH489kwBqLpeRZ0b30e1lQpFKsa2Df3Z1j72vdvWtuj7VjHGRcfGHu/BthiX7uvY923x/kxYbutxteM9aOnYGXts7OrP7DGO7Wtr80ON/TFxPNaYd2uM/6M/X+8R59LHeY1jtakiRxPGcZX+RIV5dUqMjR3no+f5Cdtc+rzbLVdmK8kJN33n7hvfD24rCxfqGdN22T48PPi926Nt9fdtphhXHxu72q/EuFhfh4cHv29Tjrmx/amxXJlQbGKPtsWOnV3x77GvZo2x1r7a1LbPemvEseJ4XKyvNdqO6Xzd2b6a/Vw2sT+jxs6Yvqw4zy8a46bl9ll26bbK5+TV545eSGYryWkFoNYs4nLIjeaj2kpfhRhGvW/HEuOGOOYqRFBjf0wpxlOjSMPsBZCuLfemKeoWgGqmcFTF+bFGYZRDjqtD1tvM/oh643Hs+epYCtUser5eaF9d70/tMb4pxpbOyaPOASvNgWvEWKNA3rEUZaxSIO36+noima0kpxWAaqmIy+xtpa9CDCcd4x5xLF00olYxnhpFGhZdruLcMbZt9gIWK8VYa18tPT8sXlCk0nF1CoVq1jgnH8O+au2YG/u+3bhcg/N8rRhb2lejYmzwfLW1r9ExyWxFOa2gREtFXGZv22PZqcU/5myrHeOc+3j2sTHlPVhhf9Q+5ubsz9j3bde+6mJcjY1/j/dnrcIom/rS2vxQ4zieYzwetN6W5tWK21zjnLzoObLBc1lL8+MaY272Qk4TY5z62WL1/XhAX+dsGz0H9EoBqLpexfiCEmOX3db205y5SMWEtq3tZXzxixptPzjw92kb4tjWXmMf1xgbu9qX3o813tOt7ZX6M/Z92zrmdmyzpXE1Kv492heNY8q+WmF+qHEcjx6Plebdpcd4rfUufk5e4Ry5+LmspbEzoS9V+rOjr6Pm3Ikxjj1HtHSeW+Pz7OjPrL1yZbaSnF4A6uBl92h7FTMWqRjbVnYUzSiNFKSq0XbZPjyssY9nHRu79tWuZW+Kv0bb2Ph3HXMV+zP7+1Zx7lhszmn0uFpkjK8VR60YaxxXNeaAsW3Hcr7eNR4721ejYqzYn4PH/67llp7np/R1YowHz0k14t+jrZnjqsY5qQeS2UqyvQJQzbTtEeOtmF78okbbm27uaivtFZtYo8BJ7f34ZnNRtwDUvnHM0p+x2yvtFYBaoyBZS0VcjmV+qDUexx5XNY651gpArTHP71vor+d91dIxd3X8792XlcbctmN1VIG4iTGO7U+vx9Uin1mvx9ATyWwl2V4BqGba9ohx0eIXNdpKXwUMbmybOB6X3h+1Yly6wMkaRVxaauvpuDqF+aHWeGxmDhjbdizH3B7j8Vj2VUvHXE8FoMbux1oxNjOu9ohj6eNq9HwdHZPMVpTtFZRopm2PZd8ujRSrGtu2x7ItFbmao+DOpral90dr43GNomPNHOcT2no6rmrPD03vjwPG41ksMz+0di7rom3ie770/DjlXLbW2NnU11F9WaCvh84Ba8TYzLiaOOaaaeuZAlB1vYq2Ckq01Lar/afZTrGqsW1b20tbRa5ubJvY1x9MmjXborHxuPT7tkccvbT1dFyN3leVxvLS+2Prvlp6fqgU/ymcryMWPkeudC5bdOyMHf87tje6PxPaxu7HWjE2M66mjLmW2q4/3xNXZivJNgtANdG2Z4yvooFiVWPbyoQCBj20tdafrFgAalh21BhYMv4jmztO+rgaHja/P7a1rTF3TIzxZM/Xu46rYdnZz5GxwTEdczXG8Ypjbtb9OEOMTY+r3j4/betrDySzlaQCUGLss8jV99rK7oI7+673TfgV22oVgKpdOGe2Ag6Oq1WPqzdd2dW24bi6vmwzRUzGtpXDip9cj3/ROWBijN0fcyvFseixutIxt2+Me/elwTHXWozNfLbaY8wt/Rlp6zkpOiaZrSQVgBLjERS5Kh0VMIj2xuMa+8px1c54PJYCUKPaVpo71oix+2NupThO4ZibfRw3OOZai7Gneb6pvkbHJLMV5YkXlBDjzmXfLo3c+L+trbX+7Ghbo8hVMwWwrvVn9WPAcdVVIatjKX7S2njsom2l/pzCMTf7ON7V3tF+rHVO7mmeb6atZwpA1fUqTrughBjrFFVZuq21/tzYVtYpcrWt7QcnjZptwz9bOgYcV/0Uspq9bYhj6WOgtfHYS9sa2zyFY67GOI4d7V3sx4rn5EXnnF3n5Jb6s8fnhy65MltJKgAlxiMoclVWKAwypa+xQZ5AkYbLNsdVRDQyHre17TquWhtXc7bV3ObwsLXx2HzbinE45ja0DQ9b21ez7sfW5o5jmQNr9LUHktlKUgEoMR5BjJ3FcSvWLXJ1SPGL2duKAlBN9XViHKPGckttG46rNyFuWHauNgWgTjiOiTHWPubebG6Ptp4KQM1+vp7xnPxmk2u0rTQHju5rdEwyW0kqACXGI4ixsziOvkjDHnH0sq9GtR1LjHvE0cy4Gtu20nF1Fm3tx0X7c+pxTIyxmWNnYhzNvOc72lqbO45lDlQAinllX4UYTqHYhBiPP463y5EXadgjjmYKANVoa60/FeNoZlx1dlyZH084jokxNnPsnMIc2ODccSxz4Oi+9koBqLpeRT+FGJZua60/YjyOOH6aR17kalccpaECQDXajiXGPeL4wQeO3tqGGJfepvnxtOOYEmMzc/kpzIGtzR3HMgdO6WuvXJmtJBWAEuMRxNhhHK/iiItc7YojbpANFZSo0dZaf049jrViHB6aH080jhlibHouN3e01Z9TiKMXktlKUgEoMR5BjMcSxynEOMRRu5DVqm1lmWJdrcVxqau2DTEu0R8FoE44jiOK8VYc+RwY7c0dxzIHju5rdEwyW0kqACXGI4jxWOI4hRhLR8UmxrYdS4zHEkeDMZ6FueNk4ziiGFs7rk5h7jjpOIoCUNwkFWIQY0P9OfU4TiHGof3t0khBiRptrfXn1ONoMMbWCnl10dZaf8TY3HF1CnPHScfRMwWg6noVCjGIsZ3+nHocpxBjRENFTCq1tdafU4+jqRhLe4W8umg7ljiOKMYfJBzZYVGhsW3HEmNPcVx/vieuzFaSCkCJ8QhiPJY4TiHGK3G8igaKmNRoO5YYjyWO1mIsDRVNWXqb4jjtGFvrjxiPI45eSGYrSQWgxHgEMR5LHKcQ47HEcQoxHkscDcZ4K9ot5NVs27HEcaQxXropju+1b1u2l7ZjibGnOIoCUGySCkCJ8QhiPJY4TiHGY4njFGI8ljgajFGBlxOO4xRiPJY4TiHGnuIoCkBxkzyNIgViPPIYW+uPGI8/jlOIsbX+HFGMbxcFXk42jlOIsbX+iPE44uiZZBYAAIDu/GjtDgAAAMChJLMAAAB0RzILAABAdySzADBCZj7LzAdb2r/NzM8X6sti2wKAVry1dgcA4Eh9FhEvjnBbANAEV2YBoIJSylellOeX/87MB5n5eN/lD3n99W0BwCmQzAIAANAdySwAjPd+Zj7KzD8O963euWy4ek9tZj6KiC8j4t7la6+87svhuTIsc77j9Y8z835mfn75/PX7d4fXPBj+/8fL9V5pP7/S9niI4dvM/PSmPtV7CwFgHMksAIx3LyI+K6W8ExFPIuLrTS8qpXwcEZ9ExJNSyjullPcjIjLzfkTcHZ7LiPhlRLy66fWDWxHxm4g4i4iPbujXrYj4fFjHT4fnPrvS/igiHg39fhERZ6WU90spX9zUpwPeEwBYhGQWAMb7bSnlRUREKeWTiDjLzHsHruM8M+9l5lkp5Xkp5fUeyzwtpXxyue0b/K6U8mJY328j4urV1TtxkXxHXFwBvjtDnwBgUZJZAJjPi/h+0rhVKeWriPh1XCSUl1/5Pdtj0X0KQz3b0vY8Iu4Pj+/Fd4ntlD4BwKIkswAwn/OIeHrIAqWUL4avEb8TF18PvvG3a694fXjXvudVRPwiM/8YF19V/uUMfQKARUlmAWC8jzLzbPjvUUS82PITOa8i4u7w2nsREcNXee9de82Nr5/R3bhIYD+IiI+vfo14R58AoBmSWQAY50VcfF33UUT8MS4KMn2w5fVP4iIx/H1cFGa69NlwhfT3EfG6lPLFjtfP4UVcfA3527j4KnHJzM/36BMANCNLKWv3AQBYyPATPh+XUj668tyduEhuP9hyZRkAmuLKLACcnlsbfjv2dUy/FxcAFvPW2h0AAJZTSnmYmRERj64ktE/j4mrttp/6AYCm+JoxAAAA3fE1YwAAALojmQUAAKA7klkAAAC6I5kFAACgO5JZAAAAuiOZBQAAoDv/HzPQy/EWah5pAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dw_preserved =['1110000000', '0111000000', '0011100000', '0001110000', \n", + " '0000111000', '0000011100', '0000001110', '0000000111', '1000000011', '1100000001']\n", + "\n", + "for n_cycle in [2*k for k in range(int(N_cycles/2))]:\n", + " color_dict = {key: 'red' if key in dw_preserved else 'black' for key in samples_evol[n_cycle]}\n", + " plt.figure(figsize=(16, 5))\n", + " plt.title(r'Cycle $= {}$'.format(n_cycle), fontsize=18)\n", + " plt.bar(samples_evol[n_cycle].keys(), samples_evol[n_cycle].values(), color=color_dict.values())\n", + " plt.xlabel(\"bitstrings\", fontsize=16)\n", + " plt.ylabel(\"counts\", fontsize=16)\n", + " plt.xticks(rotation=90)\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env_xyz_quantum_sim", + "language": "python", + "name": "env_xyz_quantum_sim" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/applications/QuantumEvolutionKernel_Tutorial.ipynb b/tutorials/applications/Quantum Evolution Kernel.ipynb similarity index 100% rename from tutorials/applications/QuantumEvolutionKernel_Tutorial.ipynb rename to tutorials/applications/Quantum Evolution Kernel.ipynb From 743a2eed87b45b66b306404604002dd5f01056a7 Mon Sep 17 00:00:00 2001 From: Louis-PaulHenry <79902647+Louis-PaulHenry@users.noreply.github.com> Date: Mon, 18 Oct 2021 13:35:15 +0200 Subject: [PATCH 09/51] 3D registers (#271) * 2D registers Created a 3D device, and split the Register class into to child classes, with their respective draw() : For the 3D register, the default is to output 2 perspective views of the register, but it can be changed to three projection views on the xy, yz and xz planes * Test corrections Corrected the tests * linter changes * Implemented corrections Still having issues with typing of Register * Update register.py cls -> BaseRegister * typing of from_coordinates Found a way to correctly type `from_coordinates` using `TypeVar` * Support for 3D Registers Split the Register class into two children Register class (A 2D one and a 3D one) Implemented a method draw the 3D register and a method to check if the atoms of a 3D register are coplanar and return their coordinates in their common plane * Final typo oups! * Nicer projection of 3D registers Fixed the aspect ratio and the overlap of labels problems (up to "too many" overlapping atoms), as well as the suggested modifications * Adaptable meshgrid coarseness reduced the mesh for the 3D plots of larger registers * Better handling of label overlap and renaming of orthorhombic As in the title : better display of labels of overlapping atoms and renaming of orthorhombic into cuboid for spelling reasons ;-) * Readable projection of 3D registers fixed aspect ratio issues for projected 3D Registers * Update register.py * Implemented suggestions * Passing remaining tests --- pulser/__init__.py | 2 +- pulser/devices/__init__.py | 1 + pulser/devices/_device_datacls.py | 17 +- pulser/devices/_mock_device.py | 2 +- pulser/register.py | 638 +++++++++++++++++++++++++----- pulser/sequence.py | 6 +- pulser/tests/test_devices.py | 8 +- pulser/tests/test_register.py | 98 ++++- 8 files changed, 648 insertions(+), 124 deletions(-) diff --git a/pulser/__init__.py b/pulser/__init__.py index c2a4a8548..fbebcfedf 100644 --- a/pulser/__init__.py +++ b/pulser/__init__.py @@ -18,7 +18,7 @@ from pulser.pulse import Pulse -from pulser.register import Register +from pulser.register import Register, Register3D from pulser.sequence import Sequence diff --git a/pulser/devices/__init__.py b/pulser/devices/__init__.py index 500747e7c..40c88be5a 100644 --- a/pulser/devices/__init__.py +++ b/pulser/devices/__init__.py @@ -20,4 +20,5 @@ from pulser.devices._mock_device import MockDevice # Registers which devices can be used to avoid definition of custom devices +_mock_devices = (MockDevice,) _valid_devices = (Chadoq2,) diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index c268d5084..15ec05c43 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -20,7 +20,8 @@ import numpy as np from scipy.spatial.distance import pdist -from pulser import Register, Pulse +from pulser import Pulse +from pulser.register import BaseRegister from pulser.channels import Channel from pulser.json.utils import obj_to_dict @@ -101,14 +102,17 @@ def rabi_from_blockade(self, blockade_radius: float) -> float: """ return self.interaction_coeff / blockade_radius ** 6 - def validate_register(self, register: Register) -> None: + def validate_register(self, register: BaseRegister) -> None: """Checks if 'register' is compatible with this device. Args: register(pulser.Register): The Register to validate. """ - if not isinstance(register, Register): - raise TypeError("register has to be a pulser.Register instance.") + if not (isinstance(register, BaseRegister)): + raise TypeError( + "register has to be a pulser.Register or " + "a pulser.Register3D instance." + ) atoms = list(register.qubits.values()) if len(atoms) > self.max_atom_num: @@ -119,9 +123,10 @@ def validate_register(self, register: Register) -> None: f" ({self.max_atom_num})." ) - if register._dim != self.dimensions: + if register._dim > self.dimensions: raise ValueError( - f"All qubit positions must be {self.dimensions}D " "vectors." + f"All qubit positions must be at most {self.dimensions}D " + "vectors." ) if len(atoms) > 1: diff --git a/pulser/devices/_mock_device.py b/pulser/devices/_mock_device.py index 935017344..9d84c0d19 100644 --- a/pulser/devices/_mock_device.py +++ b/pulser/devices/_mock_device.py @@ -18,7 +18,7 @@ MockDevice = Device( name="MockDevice", - dimensions=2, + dimensions=3, max_atom_num=2000, max_radial_distance=1000, min_atom_distance=1, diff --git a/pulser/register.py b/pulser/register.py index df9505f63..2b440ccdf 100644 --- a/pulser/register.py +++ b/pulser/register.py @@ -15,29 +15,28 @@ from __future__ import annotations +from abc import ABC, abstractmethod from collections.abc import Mapping, Iterable import matplotlib.pyplot as plt from matplotlib import collections as mc import numpy as np from numpy.typing import ArrayLike from scipy.spatial import KDTree -from typing import Any, cast, Optional, Union +from typing import Any, cast, Optional, Union, TypeVar, Type +from itertools import combinations import pulser from pulser.json.utils import obj_to_dict QubitId = Union[int, str] +T = TypeVar("T", bound="BaseRegister") -class Register: - """A quantum register containing a set of qubits. - Args: - qubits (dict): Dictionary with the qubit names as keys and their - position coordinates (in μm) as values - (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). - """ +class BaseRegister(ABC): + """The abstract class for a register.""" + @abstractmethod def __init__(self, qubits: Mapping[Any, ArrayLike]): """Initializes a custom Register.""" if not isinstance(qubits, dict): @@ -50,16 +49,8 @@ def __init__(self, qubits: Mapping[Any, ArrayLike]): "Cannot create a Register with an empty qubit " "dictionary." ) self._ids = list(qubits.keys()) - coords = [np.array(v, dtype=float) for v in qubits.values()] - self._dim = coords[0].size - if any(c.shape != (self._dim,) for c in coords) or ( - self._dim != 2 and self._dim != 3 - ): - raise ValueError( - "All coordinates must be specified as vectors of" - " size 2 or 3." - ) - self._coords = coords + self._coords = [np.array(v, dtype=float) for v in qubits.values()] + self._dim = 0 @property def qubits(self) -> dict[QubitId, np.ndarray]: @@ -68,11 +59,11 @@ def qubits(self) -> dict[QubitId, np.ndarray]: @classmethod def from_coordinates( - cls, + cls: Type[T], coords: np.ndarray, center: bool = True, prefix: Optional[str] = None, - ) -> Register: + ) -> T: """Creates the register from an array of coordinates. Args: @@ -98,6 +89,203 @@ def from_coordinates( qubits = dict(cast(Iterable, enumerate(coords))) return cls(qubits) + @staticmethod + def _draw_2D( + ax: plt.axes._subplots.AxesSubplot, + pos: np.ndarray, + ids: list, + plane: tuple = (0, 1), + with_labels: bool = True, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + ) -> None: + ix, iy = plane + + ax.scatter(pos[:, ix], pos[:, iy], s=30, alpha=0.7, c="darkgreen") + + axes = "xyz" + + ax.set_xlabel(axes[ix] + " (µm)") + ax.set_ylabel(axes[iy] + " (µm)") + ax.axis("equal") + ax.spines["right"].set_color("none") + ax.spines["top"].set_color("none") + + if with_labels: + # Determine which labels would overlap and merge those + plot_pos = list(pos[:, (ix, iy)]) + plot_ids = [f"{i}" for i in ids] + # Threshold distance between points + epsilon = 1.0e-2 * np.diff(ax.get_xlim())[0] + + i = 0 + bbs = {} + while i < len(plot_ids): + r = plot_pos[i] + j = i + 1 + overlap = False + while j < len(plot_ids): + r2 = plot_pos[j] + if np.max(np.abs(r - r2)) < epsilon: + plot_ids[i] += ", " + plot_ids.pop(j) + plot_pos.pop(j) + overlap = True + else: + j += 1 + bbs[plot_ids[i]] = overlap + i += 1 + + for q, coords in zip(plot_ids, plot_pos): + bb = ( + dict(boxstyle="square", fill=False, ec="gray", ls="--") + if bbs[q] + else None + ) + v_al = "center" if bbs[q] else "bottom" + txt = ax.text( + coords[0], + coords[1], + q, + ha="left", + va=v_al, + wrap=True, + bbox=bb, + ) + txt._get_wrap_line_width = lambda: 50.0 + + if draw_half_radius and blockade_radius is not None: + for p in pos: + circle = plt.Circle( + tuple(p[[ix, iy]]), + blockade_radius / 2, + alpha=0.1, + color="darkgreen", + ) + ax.add_patch(circle) + ax.autoscale() + if draw_graph and blockade_radius is not None: + epsilon = 1e-9 # Accounts for rounding errors + edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) + bonds = pos[(tuple(edges),)] + if len(bonds) > 0: + lines = bonds[:, :, (ix, iy)] + else: + lines = [] + lc = mc.LineCollection(lines, linewidths=0.6, colors="grey") + ax.add_collection(lc) + + else: + # Only draw central axis lines when not drawing the graph + ax.axvline(0, c="grey", alpha=0.5, linestyle=":") + ax.axhline(0, c="grey", alpha=0.5, linestyle=":") + + @staticmethod + def _register_dims( + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> np.ndarray: + """Returns the dimensions of the register to be drawn.""" + diffs = np.ptp(pos, axis=0) + diffs[diffs < 9] *= 1.5 + diffs[diffs < 9] += 2 + if blockade_radius and draw_half_radius: + diffs[diffs < blockade_radius] = blockade_radius + + return np.array(diffs) + + def draw( + self, + with_labels: bool = True, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + ) -> None: + """Draws the entire register. + + Keyword Args: + with_labels(bool, default=True): If True, writes the qubit ID's + next to each qubit. + blockade_radius(float, default=None): The distance (in μm) between + atoms below the Rydberg blockade effect occurs. + draw_half_radius(bool, default=False): Whether or not to draw the + half the blockade radius surrounding each atoms. If `True`, + requires `blockade_radius` to be defined. + draw_graph(bool, default=True): Whether or not to draw the + interaction between atoms as edges in a graph. Will only draw + if the `blockade_radius` is defined. + + Note: + When drawing half the blockade radius, we say there is a blockade + effect between atoms whenever their respective circles overlap. + This representation is preferred over drawing the full Rydberg + radius because it helps in seeing the interactions between atoms. + """ + # Check spacing + if blockade_radius is not None and blockade_radius <= 0.0: + raise ValueError( + "Blockade radius (`blockade_radius` =" + f" {blockade_radius})" + " must be greater than 0." + ) + + if draw_half_radius: + if blockade_radius is None: + raise ValueError("Define 'blockade_radius' to draw.") + if len(self._ids) == 1: + raise NotImplementedError( + "Needs more than one atom to draw " "the blockade radius." + ) + + +class Register(BaseRegister): + """A 2D quantum register containing a set of qubits. + + Args: + qubits (dict): Dictionary with the qubit names as keys and their + position coordinates (in μm) as values + (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). + """ + + def __init__(self, qubits: Mapping[Any, ArrayLike]): + """Initializes a custom Register.""" + super().__init__(qubits) + self._dim = self._coords[0].size + if any(c.shape != (self._dim,) for c in self._coords) or ( + self._dim != 2 + ): + raise ValueError( + "All coordinates must be specified as vectors of size 2." + ) + + @classmethod + def square( + cls, side: int, spacing: float = 4.0, prefix: Optional[str] = None + ) -> Register: + """Initializes the register with the qubits in a square array. + + Args: + side (int): Side of the square in number of qubits. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: A register with qubits placed in a square array. + """ + # Check side + if side < 1: + raise ValueError( + f"The number of atoms per side (`side` = {side})" + " must be greater than or equal to 1." + ) + + return cls.rectangle(side, side, spacing=spacing, prefix=prefix) + @classmethod def rectangle( cls, @@ -152,33 +340,6 @@ def rectangle( return cls.from_coordinates(coords, center=True, prefix=prefix) - @classmethod - def square( - cls, side: int, spacing: float = 4.0, prefix: Optional[str] = None - ) -> Register: - """Initializes the register with the qubits in a square array. - - Args: - side (int): Side of the square in number of qubits. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register: A register with qubits placed in a square array. - """ - # Check side - if side < 1: - raise ValueError( - f"The number of atoms per side (`side` = {side})" - " must be greater than or equal to 1." - ) - - return cls.rectangle(side, side, spacing=spacing, prefix=prefix) - @classmethod def triangular_lattice( cls, @@ -445,8 +606,6 @@ def rotate(self, degrees: float) -> None: Args: degrees (float): The angle of rotation in degrees. """ - if self._dim != 2: - raise NotImplementedError("Can only rotate arrays in 2D.") theta = np.deg2rad(degrees) rot = np.array( [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]] @@ -480,24 +639,18 @@ def draw( This representation is preferred over drawing the full Rydberg radius because it helps in seeing the interactions between atoms. """ - # Check dimensions - if self._dim != 2: - raise NotImplementedError("Can only draw register layouts in 2D.") - - # Check spacing - if blockade_radius is not None and blockade_radius <= 0.0: - raise ValueError( - "Blockade radius (`blockade_radius` =" - f" {blockade_radius})" - " must be greater than 0." - ) - + super().draw( + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) pos = np.array(self._coords) - diffs = np.max(pos, axis=0) - np.min(pos, axis=0) - diffs[diffs < 9] *= 1.5 - diffs[diffs < 9] += 2 - if blockade_radius and draw_half_radius: - diffs[diffs < blockade_radius] = blockade_radius + diffs = super()._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) big_side = max(diffs) proportions = diffs / big_side Ls = proportions * min( @@ -505,45 +658,336 @@ def draw( ) # Figsize is, at most, (10,10) fig, ax = plt.subplots(figsize=Ls) - ax.scatter(pos[:, 0], pos[:, 1], s=30, alpha=0.7, c="darkgreen") + super()._draw_2D( + ax, + pos, + self._ids, + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) - ax.set_xlabel("µm") - ax.set_ylabel("µm") - ax.axis("equal") - ax.spines["right"].set_color("none") - ax.spines["top"].set_color("none") + plt.show() - if with_labels: - for q, coords in zip(self._ids, self._coords): - ax.annotate(q, coords, fontsize=12, ha="left", va="bottom") + def _to_dict(self) -> dict[str, Any]: + qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) + return obj_to_dict(self, qs) - if draw_half_radius: - if blockade_radius is None: - raise ValueError("Define 'blockade_radius' to draw.") - if len(pos) == 1: - raise NotImplementedError( - "Needs more than one atom to draw " "the blockade radius." - ) - for p in pos: - circle = plt.Circle( - tuple(p), blockade_radius / 2, alpha=0.1, color="darkgreen" - ) - ax.add_patch(circle) +class Register3D(BaseRegister): + """A 3D quantum register containing a set of qubits. + + Args: + qubits (dict): Dictionary with the qubit names as keys and their + position coordinates (in μm) as values + (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). + """ + + def __init__(self, qubits: Mapping[Any, ArrayLike]): + """Initializes a custom Register.""" + super().__init__(qubits) + coords = [np.array(v, dtype=float) for v in qubits.values()] + self._dim = coords[0].size + if any(c.shape != (self._dim,) for c in coords) or (self._dim != 3): + raise ValueError( + "All coordinates must be specified as vectors of size 3." + ) + self._coords = coords + + @classmethod + def cubic( + cls, side: int, spacing: float = 4.0, prefix: Optional[str] = None + ) -> Register3D: + """Initializes the register with the qubits in a cubic array. + + Args: + side (int): Side of the cube in number of qubits. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register3D : A 3D register with qubits placed in + a cubic array. + """ + # Check side + if side < 1: + raise ValueError( + f"The number of atoms per side (`side` = {side})" + " must be greater than or equal to 1." + ) + + return cls.cuboid(side, side, side, spacing=spacing, prefix=prefix) + + @classmethod + def cuboid( + cls, + rows: int, + columns: int, + layers: int, + spacing: float = 4.0, + prefix: Optional[str] = None, + ) -> Register3D: + """Initializes the register with the qubits in a cuboid array. + + Args: + rows (int): Number of rows. + columns (int): Number of columns. + layers (int): Number of layers. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) + + Returns: + Register3D : A 3D register with qubits placed in + an cuboid array. + """ + # Check rows + if rows < 1: + raise ValueError( + f"The number of rows (`rows` = {rows})" + " must be greater than or equal to 1." + ) + + # Check columns + if columns < 1: + raise ValueError( + f"The number of columns (`columns` = {columns})" + " must be greater than or equal to 1." + ) + + # Check layers + if layers < 1: + raise ValueError( + f"The number of layers (`layers` = {layers})" + " must be greater than or equal to 1." + ) + + # Check spacing + if spacing <= 0.0: + raise ValueError( + f"Spacing between atoms (`spacing` = {spacing})" + " must be greater than 0." + ) + + coords = ( + np.array( + [ + (x, y, z) + for z in range(layers) + for y in range(rows) + for x in range(columns) + ], + dtype=float, + ) + * spacing + ) + + return cls.from_coordinates(coords, center=True, prefix=prefix) + + def to_2D(self, tol_width: float = 0.0) -> Register: + """Converts a Register3D into a Register (if possible). + + Args: + tol_width (float): The allowed transverse width of + the register to be projected. + + Returns: + Register : Returns a 2D register with the coordinates + of the atoms in a plane, if they are coplanar. + + Raises: + If the atoms are not coplanar, raises an error. + """ + coords = np.array(self._coords) + prefix = str(self._ids[0])[:-1] + + barycenter = coords.sum(axis=0) / coords.shape[0] + # run SVD + u, s, vh = np.linalg.svd(coords - barycenter) + e_z = vh[2, :] + perp_extent = [e_z.dot(r) for r in coords] + width = np.ptp(perp_extent) + # A set of vector is coplanar if one of the Singular values is 0 + if width > tol_width: + raise ValueError( + f"Atoms are not coplanar (`width` = {width:#.2f} µm)" + ) + else: + e_x = vh[0, :] + e_y = vh[1, :] + coords_2D = np.array( + [np.array([e_x.dot(r), e_y.dot(r)]) for r in coords] + ) + return Register.from_coordinates(coords_2D, prefix=prefix) + + def draw( + self, + with_labels: bool = False, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + projection: bool = False, + ) -> None: + """Draws the entire register. + + Keyword Args: + with_labels(bool, default=True): If True, writes the qubit ID's + next to each qubit. + blockade_radius(float, default=None): The distance (in μm) between + atoms below the Rydberg blockade effect occurs. + draw_half_radius(bool, default=False): Whether or not to draw the + half the blockade radius surrounding each atoms. If `True`, + requires `blockade_radius` to be defined. + draw_graph(bool, default=True): Whether or not to draw the + interaction between atoms as edges in a graph. Will only draw + if the `blockade_radius` is defined. + projection(bool, default=False): Whether to draw a 2D projection + instead of a perspective view. + + Note: + When drawing half the blockade radius, we say there is a blockade + effect between atoms whenever their respective circles overlap. + This representation is preferred over drawing the full Rydberg + radius because it helps in seeing the interactions between atoms. + """ + super().draw( + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) + + pos = np.array(self._coords) + if draw_graph and blockade_radius is not None: epsilon = 1e-9 # Accounts for rounding errors edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) - lines = pos[(tuple(edges),)] - lc = mc.LineCollection(lines, linewidths=0.6, colors="grey") - ax.add_collection(lc) + + if projection: + diffs = super()._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + + proportions = [] + for (ix, iy) in combinations(np.arange(3), 2): + big_side = max(diffs[[ix, iy]]) + Ls = diffs[[ix, iy]] / big_side + Ls *= max( + min(big_side / 4, 10), 4 + ) # Figsize is, at most, (10,10), and, at least (4,*) or (*,4) + proportions.append(Ls) + + fig_height = np.max([Ls[1] for Ls in proportions]) + + max_width = 0 + for i, (width, height) in enumerate(proportions): + proportions[i] = (width * fig_height / height, fig_height) + max_width = max(max_width, proportions[i][0]) + widths = [max(Ls[0], max_width / 5) for Ls in proportions] + fig_width = min(np.sum(widths), fig_height * 4) + + rescaling = 20 / max(max(fig_width, fig_height), 20) + figsize = (rescaling * fig_width, rescaling * fig_height) + + labels = "xyz" + + fig, axes = plt.subplots( + ncols=3, + figsize=figsize, + gridspec_kw=dict(width_ratios=widths), + ) + + for ax, (ix, iy) in zip(axes, combinations(np.arange(3), 2)): + super()._draw_2D( + ax, + pos, + self._ids, + plane=( + ix, + iy, + ), + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) + ax.set_title( + "Projection onto\n the " + + labels[ix] + + labels[iy] + + "-plane" + ) else: - # Only draw central axis lines when not drawing the graph - ax.axvline(0, c="grey", alpha=0.5, linestyle=":") - ax.axhline(0, c="grey", alpha=0.5, linestyle=":") + fig = plt.figure(figsize=2 * plt.figaspect(0.5)) + + if draw_graph and blockade_radius is not None: + bonds = {} + for i, j in edges: + xi, yi, zi = pos[i] + xj, yj, zj = pos[j] + bonds[(i, j)] = [[xi, xj], [yi, yj], [zi, zj]] + + for i in range(1, 3): + ax = fig.add_subplot( + 1, 2, i, projection="3d", azim=-60 * (-1) ** i, elev=15 + ) - plt.show() + ax.scatter( + pos[:, 0], + pos[:, 1], + pos[:, 2], + s=30, + alpha=0.7, + c="darkgreen", + ) - def _to_dict(self) -> dict[str, Any]: - qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) - return obj_to_dict(self, qs) + if with_labels: + for q, coords in zip(self._ids, self._coords): + ax.text( + coords[0], + coords[1], + coords[2], + q, + fontsize=12, + ha="left", + va="bottom", + ) + + if draw_half_radius and blockade_radius is not None: + mesh_num = 20 if len(self._ids) > 10 else 40 + for r in pos: + x0, y0, z0 = r + radius = blockade_radius / 2 + + # Strange behavior pf mypy using "imaginary slice step" + # u, v = np.pi * np.mgrid[0:2:50j, 0:1:50j] + + v, u = np.meshgrid( + np.arccos(np.linspace(-1, 1, num=mesh_num)), + np.linspace(0, 2 * np.pi, num=mesh_num), + ) + x = radius * np.cos(u) * np.sin(v) + x0 + y = radius * np.sin(u) * np.sin(v) + y0 + z = radius * np.cos(v) + z0 + # alpha controls opacity + ax.plot_surface(x, y, z, color="darkgreen", alpha=0.1) + + if draw_graph and blockade_radius is not None: + for x, y, z in bonds.values(): + ax.plot(x, y, z, linewidth=1.5, color="grey") + + ax.set_xlabel("x (µm)") + ax.set_ylabel("y (µm)") + ax.set_zlabel("z (µm)") + plt.show() diff --git a/pulser/sequence.py b/pulser/sequence.py index 7518eff36..418d5f838 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -164,14 +164,14 @@ def __init__(self, register: Register, device: Device): " device from 'pulser.devices'." ) cond1 = device not in pulser.devices._valid_devices - cond2 = device != MockDevice + cond2 = device not in pulser.devices._mock_devices if cond1 and cond2: names = [d.name for d in pulser.devices._valid_devices] warns_msg = ( "The Sequence's device should be imported from " + "'pulser.devices'. Correct operation is not ensured" - + " for custom devices. Choose 'MockDevice' or one of" - + " the following real devices:\n" + + " for custom devices. Choose 'MockDevice'" + + " or one of the following real devices:\n" + "\n".join(names) ) warnings.warn(warns_msg, stacklevel=2) diff --git a/pulser/tests/test_devices.py b/pulser/tests/test_devices.py index 0a8f83519..d2410933c 100644 --- a/pulser/tests/test_devices.py +++ b/pulser/tests/test_devices.py @@ -20,7 +20,7 @@ import pulser from pulser.devices import Chadoq2 -from pulser.register import Register +from pulser.register import Register, Register3D def test_init(): @@ -43,7 +43,7 @@ def test_init(): def test_mock(): dev = pulser.devices.MockDevice - assert dev.dimensions == 2 + assert dev.dimensions == 3 assert dev.max_atom_num > 1000 assert dev.min_atom_distance <= 1 assert dev.interaction_coeff == 5008713 @@ -83,9 +83,9 @@ def test_validate_register(): with pytest.raises(ValueError, match="at most 50 μm away from the center"): Chadoq2.validate_register(Register.from_coordinates(coords)) - with pytest.raises(ValueError, match="must be 2D vectors"): + with pytest.raises(ValueError, match="at most 2D vectors"): coords = [(-10, 4, 0), (0, 0, 0)] - Chadoq2.validate_register(Register(dict(enumerate(coords)))) + Chadoq2.validate_register(Register3D(dict(enumerate(coords)))) with pytest.raises(ValueError, match="don't respect the minimal distance"): Chadoq2.validate_register( diff --git a/pulser/tests/test_register.py b/pulser/tests/test_register.py index 1afab9411..b7cbfb2a0 100644 --- a/pulser/tests/test_register.py +++ b/pulser/tests/test_register.py @@ -17,7 +17,7 @@ import numpy as np import pytest -from pulser import Register +from pulser import Register, Register3D from pulser.devices import Chadoq2 @@ -33,11 +33,11 @@ def test_creation(): Register(coords) Register(ids) - with pytest.raises(ValueError, match="vectors of size 2 or 3"): + with pytest.raises(ValueError, match="vectors of size 2"): Register.from_coordinates([(0, 1, 0, 1)]) - with pytest.raises(ValueError, match="vectors of size 2 or 3"): - Register.from_coordinates([((1, 0),), ((-1, 0),)]) + with pytest.raises(ValueError, match="vectors of size 3"): + Register3D.from_coordinates([((1, 0),), ((-1, 0),)]) reg1 = Register(qubits) reg2 = Register.from_coordinates(coords, center=False, prefix="q") @@ -247,9 +247,6 @@ def test_max_connectivity(): def test_rotation(): - with pytest.raises(NotImplementedError): - reg_ = Register.from_coordinates([(1, 0, 0), (0, 1, 4)]) - reg_.rotate(20) reg = Register.square(2, spacing=np.sqrt(2)) reg.rotate(45) coords_ = np.array([(0, -1), (1, 0), (-1, 0), (0, 1)], dtype=float) @@ -257,13 +254,13 @@ def test_rotation(): def test_drawing(): - with pytest.raises(NotImplementedError, match="register layouts in 2D."): - reg_ = Register.from_coordinates([(1, 0, 0), (0, 1, 4)]) - reg_.draw() - with pytest.raises(ValueError, match="Blockade radius"): reg = Register.from_coordinates([(1, 0), (0, 1)]) - reg.draw(blockade_radius=0.0) + reg.draw(blockade_radius=0.0, draw_half_radius=True) + + reg = Register.from_coordinates([(1, 0), (0, 1)]) + with patch("matplotlib.pyplot.show"): + reg.draw(blockade_radius=0.1, draw_graph=True) reg = Register.triangular_lattice(3, 8) with patch("matplotlib.pyplot.show"): @@ -279,3 +276,80 @@ def test_drawing(): reg = Register.square(1) with pytest.raises(NotImplementedError, match="Needs more than one atom"): reg.draw(blockade_radius=5, draw_half_radius=True) + + +def test_orthorombic(): + # Check rows + with pytest.raises(ValueError, match="The number of rows"): + Register3D.cuboid(0, 2, 2) + + # Check columns + with pytest.raises(ValueError, match="The number of columns"): + Register3D.cuboid(2, 0, 2) + + # Check layers + with pytest.raises(ValueError, match="The number of layers"): + Register3D.cuboid(2, 2, 0) + + # Check spacing + with pytest.raises(ValueError, match="Spacing"): + Register3D.cuboid(2, 2, 2, 0.0) + + +def test_cubic(): + # Check side + with pytest.raises(ValueError, match="The number of atoms per side"): + Register3D.cubic(0) + + # Check spacing + with pytest.raises(ValueError, match="Spacing"): + Register3D.cubic(2, 0.0) + + +def test_drawing3D(): + with pytest.raises(ValueError, match="Blockade radius"): + reg = Register3D.from_coordinates([(1, 0, 0), (0, 0, 1)]) + reg.draw(blockade_radius=0.0) + + reg = Register3D.cubic(3, 8) + with patch("matplotlib.pyplot.show"): + reg.draw() + + reg = Register3D.cuboid(1, 8, 2) + with patch("matplotlib.pyplot.show"): + reg.draw(blockade_radius=5, draw_half_radius=True, draw_graph=True) + + with pytest.raises(ValueError, match="'blockade_radius' to draw."): + reg.draw(draw_half_radius=True) + + reg = Register3D.cuboid(2, 2, 2) + with patch("matplotlib.pyplot.show"): + reg.draw( + blockade_radius=5, + draw_half_radius=True, + draw_graph=True, + projection=False, + with_labels=True, + ) + with patch("matplotlib.pyplot.show"): + reg.draw( + blockade_radius=5, + draw_half_radius=True, + draw_graph=False, + projection=True, + with_labels=True, + ) + + reg = Register3D.cubic(1) + with pytest.raises(NotImplementedError, match="Needs more than one atom"): + reg.draw(blockade_radius=5, draw_half_radius=True) + + +def test_to_2D(): + reg = Register3D.cuboid(2, 2, 2) + with pytest.raises(ValueError, match="Atoms are not coplanar"): + reg.to_2D() + reg.to_2D(tol_width=6) + + reg = Register3D.cuboid(2, 2, 1) + reg.to_2D() From dde12051af569cc84b158936158b2bce5ea63b60 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Wed, 20 Oct 2021 14:00:23 +0200 Subject: [PATCH 10/51] Adding extra info to the register validation error messages (#270) * Adding extra info to the register validation error messages * Setting minimum atom distance from the min_atom_distance (as it should) --- pulser/devices/_device_datacls.py | 25 +++++++++++++++++-------- pulser/tests/test_devices.py | 2 +- 2 files changed, 18 insertions(+), 9 deletions(-) diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index 15ec05c43..2ef495617 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -18,7 +18,7 @@ from typing import Any import numpy as np -from scipy.spatial.distance import pdist +from scipy.spatial.distance import pdist, squareform from pulser import Pulse from pulser.register import BaseRegister @@ -114,6 +114,7 @@ def validate_register(self, register: BaseRegister) -> None: "a pulser.Register3D instance." ) + ids = list(register.qubits.keys()) atoms = list(register.qubits.values()) if len(atoms) > self.max_atom_num: raise ValueError( @@ -131,17 +132,25 @@ def validate_register(self, register: BaseRegister) -> None: if len(atoms) > 1: distances = pdist(atoms) # Pairwise distance between atoms - if np.min(distances) < self.min_atom_distance: + if np.any(distances < self.min_atom_distance): + sq_dists = squareform(distances) + mask = np.triu(np.ones(len(atoms), dtype=bool), k=1) + bad_pairs = np.argwhere( + np.logical_and(sq_dists < self.min_atom_distance, mask) + ) + bad_qbt_pairs = [(ids[i], ids[j]) for i, j in bad_pairs] raise ValueError( - "Qubit positions don't respect the minimal " - "distance between atoms for this device." + "The minimal distance between atoms in this device " + f"({self.min_atom_distance} µm) is not respected for the " + f"pairs: {bad_qbt_pairs}" ) - if np.max(np.linalg.norm(atoms, axis=1)) > self.max_radial_distance: + too_far = np.linalg.norm(atoms, axis=1) > self.max_radial_distance + if np.any(too_far): raise ValueError( - "All qubits must be at most " - f"{self.max_radial_distance} μm away from the " - "center of the array." + f"All qubits must be at most {self.max_radial_distance} μm " + f"away from the center of the array, which is not the case " + f"for: {[ids[int(i)] for i in np.where(too_far)[0]]}" ) def validate_pulse(self, pulse: Pulse, channel_id: str) -> None: diff --git a/pulser/tests/test_devices.py b/pulser/tests/test_devices.py index d2410933c..107a62b61 100644 --- a/pulser/tests/test_devices.py +++ b/pulser/tests/test_devices.py @@ -87,7 +87,7 @@ def test_validate_register(): coords = [(-10, 4, 0), (0, 0, 0)] Chadoq2.validate_register(Register3D(dict(enumerate(coords)))) - with pytest.raises(ValueError, match="don't respect the minimal distance"): + with pytest.raises(ValueError, match="The minimal distance between atoms"): Chadoq2.validate_register( Register.triangular_lattice(3, 4, spacing=3.9) ) From ff6e20749fd06c03233900c40828cf53cf542a95 Mon Sep 17 00:00:00 2001 From: Louis-PaulHenry <79902647+Louis-PaulHenry@users.noreply.github.com> Date: Mon, 8 Nov 2021 10:05:41 +0100 Subject: [PATCH 11/51] Custom labels (#279) * Custom labels Minor fix : `Register.from_coordinates()`now has an additional optional argument to specify custom labels on each qubit. This change is applied to the `to_2D()`method * Implemented suggestions Implemented the few changes suggested --- pulser/register.py | 20 ++++++++++++++++++-- pulser/tests/test_register.py | 11 +++++++++++ 2 files changed, 29 insertions(+), 2 deletions(-) diff --git a/pulser/register.py b/pulser/register.py index 2b440ccdf..e416a6f2c 100644 --- a/pulser/register.py +++ b/pulser/register.py @@ -17,6 +17,7 @@ from abc import ABC, abstractmethod from collections.abc import Mapping, Iterable +from collections.abc import Sequence as abcSequence import matplotlib.pyplot as plt from matplotlib import collections as mc import numpy as np @@ -63,6 +64,7 @@ def from_coordinates( coords: np.ndarray, center: bool = True, prefix: Optional[str] = None, + labels: Optional[abcSequence[QubitId]] = None, ) -> T: """Creates the register from an array of coordinates. @@ -76,6 +78,8 @@ def from_coordinates( prefix (str): The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + labels (ArrayLike): The list of qubit ids. If defined, each qubit + id will be set to the corresponding value. Returns: Register: A register with qubits placed on the given coordinates. @@ -85,6 +89,19 @@ def from_coordinates( if prefix is not None: pre = str(prefix) qubits = {pre + str(i): pos for i, pos in enumerate(coords)} + if labels is not None: + raise NotImplementedError( + "It is impossible to specify a prefix and " + "a set of labels at the same time" + ) + + elif labels is not None: + if len(coords) != len(labels): + raise ValueError( + f"Label length ({len(labels)}) does not" + f"match number of coordinates ({len(coords)})" + ) + qubits = dict(zip(cast(Iterable, labels), coords)) else: qubits = dict(cast(Iterable, enumerate(coords))) return cls(qubits) @@ -807,7 +824,6 @@ def to_2D(self, tol_width: float = 0.0) -> Register: If the atoms are not coplanar, raises an error. """ coords = np.array(self._coords) - prefix = str(self._ids[0])[:-1] barycenter = coords.sum(axis=0) / coords.shape[0] # run SVD @@ -826,7 +842,7 @@ def to_2D(self, tol_width: float = 0.0) -> Register: coords_2D = np.array( [np.array([e_x.dot(r), e_y.dot(r)]) for r in coords] ) - return Register.from_coordinates(coords_2D, prefix=prefix) + return Register.from_coordinates(coords_2D, labels=self._ids) def draw( self, diff --git a/pulser/tests/test_register.py b/pulser/tests/test_register.py index b7cbfb2a0..e2255455a 100644 --- a/pulser/tests/test_register.py +++ b/pulser/tests/test_register.py @@ -36,6 +36,11 @@ def test_creation(): with pytest.raises(ValueError, match="vectors of size 2"): Register.from_coordinates([(0, 1, 0, 1)]) + with pytest.raises( + NotImplementedError, match="a prefix and a set of labels" + ): + Register.from_coordinates(coords, prefix="a", labels=["a", "b"]) + with pytest.raises(ValueError, match="vectors of size 3"): Register3D.from_coordinates([((1, 0),), ((-1, 0),)]) @@ -44,6 +49,12 @@ def test_creation(): assert np.all(np.array(reg1._coords) == np.array(reg2._coords)) assert reg1._ids == reg2._ids + reg2b = Register.from_coordinates(coords, center=False, labels=["a", "b"]) + assert reg2b._ids == ["a", "b"] + + with pytest.raises(ValueError, match="Label length"): + Register.from_coordinates(coords, center=False, labels=["a", "b", "c"]) + reg3 = Register.from_coordinates(np.array(coords), prefix="foo") coords_ = np.array([(-0.5, 0), (0.5, 0)]) assert reg3._ids == ["foo0", "foo1"] From 997bd05d48acab00cd920add39b4348505636e1a Mon Sep 17 00:00:00 2001 From: Slimane33 <47427641+Slimane33@users.noreply.github.com> Date: Tue, 9 Nov 2021 15:54:11 +0100 Subject: [PATCH 12/51] Add the possibility to save figures of register and sequences (#276) * add savefig * add test * add test * black * mypy * fix test * docstring * docstring * fix style * simulation * corrections * black --- pulser/_seq_drawer.py | 5 ++--- pulser/register.py | 20 +++++++++++++++++--- pulser/sequence.py | 11 +++++++++++ pulser/simulation/simulation.py | 11 +++++++++++ pulser/tests/test_register.py | 7 ++++++- pulser/tests/test_sequence.py | 3 ++- pulser/tests/test_simulation.py | 3 ++- 7 files changed, 51 insertions(+), 9 deletions(-) diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index 0a068f554..7e8d97cf2 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -18,6 +18,7 @@ from typing import Any, cast, Optional, Union import matplotlib.pyplot as plt +from matplotlib.figure import Figure import numpy as np from scipy.interpolate import CubicSpline @@ -107,7 +108,7 @@ def draw_sequence( draw_phase_area: bool = False, draw_interp_pts: bool = True, draw_phase_shifts: bool = False, -) -> None: +) -> Figure: """Draws the entire sequence. Args: @@ -445,5 +446,3 @@ def phase_str(phi: float) -> str: if "detuning" in all_points: pts = np.array(all_points["detuning"]) b.scatter(pts[:, 0], pts[:, 1], color="indigo") - - plt.show() diff --git a/pulser/register.py b/pulser/register.py index e416a6f2c..ac391802b 100644 --- a/pulser/register.py +++ b/pulser/register.py @@ -18,13 +18,14 @@ from abc import ABC, abstractmethod from collections.abc import Mapping, Iterable from collections.abc import Sequence as abcSequence +from typing import Any, cast, Optional, Union, TypeVar, Type +from itertools import combinations + import matplotlib.pyplot as plt from matplotlib import collections as mc import numpy as np from numpy.typing import ArrayLike from scipy.spatial import KDTree -from typing import Any, cast, Optional, Union, TypeVar, Type -from itertools import combinations import pulser from pulser.json.utils import obj_to_dict @@ -635,6 +636,8 @@ def draw( blockade_radius: Optional[float] = None, draw_graph: bool = True, draw_half_radius: bool = False, + fig_name: str = None, + kwargs_savefig: dict = {}, ) -> None: """Draws the entire register. @@ -649,6 +652,11 @@ def draw( draw_graph(bool, default=True): Whether or not to draw the interaction between atoms as edges in a graph. Will only draw if the `blockade_radius` is defined. + fig_name(str, default=None): The name on which to save the figure. + If None the figure will not be saved. + kwargs_savefig(dict, default={}): Keywords arguments for + `matplotlib.pyplot.savefig`. Not applicable if + `fig_name`is `None`. Note: When drawing half the blockade radius, we say there is a blockade @@ -684,7 +692,8 @@ def draw( draw_graph=draw_graph, draw_half_radius=draw_half_radius, ) - + if fig_name is not None: + plt.savefig(fig_name, **kwargs_savefig) plt.show() def _to_dict(self) -> dict[str, Any]: @@ -851,6 +860,8 @@ def draw( draw_graph: bool = True, draw_half_radius: bool = False, projection: bool = False, + fig_name: str = None, + kwargs_savefig: dict = {}, ) -> None: """Draws the entire register. @@ -1006,4 +1017,7 @@ def draw( ax.set_xlabel("x (µm)") ax.set_ylabel("y (µm)") ax.set_zlabel("z (µm)") + + if fig_name is not None: + plt.savefig(fig_name, **kwargs_savefig) plt.show() diff --git a/pulser/sequence.py b/pulser/sequence.py index 418d5f838..7d7ffd566 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -25,6 +25,7 @@ from typing import Any, cast, NamedTuple, Optional, Tuple, Union import warnings +import matplotlib.pyplot as plt import numpy as np from numpy.typing import ArrayLike @@ -912,6 +913,8 @@ def draw( draw_phase_area: bool = False, draw_interp_pts: bool = True, draw_phase_shifts: bool = False, + fig_name: str = None, + kwargs_savefig: dict = {}, ) -> None: """Draws the sequence in its current state. @@ -923,6 +926,11 @@ def draw( on top of the respective waveforms (defaults to True). draw_phase_shifts (bool): Whether phase shift and reference information should be added to the plot, defaults to False. + fig_name(str, default=None): The name on which to save the figure. + If None the figure will not be saved. + kwargs_savefig(dict, default={}): Keywords arguments for + `matplotlib.pyplot.savefig`. Not applicable if + `fig_name`is `None`. See Also: Simulation.draw(): Draws the provided sequence and the one used by @@ -934,6 +942,9 @@ def draw( draw_interp_pts=draw_interp_pts, draw_phase_shifts=draw_phase_shifts, ) + if fig_name is not None: + plt.savefig(fig_name, **kwargs_savefig) + plt.show() def _target( self, qubits: Union[Iterable[QubitId], QubitId], channel: str diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index 6b839825e..dab48dd4b 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -26,6 +26,7 @@ import qutip import numpy as np from numpy.typing import ArrayLike +import matplotlib.pyplot as plt from pulser import Pulse, Sequence from pulser.simulation.simresults import ( @@ -351,6 +352,8 @@ def draw( draw_phase_area: bool = False, draw_interp_pts: bool = False, draw_phase_shifts: bool = False, + fig_name: str = None, + kwargs_savefig: dict = {}, ) -> None: """Draws the input sequence and the one used by the solver. @@ -362,6 +365,11 @@ def draw( on top of the respective waveforms (defaults to False). draw_phase_shifts (bool): Whether phase shift and reference information should be added to the plot, defaults to False. + fig_name(str, default=None): The name on which to save the figure. + If None the figure will not be saved. + kwargs_savefig(dict, default={}): Keywords arguments for + `matplotlib.pyplot.savefig`. Not applicable if + `fig_name`is `None`. See Also: Sequence.draw(): Draws the sequence in its current state. @@ -373,6 +381,9 @@ def draw( draw_interp_pts=draw_interp_pts, draw_phase_shifts=draw_phase_shifts, ) + if fig_name is not None: + plt.savefig(fig_name, **kwargs_savefig) + plt.show() def _extract_samples(self) -> None: """Populates samples dictionary with every pulse in the sequence.""" diff --git a/pulser/tests/test_register.py b/pulser/tests/test_register.py index e2255455a..d6368bf5c 100644 --- a/pulser/tests/test_register.py +++ b/pulser/tests/test_register.py @@ -277,6 +277,10 @@ def test_drawing(): with patch("matplotlib.pyplot.show"): reg.draw() + with patch("matplotlib.pyplot.show"): + with patch("matplotlib.pyplot.savefig"): + reg.draw(fig_name="my_register.pdf") + reg = Register.rectangle(1, 8) with patch("matplotlib.pyplot.show"): reg.draw(blockade_radius=5, draw_half_radius=True, draw_graph=True) @@ -324,7 +328,8 @@ def test_drawing3D(): reg = Register3D.cubic(3, 8) with patch("matplotlib.pyplot.show"): - reg.draw() + with patch("matplotlib.pyplot.savefig"): + reg.draw(fig_name="my_register.pdf") reg = Register3D.cuboid(1, 8, 2) with patch("matplotlib.pyplot.show"): diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 3d5a998f0..1cf2d1bf2 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -327,7 +327,8 @@ def test_sequence(): seq.phase_shift(np.pi, "q0", basis="ground-rydberg") with patch("matplotlib.pyplot.show"): - seq.draw() + with patch("matplotlib.pyplot.savefig"): + seq.draw(fig_name="my_sequence.pdf") pulse1 = Pulse( InterpolatedWaveform(500, [0, 1, 0]), diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index 8c1dbb5ac..c686babd2 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -357,7 +357,8 @@ def test_run(): sim = Simulation(seq, sampling_rate=0.01) sim.set_config(SimConfig("SPAM", eta=0.0)) with patch("matplotlib.pyplot.show"): - sim.draw(draw_phase_area=True) + with patch("matplotlib.pyplot.savefig"): + sim.draw(draw_phase_area=True, fig_name="my_fig.pdf") bad_initial = np.array([1.0]) good_initial_array = np.r_[1, np.zeros(sim.dim ** sim._size - 1)] good_initial_qobj = qutip.tensor( From e548dc21bc0615401c5ad4bc1aaa2361f7b6cc39 Mon Sep 17 00:00:00 2001 From: Mauro D'Arcangelo <32898410+darcangelomauro@users.noreply.github.com> Date: Thu, 18 Nov 2021 10:17:42 +0100 Subject: [PATCH 13/51] Added register drawing capabilities to Sequence.draw() (#272) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * draw slm mask on register as a square halo * added drawing of mask for Register3D * fixed style and tests * fixed layout issues * fixed typo * extended save figure to masked register * split sequence and register figure names * changed masked label to enclosing square brackets * improved labels of masked qubits, less options to savefig Co-authored-by: Mauro D'Arcangelo Co-authored-by: Henrique Silvério --- pulser/_seq_drawer.py | 65 ++++++++++++++- pulser/register.py | 144 ++++++++++++++++++++++++---------- pulser/sequence.py | 36 ++++++--- pulser/tests/test_sequence.py | 27 ++++++- 4 files changed, 216 insertions(+), 56 deletions(-) diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index 7e8d97cf2..1dbb71973 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -21,10 +21,12 @@ from matplotlib.figure import Figure import numpy as np from scipy.interpolate import CubicSpline +from itertools import combinations import pulser from pulser.waveforms import ConstantWaveform, InterpolatedWaveform from pulser.pulse import Pulse +from pulser import Register, Register3D def gather_data(seq: pulser.sequence.Sequence) -> dict: @@ -108,7 +110,8 @@ def draw_sequence( draw_phase_area: bool = False, draw_interp_pts: bool = True, draw_phase_shifts: bool = False, -) -> Figure: + draw_register: bool = False, +) -> tuple[Figure, Figure]: """Draws the entire sequence. Args: @@ -123,6 +126,9 @@ def draw_sequence( top of the respective waveforms (defaults to True). draw_phase_shifts (bool): Whether phase shift and reference information should be added to the plot, defaults to False. + draw_register (bool): Whether to draw the register before the pulse + sequence, with a visual indication (square halo) around the qubits + masked by the SLM, defaults to False. """ def phase_str(phi: float) -> str: @@ -148,8 +154,59 @@ def phase_str(phi: float) -> str: area_ph_box = dict(boxstyle="round", facecolor="ghostwhite", alpha=0.7) slm_box = dict(boxstyle="round", alpha=0.4, facecolor="grey", hatch="//") - fig = plt.figure(constrained_layout=False, figsize=(20, 4.5 * n_channels)) - gs = fig.add_gridspec(n_channels, 1, hspace=0.075) + pos = np.array(seq._register._coords) + + # Draw masked register + if draw_register: + if isinstance(seq._register, Register3D): + labels = "xyz" + fig_reg, axes_reg = seq._register._initialize_fig_axes_projection( + pos, + blockade_radius=35, + draw_half_radius=True, + ) + fig_reg.tight_layout(w_pad=6.5) + + for ax_reg, (ix, iy) in zip( + axes_reg, combinations(np.arange(3), 2) + ): + seq._register._draw_2D( + ax=ax_reg, + pos=pos, + ids=seq._register._ids, + plane=(ix, iy), + masked_qubits=seq._slm_mask_targets, + ) + ax_reg.set_title( + "Masked register projected onto\n the " + + labels[ix] + + labels[iy] + + "-plane" + ) + + elif isinstance(seq._register, Register): + fig_reg, ax_reg = seq._register._initialize_fig_axes( + pos, + blockade_radius=35, + draw_half_radius=True, + ) + seq._register._draw_2D( + ax=ax_reg, + pos=pos, + ids=seq._register._ids, + masked_qubits=seq._slm_mask_targets, + ) + ax_reg.set_title("Masked register", pad=10) + + fig = plt.figure( + constrained_layout=False, + figsize=(20, 4.5 * n_channels), + ) + gs = fig.add_gridspec( + n_channels, + 1, + hspace=0.075, + ) ch_axes = {} for i, (ch, gs_) in enumerate(zip(seq._channels, gs)): @@ -446,3 +503,5 @@ def phase_str(phi: float) -> str: if "detuning" in all_points: pts = np.array(all_points["detuning"]) b.scatter(pts[:, 0], pts[:, 1], color="indigo") + + return (fig_reg if draw_register else None, fig) diff --git a/pulser/register.py b/pulser/register.py index ac391802b..9e62cca37 100644 --- a/pulser/register.py +++ b/pulser/register.py @@ -117,11 +117,28 @@ def _draw_2D( blockade_radius: Optional[float] = None, draw_graph: bool = True, draw_half_radius: bool = False, + masked_qubits: set[QubitId] = set(), ) -> None: ix, iy = plane ax.scatter(pos[:, ix], pos[:, iy], s=30, alpha=0.7, c="darkgreen") + # Draw square halo around masked qubits + if masked_qubits: + mask_pos = [] + for i, c in zip(ids, pos): + if i in masked_qubits: + mask_pos.append(c) + mask_arr = np.array(mask_pos) + ax.scatter( + mask_arr[:, ix], + mask_arr[:, iy], + marker="s", + s=1200, + alpha=0.2, + c="black", + ) + axes = "xyz" ax.set_xlabel(axes[ix] + " (µm)") @@ -133,7 +150,7 @@ def _draw_2D( if with_labels: # Determine which labels would overlap and merge those plot_pos = list(pos[:, (ix, iy)]) - plot_ids = [f"{i}" for i in ids] + plot_ids: list[Union[list, str]] = [[f"{i}"] for i in ids] # Threshold distance between points epsilon = 1.0e-2 * np.diff(ax.get_xlim())[0] @@ -143,14 +160,32 @@ def _draw_2D( r = plot_pos[i] j = i + 1 overlap = False + # Put in a list all qubits that overlap at position plot_pos[i] while j < len(plot_ids): r2 = plot_pos[j] if np.max(np.abs(r - r2)) < epsilon: - plot_ids[i] += ", " + plot_ids.pop(j) + plot_ids[i] = plot_ids[i] + plot_ids.pop(j) plot_pos.pop(j) overlap = True else: j += 1 + # Sort qubits in plot_ids[i] according to masked status + plot_ids[i] = sorted( + plot_ids[i], + key=lambda s: s in [str(q) for q in masked_qubits], + ) + # Merge all masked qubits + has_masked = False + for j in range(len(plot_ids[i])): + if plot_ids[i][j] in [str(q) for q in masked_qubits]: + plot_ids[i][j:] = [", ".join(plot_ids[i][j:])] + has_masked = True + break + # Add a square bracket that encloses all masked qubits + if has_masked: + plot_ids[i][-1] = "[" + plot_ids[i][-1] + "]" + # Merge what remains + plot_ids[i] = ", ".join(plot_ids[i]) bbs[plot_ids[i]] = overlap i += 1 @@ -630,6 +665,27 @@ def rotate(self, degrees: float) -> None: ) self._coords = [rot @ v for v in self._coords] + def _initialize_fig_axes( + self, + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> tuple[plt.figure.Figure, plt.axes.Axes]: + """Creates the Figure and Axes for drawing the register.""" + diffs = super()._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + big_side = max(diffs) + proportions = diffs / big_side + Ls = proportions * min( + big_side / 4, 10 + ) # Figsize is, at most, (10,10) + fig, axes = plt.subplots(figsize=Ls) + + return (fig, axes) + def draw( self, with_labels: bool = True, @@ -671,18 +727,11 @@ def draw( draw_half_radius=draw_half_radius, ) pos = np.array(self._coords) - diffs = super()._register_dims( + fig, ax = self._initialize_fig_axes( pos, blockade_radius=blockade_radius, draw_half_radius=draw_half_radius, ) - big_side = max(diffs) - proportions = diffs / big_side - Ls = proportions * min( - big_side / 4, 10 - ) # Figsize is, at most, (10,10) - - fig, ax = plt.subplots(figsize=Ls) super()._draw_2D( ax, pos, @@ -853,6 +902,48 @@ def to_2D(self, tol_width: float = 0.0) -> Register: ) return Register.from_coordinates(coords_2D, labels=self._ids) + def _initialize_fig_axes_projection( + self, + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> tuple[plt.figure.Figure, plt.axes.Axes]: + """Creates the Figure and Axes for drawing the register projections.""" + diffs = super()._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + + proportions = [] + for (ix, iy) in combinations(np.arange(3), 2): + big_side = max(diffs[[ix, iy]]) + Ls = diffs[[ix, iy]] / big_side + Ls *= max( + min(big_side / 4, 10), 4 + ) # Figsize is, at most, (10,10), and, at least (4,*) or (*,4) + proportions.append(Ls) + + fig_height = np.max([Ls[1] for Ls in proportions]) + + max_width = 0 + for i, (width, height) in enumerate(proportions): + proportions[i] = (width * fig_height / height, fig_height) + max_width = max(max_width, proportions[i][0]) + widths = [max(Ls[0], max_width / 5) for Ls in proportions] + fig_width = min(np.sum(widths), fig_height * 4) + + rescaling = 20 / max(max(fig_width, fig_height), 20) + figsize = (rescaling * fig_width, rescaling * fig_height) + + fig, axes = plt.subplots( + ncols=3, + figsize=figsize, + gridspec_kw=dict(width_ratios=widths), + ) + + return (fig, axes) + def draw( self, with_labels: bool = False, @@ -899,40 +990,13 @@ def draw( edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) if projection: - diffs = super()._register_dims( + labels = "xyz" + fig, axes = self._initialize_fig_axes_projection( pos, blockade_radius=blockade_radius, draw_half_radius=draw_half_radius, ) - - proportions = [] - for (ix, iy) in combinations(np.arange(3), 2): - big_side = max(diffs[[ix, iy]]) - Ls = diffs[[ix, iy]] / big_side - Ls *= max( - min(big_side / 4, 10), 4 - ) # Figsize is, at most, (10,10), and, at least (4,*) or (*,4) - proportions.append(Ls) - - fig_height = np.max([Ls[1] for Ls in proportions]) - - max_width = 0 - for i, (width, height) in enumerate(proportions): - proportions[i] = (width * fig_height / height, fig_height) - max_width = max(max_width, proportions[i][0]) - widths = [max(Ls[0], max_width / 5) for Ls in proportions] - fig_width = min(np.sum(widths), fig_height * 4) - - rescaling = 20 / max(max(fig_width, fig_height), 20) - figsize = (rescaling * fig_width, rescaling * fig_height) - - labels = "xyz" - - fig, axes = plt.subplots( - ncols=3, - figsize=figsize, - gridspec_kw=dict(width_ratios=widths), - ) + fig.tight_layout(w_pad=6.5) for ax, (ix, iy) in zip(axes, combinations(np.arange(3), 2)): super()._draw_2D( diff --git a/pulser/sequence.py b/pulser/sequence.py index 7d7ffd566..0fed7efb6 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -24,6 +24,7 @@ from sys import version_info from typing import Any, cast, NamedTuple, Optional, Tuple, Union import warnings +import os import matplotlib.pyplot as plt import numpy as np @@ -37,7 +38,7 @@ from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, Variable from pulser.pulse import Pulse -from pulser.register import Register +from pulser.register import BaseRegister from pulser._seq_drawer import draw_sequence if version_info[:2] >= (3, 8): # pragma: no cover @@ -148,7 +149,7 @@ class Sequence: generated from a single "parametrized" ``Sequence``. Args: - register(Register): The atom register on which to apply the pulses. + register(BaseRegister): The atom register on which to apply the pulses. device(Device): A valid device in which to execute the Sequence (import it from ``pulser.devices``). @@ -157,7 +158,7 @@ class Sequence: they are the same for all Sequences built from a parametrized Sequence. """ - def __init__(self, register: Register, device: Device): + def __init__(self, register: BaseRegister, device: Device): """Initializes a new pulse sequence.""" if not isinstance(device, Device): raise TypeError( @@ -180,7 +181,7 @@ def __init__(self, register: Register, device: Device): # Checks if register is compatible with the device device.validate_register(register) - self._register: Register = register + self._register: BaseRegister = register self._device: Device = device self._in_xy: bool = False self._mag_field: Optional[tuple[float, float, float]] = None @@ -913,6 +914,7 @@ def draw( draw_phase_area: bool = False, draw_interp_pts: bool = True, draw_phase_shifts: bool = False, + draw_register: bool = False, fig_name: str = None, kwargs_savefig: dict = {}, ) -> None: @@ -926,24 +928,36 @@ def draw( on top of the respective waveforms (defaults to True). draw_phase_shifts (bool): Whether phase shift and reference information should be added to the plot, defaults to False. - fig_name(str, default=None): The name on which to save the figure. - If None the figure will not be saved. + draw_register (bool): Whether to draw the register before the pulse + sequence, with a visual indication (square halo) around the + qubits masked by the SLM, defaults to False. + fig_name(str, default=None): The name on which to save the + figure. If draw_register is True, both pulses and register + will be saved as figures, with a suffix "_pulses" and + "_register" in the file name. If draw_register is False, only + the pulses are saved, with no suffix. If fig_name is None, + no figure is saved. kwargs_savefig(dict, default={}): Keywords arguments for - `matplotlib.pyplot.savefig`. Not applicable if - `fig_name`is `None`. + `matplotlib.figure.Figure.savefig`. + Not applicable if `fig_name`is `None`. See Also: Simulation.draw(): Draws the provided sequence and the one used by the solver. """ - draw_sequence( + fig_reg, fig = draw_sequence( self, draw_phase_area=draw_phase_area, draw_interp_pts=draw_interp_pts, draw_phase_shifts=draw_phase_shifts, + draw_register=draw_register, ) - if fig_name is not None: - plt.savefig(fig_name, **kwargs_savefig) + if fig_name is not None and draw_register: + name, ext = os.path.splitext(fig_name) + fig.savefig(name + "_pulses" + ext, **kwargs_savefig) + fig_reg.savefig(name + "_register" + ext, **kwargs_savefig) + elif fig_name: + fig.savefig(fig_name, **kwargs_savefig) plt.show() def _target( diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 1cf2d1bf2..60c31d091 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -18,7 +18,7 @@ import pytest import pulser -from pulser import Sequence, Pulse, Register +from pulser import Sequence, Pulse, Register, Register3D from pulser.devices import Chadoq2, MockDevice from pulser.devices._device_datacls import Device from pulser.sequence import _TimeSlot @@ -327,8 +327,9 @@ def test_sequence(): seq.phase_shift(np.pi, "q0", basis="ground-rydberg") with patch("matplotlib.pyplot.show"): - with patch("matplotlib.pyplot.savefig"): + with patch("matplotlib.figure.Figure.savefig"): seq.draw(fig_name="my_sequence.pdf") + seq.draw(draw_register=True, fig_name="both.pdf") pulse1 = Pulse( InterpolatedWaveform(500, [0, 1, 0]), @@ -547,3 +548,25 @@ def test_slm_mask(): # Check drawing method with patch("matplotlib.pyplot.show"): seq_xy2.draw() + + +def test_draw_register(): + # Draw 2d register from sequence + reg = Register({"q0": (0, 0), "q1": (10, 10), "q2": (-10, -10)}) + targets = ["q0", "q2"] + pulse = Pulse.ConstantPulse(100, 10, 0, 0) + seq = Sequence(reg, MockDevice) + seq.declare_channel("ch_xy", "mw_global") + seq.add(pulse, "ch_xy") + seq.config_slm_mask(targets) + with patch("matplotlib.pyplot.show"): + seq.draw(draw_register=True) + + # Draw 3d register from sequence + reg3d = Register3D.cubic(3, 8) + seq3d = Sequence(reg3d, MockDevice) + seq3d.declare_channel("ch_xy", "mw_global") + seq3d.add(pulse, "ch_xy") + seq3d.config_slm_mask([6, 15]) + with patch("matplotlib.pyplot.show"): + seq3d.draw(draw_register=True) From 0259a1a53e4f1308ac5e0d12db7ed32b543afe66 Mon Sep 17 00:00:00 2001 From: Lucas Leclerc <71789668+LucasGitQ@users.noreply.github.com> Date: Mon, 22 Nov 2021 14:21:09 +0100 Subject: [PATCH 14/51] Changing rydberg level (#281) * Starting branch for changing rydberg level * Method to change rydberg_level attribute * Adding error check * Try to pass the checks 1 * Try passing checks 2 * Create interaction_coeffs module and update device_datacls * Small changes and correcting black, style and typing * Update test_devices.py * Passing tests [FIXED] * Adding test change_rydberg_level * Small changes * Adding disclaimer to the scope of the `change_rydberg_level` method Co-authored-by: HGSilveri --- pulser/devices/_device_datacls.py | 31 +++++++++-- pulser/devices/_devices.py | 1 + pulser/devices/_mock_device.py | 1 + .../interaction_coefficients/C6_coeffs.json | 53 +++++++++++++++++++ .../interaction_coefficients/__init__.py | 20 +++++++ pulser/tests/test_devices.py | 24 +++++++-- pulser/tests/test_sequence.py | 2 +- pulser/tests/test_simresults.py | 20 +++---- pulser/tests/test_simulation.py | 2 +- 9 files changed, 134 insertions(+), 20 deletions(-) create mode 100644 pulser/devices/interaction_coefficients/C6_coeffs.json create mode 100644 pulser/devices/interaction_coefficients/__init__.py diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index 2ef495617..3c8cb0054 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -24,6 +24,7 @@ from pulser.register import BaseRegister from pulser.channels import Channel from pulser.json.utils import obj_to_dict +from pulser.devices.interaction_coefficients import c6_dict @dataclass(frozen=True, repr=False) @@ -37,10 +38,8 @@ class Device: max_radial_distance: The furthest away an atom can be from the center of the array (in μm). min_atom_distance: The closest together two atoms can be (in μm). - interaction_coeff: :math:`C_6/\hbar` - (in :math:`\mu m^6 / \mu s`), - which sets the van der Waals interaction strength between atoms in - the same Rydberg state. + rybderg_level : The value of the principal quantum number n + when the Rydberg level used is of the form |nS_1/2, m_j = +1/2>. interaction_coeff_xy: :math:`C_3/\hbar` (in :math:`\mu m^3 / \mu s`), which sets the van der Waals interaction strength between atoms in different Rydberg states. @@ -48,12 +47,12 @@ class Device: name: str dimensions: int + rydberg_level: int max_atom_num: int max_radial_distance: int min_atom_distance: int _channels: tuple[tuple[str, Channel], ...] # Ising interaction coeff - interaction_coeff: float = 5008713.0 interaction_coeff_xy: float = 3700.0 def __post_init__(self) -> None: @@ -70,6 +69,11 @@ def supported_bases(self) -> set[str]: """Available electronic transitions for control and measurement.""" return {ch.basis for ch in self.channels.values()} + @property + def interaction_coeff(self) -> float: + """C_6/hbar coefficient of chosen Rydberg level.""" + return float(c6_dict[self.rydberg_level]) + def print_specs(self) -> None: """Prints the device specifications.""" title = f"{self.name} Specifications" @@ -80,6 +84,23 @@ def print_specs(self) -> None: def __repr__(self) -> str: return self.name + def change_rydberg_level(self, ryd_lvl: int) -> None: + """Changes the Rydberg level used in the Device. + + Args: + ryd_lvl(int): the Rydberg level to use (between 50 and 100). + + Note: + Modifications to the `rydberg_level` attribute only affect the + outcomes of local emulations. + """ + if not isinstance(ryd_lvl, int): + raise TypeError("Rydberg level has to be an int.") + if not ((49 < ryd_lvl) & (101 > ryd_lvl)): + raise ValueError("Rydberg level should be between 50 and 100.") + + object.__setattr__(self, "rydberg_level", ryd_lvl) + def rydberg_blockade_radius(self, rabi_frequency: float) -> float: """Calculates the Rydberg blockade radius for a given Rabi frequency. diff --git a/pulser/devices/_devices.py b/pulser/devices/_devices.py index b71d6c182..0fa9e50e0 100644 --- a/pulser/devices/_devices.py +++ b/pulser/devices/_devices.py @@ -21,6 +21,7 @@ Chadoq2 = Device( name="Chadoq2", dimensions=2, + rydberg_level=70, max_atom_num=100, max_radial_distance=50, min_atom_distance=4, diff --git a/pulser/devices/_mock_device.py b/pulser/devices/_mock_device.py index 9d84c0d19..ac4f8dcef 100644 --- a/pulser/devices/_mock_device.py +++ b/pulser/devices/_mock_device.py @@ -19,6 +19,7 @@ MockDevice = Device( name="MockDevice", dimensions=3, + rydberg_level=70, max_atom_num=2000, max_radial_distance=1000, min_atom_distance=1, diff --git a/pulser/devices/interaction_coefficients/C6_coeffs.json b/pulser/devices/interaction_coefficients/C6_coeffs.json new file mode 100644 index 000000000..d3071eac9 --- /dev/null +++ b/pulser/devices/interaction_coefficients/C6_coeffs.json @@ -0,0 +1,53 @@ +{ +"50": 96120.72, +"51": 122241.6, +"52": 154693.02, +"53": 194740.36, +"54": 243973.91, +"55": 304495.01, +"56": 378305.98, +"57": 468027.05, +"58": 576714.85, +"59": 707911.38, +"60": 865723.02, +"61": 1054903.11, +"62": 1281042.11, +"63": 1550531.15, +"64": 1870621.31, +"65": 2249728.57, +"66": 2697498.69, +"67": 3224987.51, +"68": 3844734.37, +"69": 4571053.32, +"70": 5420158.53, +"71": 6410399.4, +"72": 7562637.31, +"73": 8900342.14, +"74": 10449989.62, +"75": 12241414.53, +"76": 14308028.03, +"77": 16687329.94, +"78": 19421333.62, +"79": 22557029.94, +"80": 26146720.74, +"81": 30248886.65, +"82": 34928448.69, +"83": 40257623.67, +"84": 46316557.88, +"85": 53194043.52, +"86": 60988354.64, +"87": 69808179.15, +"88": 79773468.88, +"89": 91016513.07, +"90": 103677784.57, +"91": 117933293.96, +"92": 133943541.9, +"93": 151907135.94, +"94": 172036137.34, +"95": 194562889.89, +"96": 219741590.56, +"97": 247850178.91, +"98": 279192193.77, +"99": 314098829.39, +"100": 352931119.11 +} \ No newline at end of file diff --git a/pulser/devices/interaction_coefficients/__init__.py b/pulser/devices/interaction_coefficients/__init__.py new file mode 100644 index 000000000..395e0e57a --- /dev/null +++ b/pulser/devices/interaction_coefficients/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2020 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""C_6/hbar (in um^6 / us`), coeffs for Rydberg levels between 50 and 100.""" + +import json +from pathlib import PurePath + +_json_dict = json.load(open(PurePath(__file__).parent / "C6_coeffs.json")) +c6_dict = {int(key): value for key, value in _json_dict.items()} diff --git a/pulser/tests/test_devices.py b/pulser/tests/test_devices.py index 107a62b61..d71807166 100644 --- a/pulser/tests/test_devices.py +++ b/pulser/tests/test_devices.py @@ -26,6 +26,8 @@ def test_init(): for dev in pulser.devices._valid_devices: assert dev.dimensions in (2, 3) + assert dev.rydberg_level > 49 + assert dev.rydberg_level < 101 assert dev.max_atom_num > 10 assert dev.max_radial_distance > 10 assert dev.min_atom_distance > 0 @@ -44,9 +46,11 @@ def test_init(): def test_mock(): dev = pulser.devices.MockDevice assert dev.dimensions == 3 + assert dev.rydberg_level > 49 + assert dev.rydberg_level < 101 assert dev.max_atom_num > 1000 assert dev.min_atom_distance <= 1 - assert dev.interaction_coeff == 5008713 + assert dev.interaction_coeff > 0 assert dev.interaction_coeff_xy == 3700 names = ["Rydberg", "Raman", "Microwave"] basis = ["ground-rydberg", "digital", "XY"] @@ -62,10 +66,24 @@ def test_mock(): assert ch.max_targets == int(ch.max_targets) +def test_change_rydberg_level(): + dev = pulser.devices.MockDevice + dev.change_rydberg_level(60) + assert dev.rydberg_level == 60 + assert np.isclose(dev.interaction_coeff, 865723.02) + with pytest.raises(TypeError, match="Rydberg level has to be an int."): + dev.change_rydberg_level(70.5) + with pytest.raises( + ValueError, match="Rydberg level should be between 50 and 100." + ): + dev.change_rydberg_level(110) + dev.change_rydberg_level(70) + + def test_rydberg_blockade(): dev = pulser.devices.MockDevice - assert np.isclose(dev.rydberg_blockade_radius(3 * np.pi), 9) - assert np.isclose(dev.rabi_from_blockade(9), 3 * np.pi) + assert np.isclose(dev.rydberg_blockade_radius(3 * np.pi), 9.119201) + assert np.isclose(dev.rabi_from_blockade(9), 10.198984) rand_omega = np.random.rand() * 2 * np.pi assert np.isclose( rand_omega, diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 60c31d091..9934d2ca3 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -37,7 +37,7 @@ def test_init(): with pytest.raises(TypeError, match="must be of type 'Device'"): Sequence(reg, Device) - fake_device = Device("fake", 2, 100, 100, 1, Chadoq2._channels) + fake_device = Device("fake", 2, 70, 100, 100, 1, Chadoq2._channels) with pytest.warns(UserWarning, match="imported from 'pulser.devices'"): Sequence(reg, fake_device) diff --git a/pulser/tests/test_simresults.py b/pulser/tests/test_simresults.py index 0adf2f9a8..d76c26a89 100644 --- a/pulser/tests/test_simresults.py +++ b/pulser/tests/test_simresults.py @@ -175,10 +175,10 @@ def test_get_state_float_time(): state.full(), np.array( [ - [0.79602211 + 0.0j], - [0.02417478 - 0.37829574j], - [0.02417478 - 0.37829574j], - [-0.27423657 - 0.06131009j], + [0.76522918 + 0.0j], + [0.08126436 - 0.39418836j], + [0.08126436 - 0.39418836j], + [-0.28037007 - 0.10881273j], ] ), ).all() @@ -243,7 +243,7 @@ def test_expect_noisy(): with pytest.raises(ValueError, match="non-diagonal"): results_noisy.expect([bad_op]) op = qutip.tensor([qutip.qeye(2), qutip.basis(2, 0).proj()]) - assert np.isclose(results_noisy.expect([op])[0][-1], 0.7733333333333334) + assert np.isclose(results_noisy.expect([op])[0][-1], 0.76) def test_plot(): @@ -258,7 +258,7 @@ def test_sample_final_state(): sim_no_meas = Simulation(seq_no_meas, config=SimConfig(runs=1)) results_no_meas = sim_no_meas.run() assert results_no_meas.sample_final_state() == Counter( - {"00": 77, "01": 140, "10": 167, "11": 616} + {"00": 88, "01": 156, "10": 188, "11": 568} ) with pytest.raises(NotImplementedError, match="dimension > 3"): results_large_dim = deepcopy(results) @@ -286,7 +286,7 @@ def test_sample_final_state(): def test_sample_final_state_noisy(): np.random.seed(123) assert results_noisy.sample_final_state(N_samples=1234) == Counter( - {"11": 787, "10": 219, "01": 176, "00": 52} + {"00": 67, "01": 176, "10": 219, "11": 772} ) res_3level = Simulation( seq_no_meas_noisy, config=SimConfig(noise=("SPAM", "doppler"), runs=10) @@ -296,10 +296,10 @@ def test_sample_final_state_noisy(): final_state.full(), np.array( [ - [0.62 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.16 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], + [0.58 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], + [0.0 + 0.0j, 0.18 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], [0.0 + 0.0j, 0.0 + 0.0j, 0.18 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.04 + 0.0j], + [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.06 + 0.0j], ] ), ).all() diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index c686babd2..87973fb87 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -295,7 +295,7 @@ def test_get_hamiltonian(): np.array( [ [ - 4.0683997 + 0.0j, + 4.47984523 + 0.0j, 0.09606404 + 0.0j, 0.09606404 + 0.0j, 0.0 + 0.0j, From 6074064746d0e1766a2ba4d884d10fff082b0984 Mon Sep 17 00:00:00 2001 From: Seb Grijalva <13460713+sebgrijalva@users.noreply.github.com> Date: Thu, 25 Nov 2021 11:16:56 +0100 Subject: [PATCH 15/51] Evaluation Times and Sampling Times (#282) * Extend sampling times to include final instant * Change `self._times` to `self.sampling_times` * Make `self.evaluation_times` return array not instruction * Make simulation tests comply with new names * Include changes in `test_simresults` * Add flake8, black * Typing * Assign evaluation times instruction at __init__() * Ignore typing on setter evaluation_times * Updating tutorials using `Simulation._times` * Fix bug where `sim._times` should actually be `sim.evaluation_times` Co-authored-by: HGSilveri --- pulser/simulation/simulation.py | 38 ++-- pulser/tests/test_simresults.py | 26 +-- pulser/tests/test_simulation.py | 54 ++++-- .../Building 1D Rydberg Crystals.ipynb | 176 +++++++++--------- tutorials/simulating_sequences.ipynb | 2 +- 5 files changed, 162 insertions(+), 134 deletions(-) diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index dab48dd4b..e535d76a5 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -106,11 +106,12 @@ def __init__( self._sampling_rate = sampling_rate self._qid_index = {qid: i for i, qid in enumerate(self._qdict)} self._collapse_ops: list[qutip.Qobj] = [] - self._times = self._adapt_to_sampling_rate( + + self.sampling_times = self._adapt_to_sampling_rate( np.arange(self._tot_duration, dtype=np.double) / 1000 ) + self.evaluation_times = evaluation_times # type: ignore - self.evaluation_times = evaluation_times self._bad_atoms: dict[Union[str, int], bool] = {} self._doppler_detune: dict[Union[str, int], float] = {} # Sets the config as well as builds the hamiltonian @@ -271,7 +272,7 @@ def initial_state(self, state: Union[str, np.ndarray, qutip.Qobj]) -> None: self._initial_state = qutip.Qobj(state, dims=legal_dims) @property - def evaluation_times(self) -> Union[str, float, ArrayLike]: + def evaluation_times(self) -> np.ndarray: """The times at which the results of this simulation are returned. Args: @@ -288,17 +289,19 @@ def evaluation_times(self) -> Union[str, float, ArrayLike]: - A float to act as a sampling rate for the resulting state. """ - return self._evaluation_times + return np.array(self._eval_times_array) @evaluation_times.setter def evaluation_times(self, value: Union[str, ArrayLike, float]) -> None: """Sets times at which the results of this simulation are returned.""" if isinstance(value, str): if value == "Full": - self._eval_times_array = self._times + self._eval_times_array = np.append( + self.sampling_times, self._tot_duration / 1000 + ) elif value == "Minimal": self._eval_times_array = np.array( - [self._times[0], self._times[-1]] + [self.sampling_times[0], self._tot_duration / 1000] ) else: raise ValueError( @@ -311,17 +314,20 @@ def evaluation_times(self, value: Union[str, ArrayLike, float]) -> None: raise ValueError( "evaluation_times float must be between 0 " "and 1." ) + extended_times = np.append( + self.sampling_times, self._tot_duration / 1000 + ) indices = np.linspace( 0, - len(self._times) - 1, - int(value * len(self._times)), + len(extended_times) - 1, + int(value * len(extended_times)), dtype=int, ) - self._eval_times_array = self._times[indices] + self._eval_times_array = extended_times[indices] elif isinstance(value, (list, tuple, np.ndarray)): t_max = np.max(value) t_min = np.min(value) - if t_max > self._times[-1]: + if t_max > self._tot_duration / 1000: raise ValueError( "Provided evaluation-time list extends " "further than sequence duration." @@ -335,8 +341,8 @@ def evaluation_times(self, value: Union[str, ArrayLike, float]) -> None: eval_times = np.array(np.sort(value)) if t_min > 0: eval_times = np.insert(eval_times, 0, 0.0) - if t_max < self._times[-1]: - eval_times = np.append(eval_times, self._times[-1]) + if t_max < self._tot_duration / 1000: + eval_times = np.append(eval_times, self._tot_duration / 1000) self._eval_times_array = eval_times # always include initial and final times else: @@ -345,7 +351,7 @@ def evaluation_times(self, value: Union[str, ArrayLike, float]) -> None: "be `Full`, `Minimal`, an array of times or a " + "float between 0 and 1." ) - self._evaluation_times: Union[str, ArrayLike, float] = value + self._eval_times_instruction = value def draw( self, @@ -799,7 +805,7 @@ def build_coeffs_ops(basis: str, addr: str) -> list[list]: if not qobj_list: # If qobj_list ends up empty qobj_list = [0 * self.build_operator([("I", "global")])] - ham = qutip.QobjEvo(qobj_list, tlist=self._times) + ham = qutip.QobjEvo(qobj_list, tlist=self.sampling_times) ham = ham + ham.dag() ham.compress() self._hamiltonian = ham @@ -816,11 +822,11 @@ def get_hamiltonian(self, time: float) -> qutip.Qobj: extracted from the effective sequence (determined by `self.sampling_rate`) at the specified time. """ - if time > 1000 * self._times[-1]: + if time > self._tot_duration: raise ValueError( f"Provided time (`time` = {time}) must be " "less than or equal to the sequence duration " - f"({1000 * self._times[-1]})." + f"({self._tot_duration})." ) if time < 0: raise ValueError( diff --git a/pulser/tests/test_simresults.py b/pulser/tests/test_simresults.py index d76c26a89..f8219c884 100644 --- a/pulser/tests/test_simresults.py +++ b/pulser/tests/test_simresults.py @@ -154,11 +154,11 @@ def test_get_final_state_noisy(): res3._meas_basis = "ground-rydberg" assert ( final_state[0, 0] == 0.06666666666666667 + 0j - and final_state[2, 2] == 0.9333333333333333 + 0j + and final_state[2, 2] == 0.92 + 0j ) assert res3.states[-1] == final_state assert res3.results[-1] == Counter( - {"10": 0.9333333333333333, "00": 0.06666666666666667} + {"10": 0.92, "00": 0.06666666666666667, "11": 0.013333333333333334} ) @@ -175,10 +175,10 @@ def test_get_state_float_time(): state.full(), np.array( [ - [0.76522918 + 0.0j], - [0.08126436 - 0.39418836j], - [0.08126436 - 0.39418836j], - [-0.28037007 - 0.10881273j], + [0.76522907 + 0.0j], + [0.08339973 - 0.39374219j], + [0.08339973 - 0.39374219j], + [-0.27977623 - 0.1103308j], ] ), ).all() @@ -200,7 +200,7 @@ def test_expect(): op = [qutip.basis(2, 0).proj()] exp = results_single.expect(op)[0] assert np.isclose(exp[-1], 1) - assert len(exp) == duration + assert len(exp) == duration + 1 # +1 for the final instant np.testing.assert_almost_equal( results_single._calc_pseudo_density(-1).full(), np.array([[1, 0], [0, 0]]), @@ -243,7 +243,7 @@ def test_expect_noisy(): with pytest.raises(ValueError, match="non-diagonal"): results_noisy.expect([bad_op]) op = qutip.tensor([qutip.qeye(2), qutip.basis(2, 0).proj()]) - assert np.isclose(results_noisy.expect([op])[0][-1], 0.76) + assert np.isclose(results_noisy.expect([op])[0][-1], 0.6933333333333334) def test_plot(): @@ -286,7 +286,7 @@ def test_sample_final_state(): def test_sample_final_state_noisy(): np.random.seed(123) assert results_noisy.sample_final_state(N_samples=1234) == Counter( - {"00": 67, "01": 176, "10": 219, "11": 772} + {"00": 140, "01": 227, "10": 221, "11": 646} ) res_3level = Simulation( seq_no_meas_noisy, config=SimConfig(noise=("SPAM", "doppler"), runs=10) @@ -296,10 +296,10 @@ def test_sample_final_state_noisy(): final_state.full(), np.array( [ - [0.58 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.18 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.0 + 0.0j, 0.18 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.06 + 0.0j], + [0.64 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], + [0.0 + 0.0j, 0.14 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], + [0.0 + 0.0j, 0.0 + 0.0j, 0.1 + 0.0j, 0.0 + 0.0j], + [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.12 + 0.0j], ] ), ).all() diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index 87973fb87..0844d6c2f 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -93,11 +93,13 @@ def test_initialization_and_construction_of_hamiltonian(): Simulation(seq, sampling_rate=-1) assert sim._sampling_rate == 0.011 - assert len(sim._times) == int(sim._sampling_rate * sim._tot_duration) + assert len(sim.sampling_times) == int( + sim._sampling_rate * sim._tot_duration + ) assert isinstance(sim._hamiltonian, qutip.QobjEvo) # Checks adapt() method: - assert bool(set(sim._hamiltonian.tlist).intersection(sim._times)) + assert bool(set(sim._hamiltonian.tlist).intersection(sim.sampling_times)) for qobjevo in sim._hamiltonian.ops: for sh in qobjevo.qobj.shape: assert sh == sim.dim ** sim._size @@ -423,7 +425,7 @@ def test_eval_times(): match="Provided evaluation-time list contains " "negative values.", ): sim = Simulation(seq, sampling_rate=1.0) - sim.evaluation_times = [-1, 0, sim._times[-2]] + sim.evaluation_times = [-1, 0, sim.sampling_times[-2]] with pytest.raises( ValueError, @@ -431,38 +433,58 @@ def test_eval_times(): "further than sequence duration.", ): sim = Simulation(seq, sampling_rate=1.0) - sim.evaluation_times = [0, sim._times[-1] + 10] + sim.evaluation_times = [0, sim.sampling_times[-1] + 10] sim = Simulation(seq, sampling_rate=1.0) sim.evaluation_times = "Full" - assert sim.evaluation_times == "Full" - np.testing.assert_almost_equal(sim._eval_times_array, sim._times) + assert sim._eval_times_instruction == "Full" + np.testing.assert_almost_equal( + sim._eval_times_array, + np.append(sim.sampling_times, sim._tot_duration / 1000), + ) sim = Simulation(seq, sampling_rate=1.0) sim.evaluation_times = "Minimal" np.testing.assert_almost_equal( - sim._eval_times_array, np.array([sim._times[0], sim._times[-1]]) + sim._eval_times_array, + np.array([sim.sampling_times[0], sim._tot_duration / 1000]), ) sim = Simulation(seq, sampling_rate=1.0) - sim.evaluation_times = [0, sim._times[-3], sim._times[-1]] + sim.evaluation_times = [ + 0, + sim.sampling_times[-3], + sim._tot_duration / 1000, + ] np.testing.assert_almost_equal( - sim._eval_times_array, np.array([0, sim._times[-3], sim._times[-1]]) + sim._eval_times_array, + np.array([0, sim.sampling_times[-3], sim._tot_duration / 1000]), ) sim = Simulation(seq, sampling_rate=1.0) - sim.evaluation_times = [sim._times[-10], sim._times[-3]] + sim.evaluation_times = [sim.sampling_times[-10], sim.sampling_times[-3]] np.testing.assert_almost_equal( sim._eval_times_array, - np.array([0, sim._times[-10], sim._times[-3], sim._times[-1]]), + np.array( + [ + 0, + sim.sampling_times[-10], + sim.sampling_times[-3], + sim._tot_duration / 1000, + ] + ), ) sim = Simulation(seq, sampling_rate=1.0) sim.evaluation_times = 0.4 + extended_tlist = np.append(sim.sampling_times, sim._tot_duration / 1000) np.testing.assert_almost_equal( - sim._times[ + extended_tlist[ np.linspace( - 0, len(sim._times) - 1, int(0.4 * len(sim._times)), dtype=int + 0, + len(extended_tlist) - 1, + int(0.4 * len(extended_tlist)), + dtype=int, ) ], sim._eval_times_array, @@ -585,7 +607,7 @@ def test_cuncurrent_pulses(): config_doppler = SimConfig(noise=("doppler")) sim_with_noise.set_config(config_doppler) - for t in sim_no_noise._times: + for t in sim_no_noise.evaluation_times: ham_no_noise = sim_no_noise.get_hamiltonian(t) ham_with_noise = sim_with_noise.get_hamiltonian(t) assert ham_no_noise[0, 1] == ham_with_noise[0, 1] @@ -724,7 +746,7 @@ def test_xy_mask_equals_remove(): sim_two = Simulation(seq_two) # Check equality - for t in sim_two._times: + for t in sim_two.evaluation_times: ham_masked = sim_masked.get_hamiltonian(t) ham_two = sim_two.get_hamiltonian(t) assert ham_masked == qutip.tensor(ham_two, qutip.qeye(2)) @@ -769,7 +791,7 @@ def test_xy_mask_two_pulses(): ti = seq_masked._slm_mask_time[0] tf = seq_masked._slm_mask_time[1] - for t in sim_masked._times: + for t in sim_masked.evaluation_times: ham_masked = sim_masked.get_hamiltonian(t) ham_three = sim_three.get_hamiltonian(t) ham_two = sim_two.get_hamiltonian(t) diff --git a/tutorials/applications/Building 1D Rydberg Crystals.ipynb b/tutorials/applications/Building 1D Rydberg Crystals.ipynb index c07180c85..faf388314 100644 --- a/tutorials/applications/Building 1D Rydberg Crystals.ipynb +++ b/tutorials/applications/Building 1D Rydberg Crystals.ipynb @@ -74,7 +74,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Blockade Radius is: 8.57865851586716µm.\n" + "Blockade Radius is: 8.692279598222772µm.\n" ] } ], @@ -119,9 +119,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": { @@ -133,23 +133,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 0.12s. Est. time left: 00:00:00:01\n", - "20.0%. Run time: 0.20s. Est. time left: 00:00:00:00\n", - "30.0%. Run time: 0.25s. Est. time left: 00:00:00:00\n", - "40.0%. Run time: 0.32s. Est. time left: 00:00:00:00\n", - "50.0%. Run time: 0.36s. Est. time left: 00:00:00:00\n", - "60.0%. Run time: 0.41s. Est. time left: 00:00:00:00\n", - "70.0%. Run time: 0.45s. Est. time left: 00:00:00:00\n", - "80.0%. Run time: 0.49s. Est. time left: 00:00:00:00\n", - "90.0%. Run time: 0.56s. Est. time left: 00:00:00:00\n", - "Total run time: 0.60s\n" + "10.1%. Run time: 0.02s. Est. time left: 00:00:00:00\n", + "20.1%. Run time: 0.03s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.05s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.07s. Est. time left: 00:00:00:00\n", + "50.0%. Run time: 0.10s. Est. time left: 00:00:00:00\n", + "60.0%. Run time: 0.12s. Est. time left: 00:00:00:00\n", + "70.0%. Run time: 0.15s. Est. time left: 00:00:00:00\n", + "80.0%. Run time: 0.19s. Est. time left: 00:00:00:00\n", + "90.0%. Run time: 0.24s. Est. time left: 00:00:00:00\n", + "Total run time: 0.30s\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { @@ -161,23 +161,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 0.06s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.14s. Est. time left: 00:00:00:00\n", - "30.0%. Run time: 0.20s. Est. time left: 00:00:00:00\n", - "40.0%. Run time: 0.29s. Est. time left: 00:00:00:00\n", - "50.0%. Run time: 0.35s. Est. time left: 00:00:00:00\n", - "60.0%. Run time: 0.43s. Est. time left: 00:00:00:00\n", - "70.0%. Run time: 0.52s. Est. time left: 00:00:00:00\n", - "80.0%. Run time: 0.59s. Est. time left: 00:00:00:00\n", - "90.0%. Run time: 0.66s. Est. time left: 00:00:00:00\n", - "Total run time: 0.74s\n" + "10.1%. Run time: 0.03s. Est. time left: 00:00:00:00\n", + "20.1%. Run time: 0.07s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.12s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "50.0%. Run time: 0.21s. Est. time left: 00:00:00:00\n", + "60.0%. Run time: 0.25s. Est. time left: 00:00:00:00\n", + "70.0%. Run time: 0.29s. Est. time left: 00:00:00:00\n", + "80.0%. Run time: 0.33s. Est. time left: 00:00:00:00\n", + "90.0%. Run time: 0.38s. Est. time left: 00:00:00:00\n", + "Total run time: 0.43s\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { @@ -189,16 +189,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 0.97s. Est. time left: 00:00:00:08\n", - "20.0%. Run time: 1.44s. Est. time left: 00:00:00:05\n", - "30.0%. Run time: 1.96s. Est. time left: 00:00:00:04\n", - "40.0%. Run time: 2.70s. Est. time left: 00:00:00:04\n", - "50.0%. Run time: 3.37s. Est. time left: 00:00:00:03\n", - "60.0%. Run time: 3.85s. Est. time left: 00:00:00:02\n", - "70.0%. Run time: 4.34s. Est. time left: 00:00:00:01\n", - "80.0%. Run time: 4.84s. Est. time left: 00:00:00:01\n", - "90.0%. Run time: 5.63s. Est. time left: 00:00:00:00\n", - "Total run time: 6.09s\n" + "10.1%. Run time: 0.43s. Est. time left: 00:00:00:03\n", + "20.1%. Run time: 0.88s. Est. time left: 00:00:00:03\n", + "30.1%. Run time: 1.35s. Est. time left: 00:00:00:03\n", + "40.1%. Run time: 1.83s. Est. time left: 00:00:00:02\n", + "50.0%. Run time: 2.30s. Est. time left: 00:00:00:02\n", + "60.0%. Run time: 2.77s. Est. time left: 00:00:00:01\n", + "70.0%. Run time: 3.26s. Est. time left: 00:00:00:01\n", + "80.0%. Run time: 3.74s. Est. time left: 00:00:00:00\n", + "90.0%. Run time: 4.21s. Est. time left: 00:00:00:00\n", + "Total run time: 4.68s\n" ] } ], @@ -220,7 +220,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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JOR3HMCUiRdMszxcRE6MIMP/zSiOXLcufV9PYQ1JCHFetLOSF2i4Gx0zHmr946GArU27l1g0lC/r+9Wus2+mxo2f9aVZM45py89CBNm5YW0xSwvxFHbJSErlkaR5PHDuHa8o99xeiGKf/vZniF8mAGTIYQOq7hmnuGZ13uMKbv9hRxeCYy7Qa/Mizp7ooyU5hbUnWgr6/sjiTdaVZ/OnYOT9bFrvsa+pjaNzFn61Z+L3yhi1ltPWPcbRtwI+WRR6ziuiJyEftjwr8rYgMea2OB64ETgTINgPw8EErS2Khb6YAmypyKM9N5cXaLt55abWfLItdhsddPH2yg7++vHpRQoVXLC/gey/W0zEwRlFWih8tjE1+9HIDmSkJXLv6NcENx1xak0+cWC25TTGshjtXi+GD9iTAu73mP2jPJwN/O+O3DYvmxTNdrCzOoHiRD47rVhfx9IlOhsdNiuRi2VXfw5RbuWweGWLTcfcllbjcyq/2tvjJstjF7VaePdXJbRtKyFiE5HxRVgrbl+bz/OkuP1oXeczqGFR1qaouBZ4FNnnm7WmVqt6kqq8Ex9TYo29kgp11Pdy8fuGtBQ/XripiYsrNwZa+xRsW4/zklSYKMpK4bFn+ovZTXZDO8sIMdtX3+Mmy2GVPYy+DYy621+Qtel/ba/I42tZPa9/o3BtHKU7TVa9V1d5AG2O4kN0N1r98x9LF/9i3VeeSmhjPHw6bzs7FMOVWXjrTxW0bSkhOWHxVtqtWFvLSmS76R01iwGJ4/nQn8XHCjWvnn43ky5vtcSm/3hO7LTnHbS4RWQm8CajEGs9wHlX9az/bZQB+taeZgowktlUv3jFkpiSyrTqXvY3Gvy+GfU29jExMsXUWEcP5cM2qQv77hXoOt/RzxYrFhaZimRdqu1i9JNMvlQvLclJZWZzJ3qbYvVecpqveBhwCXg/8NbAKuBVLWdX8mgPAlFt5/nQXt24oWVDq3XRsX5rHsfYBkzu/CJ4/Zb2ZLibzxZtNFTkkJcTxxHGTnbRQ+kcn2d/Ut6CxCzOxoyafnXXdMZvi7fSJcz/wGVW9FBjHksKoBp4AngmIZTHO8fYBRien2FSe47d93rm1HBFLXsOwMPY09rK8MMNvNbWzUhK5pDqPPY2mn2Gh7LPf7P2ZRXTt6iImXG4Ox+goaKeOYRXwC/vzJJCmqmNYDuPDAbAr5nnyeAciVjaRvyjNSaU6P51DpgN6QQyNu3i5rvv84DR/sbE8mxPtg/QMmyFBC+G3+1rJTk1kux/64jxsKMsmTuDlM9OJSkc/Th3DIODJl2wHltufE7AkuQ1+5khbP0sL0slNT5p743mwtTKXl850M+4yA9bny4n2AVRhiwPZ8/nw+k2luNzKw4dMS26+qCov1HbxZ2uKFyRNMhN56UlcVJXLi7Wxmbbq1DG8Alxhf34E+LKI/CvwfaxynQY/Mu6a4oXTXeyoWVw65HRcu7qQwTEXp88Nzb2x4QIeO3qWhDhhm586nj2sKcmiNDvFJAYsgLb+MXqGJ1hftrAR6LOxrjSbE2cHY/Ilyqlj+Ciw0/58H/A48Eas+szv9r9ZsU1txxCjk1OLzpOfDo+Ew/7mPr/vO9r549GzXL2y0O+tOIC1pdnsb+ozNbrnyVMnOgAr7dffXL2ykJGJKZ49GXvKxE7HMdSp6iH784iqvk9VN6rqm1S1KbAmxh577PELCxHNm4ulBelU5afF5I99MQyOTdLcM+q3NFVfrl1dSFPPCA2m5Oe8ONY2QE5aIjUF6X7f9xUrCshKSeDpkx1+33e44zRdtU5EXvP6KiI5ImIKpfqZ3x9sY/WSTJYG4McuIqwvy+bkudgWCZsvnk5If2aJebOhzHoJONFurst82FnXzYay7EVpVs1EYnwca0qyOHF20O/7DnechpKqsUTzfEkGyvxmjYEJl5sDzX1cs8q/mS/ebK3MpblnlLpO08/glL2NvSTFx/lFcmE6VhZnkp4Uz/Mx2tm5ELqGxqnvGubqAISRPGytyuVQSz99I7GVMTarYxCRO0XkTnv2Ns+8Pb0Z+AzQEGgjY4kznUO43MqqJRkBO8Yt662BQI8dNYOqnHKsfYCawnQS4/0z2NCXlMR4LqrOM6nE8+C43bpatSQzYMe4fFkBU27lWIzJcM81SufX9l8F/ttn3SSWU/h7P9sU0zx3yor9ByIjyUNpTioVeakcbYvNwTvzZWxyilfqenj7jsqAHmdlUQY/3tnN2OSUX1Mvo5Unj3eQnBDn9/Rhb1baL2hH2wYWraYbScylrhqnqnFAE1DkmbenZFth9eHgmBobnDg7SGl2CiXZqQE9zqriTA639pssGAfUdQ4zMeVmawAfQGB1do673LwQ45LPTnnqRAdXrihclMz2XBRlprCiKIPnYqxmutOspKWqesGvVUQSA2NSbHOopY+VAWwae7hieQGN3SMxLS3slMOtfUBgQxYAly0rID0pPuYeQgthZMJFU88IG8v9n7nny4ay7Jgb9+M0K+lDIvJGr/nvAaMiclJEVgXMuhijY2CMM53DXBGEJutaOxW2tiO2fvAL4fGj5yjLSWVFUeD6fcCqz72iONNcEwccarHCoIF21gCrSzI5OzBGe3/svEQ57Un7ENAJICJXAW8G3gYcAL4cEMtiEM8DYc0C6wjPh1XFmcQJ7DOjbWdFVXmlvodrVxcGJCXSlzUlWRxu6WfCFdvF6OfilboeRAjIIFBfrlttKenGUn1up46hDKi3P78e+JWq/hJrFPSOANgVk+xt7EUEVgfhLSg7LZEN5TnniwEZpqdjcJyhcRerigN/TcAK8Q2Ouzh1LvZy5+fDmc4hynJSyUwJfER7eVEGZTmpMXWvOHUMA4Ansf4G4En78ySviusZFskTx8+xuSKH/IzkoBxveWEGdV0mbDEbuxssOey1pYFvxYH1EALrwWeYHlVlT0PPeXmXYLCsKIMzMRTic+oYHgceFJHvYimr/sFevo5XWxKGReDRfg9G09jD+rIszg2M02RkGGbkQFMfKYlxbK4Ijojw0oJ00pLiTR3oWegcHKetf4xLg3ivbCjL4uS5wZgpwerUMXwAeBEoBN6kqp5f7VbgZ04PJiI32x3WtSLyiWnWV4rI0yKyX0QOicitTvcd6TT1DONWWFYY2A5Obzy1Hkz1sJmp6xqmOj+d+LjA9y+A1QF9UVVuzBaIccKZzmEguPfK9qX5TLn1/KC6aMdpuuqAqn5QVe9Q1T96Lf9XVf03J/sQkXjgm8AtwFrgbhFZ67PZPwO/VNUtwFuBbznZdzSwq96KX3o0c4JBVX46JdkpHDEPoWlxTbnZ09DD+iBeE4CagnTqOofNGJMZ2N1gdTwHK7wHUFNo6ZbV2U4p2gnM+P7puQSotZVaJ4CfA3f4bKOA52pnAzFTueRwaz85aYnnY8zBYmlBOme6YuPHPl+ae0cZGHNxiR8rgzlhfVk2Q+MujsXI2+l8eepEB5vKcygIUl8cQGl2KvnpSbxSHxsV3YLpGMqAZq/5Fl4rwHcf8A4RaQEeBT443Y5E5L0iskdE9nR2RsdgoPquIWoK0oOSEunNhrJsjrX1MzTuCupxI4EG22EGQtJ5Nq5eZYnCPWOk0V+DR7co2M46Lk64qCqXozGimRRMx+CEu4EfqGo5cCvwYxF5jY2q+oCqblPVbYWFgVNWDBbjrikONvcHpP7CXGyrzmNySjlt0iNfw66GHhLiJCiDqLwpykxhaUG60bKahvb+USam3AGRpJ+LpQXpNHWPMOWO/hBfMB1DK1DhNV9uL/PmXcAvAVT1ZaxU2KhXrqrvGmZ0copt1cEvn720IO28DYYLeeZkJxdV5QYlV96X6vy0mIlnz4dXi1gFr3/Bw7qybCam3DGhgBtMx7AbWCEiS0UkCatz+SGfbZqA6wFEZA2WY4j69nRDl5UuGoq3oKr8dNKT4k29YR8mXG5Onh3g4urghiw8rC/L5nTHUMykRzrlSGs/KYlxrA9B6/rK5QXESWyE+BzJEtraSNOhwBhW7edfqOqMncWq6hKRe4HHsIr+fE9Vj4rI/cAeVX0IS8L7QRH5iL3vezQGUjP2NVlFYILd8QxWlarNlTkciZHYqVNa+0ZxK1SHwFmDFeKbctdy8uxg0OPp4UxD9whVeenEBSl92Jvc9CRWFGXGRD+DU73aQuBKwA0csZetBwTYC9wJ3C8iV6rqgZl2oqqPYnUqey/7tNfnY8DlTo2PFp471cm26lzSkgInHzwbVfnpPHq4PSTHDlc8GlLBkCeZjup8K8TX0DVsHIONqnKguS+og0B9qcpPoy4Gwq5OQ0kvYo12LlfVq1T1Kqw+gkexRkVXAY9gBPXmzdjkFCfODoYsZAHWw69vZJLG7uj/wTvlcGs/GckJQZVd8KYsJ5X0pHgOxEA82ynt/WN0DY1zcQgd5eolmdR1Rn+Iz6lj+DvgflU9r51gf/4c8BF7XMIXgc1+tzDKae4JXf+CB4/M9/OmQMx5GruHqcxLC0nIAiAhPs6uN9wXkuOHI422dMvS/NDdKxcvzcOtRH3GmFPHkAGUTLN8ib0OLKG90MRCIhhPvDIU/QselhakU5SZzP6mvpDZEE6oKkfbBkJ6TQCq89ONjpUXnodxKK9Lte2Uov26OHUMvwX+W0TeLCLV9vRmrDrQv7G3uQQ4FQgjo5n9Tb1kJCcEpQbDTIgI1QXp51svsU5r3ygdg+Mhj+0vK0xnYMxFS6+5LgAv1HZRU5jOkuzQCTqX5qSSmhgf9aPSnTqGv8XKJvof4Iw9/Q/wR+D99jbHgff428Bop7FnhKr8tKCJtM1ETUE6J88N4o6BwTtz0WQ7yGCPePbl0mVWiO+l2tiQYZgNVWVvQy+X1oSu4xkg3h4Bva8putO753QMIpIAXIMlcJcHbLGnPFV9n6oOA6jqgdkykgyvRVU5fW4oZCmR3lxcnUf/6CS1pg7A+fq+VSG+LiuLMyjISGJXg5Hg7h2ZZHDcFVRF1ZmoKUynsXskqkUO53QMqurCChdlqOqwqh6yJ5PCskja+8do7Rvl4qrgj3j2xRO3bYzy2KkTXjrTRVlOKqUhDFmAFeKrKcyI+ni2EzyFi6rsNN5QUpWfzuCYi87B8VCbEjCchpIOYhXoMfiRBjs9dEWQykbORmWedcPVxXiLwVPj+bJl+UEXNJyOyrw06rqGovrt1AmewkVbK0P/ErXNfpF7uS56Q3xOHcN9wJdF5M9FpEJE8rynANoX1Xjezj0P5VCSm55EdX5aTNW1nY6+kUn6RiZZHcJkAG+2VubSNTQR81pW9V3DFGclk5ueFGpTWF+WTVZKAjuj2DE4TS99xP77GyypCg9iz8f706hYYXd9D/npSZTlpIbaFADWlFjlC2OZRrvjuSI3XK6J1Zqs6xymJgzi66GiqXuEitzQv0CB1QG9aknm+Upy0YhTx3BtQK2IUV46082OZfkhG0TlS0VeGk+e6MDt1rCxKdh4pDDWBblq20xU2K3JphhOJfbUQ79rW3moTTlPRV4aL5+J8RaDqj4baENijf6RSc4OjLGpPDweQGAN959wuTlxdjCoZRPDiRNnByjISA6bVlx+ehIFGckcjOER0E09I4xOTrGpIifUppxn9ZJMfrOvlXMDYxRnhTZJIRA4lt0WkQ0i8g0R+YOIlNjL/lxEtgTOvOiluTd8+hc87LBzxF86E7vSGM09o1TkhYdTACszaUtlDsdiQNFzJlrC8F7ZYneCR+t1ceQYRORGrHoKZcB1gOfOWQb8a2BMi25OnLVi+UsLwiduXJqTSllOKodaolsHZiZUlVPnBkOqWzUdlXlptPSOxmxm0kn7XgmH8T4ePE4qWkelO20xfBb4qKq+AZjwWv4MlhSGYZ7sb+olMyWBFSHW4/GlKj8tan/sc9HcM0r38ATbqsIr0a46P43RySla+0ZDbUpIeLmum2WF6RRkJIfalPMUZiSTlhTP6Y7oTO926hjW41NHwaYHazS0YZ40945SnR+agiOzUZmXRn3XcEy+nXrCe+HWYthmS7LvrIu9EdAeKYztIZbC8CUuTthamRu1lQ+dOoYerDCSL1uBFv+ZEzs0dQ+HVSzbw5bKHHpHJqM6FW8mPAMOw+26rCrOJCctkd31secYPFIYy8MwVXdZYXrUZos5dQw/Bb4kIuVY4xYSRORq4P8BPwqUcdFK19A4Dd0jbCzPCbUpr2GlPQo7Fov27G3oDatxJR7i4oQVRRnUx+A18Tx4y8JkXIk35blpDI656BuZmHvjCMOpY/hnoB5oxKq/cAx4CngBq1iPYR7U2W/joaoONhvl9iCiWJTg3lnXzfaavLCQwvClPDeNlhi8JgdsFdN1YZg+7Unp3t/cF1pDAoAjx6Cqk6r6dmAlcBfwNmC1qv6Fqk4F0sBoxNO5Wx6Gb0EFGUnkpiVyJErT8GZiYGyStv4xNpTlhNqUaVlRnEGbXdoyljh5bpC89KTzLyzhxNbKXBLjhZ1RONBtXhXXVPWMiJyzP0dnd3wQOHF2kKT4uLBsHosImytyONIaWymrrb1Wxk+49S942FJh5c0fbx/gyhWFIbYmeLT0joaNPIkvqUnxrCnJOl+FMZqYzwC3D4tIE9AP9ItIs4h8RMKx3R3m7KzrZnNFDskJ4SkxVZmXFnOpkbV22mFVXnhlJHmotOWmPQ4sVjjTMURlCGs8z0VFbnTeK04HuP07lsLqd4Ab7OnbwKeBLwbKuGhkbHKKI639XLw09PLBM1GRZ3WqxVLYYn9TH6mJ8awuCb0E+nQUZyaTGC8xpbJ6tn+Mtv4xtlbmhNqUGSnPS6W1d5RxV3RF1J22GN4NvFtVP6eqT9nT57BKeb4rcOZFH219o7gVasJoxLMvm21Nmj0xVDmsqWeEyrw0EuMdN6KDSkJ8HBvKsqM2b346zpdYDcNUVQ9bKnKYmHJHXThpPnfBoRmWheedFKa02KGAcOxf8LCxPIeUxDheiaG8+ZbekbC+JgArijJpiKFqbp4kjXBLH/ZmRZSmdzt9qP8I+MA0y98H/Nh/5kQ/h+1O3VVhULVtJpIS4lhRFN16896MTLio7Rhi9ZLwvSZgZbF1DY0zNhldYYuZONTST0piXFiJ5/nicVotPdHVzzBjVpKIfN1nu3eIyE3ATnvZdqAU+EngzIs+jrcPUJmXFhaVqGajLCeV0x2xUbSntmMIl1vDcsChN8tsXa3j7QPn1T2jmV31PVxUlUtSQvgGJVIS4ynLSeX42dgJJW3wmtYAe4F2oMqezgL7gNUBtjGqaO0bDcvxC75UF6TT3DMaE2+nnkyfcL8uF9uaSbtiIMRn1QUZCIsaz3OxtSo36hSJZ2wxqKqp2uZnVJWGrmFuXLsk1KbMyZZKq1PtWHtk3JyLwSM1ES6lI2eiMDOZ6vw09jf1hdqUgNPebyVpVIRxGMlDRW4qfzjczpRbiQ8zUcyFEr5ttCikoXuE3pFJNodx+p0Hj8JoLEhj7G3opaYwney0xFCbMifVBem09EX/NWm2Y/blYdzx7KE8Nw2XW2mLovEMs/UxPI0lmDcnqnqdk+1E5Gbga0A88F1V/cI029yFNWZCgYOq+jYn+44EPOqd4VaDYTrOd6pF+YAqt1vZ3dDDrRtKQm2KI0pzUjnQ3IeqhqWmk7840Gyl5UZCidkNdn3w/c19EdHCccJsLYYjwFF7OgFchCW93WJPpfay404OJCLxwDeBW4C1wN0istZnmxXAPwGXq+o64MPzOJewpzUCUlU9pCcnUJ6byuEoi5360jU8zsCYizVhKGg4HauXZNI3Mhn1DvtM5zBlOankpIV3kgbAmpJM0pPio0oWfbY+hg96PovIfwA/BP5OvSq4iMhXAaevLZcAtapaZ3/358AdWEqtHt4DfFNVe20bOhzuOyI4eXaQ9KR4ijIjo3j4pvIcjrVHV7aFL219Y0B458p748mcOt4+EDVvp9PR2jsaMdckIT6OdaXZnIiizCSnfQzvBL6hry3r9S3gLxzuowxo9ppv4bXFf1YCK0XkRRHZaYeeXoOIvFdE9ojIns7OToeHDz27G3rYWpUbMR1UZbmptPZFd63hM7ZGkkeLKNzxPCyjKZ7ti6pS1zUUMdcErIw2z0tGNODUMQhW2qov0y1bDAnACuAa4G7gQRHJ8d1IVR9Q1W2quq2wMDKUJofHXZw8Nxh29YRnozw3lQmXm7MD0fOD9+VAcx+ZyQlhWSFsOgoykkhJjIvqEdAtvaN0DU2wJQKSNDyU56ZydmAsatK7nTqG7wHfFZFPiMg19vQJ4EHg+w730QpUeM2X28u8aQEesus/1AOnsBxFxGO9ecPSwvBVivTFE7aIZn2epp4RqgvCr/b2TIgIG8tzorI4jAePRlK41d6ejY3lOUy5NWrGMzh1DB8HPg98EKty21P25y/Y65ywG1ghIktFJAl4K/CQzza/w2otICIFWKGlOof7D2vOdzznREb/AlhVs1IT49nTEL2OobVvlNIIuiZgZbU1RZk2jzceGetI6WOAV0viNkTJdXFawc2tqv+uqmVADpCjqmX2MkdtJ1V1AfcCj2FlMv1SVY+KyP0icru92WNAt4gcA54GPqaqUVEe6Wib9SaxLEJCFgCJ8XGsWpJ5vlZBtDEy4aK+a5jlEZA+7E1Zbiq9I5OMTLhCbUpAONY2QGpiPKUR5BiWZKcgEj31MhxVcLOzj76rqkdUdcFd76r6KPCoz7JPe31W4KP2FFUcax9gaUF6RKTfeVOWm8qxKJMU9nDq3BBTbmVTmGsk+VJtF645dW7ovER6NLG7oYctlTlhK4E+HUkJcVGlL+b0P38xcFBEdtkZQeEtQxmGtPZGhkaSLxW5aVFZiARezeyJtLTPi6osiZJorJcx7prieITKsGypzOVgcwz1Majq5ViD0p4G/hVoF5EficjVgTQuWlBVmiMoL9ubzRXZlmZSFLYaPJ2ckRSyACjOSqEyLy0qkwLO9o/hVkv6I9Koykvj7MAYril3qE1ZNI7baqp6UlX/ESuz6K1ABvC4iJy2s5UiJw8zyLT0jtIzPME6e+h8JOGJvzdFoWbSgaY+KvJSyU4Nf40kX5YVpkflNfHE6CMtIQCsF4wpt3JuMPJL4i4kiJcIZAHZWJpHTViD3JpEJGp0jfzJmU6r83ZNmBeCmY6SbOttOtoKnqsqexp7uDiCxpV4U5qTGpWD3I7YSRoriiLvXlm1xHqJigYZGceOQUS2ici3sGoy/DtWwZ4Vqnq9rWv0MeA/AmNmZHNediEC+xjSkxMoykzm1Nno6FTz0DU0QdfQBBvLI68VB1aOf+/IJB1RNvjwePsgpdkpFGYmh9qUebO+LJukhLio6Ptx5BhE5DDwElYY6R6gSlU/ZQ9C8/ArIDKGIQeZ+q4hkhLiIkYjyZfNFTkcao38tyBvPG/b5WFeg2EmPKOCD0fZdWntG6U8wpIBPCQnxLOhLDsq7hWnLYZfAktV9fWq+pD32AURSQdQ1S5VjZz8siCyp7GXTeXZEaOR5EtVfhptUaaZFKkdzx48mVTRFk5q7hmJyCQND5V5aVExlsFpVtJnVfUC+QoRSRGRjwH1M3zNAIxNTnGktZ9t1ZEZywbr4Tk26aZ7eCLUpviNA819JCfERdzgNg8F6ckkxcdFlfx2x8AY7f1jrI/AJA0PpTkpUZGZNKtjEJEkEfmciOwWkZdE5M/t5e/Ekqr4MKZfYVZaekeYnFJWFUdeZ5qHVXaneTSFLeq7hqkpzAjrQvOzERcnrFySETXaPGDVYAAi/F7JYsqtnIjwPrm57or7sGQsGoGlwK/sDuhPYRXUqVbVzwfUwginNYI7nj1srsghIU6iolPNQ1tfZI4r8WZDWTYnz0X2A8gbT1gsku8VTzW3SHcMc0li3AXco6q/FZFNwH4gF1hnax8Z5qDOTlUN90Lzs5GWlMCqJZkcbo2OQW6uKTeN3SNcuiw/1KYsirKcVHqGJxibnCIlMT7U5iya+q5h4uOEkuzITNIAztveHuF9P3O1GCqwVFFR1YPABPBF4xScc7C5jyVZKSyJ4B87WJ1q0dLRefLcIKOTU2yJQNkFbzxv1tEy0G1PYw/rSrMi2smlJMZTkJFMY4Rfk7kcQyLgPYxvEoieoGYQaO4dpSaCajDMRGlOKq29o7jdkZ+Z1NxjObiaCJRd8GZDWQ4A+5siXxrDNeXmQHPfeR2oSGZTeXbEXxMn6qqfFxGP+0sC7hORC5yDqn7I75ZFCW19o1y2rCDUZiyaVUsyGZ2c4kznECsiuHMQXo1lR2qqqodlhenkpiWyu6GXt1xcGWpzFkV7/xhjk25WR6A6gC8rijN57nQnbrdGTAEoX+ZyDM8By7zmXwJ8f4GR/woZIHqHJ2jvH4uKFsP6UqtT7dS5yHcMJ84OkJOWSG5a5GkkeSMirCvN5nQU1MuIFmcNVjGuySmla2icoqzIDCHP6hhU9Zog2RGVHLUVSbdEgWa+J54dDf0Mexp62VaVi0hkvs15U5aTylMnO0JtxqKp67JSVSM5ScODZ/BhbcdQxDqGyEzijhAiVe9/OrJSEshMTqCxJ7JLF/aPTFLXNczWKIhlg1WEvnNwnOHxyM4HOdjcR25aIlX5kX+vbKmw62VEsCy6cQwBpKV3BBFLPz/SERE2VmSzv6kv1KYsiuZeu9B8fuSH9wA22q3Rg819IbVjsTT3jlBdkB4VrbjstERWFWdGdL0M4xgCyMGWflYURe7oWl9WFGXS2B3ZaXjt/daAw2iIZQOsLLYkPRoi/Lq09Y1FzTUBWFGcEdFpxNHxxApDptzKvsbeiNZI8qU0J4WhcRcDY5OhNmXBHG8fQCQyK4RNR1FmCvFxEtF9P0PjLhq7h1kWJdcErL6f1ggWnpzRMYjI9zy1nUXkKhFxktpqsGnuGWFw3MXmCCs0PxuVedaNWxvBWTBHWvupKUiPyKpt0xEfJ5TnRnYR+hPtA7gVNkVBkoaHyvw0Jlzu82NmIo3ZWgzvADwu/Gkgel59g8B5vf+86Gkeb63KAWBvQ+TGTtv6R6mMgmQAb7ZU5ER0EXpPdcBoui6eDugDLX2hNWSBzNYKaAA+KCKPAwJcKiLTPhFU9bkA2BbReOSQS7OjxzEUZaZQnZ/GnsYe3kNNqM2ZN6pKS+8oG6OoFQdQlZ/O/x1sY8Lljsj+LI9jKImiPgZPdlVLb2T2M8zmGD4GfBdLRVWB386wnWLVfjZ4caClj8zkhKhIVfVmeQR3QDf1jNA3MsnakqxQm+JXynJSUYWz/WNURmC658HmPqry08hIjp5odXpyAtmpiRFbtGfG1wtV/T9VLcQKIQmwDqt0p+9UFAQ7I47ajiHWlGRFbNW2mSjLSYnYjk5P38i60uhyDMvtzKRj7ZEXTlJVe8Bh9EWqVxRlcKw9MhWJ52x3qmofcC1wWlW7p5sCbmUE0tY3GtG68jNRlZ/OwJiLs/2RV4Q+GvT+p2N9aTbJCXHsjsC+n3MD43QPT7CpInKrts3EpoocjrUNRGRmkqO2m6o+KyLJduW2tVjho2PAT1V1fPZvxx5jk1O0949FfCGY6dhsF6E/2NLHkuwloTVmntR3jZCcEEdBenKoTfErSQlxbCzPjshBbp7+hWiQwvClPDeVcZebnuEJ8jMi6zfnqKdKRNYCp4CvANuBHVglPU+JyJrAmReZHGntZ8qtUZV+56HaHjEcibHTvY09bKrIiVjFy9moyk+PyPrPns7ZaBrc5sHzYhiJA92cpjB8DTgAVKrqlap6JZbK6kHgq4ExLXLxjEJdEaGF5mcjNy2RlMS48296kcLoxBRH2ga4uDo6NJJ8Kc1J5dzgGBOuyCpCv7+pj9TE+KhQIPZlvV3m80AEtuScOobLgU+q6vmeFPvzp4ArAmFYJOOJZUd61bbpEBFWFWdyuDWyOjobuoeZcitroiwjycOq4kxUibjOTqu+RwaJ8ZGXZjsXpTmplOWksjsCa6U7vRpjQM40y7PtdQYvTp4dpCwnNaJLFM7GxvIcTkTYA+h8x3MUhiwANpbbRegj8LpE6zUB67qcaI+8UelOHcPvgQdF5HIRibenK4DvAA85PZiI3CwiJ0WkVkQ+Mct2bxQRFZFtTvcdLqgquxt62BalIQuwsnoGxlwMRpBmUr1H7z/KxpV4WJKdQpxAWwRli0243DT3jkbtNQGrA7qtP/I0k5w6hr8DTgPPY7UQxoBnsTqkP+xkByISD3wTuAUrs+luu1Pbd7tM+3ivOLQtrGjtG6VjcDyqxPN8KbfTPSNpoNv+pj7Kc1MpiLDsEKckxsexJCuFxu7IqZdxrH2ACZc7KgpZzURZTipjk246BiMredORY1DVPlW9A1gJ3GlPq1T1DarqNNh8CVCrqnWqOgH8HLhjmu0+C3yRCA1ReTIQokkp0pdNtqREJBU8b+oZoaYw+pIBvNlYnhNR9TI890o0X5cNEXivwDxlt1W1VlV/b0+18zxWGdDsNd9iLzuPiGwFKlT1kdl2JCLvFZE9IrKns7NznmYEFk8aZzSm33koz01lSVYKuyJoQFVrlMeyAVYuyaS5dyRiMpNerfMcfUkaHlYtseqj13dFTusawqgeg4jEYY2T+Pu5tlXVB1R1m6puKywsDLxx8+BY+wDJCXFR7RhEhPVlWZw+Fxmdau39o/QMT7A8CtOHvSm3NZPODURGY/to2wDFWclkpkSHBPp0ZNiaSZEmIxNMx9AKVHjNl9vLPGQC64FnRKQBaxDdQ5HWAX20bYD1ZdkRqXI5HzyFSCKBo61Wps7mKI5lw6uKnqciwGGrKrvre7hkaX6oTQk41QXpnIyAa+JNMJ9eu4EVIrJURJKAt+KV0aSq/apaoKrVqloN7ARuV9U9QbRx0bT2Rp/e/3RU5KUxOOaiayj8O9WiUe9/OjZV5JAYL+yKgLz5cwPjnB0Y4yJbYiWa2VqZw6GWvojKTAqaY1BVF3Av8BhwHPilqh4VkftF5PZg2RFIxl1TnB2ITo0kX7bYN/S+CCh43tQzQlJCHPnpSaE2JaCkJMazoSw7Iq6JRwojWkqszkZVXhpjk266hydCbYpjHInoicgBrNoMP1HVBf/qVPVR4FGfZZ+eYdtrFnqcUHG4xdJI2lAefUqRviyzM0kiQQdmX1MvG8uyo1IjyZdlhRk8eyq8EjKmw6PrVB5lSrfTUWYLBDb3jERMurTTFsMjwMeBNhH5mYhcH0CbIpa6TiuHPNoKwUxHdmoi6UnxYS/cNjoxxeGWfi5eGr3jSrwpy02lY3CccddUqE2ZlYMtfaQkxp2vIx7NrCmxMpMOtUSOjIzTcQyfAqqwxi/EA4+ISL2IfFpEKgNpYCTR0jeKCBRnRW/6nQcRYXlxZthr85zpHMLlVjaWRX8rDmBFkfUQOh7mMgwnzw6yeklW1CdpAJTnplGWk8or9ZFTusbxVVGLP6jqXUAp8ADwSaBORB4TkZsDZWSkcPrcIOW5qTHxYwerCP2R1v6w7lR7NWQR3R3PHjx9P+EuctjaF91SGL5srszhSGt4v0R5M+8nmIjsAL4AfAJoAz4DnAF+LSJf9at1EYSqsqu+h4ujWArDl4q8NEYmpugbCV/NpFc1kqI/lg2wJCuFxHgJ63oZY5NTtPaOUhED/QseKnLTaO8fZcodvi9R3jgt1FMkIv8gIkeBZ7CUVt+kqjWq+llVfT9wA/DugFka5jR0j9A9PMElMeQYPB2HDWGsz7O3sZeagnRy0qI7I8lDXJxQlpNKU0/4XpPDrf243MrWyugVmvSlIi+VySmNmIFuTlsMLcBfA98DylX1LlX9k882R7HGKsQkHvGyaB9d641nwNjeME6PbOoZjqlrApZmUjhfE4/4YixdF4++2L4I0Uxy6hiuV9W1qvplVe2abgNVHVDVa/1oW0ThiWVHW6H52SjOSqE6P42ddeE5oEpVae0djalrArC6JJNzA+MMj7tCbcq0eMYwlESxRpIvK4szEXk1tBnuOHUMnxGRHN+FIpIlIk/516TI5FBLH9mpiRRnxs6PHazyhbUd4ZkBU9c1zPDEFKuKM0NtSlDxDLAMV8mSwy391BSkk5wQnYWspiMpIY7izBSae8Lzmvji1DFcDUwXpE0BrvSfOZHL8fbBqC00PxvluWm09o3iDsNOtaNtVhbIpijXSPLFM/jwWFv4ZcFMua0kje010a+R5MuyonSOh3l6t4dZHYOIbLWlsAXY6Jm3p4uB93KhEF7M0tI7QmWMZL54s7QgjckppT4MO6A9IYtYSosEWFOSRWZKQljmzdd3DTM47uKiqtjpePZwUWUux88OhP3gQ5hbEmMPoPb0+DTrR4EP+tuoSKNneILekUkqYiRX3hvPDb6/qe/8m2q4cKZjmPz0JDKSHSm/RA3xccLF1Xlh2QHd7NFIyo+9e6UyPx1VaO8bC3uNqLnumKVYrYU6rAps3iIsE0CHqoa/+wswnhswFt+CKvPSEbF0YMKNXQ3dUV17ezaWFqTz8pluVBWR8AlvNtidr7Ey4NAbz7iN+q7hsHcMs4aSVLVRVRtUNU5V99jznqndOAULT6qqR44glkhKCM9aw+39ozT3jLI9BvT+p6M8N5XRySk6w0wWfW9jLyXZKRRnRYaYnD9ZX5ZNQlxkyKLP2GIQkTuB36vqpP15RlT1N363LIJo7hkhMzmBrNTYCll42FCWzb4wqzXsETRcEwOChtOx0Vb43dfYy83rS0JszavUdQ6zeklmWLVigkV6cgIby7PZWRd+fT++zPYk+zWwBOiwP8+EYgnrxSz7m/tYU5oVkz92sN6EHj92jrHJKVISw+On4AltxYKs83SsK7Ucw+lzQ9y8PsTG2Kgqzb0jMRveA+te+e3+8M/XmTGUZIePOrw+zzSFx5MgRPSPTnKktZ/LlsVmyAJe1SEKJwnugy39ZCYnRHXt7dlISYynMDP5fGdvONDYPcLgmIsVMTauxJuKXKvyYX8Y64tBcEt7RiXH2wdwKzGl++KLp2/laFv4KHoebetnU0UO8TE2rsSb5YUZYaXo6VF83RoD5TxnwiMDciSM7pXpmKuPwRGx3MfgqWBWFYPpdx7WlGSRlZLAS7Xd3LG5LNTmANZ1uXVD+MTWQ8G26ly+8XQt466psBhl7Gm9VOWHd0ZOILnIDqMdaO7j8uUFIbZmZubqY3BCTPcxnD43SGK8xGzIAl7Nmw8XgbDOwXH6RiZZGsMPIIBqO2++rW+MpWGQHll7boiCjOSYG1fiTVZKIvnpSecHX4Yrc/UxOJli1ikA7GnsZUtFLonxsR2VqylMp6lnJCyK9uxttNIBY7mTE6C6wGrF1nYMhdgSq+P5pTPdbK+JHVn6majKT+NMR3ild/sS208zP9DUPcKyGJIPnonK/HTGXe6wEG5rsGWdY/26rCvNJjkhjpfPhD49sqlnhLMDYzGdpOFhW3Ue+5t7mXC5Q23KjJhxDIugf3SS7uGJmKkONhsX2Z3vuxt6Qj6qtb5zmNy0RLJSEkNqR6hJSYzn4uo8Xg6DvPk6e8RzrCndTseKoozzRXvCdQS0GcewCHbXWyGLLRWxHbIAWFGcQZy8OrAslLxS3x3TWWLerF6SyY93NoZcGqPRdgyVMSZoOB2ezvdwlsYw4xgWQW2nFbtdXxabo2u9SYyPozQn9XwYJ1S09o3S0D0S1hkfwaQyP41xl5tzA6GVxthtS2EUZsaeFIYva0uzSIgTXqkPX2kM08ewCBq7h8lLTyIzxkMWHjaWZ7MnxDowp84NnrfFYMmVgBXiCyWnzg6yoSw7ZtUBvMlITmBrZS4v1k5bDDMscOwY7BoMPxKRPfb0Y7tWQ8yyp6GXdaWmteBhU3kO7f1j9I+GblRnk91iqYzhcSXerC/LJj5OOHk2dFX23G6lqWckpsf6+LKpIpuT5wbDssAVOHQMIvJ2YDdQAjxqT8XALhF5R+DMC1/a+0c53THEVSsKQ21K2OCJnTaFMJy0t7GXwsxkCjNMyAKsEF9ZTioNIVS/PdY+wLjLHbOChtNRmZ/OhMvNucGxUJsyLU5bDJ8D/kVVb1DVT9vTjcC/AP9f4MwLX47aUgNbY7AGw0ystW/8UA50O9LWz0WVuSZk4cWakkz2N/WFbIzJEVsKY1uVGcPg4fy90tgXWkNmwKljKAR+Oc3yXwFF/jMncmi0pTDCYURpuFCZn0ZVfhrPnuqce+MAMOVWmntGqCowIQtvLq7Oo7VvlL4QCbc19ozY6gApITl+OLKpPJvEeDmvHxVuOHUMTwPXTLP8GuBZfxkTSRxo7qMwM5ncNNPx7M0l1XkcagnNj/1Y2wCTU8rqJSZX3ptqT3pkiMJJB5r6WF6USUKMqwN4kxAfR0VeWtgVuPIw45USkTs9E/AH4PMi8m0Ruceevg38G/B7pwcTkZtF5KSI1IrIJ6ZZ/1EROSYih0TkSRGpWshJBYODzX1csjTPhCx8WFqYTtfQOEPjrqAf+0BLHwCXxGjVtplYZTvKAyEopjQ07mJPYw9XrTTpw76sXpLJgebQhfhmYzYX/muv6T+BfOC9wPfs6b1Agb1uTkQkHvgmcAuwFrhbRNb6bLYf2KaqG+3j/rvjMwkiEy43Lb0jLDNhpNewOoQPoYauYVIS4yjJMiELbyry0qgpTOeZEIT4jrb2Mzml7KgxztqX7Uvzae8fo2MwvMqvQnBF9C4BalW1TlUngJ8Dd/gc82lV9aS07ATKF3JSgeZwaz9uhZUmZPEaPG/rB5qD3wF9sLmPVcWZxMVwDYaZuLQmnwNNvUF/O/VkQy0riG3dqunw9E82dIVfOCmYQb8yoNlrvsVeNhPvwgphvQYRea9nPEVnZ/DfgvbbWTfmLei1ZCQnsCQrhfqu4Kas9o1MsK+pl6tXmvTh6agpzGBgzEVvkDugDzT3kZGcYDqep8HTug63eukwu1bSBYhILlYYqBJI8l6nqvf70yh7bMQ24Orp1qvqA8ADANu2bQt6gK6+a5jsVEtX3fBaVpdknneeweJgi9WKu3SZiWVPxxr7IbS3sZcb1hYH7bj7Gq2+ONPx/FqKslJYW5LF0yc6eN81y0JtzgU4cgwisgN4BBjHSl1txRrsNg40AE4cQytQ4TVfbi/zPdafAZ8CrlbV8Au+Yd1cq5dkmo7nGdhRk88zJzvpG5kgJy04zrPe1q1aHuNS2zOxrTqPhDjhQHPwHIPbrdR3D3P1KtOKm4nLluXzo52NTLk1rMrQOnXjXwJ+ghX6GQOuw2o57AG+6HAfu4EVIrJURJKAtwIPeW8gIluA7wC3ewT8wo3WvlFOnB3k+jUxOXzDEcsKrYdzXRBjp3saeynISKYgw7TipiMpIY7KvLSgqt8eaetnwuVmTYnpi5uJZUUZTLjctIVBHRNvnDqGjcA31Oq5mgKSVfUc8I/AfU52oKou4F7gMeA48EtVPSoi94vI7fZmXwIygF+JyAEReWiG3YUMT7bNZSZkMSMe/ajdQVSP3N/Ux6XL8k0rbhbWlmaxu6E3aPo8++17xfTFzcz6UkvkcFeYKa06dQwTXp/PAZ7xBUNAqdODqeqjqrpSVZep6ufsZZ9W1Yfsz3+mqsWqutmebp99j8HnjB2yqCk0qaozUZqTytqSLJ44fi4oxxudmKKtf5TlhSaMNBs7avLpGhqnrT84b6d1nUPnkxEM07O+LIvM5AQO2WNwwgWnjmEfcLH9+Rng/xORvwS+DhwKgF1hy8tnullZnEFaUuwWNHfCjpp8jrQOBOXtdGd9N6qwscJIbc+Gp//ldJBqQL94ppuN5UZqezZEhJrC9KBdE6c4dQyfAtrsz/8MdGINbMvFGugWE6gqR1r72W5G1s7J8qIMRienaOkN/NvpEVuC45JqI9I2G+vLskmKj+OF04GvA9DQNUxtxxA3BjEDKlLZUpnLnsZexianQm3KeRw5BlXdo6pP2587VfUWVc1S1W2qejiwJoYP7f1jDI67TOaLAy5ZaqnOPl8b+HEmJ88NUpaTSnqyacXNRkZyApcvzw9KiO9om6U+vM046zm5uDqPCZeb2jBqNcwruVhElonI6+ypJlBGhSvPn7YecttrzI99LpYVZpCVksDx9oGAHmfC5ea5U52mg9MhF1Xl0tg9wnCAtayOtw8g8mqGmmFmVhRb/6NjAb5X5oPTQj35IvI74DTwO3s6LSL/JyIxc0cebx8kLSmelUUm/W4uRISVxZkcbg3sj31fUy8DYy5uWmdCFk5YUWz9do8EWO75udOdXFSZS2pSzJaEd8zywgyWZKXwp2PBSdZwgtMWw3eB5cCVQIo9XQUsBR4MjGnhx/7mPlYtMVo8TrlqZSEHm/sCWurTU7JyU0VOwI4RTVy2LJ/4OOGFANYbdruVU+cGzTVxSFyccMPaYp4/3clUmJT6dOoYbgLeo6ovqqrLnl4E/sZeF/W09o1ysLkvqHICkY5nPENtR+DqDe9q6KEgI4miTFPK0wmZKYlU56cFtAb00bYBxibd56uUGeZmfVkWY5NumntCVxbXG6eOoROYbsjkCNDtP3PCl1311mlev9o4BqdsKM9GBJ4PYBbMK3U9XLWy0KREzoNNFTm8Ut/D5JQ7IPvfWWfdK0YKwzme1tWLZwKfMeYEp47hfuCrInJeDdX+/GWc6SRFPMfaBkiMFzOwbR4UZaZwSXUejxxqD8j+OwbH6BoaZ80S82Y6H65aUUj/6GTA5DGOtQ9QmJlMQYZpxTllVXEmRZnJYVMDesb8PhE5DHgHvJYCDSLiEb7z6CYVYfVBRDVPnejgkqV5JBqVyHlx+fIC/uOJUwyPu/yeTvrMCStL7LLlMZP/4BdW29pFB1v6zld38xfjrimePH6O69eYlvV8EBFWl2SFzQjo2e7UXwfNijBneNxFXdcwt2+arXyEYTrWlWahamUPXbnCv6GFI239ZCQnmFj2PFlZlEl5biq/P9jGXdsq5v7CPNjTYGWJvX5TiV/3Gwtcs7KQ+x8+RnPPCBV5aSG1ZUbHoKqfCaYh4czOOktyYZORXJg3ly8vICkhjudOdfrVMagqL9R2GcmFBRAXJ9y6oYTvvVDPuGuK5AT/pZQebbPSYLdU5Pptn7HC5socwOq8D7VjmO8At+tE5F4R+YCIXBMYk8KPl890k5wQZxRVF0BKYjyrijM51OLfvPnDrf3UdQ7z55tNK24hrC/LxuVWTrT7NzvpqRMdLC/KINcUsZo3a0ssQb1wGM/gdIBbmYjsAv6EJbX9CeBJEXlFRByrq0Yqexp7WV2SRVKC6V9YCFevLGR3Qw/9fiwrua/RqhBnMl8WxhXLC4iPE7/KY7im3Bxo7uMqP4cMY4WUxHhu3VDCH4604wpQxphTnD7pvo5Vh2G5qlaoagWwwl729UAZFw7UdgxyoLmP2zYsCbUpEcuOmnzcar3l+4vnT3dRkp1ixi8skLz0JFYWZ3Kguc9v+7SE4NznQyKG+bNjWR4jE1PUdoZWN8mpY7gB+ICq1nsWqGod8CF7XdTyYq2Vk33bxqhvGAWMTRXZpCTG8chh/6StTrmVl850c+PaYtO/sAh21OTxSl0PA2P+acm9WNtFfJxw/WpT3XChbF+ajwj86Whow0nziY1MN1Y7PMZvB5CXznRRlJlMabYpNrJQMlMSuXVDCQ8fbPPLkP8Dzb2MTk6xpdJ0cC6G61YXMTHl5lCzf1pyL9R2sao406jcLoLSnFRWL8liV0NoK7o5dQxPAv8pIudz20SkEviqvS4qmZxy88zJTm7dUGLeTBfJZcsKGBx3cerc4js7nzjeQUKc8GdGnmRRbKrIISkhjkePLL4ld/LsIPub+rhzq0kGWCzbl+bxSr1/++Tmi1PH8CEgHagTkUYRaQTO2Ms+FCjjQs2ehl7GXW62VZs308Vy1coCEuJk0aOgVZUXTnextjSLDPNmuiiyUhK5fVMpv9vfumh5jJdsKYfbNprxC4vlpnVLmHC52dsUulaDU8fQDVwC3Ar8P3u6RVW3qmpLoIwLNY8fO0tKYpzRR/IDRZkprC3NOq+js1AOtvRzuLWfN19U7ifLYpurVxYyMjG16HTiPxw5y9KCdEqyU/1kWeyyqSKb9KR4fn8wMFIyTpjTMYhIPNAPrFDVP6nqf9rTE4E3L3RMuZWnT3SwpcJoyvuLm9YtYU9jL619Cy/3+fwpSwbj9ZtMMoA/uGpFISmJcfz+YNvcG8/A8LiLvY293LbBtBb8QVpSAm/eVsHvD7YFvKDSTMzpGFR1CmgEYmrEyjMnO2joHuEdO6pCbUrUcI095uDZkwsv9/m7A61sqcwhJy2mfo4BIzstkW1VeTx3qhP3AhMDHj5kJRVcY8aU+I3rVhfhcisvBrBuxmw4DSV9FviCiMTM0N+nT3aQkhhn6i/4kbUlWawrzeLB5+tQnf9DqKl7hDOdw7zepA77lds3l1LXNbzgcSZPn+hkSVYKF1WZvjh/saMmn8LMZP5vES25xeDUMfwDcAXQKiJnROSQ9xRA+0LC5JSb3+1v46Z1S8xoZz8iIrxteyX1XcOcXkDh81/tbSY+TrhpvRls6E+uWVVInFitsflS1znE48fOcvvmUpO550eSEuK4emUhz57sDGgFxJlw+tT7X+BLwL8BP7Lnvaeo4qkTHQyNu7jVxEz9zg1riklKiONnu5rm9b3JKTcPH2pnc0UOZTmmg9OfFGWm8Mat5fzklaZ5x7QfOdSOW+HdVywNkHWxy1surmBo3MUzJzuCfmxH+X6qel+A7QgrfrOvheKsZDOCMwAUZaVw+bJ8HjnUzj/evJqURGcd+48ebqe+a5hP3romwBbGJnduLedXe1t45FA7d13sTIrb7VZ+uquJHTV5FGWZAaD+ZktFDhV5qXzz6Vpev7E0qLXmZ20xiEiaiHxTRFpFpENEfhrt/QyHW/p57Og53rClnARTlCcgvPPSajoGx3n6hPM3oZ/vaqYo0zjrQLGjJo8VRRn8fHeT4/6fZ0930t4/xlscOhLD/EiIj+OD163g1Lkh9jX1BvXYcz35PgPcAzwC/BxLF+m/AmxTSPnhyw2kJMbxgWuXhdqUqOXy5QVU5afxhT+ecCSRcfLsIC/XdXPP5dVBfWuKJUSEuy+pZF9TH/ua+ubcXlX5wYsNFGYmc9sGkwwQKG5ev4TctEQefL4uqMedyzHcCbxLVd+rqh8CbgP+3B7bEHW09Y3y2/2tvGVbBZkpiaE2J2pJSojjYzetorF7hD84kGP4r2dqyUhO4M0XmTfTQPKmbeVkJifw+UePz9lqePpkB8+e6uRdVyw1CRoBJCslkTdvq+DxY+c47Qc5GafMdUUrgOc9M6q6C3ABUfmK8JU/nSIhTnjXFTWhNiXquWndEpYVpvO1J04z4ZpZjuGF01387kAbb9teSaGR2A4oWSmJ/NOta9jT2DtrsRhV5cuPn6I4K9l0OgeBv716GYnxcdz/8LEFpXkvhLkcQzww4bPMhcNO60ji8aNn+fXeFt6xo4rK/NCW1YsFEuPj+IcbV3G6Y4gHnjsz7TYTLjf3/f4oJdkp/P2NK4NsYWxy59YylhWm87lHj8+YofSzXc0cbRvg4zetNv1wQSAvPYlP3bqG5093LSileCHMdVUF+B8RecgzASnAgz7LHCEiN4vISRGpFZFPTLM+WUR+Ya9/RUSq53U2C6S5Z4R//N9DrCjK4B9vXh2MQxqw4qe3bljCl/90ihdOXzjCU1X53CPHqO0Y4jO3r/NrXWLDzKQkxvOZ29fT2D3Cx//30GtGQ++s6+azDx/jkuo83rDFKKkGi7dtr2RTeTb3PXSMhq7hgB9vLsfwQ6ANS0TPM/0P0OyzbE7sfolvArcAa4G7RWStz2bvAnpVdTnwH8AXnZ3Gwtnf1Mtffn8XrinlgXduM/HSICIifOGNG6kpSOdvfryHhw+14XYrIxMu/uX/jvDDlxt556VV3LjODGgLJlesKOBjN63ikUPtfOjn++kcHEdVeerEOf7q+7spy03lG2/fYhIBgkhifBxffesW3G7lDd96kT/4qejVTEiwYlYicilwn6reZM//E4Cqft5rm8fsbV4WkQTgLFCosxi5bds23bNnz7zt+cXuJr7+ZC2tfaOUZKfwlbs2c+my/Hnvx7B4WvtG+avv7+LUuSEKMpIZnXAxPDHFu69YyidvXWMeQCFAVfnqE6f5z6dOkxAfR2ZyAt3DE6woyuCn79lh+ntCRG3HIB/5xUEOt/ZTkZfKP9y4ijs2L6zlJiJ7VXXbdOuC2VdQhtXS8NACbJ9pG1V1iUg/kA9cEGcQkfcC7wWorKxckDEFGclsq87lL0qqePv2SpOFFELKclJ59ENX8sjhdp4/3UV6Ujy3by7loqq8UJsWs4gIH7lhJX++pYyfvtJIz/AkO2ryuHVDianQFkKWF2Xym/dfxv/sbGR/Ux8FGYFx0MFsMbwJuFlV323P/wWwXVXv9drmiL1Niz1/xt5mRonBhbYYDAaDIZaZrcUQzIB6K1b6q4dye9m029ihpGwc9mEYDAaDwT8E0zHsBlaIyFIRSQLeCvhmND0E/KX9+U3AU7P1LxgMBoPB/wQtWGj3GdwLPIY1PuJ7qnpURO4H9qjqQ8B/Az8WkVqgB8t5GAwGgyGIBLUXSVUfBR71WfZpr89jwJuDaZPBYDAYLsQk7RsMBoPhAoxjMBgMBsMFGMdgMBgMhgswjsFgMBgMFxC0AW6BQkQ6gcYFfr0An1HVUYo5z+jCnGd0EarzrFLVwulWRLxjWAwismemkX/RhDnP6MKcZ3QRjudpQkkGg8FguADjGAwGg8FwAbHuGB4ItQFBwpxndGHOM7oIu/OM6T4Gg8FgMLyWWG8xGAwGg8EH4xgMBoPBcAEx4RhE5GYROSkitSLyiWnWJ4vIL+z1r4hIdQjMXDQOzvOjInJMRA6JyJMiUhUKOxfLXOfptd0bRURFJKxSAZ3i5DxF5C77mh4VkZ8G20Z/4OB3WykiT4vIfvu3e2so7FwMIvI9Eemwi5FNt15E5Ov2/+CQiGwNto0XoKpRPWFJfJ8BaoAk4CCw1meb9wPftj+/FfhFqO0O0HleC6TZn98Xredpb5cJPAfsBLaF2u4AXc8VwH4g154vCrXdATrPB4D32Z/XAg2htnsB53kVsBU4MsP6W4E/AALsAF4Jpb2x0GK4BKhV1TpVnQB+Dtzhs80dwA/tz78GrheRSKtAP+d5qurTqjpiz+7EqqIXaTi5ngCfBb4IjAXTOD/i5DzfA3xTVXsBVLUjyDb6AyfnqUCW/TkbaAuifX5BVZ/DqjEzE3cAP1KLnUCOiJQEx7rXEguOoQxo9ppvsZdNu42quoB+ID8o1vkPJ+fpzbuw3lAijTnP026GV6jqI8E0zM84uZ4rgZUi8qKI7BSRm4Nmnf9wcp73Ae8QkRasei4fDI5pQWW+929ACWqhHkN4ICLvALYBV4faFn8jInHAV4B7QmxKMEjACiddg9X6e05ENqhqXyiNCgB3Az9Q1S+LyKVYVR7Xq6o71IZFK7HQYmgFKrzmy+1l024jIglYzdXuoFjnP5ycJyLyZ8CngNtVdTxItvmTuc4zE1gPPCMiDVjx2ocisAPayfVsAR5S1UlVrQdOYTmKSMLJeb4L+CWAqr4MpGAJz0UTju7fYBELjmE3sEJElopIElbn8kM+2zwE/KX9+U3AU2r3CEUQc56niGwBvoPlFCIxHg1znKeq9qtqgapWq2o1Vl/K7aq6JzTmLhgnv9vfYbUWEJECrNBSXRBt9AdOzrMJuB5ARNZgOYbOoFoZeB4C3mlnJ+0A+lW1PVTGRH0oSVVdInIv8BhWBsT3VPWoiNwP7FHVh4D/xmqe1mJ1EL01dBYvDIfn+SUgA/iV3bfepKq3h8zoBeDwPCMeh+f5GHCjiBwDpoCPqWpEtXQdnuffAw+KyEewOqLvibQXNxH5GZYTL7D7Sv4VSARQ1W9j9Z3cCtQCI8BfhcZSCyOJYTAYDIYLiIVQksFgMBjmgXEMBoPBYLgA4xgMBoPBcAHGMRgMBoPhAoxjMBgMBsMFGMdgiFlE5B4RGQrh8XNF5JyILPPjPneJyBv9tT9DbGLSVQ1RiYjM9cP+IZbCbGaoBvuJyJeAAlX1W866iLweSxJklZGMMCwU4xgMUYmILPGafR3wIOCtVjmqqv3BtepVRCQNS/Lg9ar6gh/3G2/v910RLiJoCCEmlGSISlT1rGcC+nyXqWq/byhJRO4TkSMi8pci0iAiwyLyfRFJEpH3i0iziHSLyFdssT7P95JE5Isi0iIiIyKyW0RumsPEW7FG8b7otZ9r7MJCBV7Lqr2LDYlIol3QpU1Exm2bvuB13lNYo2jvXtQ/0BDTRL0khsEwT6qxtPFfhyV7/L9YLY124EZgNZag24v2OoDvA8uAt2EJ290K/F5ELlbVgzMc50pg7wKkHT4EvAFLtqUBS2xtlc82u7CEEg2GBWEcg8FwIfHAX9lhpiMi8kcsefIyu5DMcRF5Easa3v/aHcd3A9Wq2mTv4xu2iu3fYFUHnI4qFlZwpgpLRfV526k0AS/5bNMGlIlIgl1fxGCYFyaUZDBcSJNP38M54JTtFLyXFdmft2KVYzwmIkOeCbgNqxUxE6ksrLrcD4DNwCkR+aaI3OYd1rIZtW1KWcD+DQbTYjAYfJj0mdcZlsXbn+Ps+Yun2W50luN0AbkO7In3nlHVfSJSDdyEJUX9Q+CgiNzglYWUB4ypashScQ2RjXEMBsPi2I/1dr5EVZ+e5/fumWFdMZbjAKjxXamqg1i1yX8tIj/AqjmxHCvEBFahon3zsMVguAATSjIYFoGqngJ+AvxARN4kIjUisk1E/kFE7pzlq48Ba0RkutriXxCRNSJyMfB5e9kmEckQkY+KyN32+uVYHd4DWJ3eHq4E/rj4szPEKqbFYDAsnr/CygL6d6wsoR6szKAZWxCqelhEdmFlF33TZ/U+rKwnN/AvwCCWg3jC/vwxrBKeitXyuEVVRwBEpAy4DHiHn87NEIOYAW4GQ4gQkZuBrwFrVXVKRK7BciaFqto123dn2eeXgGxVfa/fDDXEHCaUZDCECFX9I1ZrodyPu+3AamUYDAvGtBgMhjDBHy0Gg8EfGMdgMBgMhgswoSSDwWAwXIBxDAaDwWC4AOMYDAaDwXABxjEYDAaD4QKMYzAYDAbDBfz/i1oHKFulDyYAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -317,14 +317,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Blockade Radius is: 8.57865851586716µm.\n" + "Blockade Radius is: 8.692279598222772µm.\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -479,16 +479,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 0.20s. Est. time left: 00:00:00:01\n", - "20.0%. Run time: 0.44s. Est. time left: 00:00:00:01\n", - "30.0%. Run time: 0.71s. Est. time left: 00:00:00:01\n", - "40.0%. Run time: 0.95s. Est. time left: 00:00:00:01\n", - "50.0%. Run time: 1.15s. Est. time left: 00:00:00:01\n", - "60.0%. Run time: 1.39s. Est. time left: 00:00:00:00\n", - "70.0%. Run time: 1.82s. Est. time left: 00:00:00:00\n", - "80.0%. Run time: 2.41s. Est. time left: 00:00:00:00\n", - "90.0%. Run time: 2.88s. Est. time left: 00:00:00:00\n", - "Total run time: 3.60s\n" + "10.2%. Run time: 0.21s. Est. time left: 00:00:00:01\n", + "20.2%. Run time: 0.47s. Est. time left: 00:00:00:01\n", + "30.1%. Run time: 0.77s. Est. time left: 00:00:00:01\n", + "40.1%. Run time: 1.03s. Est. time left: 00:00:00:01\n", + "50.1%. Run time: 1.26s. Est. time left: 00:00:00:01\n", + "60.1%. Run time: 1.47s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 1.73s. Est. time left: 00:00:00:00\n", + "80.0%. Run time: 2.04s. Est. time left: 00:00:00:00\n", + "90.0%. Run time: 2.37s. Est. time left: 00:00:00:00\n", + "Total run time: 2.68s\n" ] } ], @@ -531,7 +531,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -563,7 +563,7 @@ " N = len(reg.qubits)\n", " time_window = []\n", " x =[]\n", - " detunings = simul.samples['Global']['ground-rydberg']['det'][[int(1000*t) for t in simul._times]]\n", + " detunings = simul.samples['Global']['ground-rydberg']['det'][[int(1000*t) for t in simul.sampling_times]]\n", "\n", " for t,d in enumerate(detunings):\n", " if start <= d <= end:\n", @@ -592,7 +592,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -654,21 +654,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 1.46s. Est. time left: 00:00:00:13\n", - "20.0%. Run time: 2.70s. Est. time left: 00:00:00:10\n", - "30.0%. Run time: 3.62s. Est. time left: 00:00:00:08\n", - "40.0%. Run time: 4.66s. Est. time left: 00:00:00:06\n", - "50.0%. Run time: 5.55s. Est. time left: 00:00:00:05\n", - "60.0%. Run time: 6.62s. Est. time left: 00:00:00:04\n", - "70.0%. Run time: 7.76s. Est. time left: 00:00:00:03\n", - "80.0%. Run time: 9.08s. Est. time left: 00:00:00:02\n", - "90.0%. Run time: 10.34s. Est. time left: 00:00:00:01\n", - "Total run time: 11.79s\n" + "10.3%. Run time: 0.86s. Est. time left: 00:00:00:07\n", + "20.3%. Run time: 1.83s. Est. time left: 00:00:00:07\n", + "30.2%. Run time: 2.69s. Est. time left: 00:00:00:06\n", + "40.2%. Run time: 3.52s. Est. time left: 00:00:00:05\n", + "50.2%. Run time: 4.39s. Est. time left: 00:00:00:04\n", + "60.1%. Run time: 5.23s. Est. time left: 00:00:00:03\n", + "70.1%. Run time: 6.06s. Est. time left: 00:00:00:02\n", + "80.1%. Run time: 6.90s. Est. time left: 00:00:00:01\n", + "90.0%. Run time: 7.73s. Est. time left: 00:00:00:00\n", + "Total run time: 8.52s\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -754,9 +754,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -780,21 +780,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 5.21s. Est. time left: 00:00:00:46\n", - "20.0%. Run time: 7.61s. Est. time left: 00:00:00:30\n", - "30.0%. Run time: 10.91s. Est. time left: 00:00:00:25\n", - "40.0%. Run time: 15.14s. Est. time left: 00:00:00:22\n", - "50.0%. Run time: 21.00s. Est. time left: 00:00:00:21\n", - "60.0%. Run time: 23.79s. Est. time left: 00:00:00:15\n", - "70.0%. Run time: 29.51s. Est. time left: 00:00:00:12\n", - "80.0%. Run time: 31.96s. Est. time left: 00:00:00:07\n", - "90.0%. Run time: 34.27s. Est. time left: 00:00:00:03\n", - "Total run time: 36.58s\n" + "10.0%. Run time: 1.94s. Est. time left: 00:00:00:17\n", + "20.0%. Run time: 4.00s. Est. time left: 00:00:00:15\n", + "30.0%. Run time: 6.26s. Est. time left: 00:00:00:14\n", + "40.0%. Run time: 9.07s. Est. time left: 00:00:00:13\n", + "50.0%. Run time: 11.38s. Est. time left: 00:00:00:11\n", + "60.0%. Run time: 13.30s. Est. time left: 00:00:00:08\n", + "70.0%. Run time: 14.95s. Est. time left: 00:00:00:06\n", + "80.0%. Run time: 16.52s. Est. time left: 00:00:00:04\n", + "90.0%. Run time: 18.18s. Est. time left: 00:00:00:02\n", + "Total run time: 20.73s\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -847,7 +847,7 @@ "\n", "phase_diagram(seq)\n", "\n", - "simul = Simulation(seq, sampling_rate=0.4)\n", + "simul = Simulation(seq, sampling_rate=0.6)\n", " \n", "occup_list = [occupation(reg, j) for j in range(simul._size)]\n", "\n", diff --git a/tutorials/simulating_sequences.ipynb b/tutorials/simulating_sequences.ipynb index 174435836..3ec413195 100644 --- a/tutorials/simulating_sequences.ipynb +++ b/tutorials/simulating_sequences.ipynb @@ -220,7 +220,7 @@ "source": [ "expect_magnetization = results.expect(magn_list)\n", "for data in expect_magnetization:\n", - " plt.plot(sim._times, data)" + " plt.plot(sim.evaluation_times, data)" ] }, { From 01d4c3d8b73bb8ec89203d5fb8251577016e6938 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Thu, 25 Nov 2021 13:45:08 +0100 Subject: [PATCH 16/51] Minimizing number of evolution simulations for specific noise configs (#273) Co-authored-by: Seb Grijalva <13460713+sebgrijalva@users.noreply.github.com> --- pulser/simulation/simulation.py | 49 ++++++++++++++++++++++++++++----- 1 file changed, 42 insertions(+), 7 deletions(-) diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index e535d76a5..2c96ac7f1 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -619,14 +619,19 @@ def _build_basis_and_op_matrices(self) -> None: self.basis[proj[0]] * self.basis[proj[1]].dag() ) - def _construct_hamiltonian(self) -> None: + def _construct_hamiltonian(self, update_and_extract: bool = True) -> None: """Constructs the hamiltonian from the Sequence. Also builds qutip.Qobjs related to the Sequence if not built already, and refreshes potential noise parameters by drawing new at random. + + Args: + update_and_extract(bool=True): Whether to update the noise + parameters and extract the samples from the sequence. """ - self._update_noise() - self._extract_samples() + if update_and_extract: + self._update_noise() + self._extract_samples() if not hasattr(self, "basis_name"): self._build_basis_and_op_matrices() @@ -919,20 +924,50 @@ def _run_solver() -> CoherentResults: if "SPAM" not in self.config.noise or self.config.eta == 0: return _run_solver() - # Did not obey the conditions above, will return NoisyResults + else: + # Stores the different initial configurations and frequency + initial_configs = Counter( + "".join( + ( + np.random.uniform(size=len(self._qid_index)) + < self.config.eta + ) + .astype(int) + .astype(str) # Turns bool->int->str + ) + for _ in range(self.config.runs) + ).most_common() + loop_runs = len(initial_configs) + update_ham = False + else: + loop_runs = self.config.runs + update_ham = True + + # Will return NoisyResults time_indices = range(len(self._eval_times_array)) total_count = np.array([Counter() for _ in time_indices]) # We run the system multiple times - for _ in range(self.config.runs): + for i in range(loop_runs): + if not update_ham: + initial_state, reps = initial_configs[i] + # We load the initial state manually + self._bad_atoms = dict( + zip( + self._qid_index, + np.array(list(initial_state)).astype(bool), + ) + ) + else: + reps = 1 # At each run, new random noise: new Hamiltonian - self._construct_hamiltonian() + self._construct_hamiltonian(update_and_extract=update_ham) # Get CoherentResults instance from sequence with added noise: cleanres_noisyseq = _run_solver() # Extract statistics at eval time: total_count += np.array( [ cleanres_noisyseq.sample_state( - t, n_samples=self.config.samples_per_run + t, n_samples=self.config.samples_per_run * reps ) for t in self._eval_times_array ] From d5abd8f0676177b19e7eb5a0e28fceb3307869f8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Fri, 26 Nov 2021 14:15:51 +0100 Subject: [PATCH 17/51] Restructuring the Tutorials (#287) * Changing Register drawing in Rydberg crystal tutorial * Reordering file structure for the tutorials * Fixing existing docs * Add tutorials to the docs * Reviewed notebooks in "Advanced Features" * Combined QAOA notebooks * Updating the QAOA notebook * Replicating Seb's changes to the Floquet engineering notebook * Typo in QAOA notebook --- docs/source/index.rst | 17 +- docs/source/tutorials/1D_crystals.nblink | 2 +- docs/source/tutorials/afm_prep.nblink | 2 +- docs/source/tutorials/composite_wfs.nblink | 2 +- docs/source/tutorials/interpolated_wfs.nblink | 2 +- docs/source/tutorials/mw_engineering.nblink | 3 + docs/source/tutorials/noisy_sim.nblink | 2 +- docs/source/tutorials/optimization.nblink | 2 +- docs/source/tutorials/paramseqs.nblink | 2 +- .../tutorials/phase_shifts_vz_gates.nblink | 2 +- docs/source/tutorials/qaoa_param_seq.nblink | 3 - docs/source/tutorials/serialization.nblink | 2 +- docs/source/tutorials/shadow_est.nblink | 3 + docs/source/tutorials/xy_spin_chain.nblink | 3 + pulser/devices/_device_datacls.py | 5 +- .../Composite Waveforms.ipynb | 0 .../Interpolated Waveforms.ipynb | 0 .../Parametrized Sequences.ipynb | 0 .../Phase Shifts and Virtual Z gates.ipynb | 0 .../Serialization.ipynb | 0 ...ting Sequences with Errors and Noise.ipynb | 873 ++++++++++++++++++ ...ltonians in arrays of Rydberg atoms .ipynb | 718 -------------- .../QAOA and Parametrized Sequences.ipynb | 386 -------- ...ting Sequences with Errors and Noise.ipynb | 873 ------------------ .../Using QAOA to solve a MIS problem.ipynb | 127 ++- ... antiferromagnetic state preparation.ipynb | 2 +- .../Building 1D Rydberg Crystals.ipynb | 2 +- ...ltonians in arrays of Rydberg atoms .ipynb | 717 ++++++++++++++ ...rromagnetic order in the Ising model.ipynb | 2 +- .../Shadow estimation for VQS.ipynb | 97 +- .../Spin chain of 3 atoms in XY mode.ipynb | 2 +- 31 files changed, 1725 insertions(+), 2126 deletions(-) create mode 100644 docs/source/tutorials/mw_engineering.nblink delete mode 100644 docs/source/tutorials/qaoa_param_seq.nblink create mode 100644 docs/source/tutorials/shadow_est.nblink create mode 100644 docs/source/tutorials/xy_spin_chain.nblink rename tutorials/{composition => advanced_features}/Composite Waveforms.ipynb (100%) rename tutorials/{composition => advanced_features}/Interpolated Waveforms.ipynb (100%) rename tutorials/{composition => advanced_features}/Parametrized Sequences.ipynb (100%) rename tutorials/{composition => advanced_features}/Phase Shifts and Virtual Z gates.ipynb (100%) rename tutorials/{composition => advanced_features}/Serialization.ipynb (100%) create mode 100644 tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb delete mode 100644 tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb delete mode 100644 tutorials/applications/QAOA and Parametrized Sequences.ipynb delete mode 100644 tutorials/applications/Simulating Sequences with Errors and Noise.ipynb rename tutorials/{applications => quantum_simulation}/Bayesian Optimisation for antiferromagnetic state preparation.ipynb (99%) rename tutorials/{applications => quantum_simulation}/Building 1D Rydberg Crystals.ipynb (99%) create mode 100644 tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb rename tutorials/{applications => quantum_simulation}/Preparing state with antiferromagnetic order in the Ising model.ipynb (99%) rename tutorials/{applications => quantum_simulation}/Shadow estimation for VQS.ipynb (98%) rename tutorials/{applications => quantum_simulation}/Spin chain of 3 atoms in XY mode.ipynb (99%) diff --git a/docs/source/index.rst b/docs/source/index.rst index 286484e5f..c4fb217b2 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -63,6 +63,7 @@ computers and simulators, check the pages in :doc:`review`. :maxdepth: 1 :caption: Advanced Features + tutorials/noisy_sim tutorials/phase_shifts_vz_gates tutorials/composite_wfs tutorials/paramseqs @@ -71,15 +72,21 @@ computers and simulators, check the pages in :doc:`review`. .. toctree:: :maxdepth: 1 - :caption: Applications + :caption: Quantum Simulation - tutorials/noisy_sim - tutorials/cz_gate tutorials/afm_prep - tutorials/1D_crystals tutorials/optimization + tutorials/xy_spin_chain + tutorials/mw_engineering + tutorials/shadow_est + tutorials/1D_crystals + +.. toctree:: + :maxdepth: 1 + :caption: Other Applications + + tutorials/cz_gate tutorials/qaoa_mis - tutorials/qaoa_param_seq tutorials/qek .. toctree:: diff --git a/docs/source/tutorials/1D_crystals.nblink b/docs/source/tutorials/1D_crystals.nblink index 37e7ea646..40da06e50 100644 --- a/docs/source/tutorials/1D_crystals.nblink +++ b/docs/source/tutorials/1D_crystals.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/applications/Building 1D Rydberg Crystals.ipynb" + "path": "../../../tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb" } diff --git a/docs/source/tutorials/afm_prep.nblink b/docs/source/tutorials/afm_prep.nblink index 000114943..22103200b 100644 --- a/docs/source/tutorials/afm_prep.nblink +++ b/docs/source/tutorials/afm_prep.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/applications/Preparing state with antiferromagnetic order in the Ising model.ipynb" + "path": "../../../tutorials/quantum_simulation/Preparing state with antiferromagnetic order in the Ising model.ipynb" } diff --git a/docs/source/tutorials/composite_wfs.nblink b/docs/source/tutorials/composite_wfs.nblink index 826dda985..510b35328 100644 --- a/docs/source/tutorials/composite_wfs.nblink +++ b/docs/source/tutorials/composite_wfs.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/composition/Composite Waveforms.ipynb" + "path": "../../../tutorials/advanced_features/Composite Waveforms.ipynb" } diff --git a/docs/source/tutorials/interpolated_wfs.nblink b/docs/source/tutorials/interpolated_wfs.nblink index 7afdf7575..59498dc0e 100644 --- a/docs/source/tutorials/interpolated_wfs.nblink +++ b/docs/source/tutorials/interpolated_wfs.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/composition/Interpolated Waveforms.ipynb" + "path": "../../../tutorials/advanced_features/Interpolated Waveforms.ipynb" } diff --git a/docs/source/tutorials/mw_engineering.nblink b/docs/source/tutorials/mw_engineering.nblink new file mode 100644 index 000000000..7ef1dadd1 --- /dev/null +++ b/docs/source/tutorials/mw_engineering.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb" +} diff --git a/docs/source/tutorials/noisy_sim.nblink b/docs/source/tutorials/noisy_sim.nblink index ace7494f5..34ad04efc 100644 --- a/docs/source/tutorials/noisy_sim.nblink +++ b/docs/source/tutorials/noisy_sim.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/applications/Simulating Sequences with Errors and Noise.ipynb" + "path": "../../../tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb" } diff --git a/docs/source/tutorials/optimization.nblink b/docs/source/tutorials/optimization.nblink index e267ff8bf..5bc44e2ae 100644 --- a/docs/source/tutorials/optimization.nblink +++ b/docs/source/tutorials/optimization.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/applications/Bayesian Optimisation for antiferromagnetic state preparation.ipynb" + "path": "../../../tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb" } diff --git a/docs/source/tutorials/paramseqs.nblink b/docs/source/tutorials/paramseqs.nblink index fb4c13249..d1022fcf9 100644 --- a/docs/source/tutorials/paramseqs.nblink +++ b/docs/source/tutorials/paramseqs.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/composition/Parametrized Sequences.ipynb" + "path": "../../../tutorials/advanced_features/Parametrized Sequences.ipynb" } diff --git a/docs/source/tutorials/phase_shifts_vz_gates.nblink b/docs/source/tutorials/phase_shifts_vz_gates.nblink index d3bd2e259..46a599f20 100644 --- a/docs/source/tutorials/phase_shifts_vz_gates.nblink +++ b/docs/source/tutorials/phase_shifts_vz_gates.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/composition/Phase Shifts and Virtual Z gates.ipynb" + "path": "../../../tutorials/advanced_features/Phase Shifts and Virtual Z gates.ipynb" } diff --git a/docs/source/tutorials/qaoa_param_seq.nblink b/docs/source/tutorials/qaoa_param_seq.nblink deleted file mode 100644 index f5e560474..000000000 --- a/docs/source/tutorials/qaoa_param_seq.nblink +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../../../tutorials/applications/QAOA and Parametrized Sequences.ipynb" -} diff --git a/docs/source/tutorials/serialization.nblink b/docs/source/tutorials/serialization.nblink index d136b61e0..7f3be54e0 100644 --- a/docs/source/tutorials/serialization.nblink +++ b/docs/source/tutorials/serialization.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/composition/Serialization.ipynb" + "path": "../../../tutorials/advanced_features/Serialization.ipynb" } diff --git a/docs/source/tutorials/shadow_est.nblink b/docs/source/tutorials/shadow_est.nblink new file mode 100644 index 000000000..401a03802 --- /dev/null +++ b/docs/source/tutorials/shadow_est.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../tutorials/quantum_simulation/Shadow estimation for VQS.ipynb" +} diff --git a/docs/source/tutorials/xy_spin_chain.nblink b/docs/source/tutorials/xy_spin_chain.nblink new file mode 100644 index 000000000..3912e963c --- /dev/null +++ b/docs/source/tutorials/xy_spin_chain.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb" +} diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index 3c8cb0054..e6e811403 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -34,12 +34,13 @@ class Device: Attributes: name: The name of the device. dimensions: Whether it supports 2D or 3D arrays. + rybderg_level : The value of the principal quantum number ``n`` + when the Rydberg level used is of the form + ``|nS_1/2, m_j = +1/2>``. max_atom_num: Maximum number of atoms supported in an array. max_radial_distance: The furthest away an atom can be from the center of the array (in μm). min_atom_distance: The closest together two atoms can be (in μm). - rybderg_level : The value of the principal quantum number n - when the Rydberg level used is of the form |nS_1/2, m_j = +1/2>. interaction_coeff_xy: :math:`C_3/\hbar` (in :math:`\mu m^3 / \mu s`), which sets the van der Waals interaction strength between atoms in different Rydberg states. diff --git a/tutorials/composition/Composite Waveforms.ipynb b/tutorials/advanced_features/Composite Waveforms.ipynb similarity index 100% rename from tutorials/composition/Composite Waveforms.ipynb rename to tutorials/advanced_features/Composite Waveforms.ipynb diff --git a/tutorials/composition/Interpolated Waveforms.ipynb b/tutorials/advanced_features/Interpolated Waveforms.ipynb similarity index 100% rename from tutorials/composition/Interpolated Waveforms.ipynb rename to tutorials/advanced_features/Interpolated Waveforms.ipynb diff --git a/tutorials/composition/Parametrized Sequences.ipynb b/tutorials/advanced_features/Parametrized Sequences.ipynb similarity index 100% rename from tutorials/composition/Parametrized Sequences.ipynb rename to tutorials/advanced_features/Parametrized Sequences.ipynb diff --git a/tutorials/composition/Phase Shifts and Virtual Z gates.ipynb b/tutorials/advanced_features/Phase Shifts and Virtual Z gates.ipynb similarity index 100% rename from tutorials/composition/Phase Shifts and Virtual Z gates.ipynb rename to tutorials/advanced_features/Phase Shifts and Virtual Z gates.ipynb diff --git a/tutorials/composition/Serialization.ipynb b/tutorials/advanced_features/Serialization.ipynb similarity index 100% rename from tutorials/composition/Serialization.ipynb rename to tutorials/advanced_features/Serialization.ipynb diff --git a/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb new file mode 100644 index 000000000..fecbad9e7 --- /dev/null +++ b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb @@ -0,0 +1,873 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation with Noise and Errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "$\\newcommand{\\Ket}[1]{\\left|#1\\right>} \\newcommand{\\Bra}[1]{\\left<#1\\right|}$\n", + "This notebook gives an overview of how to simulate several kinds of noise in Pulser. Quantum computers aren't perfect and are susceptible to various sources of noise. In order to realistically simulate these quantum computations, we need to take them into account.\n", + "\n", + "As of now, the types of noise implemented in Pulser are:\n", + "\n", + "- SPAM (State Preparation And Measurement) errors : There are three types of such errors, one (with probability $\\eta$) related to bad initial state preparation of the all-ground state $\\Ket{g}^{\\otimes n}$, and two (with probabilities $\\epsilon, \\epsilon '$) linked to detection errors. During the imaging process, excited Rydberg atoms in $\\Ket{r}$ might decay to the state $\\Ket{g}$, allowing them to be trapped in the tweezers : those are the false negatives modeled by $\\epsilon'$. On the contrary, some atoms in $\\Ket{g}$ might get excited due to various causes (collisions...) and tweezer recapture might fail, inferring them incorrectly as atoms in $\\Ket{r}$ : those are the false positives modeled by $\\epsilon$.\n", + "\n", + "- Doppler damping : The atoms in the register are cooled to a temperature $T \\sim 50\\mu K$, which is low but still non-zero. Therefore, the laser frequency they observe is shifted by Doppler shifting due to thermal motion. This corresponds to a shift in the detuning frequency of the laser, and leads to a damping in the Rydberg population.\n", + "\n", + "- Waist of the laser : For global pulses, the laser amplitude has a Gaussian profile and atoms at the border of the waist feel a slightly lower amplitude than those at the focus.\n", + "\n", + "- Dephasing / phase-damping: Each qubit interacts with its environment, and we can model this interaction with random $Z$-rotations on each qubit. Given a dephasing probability $p$, this noise model adds two collapse operators $M_0 = \\sqrt{1-\\frac{p}{2}} \\times \\mathbb{1}$, $M_1 = \\sqrt{\\frac{p}{2}} \\sigma_z = \\sqrt{\\frac{p}{2}} (\\Ket{r}\\Bra{r} - \\Ket{g}\\Bra{g})$ and forces the solver to adopt a density matrix formalism. See [here](https://ocw.mit.edu/courses/nuclear-engineering/22-51-quantum-theory-of-radiation-interactions-fall-2012/lecture-notes/MIT22_51F12_Ch8.pdf) for a more thorough explanation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import qutip\n", + "\n", + "from pulser import Register, Pulse, Sequence, Simulation\n", + "from pulser.simulation import SimConfig\n", + "from pulser.devices import Chadoq2\n", + "from pulser.waveforms import ConstantWaveform, RampWaveform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single atom noisy simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sequence preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare a single atom:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "reg = Register.from_coordinates([(0,0)], prefix='q')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Act on this atom with a Constant Pulse, such that it oscillates towards the excited Rydberg state and back to the original state (Rabi oscillations):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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kSaqUZpsZdg7wWeBSYBQQwGaKYth7GzlQZt4A3NDIPhstWp0ZJkmSJEmSVK+pimGZ+RTwtoh4H7Bv7fS99WuISZIkSZIkaeRqqmIYQES0AQcD04DRwJyofXVjZn6nxGhDLjv9NklJkiRJkqR6TVUMi4jnAT8DZlA8ItlJ8Ro3AxuBEVUMaxndbEvCSZIkSZIkDUyzVUu+ALQDzwKeBA4A5gF3AK8qLVVJuja5gL4kSZIkSVK9ppoZBrwAODozN0REF9CWmbdHxPuBLwHPLzfe0Hrm8VBJkiRJkiQVmm1mWFDMCANYDUyu7a8A9islUYn8NklJkiRJkqStNdvMsCUUi+ffB9wKfCAiOoE3A8vKDFaGrg4fk5QkSZIkSarXbMWwTwC71PY/DPwcuB5YA7ymrFBlaWlrtol/kiRJkiRJA9NUxbDMvKZu/z7ggIjYDViXmVlesnJk14h7yZIkSZIkSdvVNFOHImJURNwSEfvXn8/MR0ZiIQwshkmSJEmSJG2raYphmbkZmAFYAappGd00f72SJEmSJEkN0WzVkssoFssX0LXJBfQlSZIkSZLqNdWaYRSL5/9DRBwHtAMb6i9m5jtKSVWSaImyI0iSJEmSJFVKsxXDDgBur+0/d5trI+7xSYthkiRJkiRJW2uqYlhmvrTsDFXS1eFjkpIkSZIkSfWG/ZphETGjD20jIqYOZp4qaWkb9n+9kiRJkiRJDdUM1ZKbI+JbEfGinhpExMSIOAe4C5g/dNHKlV0j7slQSZIkSZKk7WqGxySfB1wA/DwiuigWzn8IeBqYCMymWEvsVuBdmXlNWUGHmsUwSZIkSZKkrQ37mWGZ+Whmvg+YDLwVWApMAGYAHcBlwCGZecRIKoQBtIwe9n+9kiRJkiRJDdUMM8MAyMyngCtrm4CuTS6gL0mSJEmSVM+pQ00sWqLsCJIkSZIkSZViMayJWQyTJEmSJEnamsWwJtbV4WOSkiRJkiRJ9SyGNbGWNv96JUmSJEmS6o2IaklEtETEtAb0MzUiro+IuyLizoh4ZyPyDZbsyrIjSJIkSZIkVUrTFMMiYkxEfCQi/jsinoqIhyPi3yJiDjAJuL8Bw3QA78nM2cDhwLkRMbsB/Q4Ki2GSJEmSJElbays7QCNExE7A9cD+wGXAPcBuwMnArcAFjRgnM1cCK2v7T0TEUmAycFcj+m+0ltFNU+uUJEmSJElqiKYohgHnU8z+2j8zV9ed/0REvBH4WqMHjIjpwCHALY3uu1G6NrmAviRJkiRJUr1mmTp0OnD+NoUwADLzUuCDQDRqsIgYB/wb8K7MfLyb62dHxKKIWLR69V9EGjLR0rCXLEmSJEmS1BSapRi2D/Dbni5m5uczsyGvNSJGURTCvp+ZP+5hvAWZOS8z502aNKkRw/aLxTBJkiRJkqStNUsx7Algr54uRsSciLhkoINERADfApZm5ucG2t9g6+rwMUlJkiRJkqR6zVIMux44t7sLEfEc4ArgDQ0Y5wjgdcBfR8Qdte2kBvQ7KFramuWvV5IkSZIkqTGapVpyEXByRHwvIg6KiJ0iYu+IeAtwG7Cml/2cANwNLKNYlH8rmXlTZv5dZrZl5qjMvCszr2rYq2iw7MyyI0iSJEmSJFVKU3ybZGYuiYgTgEuAO+oudQD/AnwJ+OMOumkFLgaOA1ZQFNEWAnfVtZlJsRj/EcA64NkNiD9oMi2GSZIkSZIk1WuKYhgUs7Yi4nnAC4AZFOuI3ZyZj0TELsBHd9DFYRQzwu6rHV8BzGfrYtibKQpm62rHqxoUf1C0jG6WiX+SJEmSJEmN0TTFMIDM7AJuqW315zew42LYZOCBuuMVwAu3aTOr9uevKWaSXQhc3c+4g65rkwvoS5IkSZIk1WuqYtgQaKN4VPIYYApwI3AQ8Gh5kXoWrVF2BEmSJEmSpErxObotHgSm1h1PqZ2rt4JiHbHNwP3APRTFMUmSJEmSJA0DFsO2uI2isDUDGA2cRlH4qvfvFLPCAPageGzyPirKb5OUJEmSJEnamsWwLTqA84BrgKXAj4A7gYuAU2ptrgHWUiyqfz3wvtpxJbWM8q9XkiRJkiSpnmuGbe2q2lbvH+v2E3h3bau87HBmmCRJkiRJUj2nDkmSJEmSJGnEsBjWxKLNb5OUJEmSJEmqZzGsiXVt7io7giRJkiRJUqVYDGti0erMMEmSJEmSpHoWwyRJkiRJkjRiWAxrYtnpt0lKkiRJkiTVa+tL44hoBWYBk4GdgSeBB4F7MrOz8fE0EC2jrHVKkiRJkiTV61UxLCKeA1wI/B2wazdNHo+IHwEXZubKxsXTQGSHM8MkSZIkSZLq7bAYFhHTgZuA5wA3AL+hmA32NLATxSyxFwFnAa+IiJdk5v2DlFeSJEmSJEnqt97MDPt0rd3czPxdT40i4mDgGuBTFDPIVLJo89skJUmSJEmS6vVmUamXAZ/fXiEMoHb9C8CxDcilBuja3FV2BEmSJEmSpErpTTFsDPBYL/t7rNZeFRCtzgyTJEmSJEmq15ti2O+AN0XE2O01ioidgTcDv29EMEmSJEmSJKnRerNm2CeAnwF3RsQ32bKA/kaKWWDPLKD/JmAqcMrgRFVfZaffJilJkiRJklRvh8WwzLwqIl4NfAn4ONBdhSWAlcBpmXlVYyOqv1pG92binyRJkiRJ0sjRm5lhZOaPI2IhcDTwAmBvYGfgSeAh4Dbg/2Rmx2AFrYqIOAH4F6AV+GZmfqrkSD3q2uQC+pIkSZIkSfV6PXUoMzsy87rM/FRmviMz31T781O18yOhENYKXAycCMwGTo+I2eWm6tmmpzeVHUEalq6+8uqyI0jDkveO1HfeN1L/eO9I/bNgwYKyI1SCz9H1zWHAssy8LzM3AVcA80vO1KP1T6wvO4I0LP3yx78sO4I0LHnvSH3nfSP1j/eO1D8Wwwq9ekyyXkRMA94CzAR2p1gvrF5m5ssakK2KJgMP1B2vAF5YUpYdGj16dNkRJEmSJEmSKiUye/+NgxFxIvATYDSwHljbXbvMnNGQdBUTEacCJ2Tmm2rHrwNemJnnbdPubOBsgJe//OVzr766nCm8y5cvZ/r06aWMLUmSJEmSNFgiYk1mTurPz/Z1ZtgngTXAKzNzUX8GHOYeBKbWHU+pndtKZi4AFgDMmzev99XGBtu4cWNZQ0uSJEmSJA2mP/b3B/u6ZtjzgC+M0EIYFN+aOTMiZkTEaOA0YGHJmSRJkiRJktRLfS2GrQZG7FcU1r4x8zzgGmAp8KPMvLPcVD1buXJl2REkSZIkSZIqpa+PSX4XeBXwxUHIMixk5lXAVWXn6I05c+aUHUGSJEmSJKlStjszLCKm1W/ApcDoiPhpRPx17XHBad20UwUsXry47AiSJEmSJEmVsqOZYcuBbReAj9qfr9jOz7X2N5Aap7XVvwZJkiRJkqR6OyqGXcRfFsM0TMyaNavsCJIkSZIkSZWy3WJYZl44RDk0CJYsWcIxxxxTdgxJkiRJkqTK6Ou3SWoYmTJlStkRJEmSJEmSKqVPxbCIODci/nM7138ZEW8ZeCw1wqZNm8qOIEmSJEmSVCl9nRn2RuAP27l+D3Bmv9OooVatWlV2BEmSJEmSpErpazFsJrB4O9fvrLUZdiLikohYFRFLergeEfHFiFgWEb+PiEOHOmNfzZ07t+wIkiRJkiRJldLXYtgoYKftXN9pB9er7FLghO1cP5Gi0DcTOBv46hBkGpD29vayI0iSJEmSJFVKX4th9wDHbef68cC9/Y9Tnsy8EXhkO03mA9/Jwm+ACRGx19Ck65+xY8eWHUGSJEmSJKlS+loM+wFwfER8LCJGP3MyIkZFxEcpimGXNzJghUwGHqg7XlE7V1nTp08vO4IkSZIkSVKl9LUY9nngRuAC4KGIuCkibgJWAv8LuAn4bGMjDj8RcXZELIqIRatXry4tx9KlS0sbW5IkSZIkqYr6VAzLzM0Us7/Op5gZdUhtewB4P3BsZm5qdMiKeBCYWnc8pXbuL2Tmgsycl5nzJk2aNCThuuPMMEmSJEmSpK219fUHagWxT9e2kWQhcF5EXAG8EHgsM1eWnGm7nnjiibIjSJIkSZIkVUqfi2HNKiJ+ABwD7BERK4CPUHx7Jpn5NeAq4CRgGfAkcEY5SXtv7dq1ZUeQJEmSJEmqFIthNZl5+g6uJ3DuEMVpiLlz55YdQZIkSZIkqVL6uoB+szsBuJti9tf522n3KiCBeUMRqr/a29vLjiBJkiRJklQpFsO2aAUuBk4EZgOn1/7c1njgncAtQxetf8aNG1d2BEmSJEmSpEqxGLbFYRQzwu4DNgFXAPO7afcx4J+Ap4cuWv/svffeZUeQJEmSJEmqFIthW0wGHqg7XlE7V+9QYCrw86EKNRD33HNP2REkSZIkSZIqxQX0e68F+BzwxpJz9Nq+++5bdgRJkiRJkqRKcWbYFg9SzPp6xpTauWeMBw4EbgCWA4cDC6nwIvpr164tO4IkSZIkSVKlWAzb4jZgJjADGA2cRlHsesZjwB7A9Nr2G+AUYNFQhuyLRx99tOwIkiRJkiRJlWIxbIsO4DzgGmAp8CPgTuAiiqLXsDN37tyyI0iSJEmSJFVK5dcMi4gxwN7AWGB1Zq4exOGuqm31/rGHtscMYo6GaG9v55hjjik7hiRJkiRJUmVUcmZYRIyPiHMi4kaKxxOXAUuAP0fEnyLiGxHxgnJTVt+ECRPKjiBJkiRJklQplSuGRcS7KRaoPxO4FpgPzAFmAS8CLqSY0XZtRFwdETNLCToM7L777mVHkCRJkiRJqpQqPiZ5OHB0Zi7p4fqtwCUR8VbgLOBo4A9DFW44uffee5k6deqOG0qSJEmSJI0QlSuGZeZretluI/CVQY4zrM2aNavsCJIkSZIkSZVSucck60XE7IjYv+74uIj4XkR8MCJay8w2HDz00ENlR5AkSZIkSaqUShfDgEuAQwAiYirwU2A34Fzg4yXmGhbWr19fdgRJkiRJkqRKqXox7HnA7bX9U4FbMvMk4HXA6aWlGibmzp1bdgRJkiRJkqRKqXoxrBXYVNt/GXBVbf9eYM9GDxYRJ0TE3RGxLCLO7+b6GyNidUTcUdve1OgMjdTe3l52BEmSJEmSpEqp3AL621gCnBMR/0FRDPtg7fxkYE0jB6qtQXYxcBywArgtIhZm5l3bNP1hZp7XyLEHy+677152BEmSJEmSpEqp+sywDwBvBm4AfpCZi2vnTwFubfBYhwHLMvO+zNwEXAHMb/AYQ2r8+PFlR5AkSZIkSaqUShfDMvNGYBKwR2aeWXfp68A5DR5uMvBA3fGK2rltvSoifh8RV9YW9a+s5cuXlx1BkiRJkiSpUipdDAPIzM7MXLfN6U7gfSXE+RkwPTOfD1wLXNZdo4g4OyIWRcSi1atXD2nAegcccEBpY0uSJEmSJFVRpdcMi4iFPVyaAuxHYwtiDwL1M72m1M79j8xcW3f4TeDT3XWUmQuABQDz5s3LBmbsk+XLl7Pnng3/ngFJkiRJkqRhq9LFMGDtNsetwHOBg4EzGjzWbcDMiJhBUQQ7Dfj7+gYRsVdmrqwdngIsbXCGhnrqqafKjiBJkiRJklQplS6GZWa3Ba+IeCdFMew7DRyrIyLOA66hKLpdkpl3RsRFwKLMXAi8IyJOATqAR4A3Nmr8wTB37tyyI0iSJEmSJFVKZJb2FF+/1WZv3ZWZY8vOsiPz5s3LRYsWlTL2DTfcwDHHHFPK2JIkSZIkSYMlItozc15/frbyC+j3YC7QXnaIqnv2s59ddgRJkiRJkqRKqfRjkhHxxW5O7wm8Ariq/npmvmPIgg0To0ePLjuCJEmSJElSpVS6GAYc1MP5W4E9ahvA8HvWcwisWLGC/fbbr+wYkiRJkiRJlVHpYlhmvrTsDMPZgQceWHYESZIkSZKkSqnkmmER8bmIODIiKplvuLjnnnvKjiBJkiRJklQpVS02jQWuAB6OiEsj4pURUflvjqyazs7OsiNIkiRJkiRVSiWLYZl5TmZOBv4GeBD4OLAmIhZGxJkRManchMPDQQf1tOSaJEmSJEnSyFTJYtgzMvPWzLwgMw8EDgb+D/BGYEVE3BQR742IyaWGrLA77rij7AiSJEmSJEmVUuliWL3MXJaZn83Mo4ApwCXAS4DTy01WXXvttVfZESRJkiRJkiql0t8m2ZPMXE1RDLuk7CySJEmSJEkaPipXDIuIXhe4MvPMwcwy3K1cuZL999+/7BiSJEmSJEmVUbliGLDt4vhHAV3A4trxgRSPd944lKGGozlz5pQdQZIkSZIkqVIqVwzLzJOf2Y+IDwJPAWdk5obauV2Ab7GlOKYeLF68mCOPPLLsGJIkSZIkSZVR9QX03wFc+EwhDKC2/zHg7aWlGiZaW1vLjiBJkiRJklQpVS+GjQP27ub8XsDOQ5xl2Jk1a1bZESRJkiRJkiql6sWwfwO+HRGnRcT02nYaxWOSPy45W+UtWbKk7AiSJEmSJEmVUrk1w7ZxDvBZ4FJgFBDAZopi2HvLizU8TJkypewIkiRJkiRJlVLpmWGZ+VRmvg3YHTgEmAPslplvy8wnGz1eRJwQEXdHxLKIOL+b62Mi4oe167dExPRGZ2ikTZs2lR1BkiRJkiSpUipdDAOIiDbgYGA2RTHsVRHx+oh4fYPHaQUuBk6sjXV6RMzeptlZwLrM3A/4PPBPjczQaKtWrSo7giRJkiRJUqVU+jHJiHge8DNgBsUjkp0UmTcDG4HvNHC4w4BlmXlfbewrgPnAXXVt5gMX1vavBL4cEZGZ2cAcDTN37tyyI0iSJEmSJFVK1WeGfQFoB54FPAkcAMwD7gBe1eCxJgMP1B2vqJ3rtk1mdgCPUTzCWUnt7e1lR5AkSZIkSaqUSs8MA14AHJ2ZGyKiC2jLzNsj4v3Al4DnlxuvexFxNnA2wNSpU1m/fj1PPfUUGzZsAGD33Xdn48aNrF+/HoCJEyfS2dnJ448/DsCECRMAePTRRwHYddddaW1tZd26dQCMGzeOMWPGsHbtWgB22WUXxo4dy5o1awAYO3Ys48aN4ycfv50rP/xbujYFnU+10Dauk2iFrs1B55MttO3SSbRBdkDHhlZad+6iZVSSndCxvpXWsV20jE6yCzoeb6VlbBetY4pJcJsfa6VlTBetO9WOH2+lZVTSOrYLgI4nWqAF2napHa8v6q5t42rHG1qgC9rGF8edT7XQtTkYtWtncfx00LWxhVHPqh1vDLqeaqFt106iBV+Tr8nX5GvyNfmafE2+Jl+Tr8nX5GvyNfmafE19eE3jdhvLBd//hyGpR6xZs4bMZKeddmLcuHGsW7eOzs5ORo8eza677sqjjz5KR0cHbW1tTJgwgccff5xNmzbR2trKxIkTWb9+PU8//TQRwR577PE/dRWAPfbY43/2+ysq+oQfABHxCDAvM++LiGXA2Zn5q4jYF1icmTs3cKwXARdm5strxx8EyMxP1rW5ptbm5tpaZn8GJm3vMcl58+blokWLGhWzTx5++GH23HPPUsaWJEmSJEkaLBHRnpnz+vOzVX9McgnF4vkAtwIfiIijgY8Cyxo81m3AzIiYERGjgdOAhdu0WQi8obZ/KvCrqq4XBrB06dKyI0iSJEmSJFVK1Ythn6BYOB/gw8A04HrgeOAdjRwoMzve+ta3fnv69Ol3T5s2bcOrX/3qP2fmnRFxUUScArB27dpdTjrppL/fZ599Ns6ePfvr73rXu77YyAyNNn369LIjSJIkSZIkVUqlH5PsTkTsBqwbhBlZrcA9wHEUi+ffBpzO1t8m+VLgForF/M8BjgH+bnudlvmY5OLFiznooINKGVuSJEmSJGmwNOVjkhExKiJuiYj9689n5iOD9GjiYRSPXt4HbAKuAOZv0+Z6ikIYwG+AKYOQo2GeWdBOkiRJkiRJhcoWwzJzMzADGKqpa5OBB+qOV9TO9eQs4BeDmmiA5s6dW3YESZIkSZKkSqlsMazmMuDNZYfoxmuBecA/lx1ke9rb28uOIEmSJEmSVCltZQfYgV2Af4iI44B2YEP9xcxs5CL6DwJT646n1M5t61jgAuBoYGMDx2+4cePGlR1BkiRJkiSpUqpeDDsAuL22/9xtrjX68cnbgJkUj2Y+CJwG/P02bQ4Bvg6cAKxq8PgNt/fee5cdQZIkSZIkqVIqXQzLzJcO4XAdwHnANRTfLHkJcCdwEbAIWEjxWOQ44F9rP/Mn4JQhzNgn99xzjwUxSZIkSZKkOpUrhkXEjMy8v5dtA5iSmQ/ssHHvXFXb6v1j3f6xDRpnSOy7775lR5AkSZIkSaqUKi6gf3NEfCsiXtRTg4iYGBHnAHcB84cu2vCydu3asiNIkiRJkiRVSuVmhgHPo1ig/ucR0UWxcP5DwNPARGA2xVpitwLvysxrygpadY8++mjZESRJkiRJkiqlcjPDMvPRzHwfMBl4K7AUmECxsH0HcBlwSGYeYSFs++bOnVt2BEmSJEmSpEqp4swwADLzKeDK2qZ+aG9v55hjjik7hiRJkiRJUmVUbmaYGmfChAllR5AkSZIkSaoUi2FNbPfddy87giRJkiRJUqVYDGti9957b9kRJEmSJEmSKsViWBObNWtW2REkSZIkSZIqxWJYE3vooYfKjiBJkiRJklQplSyGRcQ/RsRxtf2JEfGJiPh2RLw3IqYMwni7RcS1EfGH2p8Te2jXGRF31LaFjc7RaOvXry87giRJkiRJUqVUshgGvBX4c23/X4FTgP2AC4D7IuJdDR7vfOC6zJwJXFc77s5TmTmntp3S4AwNN3fu3LIjSJIkSZIkVUpVi2G7AWsiYl/g5sw8KDOPBCYB5wIfj4hXNnC8+cBltf3LgEb2XZr29vayI0iSJEmSJFVKVYthj1AUxF4GfO2Zk5nZkZnfAN4LvL+B4+2ZmStr+38G9uyh3U4RsSgiftPgYtyg2H333cuOIEmSJEmSVCltZQfowX8Cnwf2ARYBD25z/Trgn/rSYUT8J/Ccbi5dUH+QmRkR2UM3+2TmgxHxXOBXEbE4M+/tZqyzgbMBpk2b1peYDTV+/PjSxpYkSZIkSaqiqs4Mew/wOHA38OKIOC0iRtVdnw+s7kuHmXlsZh7YzfZT4OGI2Aug9ueqHvp4sPbnfcANwCE9tFuQmfMyc96kSZP6ErOhli9fXtrYkiRJkiRJVVTJYlhmrs7MU2uL1H8FOBxYGxHtEfHfFLPCFjRwyIXAG2r7bwB+um2D2rdajqnt7wEcAdzVwAwNd8ABB5QdQZIkSZIkqVIqWQyrl5ldmfkuiuLTT4FrgNdm5qcbOMyngOMi4g/AsbVjImJeRHyz1uYAYFFE/A64HvhUZla6GObMMEmSJEmSpK1Vdc2wv5CZi4HFg9T3WorF+rc9vwh4U23/v4CDBmP8wfLUU0+VHUGSJEmSJKlSKj8zTP03d+7csiNIkiRJkiRVisWwJtbe3l52BEmSJEmSpEqxGNbEnv3sZ5cdQZIkSZIkqVIshjWx0aNHlx1BkiRJkiSpUiyGNbEVK1aUHUGSJEmSJKlSLIY1sQMPPLDsCJIkSZIkSZViMayJ3XPPPWVHkCRJkiRJqhSLYU2ss7Oz7AiSJEmSJEmVYjGsiR100EFlR5AkSZIkSaoUi2FN7I477ig7giRJkiRJUqVYDGtie+21V9kRJEmSJEmSKsVimCRJkiRJkkYMi2FNbOXKlWVHkCRJkiRJqhSLYU1szpw5ZUeQJEmSJEmqFIthTWzx4sVlR5AkSZIkSaoUi2FNrLW1tewIkiRJkiRJlWIxrInNmjWr7AiSJEmSJEmVYjEMiIhXR8SdEdEVEfO20+6EiLg7IpZFxPlDmbE/lixZUnYESZIkSZKkSrEYVlgC/H/AjT01iIhW4GLgRGA2cHpEzB6aeP0zZcqUsiNIkiRJkiRVSlvZAaogM5cCRMT2mh0GLMvM+2ptrwDmA3cNesB+2rRpU9kRJEmSJEmSKsWZYb03GXig7nhF7VxlrVq1quwIkiRJkiRJlTJiZoZFxH8Cz+nm0gWZ+dMGj3U2cDbAtGnTGtl1n8ydO7e0sSVJkiRJkqpoxMwMy8xjM/PAbrb/KYRNmjRpt5tuuulKYBmw7QL5D7a0tEwDfggs+8AHPvCJffbZZ0MPYy3IzHmZOW/SpEmD9pp2pL29vbSxJUmSJEmSqmjEFMN6oXXfffed+aUvfemd1BbIr/35jNvGjx9/aHt7e0dEzP7GN76R3/ve955fTtTeGTt2bNkRJEmSJEmSKsViGBARfztmzJiV7e3tY374wx8uiIifAVfcfvvtr42IqwAys+PLX/7yH4888sgjgaWPP/74N1/ykpe8ANjuqvtlmj59etkRJEmSJEmSKsViGJCZP9m4cePbNm3adElm7pmZLwdWHHroobtm5knPtHvta1875sknn3xxZu67efPmjwGPAbuXFnwHli5dWnYESZIkSZKkShkxC+iXpb29fU1E/LGk4fcA1pQ0tjScee9I/eO9I/Wd943UP947Uv80072zT39/0GLYFg8CU+uOp9TOdddmBcU/u2cBa7fXaWaWtoJ+RCzKzHlljS8NV947Uv9470h9530j9Y/3jtQ/3jsFH5Pc4jZgJjADGA2cBizcps1C4A21/VOBXwE5VAElSZIkSZI0MM4M26IDOA+4BmgFLgHuBC4CFlEUwr4FfBdYBjxCUTCTJEmSJEnSMGExbGtX1bZ6/1i3/zTw6qGLM2ALyg4gDVPeO1L/eO9Ifed9I/WP947UP947QGT6lJ8kSZIkSZJGBtcMkyRJkiRJ0ohhMaxxTgDuplhP7Pxuro8Bfli7fgswfTDDRMQJEXF3RCyLiO7ySM1iR/feu4G7gN8D1wH7RMTyiFgcEXdExCKAiNgtIq6NiD/U/pxYOx8R8cXavfT7iDh0aF6WVL6IuCQiVkXEkrpzu0XEtePGjXvwyCOP3LBmzZr7gPO3c6+86rLLLsuxY8f+qXZ/vaH70aTm0cO9c2FEPFh777kjIk6qu/bB2r1zd0S8vO58d5/ndvS+B/Aaive+O4HLG/36pMEQEVMj4vqIuCsi7oyId9bO9/kzWkS8oda+0e87O7r/pgHXA7+l+Ox5UjdtpIbazr3TqPedsl0CrAKW9HA9gC9S3Je/B3r3+1pmug18a83MezPzuZk5OjN/l5mzt2nztsz8Wm3/tMz84WDlofgCgHuB51J8M+bvgG3zuLk1w9abe++lmblzbf+czPwhsBzYo74d8Gng/Nr++cA/1fZPAn5R+4/s4cAtFXjdbm5DsgFH1T5QLKk79+nW1tYPZua9M2bM+PSoUaP+OTN/d+KJJ76lm3tl/MqVK/9r8uTJT19zzTV/DUwE7gMmlv3a3NwGc+vh3rkQeG83bWfXPquNofhW83trn+X+4vPcrrvuemDu+H1vZmb+NjOfuc+eXfY/Dze33mzAXsChtf3xwD21+6NPn9GA3WrvNbs1+H2nN587F2TxeZPateVl/3N1a/5tO/fOgN93KlJHOCozD83MJT1cPykzf5GZkZmHZ2avfl9zZlhjHEZRhbwP2ARcAczfps184LLa/pXAyyj+wz1oeTLzvszsKY/UDHpz710PPFnb/w0wpYe+6u/Ry4BX1p3/ThZ+A0yIiL0akl6quMy8keLbk+vNv+KKKxYDy+6///7Pb968+RTgivXr15/JNvfKXXfd9dn3vOc9//fFL37xI8cff/zjmbkOuJbi/6xLTauHe6cn84ErMnNjZt5P8b52GN18nttvv/3OZcfve28GLgbW1Y5XDejFSEMkM1dm5u21/SeApcBk+v4Z7eXAtZn5SIPfd3rzuTOBXWv7zwIeasC40nZt597pSa/fd6hGHWFH76nzge9Q3H+/ASZQFAi3y2JYY0wGHqg7XsFf/stX36YDeAzYvcQ8UjPo67/rZ1H8H8QEfhkR7RFxdu3anpm5srb/Z2DPfo4hNbs9Tz311J0o7otn7pUVGzZseDZ198qECRMeW7Fixb6XX3756ilTpmys+3nvIY1k59Ue57rkmUe96Pl95i/OZ+b0HtrWm1Xbfk3xS4HFZw07ETEdOIRieZm+fkYbrM9uven3QuC1tWtXAW9vwLhSr21z78AA33cYHp/Z+pXbYpikkeK1wDzgn4GXZOahwInAuRFxVH3DLOYP+1W70g5s515pmTlz5swFCxZ8YYgjSVX2VWBfYA6wEvjsII3TBswEjgFOB75B8X/JpWEhIsYB/wa8KzMfr782DD6jnQ5cSvEkwknAd/F3bg2Rbu6doXrfGZa8MRvjQWBq3fGU2rme2rRRTJtdW2IeqRn09t/1Y4ELgFOAjZn5IEBmrgJ+QjEl+OFnHn+s/fnMYyXeT9LWHr7yyiufBqbW3StTdtlll1VsuVfGr1u3bvznP//5r1xyySUfeOKJJ6YCCykK0t5DGpEy8+HM7MzMLooC1WG1Sz29z/zF+YhY3kPbeiso7rfNwP0Ua8fMbNDLkAZVRIyi+GX++5n549rpvn5GG6zPbr3p9yzgR7X9m4GdgD0aMLa0Xd3dO41432F4fGbrV26LYY1xG8WHjBkUC82dRvEhpN5C4JlvMjkV+BWD9381bgNmRsSMiOgpj9QMenPvHQJ8naIQtioidomI8QARsQtwPMU3k9Tfo28AflrbXwi8vvaNRYcDj9VN1ZdGooWnn37684GZ06dPf9eoUaN+Bpw2bty4S9lyrxywbNmy26dOnTr1zDPP3P/nP//55uuuu+4fIuJeinvumjJfgFSGbdab/Fu2fCvWQuC0iBgTETMo3tdupZvPc8uXL/8qO37f+3eKWWFQ/BI+i2KNI6nSIiKAbwFLM/NzdZf6+hntGuD4iJhYeyysUe87vfnc+SeKtaEBDqAohq1uwNhSj3q6dxrxvsPwqCMsBF7Pli/TeIxiJtx2tQ1yqJGiAziP4j+yrRRf/XkncBGwiOIv51sU02SXUSz+dtpghcnMjojYKk9m3jlY40kl6s2998/AOOBfAe644441c+bMmVS8Z9AGXJ6ZV0fEbcCPIuIs4I8UX0sPxXoPJ1Hcu08CZwzNS5PKFxE/oPileo+IWAF8BPhUR0fHj8aNGzfmkEMOOe/WW29dBXzzF7/4xdeOPfbYM2+++eaHNmzYsIbavZKZj3zmM5/54yte8YrvABuBizKztwuLS8NSD/fOMRExh+J/hi4H3gKQmXdGxI+Auyje187NzM5aP1u9x61du/b37Ph97xqKX/7vAjqB9zF4TyNIjXQE8DpgcUTcUTv3IeBT9OEzWmY+EhEfo/jFHhr3vtObz53voZiB8/9T3OtvpNqPdao59HTvnD7Q952K1BH+5z2VYvbzR4BRtWtfo5+/r0Xx2LUkSZIkSZLU/HxMUpIkSZIkSSOGxTBJkiRJkiSNGBbDJEmSJEmSNGJYDJMkSZIkSdKIYTFMkiRJkiRJI4bFMEmSpBEmIsZExF0RsdcA+/lsRJzTqFySJElDwWKYJElSE4mI5RFx7A6anQ3cmJkrBzjcZ4APRcToAfYjSZI0ZCyGSZIkjTxvBb470E5qxbT/Bk4ZcCJJkqQhYjFMkiSpSUTEd4FpwM8iYn1EvL+bNtOA5wK31J27NCIujoifR8QTEXFLROxbuxYR8fmIWBURj0fE4og4sK7LG4C/GdQXJkmS1EAWwyRJkppEZr4O+BNwcmaOy8xPd9PsIOC+zOzY5vxpwEeBicAy4BO188cDRwGzgGcBrwHW1v3cUuDghr0ISZKkQWYxTJIkaWSZADzRzfmfZOattSLZ94E5tfObgfHA84DIzKXbrDX2RK1PSZKkYcFimCRJ0siyjqK4ta0/1+0/CYwDyMxfAV8GLgZWRcSCiNi1ru144NHBiSpJktR4FsMkSZKaS+7g+u+BGRHR1usOM7+YmXOB2RSPS76v7vIBwO/6nFKSJKkkFsMkSZKay8MUC+R3KzNXUKwJdlhvOouIF0TECyNiFLABeBroqmtyNPCL/seVJEkaWhbDJEmSmssngQ9HxKMR8d4e2nwdeF0v+9sV+AbF45V/pFg8/58BImIvitli/z6QwJIkSUMpMnc0k16SJEnNJCLGAL8FXrbNYvh97eezwL2Z+ZWGhZMkSRpkFsMkSZIkSZI0YviYpCRJkiRJkkYMi2GSJEmSJEkaMSyGSZIkSZIkacSwGCZJkiRJkqQRw2KYJEmSJEmSRgyLYZIkSZIkSRoxLIZJkiRJkiRpxLAYJkmSJEmSpBHj/wH/jG0m4sfnjQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seq = Sequence(reg, Chadoq2)\n", + "seq.declare_channel('ch0', 'rydberg_global')\n", + "duration = 2500\n", + "pulse = Pulse.ConstantPulse(duration, 2*np.pi, 0., 0.)\n", + "seq.add(pulse, 'ch0')\n", + "seq.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now run the noiseless simulation, to obtain a `CoherentResults` object in `clean_res`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "sim = Simulation(seq, sampling_rate=0.05)\n", + "clean_res = sim.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we obtain the excited population using the projector onto the Rydberg state." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "obs = qutip.basis(2,0).proj()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(clean_res._sim_times, clean_res.expect([obs])[0])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The SimConfig object" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each simulation has an associated `SimConfig` object, which encapsulates parameters such as noise types, the temperature of the register... You may view it at any time using the following command." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Options:\n", + "----------\n", + "Number of runs: 15\n", + "Samples per run: 5\n" + ] + } + ], + "source": [ + "sim.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When creating a new `SimConfig`, you may choose several parameters. `'runs'` indicates the number of times a noisy simulation is run to obtain the average result of several simulations, `'samples_per_run'` is the number of delivered samples per run - this has no physical interpretation, this is used simply to cut down on calculation time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also add `SPAM` noise to the simulation by creating a new `SimConfig` object, and assigning it to the `config` field of `sim` via the `Simulation.set_config` setter. We pass noise types as a tuple of strings to a SimConfig object. Possible strings are : `'SPAM', 'dephasing', 'doppler', 'amplitude'`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "config_spam = SimConfig(noise=('SPAM'), runs = 30, samples_per_run = 5)\n", + "sim.set_config(config_spam)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now show the new configuration to have an overview of the changes we made." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Options:\n", + "----------\n", + "Number of runs: 30\n", + "Samples per run: 5\n", + "Noise types: SPAM\n", + "SPAM dictionary: {'eta': 0.005, 'epsilon': 0.01, 'epsilon_prime': 0.05}\n" + ] + } + ], + "source": [ + "sim.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that `SimConfig.spam_dict` is the spam parameters dictionary. `eta` is the probability of a badly prepared state, `epsilon` the false positive probability, `epsilon_prime` the false negative one." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When dealing with a `SimConfig` object with different noise parameters from the config in `Simulation.config`, you may \"add\" both configurations together, obtaining a single `SimConfig` with all noises from both configurations. This adds simulation parameters to noises that weren't available in the former `Simulation.config`. Noises specified in both `SimConfigs` will keep the noise parameters in `Simulation.config`. Try it out with `Simulation.add_config`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Options:\n", + "----------\n", + "Number of runs: 30\n", + "Samples per run: 5\n", + "Noise types: doppler, SPAM, dephasing\n", + "SPAM dictionary: {'eta': 0.005, 'epsilon': 0.01, 'epsilon_prime': 0.05}\n", + "Temperature: 1000.0µK\n", + "Dephasing probability: 0.05\n" + ] + } + ], + "source": [ + "cfg2 = SimConfig(noise=('SPAM', 'dephasing', 'doppler'), eta=0.8, temperature=1000, runs=10000)\n", + "sim.add_config(cfg2)\n", + "sim.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we set the temperature in $\\mu K$. We also observe that the `eta` parameter wasn't changed, since both `SimConfig` objects had `'SPAM'` as a noise model already. This feature might be useful when running several simulations with distinct noise parameters to observe the influence of each noise independtly, then wanting to combine noises together without losing your tailored noise parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting evaluation times" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a `Simulation` field, `eval_times` refers to the times at which the result have to be returned. Choose `'Full'` for all the times the Hamiltonian has been sampled in the sequence, a list of times of your choice (has to be a subset of all times in the simulation), or a real number between $0$ and $1$ to sample the full return times array. Here, we choose to keep $\\frac{8}{10}$ of the Hamiltonian sample times for our evaluation times." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sim.evaluation_times = .8" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now obtain a `NoisyResults` object from our noisy simulation. This object represents the final result as a probability distribution over the sampled bitstrings, rather than a quantum state `QObj` in the `CleanResults` case." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "res = sim.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting noisy and clean results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new `res` instance has similar methods to the usual `SimResults` object. For example, we can calculate expectation values. Observe how different the Rydberg population in the clean case and noisy case are : we clearly see a damping due to all the noises we added." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(res._sim_times, res.expect([obs])[0])\n", + "plt.plot(clean_res._sim_times, clean_res.expect([obs])[0])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also use the `SimResults.plot(obs)` method to plot expectation values of a given observable. Here we compute the `sigma_z` local operator expectation values. You may choose to add error bars using the argument `error_bars = True` (`True` by default for `NoisyResults`.) Be wary that computing the expectation value of non-diagonal operators will raise an error, as `NoisyResults` bitstrings are already projected on the $Z$ basis." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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ASNoIrCSZqqLZnwAfAt47w9c3sx5pLgXMGxLzhofYt89VmnXRMRBI+kJEvF7SHSRzC+0nIl44zWsvBHY1bU8Ap7S8xzJgcUT8o6SOgUDSamA1wMjIyDRva2Yz1dyxYd9UcN7yxSw8/JDKVmnWqQ0ki24lgnem//5WHm8saYiklHHRdOdGxDpgHcDo6OgBQcnM5qa1Y8M5yxZV9gfSbSAH6hgIIuLH6cOLI+J9zcfSGUnfd+Cz9nMvsLhpe1G6r+FQ4ETgm5IAfgXYJOnsKjUYm5VBnTo2uFv3gYYynHNWm32vzPC8rcBSScekI5NX0TRVRUQ8GhELImJJRCwBbgEcBEpm287drN0yzraduwedFJujk48+gjVnHFf5H8VG6WdYuA0k1a2N4O3AxcCxkm5vOnQocPN0LxwRk5IuAW4g6T66ISLulHQlMBYRnr+o5FzEtjKqU+knq25tBJ8Frgf+DLi0af/PIuLhLC8eEZuBzS37Lutw7ulZXtOKw0VsKyt3695ftzaCR4FHgfMBJD0bOBh4pqRnRsSP+pNEK6q6jZx2TxOrqiyTzr2GpHfPvwfuJ1m7+AfA8/NNmhVdnYrYrgazKssysvhPgVOBr0fESZLOAN6Qb7KsLOpSxHY1mFVZll5DeyPiIWBI0lBEbAFGc06XWaG4p4lVWZYSwSOSngl8C7hW0v3AL/JNllmx1KkarJnbReohSyBYCTwOvJtk5tFnAX+cZ6KsnKr+o1GXarAGt4vUR5aqocsiYioiJiPiUxHxUaYfVWw10/jRuPrGu7hw/S0eYFYB7dpFrJryHFlsNeIfjepxu0h9ZBlZ/Nw2I4v/d94Js3Kp25iCOqhru0gdKaL9ZJ6SngUcwRxGFudhdHQ0xsY8HVERVb2NwKzMJG2LiLY9PqcdWSzpGuDhiPhZ+mKHSTolIiq5olgv1PUHsW6NqVY8df3uzVWWXkP/A1jWtP3zNvss5Z4WZoPh797sZWksVjTVH0XEFNkCSC250bTcPK12Z90+myJ8bv7uzV6WH/Qdkt5BUgqApAF5R35JKjc3mpaX7yg76/bZFOVz83dv9rIEgrcBHwU+QLJ28T+Trh9sB3JPi/LyfEKddftsivK5+bs3e9MGgoi4n2R1McvIjabl5DvKzrp9NkX63Np999yAPL2O3UefPEE6nqRa6N9FxImSXkiypOSfTvvi0grgGpIVytZHxFUtx98GrAH2kTRCr46I7d1e091HLU/+0eis22dT1M+tKNVWRdCt+2iWQHAT8F7gryPipHTf9yPixGmeNwzcTTIyeYJkDePzm3/oJR0WET9NH58NXBwRK7q9rgOBmWW1dss4V994F1MBw4L3vOJ5rDnjuEEnayC6BYIsvYaeHhH/1rJvMsPzlgPjEbEjIvYAG0kmsHtSIwiknkHSBlFaReg5UQT+HOqj6HntaTKyydJY/KCk55L+SEs6F/hxhuctBHY1bU8Ap7SeJGkN8B7gIOBl7V5I0mrSBuqRkZEMb91/LoIm/DnURxny2g3I2WQpEawB/hr4VUn3Au8i6UnUExGxNiKeSzKj6Qc6nLMuIkYjYvSoo47q1Vv3lPswJ8r0ORT9brboypLXJx99BGvOOM5BoIssvYZ2AGdKegYw1JhqIoN7gcVN24vSfZ1s5KmxCqVTpJ4Tg1SWz6EMd7NFV5a8tullWbz+SOBy4D8AIelfgSvT5Su72QoslXQMSQBYBVzQ8tpLI+KH6eargR9SUi6CJsryORSl73uZlSWvbXpZ2gg2kixTeU66fSHweeDMbk+KiElJlwA3kHQf3RARd0q6EhiLiE3AJZLOBPYCu4E3zu4yisHjBxJl+Bx8N9sbZchrm16W7qMHdBWVdEdEvCDXlHXg7qPWK0Xt+26Wh7l2H71R0ipJQ+nf60nu8s1KrdGICLjR2GotS9XQW0h6Cn0m3R4GfiHprUBExGE5pc0sd240NstQIoiIQyNiKCLmp39D6b5DHQSs7MrSBdIsT9MGAklvatkelnR5fkky6x+PPDXLVjX0cknnAG8CjgT+Frgp11SZ9Ym7QJplG1B2gaTzgDuAXwAXRMTNuafMrE/cBbK3itIbqyjpKIMsA8qWAu8Evgj8GvDbkr4TEY/lnTgzK5eiNL4XJR1lkaX76FeA/xYRbwV+k2T079ZcU2VmpTSoxvfWeaPcCWBmsrQRLG9MF50uYn+1pK/kmywzK6NBjNhud/fvkeMz0zEQSPqDiPhwRPxU0usi4n82Hb4I+MPcU2dmpTKIxvd2d/9rzjjOnQBmoFuJYBXw4fTx+4HmQLCCmgcCN0SZtdfvxvdOd//uBJBdt0CgDo/bbdeKG6LMisNdgOeuWyCIDo/bbdeKpzA2Kxbf/c9Nt0DwIkk/Jbn7PyR9TLp9cO4pKzA3RJn1n6tj89MxEETEcD8TUiYuipr1l6tj85Wl+6i14aJoOfmuspxcHZuvXAOBpBXANSRTV6+PiKtajr8HeDMwCTwA/F5E7MwzTVZfvqssL1fH5iu3QCBpGFgLnAVMAFslbYqI7U2nfQcYjYjHJL2dpLvqeXmlyerNd5Xl01yCc3VsfvIsESwHxiNiB4CkjcBK4MlAEBFbms6/BXhDjumxASpClYzvKvsra553Oq9dCa6xopz1Vp6BYCGwq2l7Ajily/lvAq5vd0DSamA1wMjISK/SZ31SlCoZN/L3T9Y873aeS3D9k2XSudxJegMwCvx5u+MRsS4iRiNi9Kijjupv4mzOijQBWGOdYv+g5Ctrnnc7z4sG9U+eJYJ7gcVN24vSffuRdCbwR8BvRsQTOabHBsRVMvWTNc+7necSXP8omVA0hxeW5gF3Ay8nCQBbSRa1ubPpnJOA64AVEfHDLK87OjoaY2NjOaTY8lSENgLrr3Z5nnWf9Z6kbREx2vZYXoEgfeNXAR8h6T66ISI+KOlKYCwiNkn6OvAC4MfpU34UEWd3e00HArNymq7dYK6Ny9Zdt0CQ6ziCiNgMbG7Zd1nT4zPzfH8zK45ujb+9aFy22StEY3FZtK6CZGbZdWv87UXjss2ep5jIyHciZnPTrfG3F43LNnsOBBm5T7PZ3HWaoytrDyH3JMqHA0FGvhPpjX429LlRsVyyTuToCR97z4EgI9+JzF0/q9dclWeWnRuLZ8CjUudmtg19s2mkd6NisbnjRbG4RGB9M5vqtdne2bsqr7hcWiseBwLrm9lUr822kd5VecXljhfF40CQgRsde2emDX1zubN3o2IxubRWPLlOMZGHfk8x4WLs4DkQV4/ztP8GNsVEFbgYO3i+s68e52mxuNfQNDwnuplVnUsE03Cjo5lVnQNBBi7G5sP1xGbF4EBgA+FGeLPicBtBBx75mK/mRvg9e6f4yNfv7tln7bwzm5lcA4GkFZLukjQu6dI2x18q6TZJk5LOzTMtM9G4W736xru4cP0t/kHJQaMRfgiYAm4ef7Ann7XzzmzmcgsEkoaBtcArgROA8yWd0HLaj4CLgM/mlY7Z8Dw1+Ws0wp+2dAFD4oDPerq7+k7HnXdmM5dnG8FyYDwidgBI2gisBLY3ToiIe9JjUzmmY8Y88rE/Tj76CN515vFsvefh/T7rLGvbdjruvDObuTwDwUJgV9P2BHDKbF5I0mpgNcDIyMjcUzYNdxntn3af9dot410H8XUb5Oe8M5u5UvQaioh1wDpIppjox3u6y2j/tH7W093VT3fceWc2M3kGgnuBxU3bi9J9Zl1Nd1fvu36z3sozEGwFlko6hiQArAIuyPH95sSDm4ql3V19ax61ths4/8xmJ7dAEBGTki4BbgCGgQ0RcaekK4GxiNgk6cXAPwBHAK+R9McR8fy80tSJBzcVX7c8cv6ZzU2u4wgiYnNEHB8Rz42ID6b7LouITenjrRGxKCKeERFHDiIIgLsclkG3PHL+mc2NRxbjGUbLoFseOf/M5sYL06Rcx1x83fLI+WfWXbeFaRwIzMxqoFsgcNWQmVnNORCYmdWcA4GZWc05EJiZ1ZwDgZlZzTkQmJnVnAOBmVnNORCYmdWcA4GZWc3VOhBMty6umVkdlGKFsl7btnM3X7xtguu2TTC5z1MXm1m91S4QNOauf2LvFI1Zltqti2tmVhe5BgJJK4BrSBamWR8RV7UcfxrwaeBk4CHgvIi4J4+0NGanvO+Rx9kz+VQQEJ662MzqLbdAIGkYWAucBUwAWyVtiojtTae9CdgdEcdJWgV8CDiv12lpXsFq3pCYNzzEvn1TDA+J140u5rXLFrk0YGa1lWeJYDkwHhE7ACRtBFYCzYFgJXBF+vg64GOSFD2eG7t5Bat9U8F5yxez8PBDPHe9mRn5BoKFwK6m7QnglE7npGscPwocCTzYy4Q0VrDaOznF/HlDnOMSgJnZk0rRWCxpNbAaYGRkZMbPP/noI7j2zad6BSszszbyDAT3Aoubthel+9qdMyFpHvAskkbj/UTEOmAdJCuUzSYxJx99hAOAmVkbeQ4o2woslXSMpIOAVcCmlnM2AW9MH58LfKPX7QNmZtZdbiWCtM7/EuAGku6jGyLiTklXAmMRsQn4JPAZSePAwyTBwszM+ijXNoKI2Axsbtl3WdPjXwKvyzMNZmbWXa3nGjIzMwcCM7PacyAwM6s5la2TjqQHgJ2zfPoCejxYrQR8zfXga66HuVzz0RFxVLsDpQsEcyFpLCJGB52OfvI114OvuR7yumZXDZmZ1ZwDgZlZzdUtEKwbdAIGwNdcD77mesjlmmvVRmBmZgeqW4nAzMxaOBCYmdVcJQOBpBWS7pI0LunSNsefJunz6fFbJS0ZQDJ7KsM1XyTpAUnfTf/ePIh09oqkDZLul/T9Dscl6aPp53G7pGX9TmOvZbjm0yU92pTHl7U7r0wkLZa0RdJ2SXdKemebcyqT1xmvt/f5HBGV+iOZ6fT/AscCBwHfA05oOedi4BPp41XA5wed7j5c80XAxwad1h5e80uBZcD3Oxx/FXA9IOBU4NZBp7kP13w68NVBp7PH1/wcYFn6+FDg7jb/tyuT1xmvt+f5XMUSwZNrJUfEHqCxVnKzlcCn0sfXAS+XpD6msdeyXHOlRMS3SKYu72Ql8OlI3AIcLuk5/UldPjJcc+VExI8j4rb08c+AH5AscdusMnmd8Xp7roqBoN1aya0f5H5rJQONtZLLKss1A5yTFp2vk7S4zfEqyfqZVM1LJH1P0vWSnj/oxPRSWoV7EnBry6FK5nWX64Ue53MVA4G19xVgSUS8EPgnnioRWXXcRjKfzIuAvwK+PNjk9I6kZwJfBN4VET8ddHryNs319jyfqxgIZrJWMt3WSi6Raa85Ih6KiCfSzfXAyX1K26Bk+X9QKRHx04j4efp4MzBf0oIBJ2vOJM0n+VG8NiK+1OaUSuX1dNebRz5XMRDUca3kaa+5pc70bJK6xyrbBPxO2qPkVODRiPjxoBOVJ0m/0mjrkrSc5Ptd5hsc0uv5JPCDiPiLDqdVJq+zXG8e+ZzrUpWDEDVcKznjNb9D0tnAJMk1XzSwBPeApM+R9J5YIGkCuByYDxARnyBZIvVVwDjwGPC7g0lp72S45nOBt0uaBB4HVpX8BgfgNOC3gTskfTfd94fACFQyr7Ncb8/z2VNMmJnVXBWrhszMbAYcCMzMas6BwMys5hwIzMxqzoHAzKzmHAisViQd2TRr408k3Zs+/rmkj+f0nu+S9DuzeN5Bkr6VDno0y427j1ptSboC+HlE/Pcc32MeyZQAy9J5rWb6/MtJJhS8tueJM0u5RGDGk3O8fzV9fIWkT0n6F0k7Jb1W0ocl3SHpa+kUAEg6WdJNkrZJuqHDjJcvA25rBAFJ35Q0mj5eIOme9PHzJf1bWjq5XdLS9PlfBi7M9+qt7hwIzNp7LsmP+NnA3wNbIuIFJCM5X50Gg78Czo2Ik4ENwAfbvM5pwLYM7/c24JqI+HVglGQGTYDvAy+ew3WYTct1j2btXR8ReyXdQTJtx9fS/XcAS4DnAScC/5RO+zIMtJvf5jlkm9fp28AfSVoEfCkifggQEfsk7ZF0aDo/vVnPORCYtfcEQERMSdrbNJfLFMn3RsCdEfGSaV7nceDgln2NRZDmN3ZExGcl3Qq8Gtgs6a0R8Y308NOAX87+Usy6c9WQ2ezcBRwl6SWQTB3cYYGQHwDHtexrVPWcTlKSQNKxwI6I+Cjwv4AXpvuPBB6MiL09vwKzlAOB2SykS4KeC3xI0veA7wK/0ebU60nWGm52pqStwJnAw5LeAbwe+H464+SJwKfTc88A/rHnF2DWxN1HzXIm6R+AP4iIH0r6JvBfI2Is43O/BFwaEXfnmUarN5cIzPJ3KUmj8Yykiwx92UHA8uYSgZlZzblEYGZWcw4EZmY150BgZlZzDgRmZjXnQGBmVnP/HxyNz2RaQCYIAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "res.plot(obs, error_bars=False, fmt='.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SPAM effects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare both clean and noisy simulations for the default SPAM parameters (taken from [De Léséleuc, et al., 2018](https://arxiv.org/abs/1802.10424))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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4CbgAuE9ELmiw2feBN5VSk4F7gd9ZpcdRnDkGxevquYXsaF8HBNJIlZ7hbIgsRYH/6Yhp9uoAXRK58jgc3G63FPexbzUMHg+de9mtpFmsHBFMBfKVUgVKqSrgDeCOBtsooGfgdS+g1EI9zqHwUx28G31t3SI72tcBMDQDkrvrOIEhouxYvZgy1Ze0Z7cC0as93xBfUTkf+Sfh84+Bvauium/XU1sDJeu1izWGsTJGkArsC3lfDFzSYJsfAh+IyH8A3YAmnWgi8gjwCMDw4cMjLjTm2P2RvvgOvbhuUVMdxaJCQpK+YzVxgsiiFOdVboaLrqHwrlttk1FXcZZuZFV9j+ztq/DG9jXLWRzcBtWnmuxNHEvYnT56H/AnpdRQ4GbgLyLSSJNS6mWlVIZSKmPAgAFRFxlVlNJT0UdeqS/CIdg2LT19up7hXFEc3f26mfI9cKLMdrfQOZej6Iqz+87Yqsd1FK/RzyE3dbGIlYagBBgW8n5oYFkoXwfeBFBKrQI6A/0t1BT7HC2AY0X2po02JFhuwpQrjhxFK/Vz2uW2yqjncvRAZtUqqGh4mhrazb610G0A9EmzW0mLWGkI1gJjRGSkiCSjg8ELGmyzF7gWQETORxuCQxZqin3qupHFkCEYeIH+MZs4QeQoXAFd+0F/e/P16xWi+8IAvJ5dsM9MIIwYxWt0fCBG5w8EscwQKKVqgCeAxcB2dHbQVhH5sYjcHtjsP4GHRWQj8DfgAaXivD/i7o+g9wjom263knN4PNpVtWe5aV8ZKYpWaLdQDFwg6lyOkzMgqRvsXW2zIpdw6rAe4Q+LbbcQWDyhTCm1CFjUYNnTIa+3AfYnUccKgQlGnH9bvQuEFU1K2kz6dNgyHw7tgIHnR2efbqWiWLv/Mh+zW0l9EhJhqNdkDkWK4rX6OcYzhsDMLI4tjuyGs8caZRhY0aSkzYTGCYwh6BhFgQttDMwfaMSwTPj0l1B5Ajr1sFuNs9m3RpdoGTLZbiWtYgxBjDBnSR77lr3Kc8kw4x9n2PVmbJSnDWqbu3QXHycPYtfCv/Pw28NiRpsjKVoBnXrBoPF2K2nM8ExQ/sCERlNfqiPs27Sc8pph3P70snrLY/G8MYYgRpg5YyxUVsPmnuSfHWJJqen2UjciefcD+q37O4U/u0G7EQzto2ilvuDa3AK0KZdjd06zqbMHz77VxhB0hNoahp3ZzrDMr1B48y2WlY+PFOZsjiWK10LqFFSF3dM7mmH4NHr4/qTjBINj8G7WCZw8BId3wqQv2a2keZfji3NNnKCjHNwK1adjfiJZkBi94sQhVafgwNbYnngyNEM/m7r17WdvYP5ADBSaa5bhmdo1VFtjtxLnss8ZE8mCGEMQK5Tm6vpCsfzD6ZvOMdUNStbZrcS57M2BxC6QMtFuJc0zPBOqTuq7WkP72LcGug+C3s4oiWMMQawQSDXzKd3BKGplptuAb+8xflqdhW/3frulOJcSnzYCicl2K2meYYGSYKYzXfspXqNv6kTq9RGJVYwhiBWK1+LrfhVZf9VlgKPacyAMgsXJ5vuvJOvAffjyTRmCtjBnSR6jZi3gzN4NvLqnj22VRsOi9zDomWoMQXs5eQjKC2HYVPv6iLQRYwhiAaWgeC05na+wp+dAGASLkyk8ujjZJlO3vi3MnDGW3d8eSRepYqM/ncLZt1A4+5aYSyOsY3imNgRmJnnbqSs0N9W+PiJtxBiCWKCiGE4eIHPUAHt6DoTBueJkiiRqyEza1erfGBpQsh6ATSqGyoc0x7BMOFEKFfta39ZQn7qJZJPs6yPSRsI2BCLS1UohcU0gPuCdOPFcAbBo9hwIg3PFyYTsfn/Ee3qF3ZKcR+l66NyLQjXYbiWt8tfSFAC+9exLdW6smHVlxRrFa2HwBEjqUr+oX4yd06G0Oo9ARKYB84DuwHARmQh8Qyn1uNXi4obidZDYGQaNxxvoQRCLP5i64mTpg3T1TEPbKFmvyw0cs7/QXGt8+fabYHsPfnNxJQs+JaYnQ8UUtdX6e/beX7fItj4ibSCcEcEc4AbgCIBSaiMQu12YnUjxWn2BaNCIJmZJ9Wq3wXHTWTRsqs/qblVDptitJDwSEvW8ERMwbhsHtkDNmdhOA2+CsFxDSqmGjsJaC7TEJzWVULbx3GQtJ5Aa0Fps5hOEzf7N4K/Bl6QNQaxmj9RjeCYc2EoPTtutxDnsC1QcdciM4iDhGIJ9AfeQEpEkEfkvdH8BQyTYvwVqK511BzH4IvAkmYllbaF0PT7/GLKW6PpCsZxKWMfwTEAx2WMSA8KmeA10Hwy9hrW+bQwRTq2hR4G56Gb0JcAHwDetFBVXBALFLxf04+evLaxbbEvPgWZoVJzsB0t5O3kY/TZ/yrAZNgpzEiXryUm8mKozOh0zmEoYy35jUjPw+cfhR0+KimmtNtHw3Pgk+WO2qjR2fKiX2d5HJEzEaQ3BMjIy1Lp1LroT/efXdYGv72yzW0nbWPT/YEM2PLXP9iqajuD5i/F1ziSr6GbOVvvpnOSJ6SwSCEwifGk5VSqB5KTEmNdrNxmzXmdd58dgxk/gsm/ZLacRIuJTSjXpg27VNSQifxSRPzR8RF5mnFK81lnxgSCpGVB9SlciNbTM2eNweBfeMcMdkUoYJKfgCFUqET+emJ4MFStM8QRSax0WH4DwYgT/BhYGHkuBnsBJK0XFDScP6paFTooPBBlqAsZhU5YLKEid7IhUwiCZ6f1IToAEaklKkJidDBUrTPHk69hZyiS7pbSZVmMESqn5oe9F5G/AZ5YpiieCF1EHGoI5vhoeVN147623eOof/euWx6L/03YCM4odkzoawDuiD9n3jCDnzWfJvPJmRxgvO5ns2QUpEyCps91S2kx7GtOMAQZGWkhcUrxWT0WP5ZLEzTDz+nFwIJP7Tuznqb1mwlGLlK6HPmnQta/dStqMd/yFjJm/hJ5VA4D77JYTu9RWM0EKYOjX7VbSLsKJEZwQkePBZ+Bd4H+slxYHlKyDQRdCUhe7lbSPoRlwaDtdOWu3ktimZIPjRgN1eDxs8Y8MuLcMzeHb4OMPtTfi6+y8+ACE5xrqEQ0h8YROOctjY6d1LKzN5LsxnFbWIqkZoPxcJHvsVhK7nDoMFXtZ3ufz3D8rNtODW2OTSmfa/iVQUxXbfRRswldUTtZbh6iqvZvkpYlkpzsv1bZZQyAiLd7CKKXWR15OfDBzxlhmehPhN6fZrEY6162S6gVgkiffZiExTCA+cNX0Gyh8IIbbU7bAFv9IPenx0HZHujGtJqfgCFW14CeB6loV+/NDmqClEcGvWlingGsirCW+KM0FYLN/pL06OkK3ftBnJJMOG0PQLKXrQTyOvoDWlc0u3eDo47CKzPR+JFNDFYkkJSY6MruqWUOglLo6mkLijrJc8CSRp5w1Fb0hvt7Xs/FgBQPNzNOmKVkP/cdBp+52K2kT9WfMDqRCdeXfby3g4NFpMe/KijbegZCd/DOerb6H/37oIUeeB2FlDYnIeOACoC4vSin1mlWi4oLSXBh0AVV7HFJxtAl8ReVk7byCKr/iT6/kkP1w7E+SiipK6RHBmOvtVtJmZs4YW/+C/9pUss4cAWMEGlO2Ea9nF0lS69jffzj9CJ4BpqMNwSLgJvQ8AmMI2otSULYR37CvAji2joueeerBD1TXOqB2TrSpKIZTh3SJcaeTMonalc9z/qy3qaL+zYsTAt6WEnDzbvGn2SqjI4QzIrgLmAhsUEo9KCKDgL9aK8vllBfiOz2ArG2XALoSpRNKDjREzzz1UF1TTZKYmaeNKA3kU6Q6NHU0lCGTSVA15P3HcEidQtqshc5Ncog0pRug93CO7XdugmU4JSbOKKX8QI2I9AQOAs52bNtN2UZy/OdT5dedqpxax8U7og/ZD2fyYML7ZI/60HGGzHJKfLrkwKDxdivpOMFRjZlP0JiyXEeWlQglHEOwTkR6A68APmA9sCqcDxeRG0Vkp4jki8isZrb5oohsE5GtIvJ6uMIdTVkumQl5jmhq3RreEX2Y4CnAe3yp3VJij5L1MHg8JHayW0nH6T0cuvTVd7+GOn733jooL+TZTXpSqFN7O4czoSzYm/glEXkf6KmU2tTa34lIAvACMAMoBtaKyAKl1LaQbcYATwGXKaXKRSQ+SleU5uJNSSb7pkzufHGlI91CoWzxp3F7xSo4dUSnlBrA79ed5y66224lkUEEhkwyhqABj487Cavhvx+8h/8efa3dctpNOCUmFojIl0Skm1KqMBwjEGAqkK+UKlBKVQFvAHc02OZh4AWlVDmAUupgW8Q7EqX0UHLIJEdVomyJzcE8c+M2OMeRfKg87o74QJAhk+Hgdt1/2aAJBIqdnhAQjmvoV8DlwDYR+aeI3CUi4ZTXSwVCex0XB5aFMhYYKyIrRCRHRG5s6oNE5BERWSci6w4dOhTGrmOYY3vhTLnjfYqhbA1mS8S5IZizJK/ONTBzjm7Zcf2bJx3nJmiWIZN13+VcPSqI+Vab0aAsF3oNd2RBwVBaNQRKqeUB91A68Hvgi+iAcSRIRFcznY4ubfhKIB7RUMPLSqkMpVTGgAEDIrRrmwheLIdMslNFRDlON11dM3h3FKfMnDGWwtm3UDj7FiZ4CiCpGx/87GH3pFYOmaz7Lr99GHBI32WrKc2FIc6fbR3OiAAR6QLcie5ffDHw5zD+rIT62UVDA8tCKQYWKKWqlVJ7gDy0YXAvpbm69PTAC+1W0iFC734B/n14EHu3rnLP3W8HmejZrcsxuKmNZ89UchIyqKrVb52a7RYxzhyD8j2uGN2HM6HsTbS//33geWB5IJ20NdYCY0RkJNoA3At8qcE2b6NHAn8Ukf5oV1FB2OqdSFkuDDjfkc0rQmk08/SzPPjwh8y8rH/zfxQv1FZzgRRBapOeTuciQmaKkLynlrPK4+hst4hQtlE/u2B0H86I4FVglFLqUaXUsjCNAEqpGuAJYDGwHXhTKbVVRH4sIrcHNlsMHBGRbcAy4P8ppdx7i6EUlOayRY2sdzft1JSzegTvioInRzxzcBudpdrxAcSm8I4eSnbSz0mkxvHZbh0m6OZNcf73HE766OL2frhSahG6LEXosqdDXivgO4GH+6nYB2eOMv7qKyl83GWzMoNVKcs2wqg4r1dY4qIZxQ0ZMhmv51kmSAHeEQ2TAOOM0lzoNcwVKdNhxQgMEcIlqWZN0rWvnnQU55lDAL6dBfyy+i58x3vbLSXyBNwgF3lMMyI9o9j5gWIwhiC6lOWCJOj2lG4kZVLcZw75isrJ2uLld7WfI+vV1e7LqumRAt0H6ayoeOZsBRwtcEV8AMLPGkoVkWkicmXwYbUwV1KaCwPPd26P4tZImaizKM4cs1uJbeTs2h+oyOpxZ1aNCAyZzPh4b08ajIW5ID4A4WUN/R9wD7ANCCSOoYBPLNTlPoIzisfeZLcS6wjeHe3fBCPj814hs9cxkqnhLB6SEhPcmVWTMonROxdD1SlI7ma3Gnuoc/NOslNFxAhnRPA5YJxS6mal1G2Bx+2t/ZGhARXFcPqIa344TRLMHIpj95CXbWQn/5yenHJdVk1w7sjXl1STIIo7n/m987Pd2ktZLvQcCt3ckS4dTj+CAiAJqLRYi7upSzWbZKcKa+nWX58c8RwwLsvF2+0wx6u6ucoIQMjckeNT4LlfMf9z3eASl2W/hUtprqtu6sIxBKeBXBFZSogxUEp9yzJVbqQ0VweKB7ugNn1LDJkU33MJyjZqY18udiuxjp46YBy3I7+zFXB0N0y8z24lESMcQ7Ag8DB0hLJcGHCeewPFQVImwY5/w9nj0Lmn3WqiS00VHNgGl35TR9TcTMrE+B35lQUKMMfTiEAp9WcRSUaXfwDYqZSqtlaWywjMKGbsDXYrsZ5gXvX+TZB2ub1aos3BbeCvdk1ueYukTIL8D6HqNCR3tVtNdHGhmzecrKHp6CJzhYAAw0TkfqWUyRpqgTlL8pi7dBcAgzlCTufDPL02kT7d8txTjbIpgndJZRvjzxDUqz2z3U4l1jNkEig/HNgKwy62W010Kc2FnqnQ3eGVkEMIxzX0K+B6pdROABEZC/wN8FopzOmEFmV7+Ls/AuDHj34ZhrnYCAB0Hwg9hsSl/3jj2uWMVF2Z8Ow2QOpqST157Rj3Gf+6kiK58WcIynJdVx0gHEOQFDQCAEqpPBFJslCT6xjv2QPicUcT8zDYnTgKtfEzrlu7sN5yV14QQ5iYUAgjvRQ+cKvdUqynZyp07R9/cYKzx3X3uQn32q0kooRjCNaJyDzgr4H3WcA66yS5j4tkjw4Ux4kvddTEy+Hjzyj80ZWkPfMJhbPjIMWwthr2b4GpD9utJDrU9TCOswyx/e4LFEN4E8oeQ+dAfCvw2BZYZggHpXSBrngIIAZJmQQofWGMFw7nQW2lqwKIrZIyEQ7FWQ/jUt2m023fczhZQ5XAc4GHoa2cKGOAVLjuh9MidQHjXGC4jUKiSDAmEm8G318DB7dCapyEDF0YKIYWDIGIvKmU+qKIbEbXFqqHUmqCpcpcgi93Azk1t5PpuTB+ous9BodMOHKnIQjNCgN4JvEd7k7ozCsb/My83kZh0SRo9Epz48IQzFmSx+2bVrBbDeGRWefiX26IfbU0Ingy8BwHkS9r8BWVk/W+nyr/3SQvOEn2oHLXlR1olpRJgXRKd5alCs0KS5u1kAdHVoBMZub159msLIr0Hg5d+sRNwHjmFYNhRRmjrv4avIerYl/NxgiUUmWBl48rpYpCH8Dj0ZHnbHIKjlDlBz8JVNe6sCRxSwyZhO9ALYlUu68mfwM8+HUQMZ7cQqADxnUGPw4IBopd6OYNJ1g8o4llLq6lHDky0/uRTA2CP+4affsSJpJV9RQ1JJI1L8fVxmCklEH1aVdeIFolZaIuq1ETBzUpXVZ6OpRmDYGIPBaID4wTkU0hjz3ApuhJdC7ePmfJTv4ZU2SX60oSt0bOqRSqSATEnQ1aQqhr0hJvIwLQF0V/tS6v4XbKcvVkye4D7VYScVqKEbwOvAf8LzArZPkJpdRRS1W5hbJcvJ5dCCqujABA5vlpJC8vohIhKTHR1aOhizx7ILEL9Hd2wLBdBEdBZRtdN9u2ES4rPR1KSzGCCqVUoVLqvkBc4Aw6e6i7iLgzFSTSlOYCwjaVZrOQ6ONN60t2+mLuT1js6tGQr6icfJWKr9cMSAhnfqbL6JMGnXu5v6RI5Qk9ozhlUp2b003uzlZjBCJym4jsAvYAy9HF596zWJc7KMuFAeM4TWe7ldiCd1Qq30/MxjvEncfvKyona14Ob9ZeRVbZ3a66MISNSHyUpC7bBCh8iRPJmpcD4KrYVzi3MD8FMoEPlVKTReRq4MvWynIJpRsg/WrYZ7cQmxgyiUTx6xnGLixMllNwhKoav84KU4qcgiOuHfm0SMokWP0ScxdvY86yxk3t3ZBnHzR0OScHU1VTAlAX+3LDdx6OIahWSh0REY+IeJRSy0Tk11YLczzHy+DkAdf6FMMi6DN2aYXKzPR+JHsU1bV+khLcHQdpkZSJUFvFkxNqePIGnVufNmuhq/LsKc2FHilknj+C5BVlnK12VyZgOOmjx0SkO/AJkC0ic4FT1spyPu+8p2ce3vXOaUCfGHHX6LtnKodVz3P1WVyGd0Qfsidt5cmE+WR/LcMVd4btItTgu5VA6WnviD5kP5QJ4KrYVzgjgjvQgeKZ6MqjvYAfWSnKDdwx8BBsF/75w29Ap+52y7EHETb7R3K1iwOJ3tMrSErYy4R096UUhk2fkdCpp3snllWegMO7YPxdAHUXf7cYAQhvRPC0UsqvlKpRSv1ZKfUb4H+sFuZ4AoHieDQCc5bk1Y2ANquR1B7Yxnmz/uW+0ZBSUJbLFv9Iu5XYi8cDgye4N3Nof6DcmovdvOGMCGbQ+MJ/UxPLDKGU5kL6dLtV2EJoHR52AG+8zY5vDnVfd7ajBXC2glw1ii/ZrcVuhkyCtfOgtsZ9abR1lWUn2anCUsKZWXxeEzOLN0dPogM5XgYn97v6DiJsgv5jN8YJSnwAbPKPsllIDJAyCWrOwuGd7suzL8uFHinQY5DdSiyjJdfQ68BtwDuB5+DDq5TKioI25xIMmrn4DiJseqRAt4EuNQTrIbELu1Sq3UrsJ1Bew7dps/vy7EtzXX8utzqzGJgLHA2pPFojIpeE8+EicqOI7BSRfBGZ1cJ2d4qIEpGMth5ATBKYUczgi+xWYj8ielTgMv/xnCV5rFu1lLVVw6glIT6zwkLpNxqSu5Oz+xBVNX4Ad9SYqjypu88NmVQv9gXuygQMx5n3IjAl5P3JJpY1QkQSgBfQMYZiYK2ILFBKbWuwXQ9074PVbdAd28RxoLhJhkyG/CVQdQqSu9mtJiLMvGYkrN4HGV+j8EYX5cu3F48HBl9E5um1JCeOdk+efTBQnDKJmePGOn9iXDOEkzUkSqm6DmVKKT/hGZCpQL5SqkApVQW8gU5FbchPgP8D3NP4NA6Gkm1iyCRQ/sBJ5RIOboeaM5Da4v1QfJEyCW/FErK/ricPuiLPPujmdXm8LxxDUCAi3xKRpMDjSaAgjL9LpX5xheLAsjpEZAowTCm1kBYQkUdEZJ2IrDt06FAYu7aRE/tNoLghQaPopjhB6Xr97PaKm21hyCSoPo23qz5HHW8EQN/UdR+s26+6mHAMwaPANKAEfTG/BHikozsWEQ/wHPCfrW2rlHpZKZWhlMoYMCDGm0bHQapZm+mZok8mNxmCkvXQuTf0TbdbSewQ7MfgpollZblxcVPXqotHKXUQuLcdn10CDAt5PzSwLEgPYDzwsYgADAYWiMjtSql17dhfbFC6ARMobgK3BYxL12u3kP7tGkD3Y0jqGvier7BbTcepOqUDxRd8zm4llhNOGeqxIrJURLYE3k8Qke+H8dlrgTEiMlJEktHGZEFwZSArqb9SKk0plQbkAM42AmACxc0xZLI+qSpP2K2k41Sd1u0Zh5j4QD08CfoGyC01h/Zv1rGtOBgRhOMaegV4CqgGUEptIowRglKqBngCWAxsB95USm0VkR+LyO3tlxzjmEBx0wyZBCh3BIz3bwZVawLFTZEyEco2IfjtVtJx4sjNG072T1el1BqpPwSuCefDlVKLgEUNlj3dzLbTw/nMWGTOkjzmLt3FAMpZ23k/P/Il8cc1C91Rhz1ShAaMR0yzVUqHqQsUG0MQypwleZSsEH6ZdIp0KavLt3fseVCWC90H6RiXywnHEBwWkVHoNpWIyF1AmaWqHEZdbZ2d78Pf4JlHsnhmxKV2y4otegyCnqnuCBiXrNdNzOPgAtEWZs4YCxPuhxd/z9J7e8FEh8+vKM2Nm6ywcFxD3wR+j645VAJ8G51JZGhIWS5+ZQLFzZIyyR0B4xKfcQs1x4DzIKlbXR0mx1J1Cg7vjAu3EIRhCAITwq4DBgDnKaUuD5SaMDSkdAP5aogJFDfHkMlwZBecPW63kvZz5hgc3R03d4ptxpOg40FONwRxFCiG8LKG+onIb4BP0amec0XE4fPGLaI0l80qzmvTt0TwpNq/yVYZHSLo2jIjguZJ9ervuKbSbiXtJ44CxRCea+gN4BBwJ3BX4PXfrRTlSCqK4eR+U5K4Jdwww9jMKG6dVC/UVsGBLXYraT9xFCiG8AxBilLqJ0qpPYHHTwH3FuZuL/vW4POP4f3ai91RetcKug+AXsOcbQhK1kPfUdDFBeUTrGJooIhwsYPdQ3GWBh6OIfhARO4VEU/g8UX03ABDCL4t28mq+i4H6OOeOuwRJFjC9/2jgynYtMK5JXxL1hu3UGv0TNV30yUOnRtadVoHiuMkPgDhpY8+jM4U+kvgfQJwSkS+ASilVE+LtDmKnL0nqCIJkLo67K4ouhUh6lJsP9kOH/2Ewmcugy697ZbVNk7shxOlZv5Aa4hAaoZzA8ZluYFAcfx8z+FkDfVQSnmUUkmBhyewrIcxAgGqz5B5ahnJHl2t2xV12K0ieDcd9LU7iZKA5lSvvTqcwFAvHMmHMw4cGRev1c9DL7ZXRxQJJ2vo6w3eJ4jIM9ZJciCluXhlB9k3JgAuqcNuFUMvplYJ7HVgH6ISH0iCmScSDkFjWeJAg79vja4q2y1+bubCiRFcKyKLRCRFRMaji8P1sFiXsyheA4DXqzt4GiPQAp16sEMNh305ditpO6XrYeAFkNzVbiWxz5DJgDjPPaSUHhHE0WgAwitD/SURuQfYDJwCvqSUWmG5MidRdwfR324ljsDnH8uFxaugtgYSwglTxQBK6Wyn891bLzGidO6ly1I7zRBUFMPJA3FnCMJxDY1B9xSeDxQBXxERc0sURCltCIZOtVuJI/AVlfO32qvxnU2Bg1vtlhM+5Xu0v9tkDIXP0AwoXqfPEYewcNE7ANzyVmVdZpsjs9vaSDi3Y+8C31RKLRVdgvQ76F4DF1qqzCmUF8KpgzDMGILW8BWVkzUvh7NqBFlV3yV7wwa8wa5WMUawomyQ2z0r+U0y/HVvP75sYsXhkToFcrPhWBH0SbNbTVjc0rcEEruw8AffIO17H1A42+GF88IkrCb0SqnjoHNFgV+JyLvWynIQwQwDYwhaJafgCFU1fkCoJpGc3QeI1WtqXborkDZrIb+5UsG6znz59httVuYcsosHkgU88Yt5/Nt/rhpvTJelLl6r4xsJSXYriSrNuoZE5L8BlFLHReTuBqsfsFKUo9i3mipPV9J/XVhXfz1ehpNtJTO9H8mJ+ieXJIrM08ttVtQGStfrpitxdoHoCFm33wSJnXn+St2kpnD2LRTOviV2jUBNpe63PCy+4gPQ8ojgXuDZwOungH+ErLsR+K5VohzFvjUkj7iYgvtvs1tJzOMd0YfshzK588WVZF9xBO+aFVBRAr1S7ZbWIgnU6gvElPvtluIsEpK08SzxAZfZraZ1yjbpGklxFiiGloPF0szrpt7HJ5Un4cBW4xZqA8HUWu+kQNE2B6SRjpESqD5tAsXtIdULZbkkhtfU0F6Cbt7UjLoSMfFSKqYlQ6Caed3U+/ikdL3uXTvsEruVOI9BF0FS15ifWOYrKieJanz+MXFVciBipHqh5izjpNhuJa1TvAZ6DcNX3pmsefoGJV7qhrVkCCaKyHEROQFMCLwOvjdTK0GnjcK5aouG8ElI1BeJGB4RBLOctqiRZFV9D99JM1GwzQTOjUmefJuFhEHxOhh6cUhSA3V1w9xOs4ZAKZWglOoZqCmUGHgdfG8iZqANQf+xpiRxexmeCfu3aBdbDBK8ICg8Ostpj/vvDCNO7xH4krwcUT1i+876eBlU7IOhF9dPaoiTumHhlJgwNIVSeihp4gNhESxDHZpZdf+HHu1ai9FyxZnp/UhO8JBALUkeFRcXhEjj23uMrJNP8oE/I7bdLCGF5oJJDRA/dcMcMr8/BglWVjQzisMiNC+/jrOXw+xn9cgqfbotulrCO6IP2Xf0JOedl8ic8cW4uCBEmpyCI1SpBPyxXp69eC0kJEPKBCAkqSEWtVqAMQTtJRgfMIHi9tO5ly7itjd24wTeyrV4ExeA91d2S3EkelQlVNfWkuRJiN1RVfE6neqa2MluJbZgXEPtpXjNucJahvYz/BJ9N+avtVtJ0xStJN8/BLoPtFuJI/GO6EP2g5N5MmE+2ZO3xuYddm21LigYh/MHghhD0F72rdFdmDzmX9ghhmVC5XE4uN1uJY3x18LeVazxn2e3EkfjHT2ESxJ24D2y0G4pTXNgC9ScievsP3MVaw9nK/SFy7iFOs7wwP8wFtNI92+GyuPk+M+3W4njWVl7oZ6dffqo3VIaUxxIVhg6tcmkhngoF2NiBO2heB2g4rImScTpPUI3Ot+7Gi5+yG41dcxZksfJj1/hB0mw2n9+3YUhpgumxTAr/Bcyk/lQ+BlcEGM9Hfatge6DoddQZs6QuPx+jSFoD8VrgUCDbkPHEIFhl1CR9xkTZzV2Hdh14Z05YywcLoeDI1n95Feivn+3sVGN1jPJ93wSM4YgWGr84+Tl7FDDefSpRUB8GntjCNrDvtU626VzT7uVuIPhmfTavoDC706BnimkzVpofx14vx/2roRx8VGP3grq93RI5OOzYxi6eiHvJj8SExfamTPGMnNqV5hzgL9UX2f/b85GLDUEInIjMBdIAOYppWY3WP8d4CGgBjgEfE0pVWSlpg7j92vX0Pgv2K3EPQzTk3fYtxou/JytUuo4tF3PE0lzQNXMGKXR3JEVu2HJD5h5SXf7RDUkfykAn/on2CzEXiwLFotIAvACcBNwAXCfiFzQYLMNQIZSagLwT86VvY5dDu3QWS4mUBw5UiZAYhdtCGKFwkBb7hHGEESM9Kv0855P7NURyu6l0GMIeWqo3UpsxcqsoalAvlKqQClVBbwB3BG6gVJqmVLqdOBtDhD730ZxsNCcmVEcMRKSdAG6WJpYVvQZ9BoGfUbYrcQ9DLpI1+WKFUNQWwMFH8Poa4j3yvpWGoJUYF/I++LAsub4OvBeUytE5BERWSci6w4dOhRBie1g31ro0hf6jbJXh9sYfgns34QvvwywuQ68UlC0EkZMs0+DG/F4IO0KKFgeGw3tS3xwtgJfz+uA+Ok90BQxMY9ARL4MZAC/aGq9UuplpVSGUipjwIAB0RXXkH2rdaE5ie87iIgzLBNfzUiy/rgesLkO/OFdcOqQcQtZQfpVcLwYjhbYrQR2L8XnH0vW0s5A/PQeaAorDUEJMCzk/dDAsnqIyHXA94DblVKVFuppN8FJJpNmvQFHdvHs1p5xMckkqgzNIMd/PlW1+k7R1jrwRZ/p57TL7dm/mxk5XT8XfGyjiAD5S8npfi1VtfHVe6AprDQEa4ExIjJSRJLRPZAXhG4gIpOB36ONwEELtXSImTPGUjj7FnIf7AXAfz/01dhuwu1EuvYls+9pkkXXHLK1DnzhCj3BqG+6Pft3M/1GQY8h9scJTh+F0vVkjhkcd70HmsIyQ6CUqgGeABYD24E3lVJbReTHIhKcUfILoDvwDxHJFZEFzXxcbJD/IadVJ9ODwCK8o1PJ7vYc4LevDrxSULRCxweM+y/yiGj30J5PdCq2XRR8DMqPN2Na3PUeaApL5xEopRYBixosezrk9XVW7j+iKAW7PmCF/0JmxGmpWssZnol3/Z8ZI6X2nZDle+BEmZk/YCUjr4KNf9PF3lJsyt/PX6qrBw+ZgjdBXwbj1QhAjASLHcHhXfiOduKFmjviNqBkOYG5GRd7dtqnoW7+gIkPWMbIK/XznuX27F8pPX8gfbrunW0whiBcfGs+Iavqu+Sq0XGdXWApfdOh1zCu8my0T0PRCujaHwaMs0+D2+mVCv1G2xcnOLhdj/pGXWvP/mMQYwjCJGfnPqpIgpCWe4bIMWdJHmlPLeK1I+dxhWcz42a9ZU9mVqGJD0SFkVfpuRq11dHf925dVuKVspFxWXK6Kcy4KBwqT5J5fDHJHi9n/fGdXWAVdXVpdiVD9hJ2fr0HjIlyCOloAVTshUu/Gd39xiPpV8G6V/WkruGZ0d13/lLoP46Hb72Sh2+N7q5jFTMiCIfCT/GynexbuwLxnV1gOWlX6HLFeU1OMreWrW/r53E3RX/f8UbaFYDoWcbRpOq0HomMdk6eSjQwhiAcdi2BpG54p14BxHd2geUkdYb0q2Hn+1EtQzBnSR5bl/yJ9f7RpP3flrh2E0SFrn11xlC04wRFK6C2MlBfyBDEuIZaQynIX6KHsiZtNDqMuxF2LoQDW2Hw+KjscuZkgRVFcMP/wjvEdW36qDHySlj9e32Xntw1OvvMXwqJnU35kAaYEUFrHM6DY3vNUDKajLlBP0fTPbTlX4DETj+EeGDkdKitgr2rorfP3Ut1MkBSl+jt0wEYQ9Aau5YAcNn8BJNdEC16DIIhUyBvcXT2pxRsmQ8jpuEr1xcIkx4cBUZcCp6k6M0nOLZP39iZtNFGGNdQa+QvgQHnseKbD9itJL4YdxMs+zmcPATdLa44e3AbHN6Jb/Q3yZqneyJkzcsxSQFWk9wNhl4cvThBIG2U0cYQNMSMCFqi8qTJMLCLsTcCCnZFYVSw5V8gHnJkElU1phJlVBl5JZTm6ragVpP/IfRMhQHnWb8vh2EMQUvs+UT7MMdcb7eS+GPwRfqkzXvf2v0E3UIjryTzvOGmEmW0Sb8KUFD4mbX7qa2Bgk9g1DVmsmATGNdQS+QvgeTuMPxSu5XEHyJs7JrJqG0LmTLr7cCsbs2T146JXAnwslxdaO6K7+Ad0YfshzK588WVxi0ULVIz9LyR3R/B+bdF9KPnLMlj7tJdAHhlJ/M7VfD46j6M6ZpnSsg3wBiC5lAKdn2op8InJtutJi6ZeM098Pp88h7qTtq8SmtSOrf8CzyJcJ6eYhq8+BsjECUSk7UbcOtbcOPsiKZo181WB+Z+/x8gHn73g+/ovsmGehjXUHMc2qHLDUS7zIHhHCOvhMQu1mUPKaUvQKOu1ROcDFEl2PnvKxvGwplyHn/6p5Zl413l2QSpXmMEmsGMCJpj3R90atu4m+1WEr8kdYFRV+PbvA24Cl9ReWTv1IvXQsU+uOb79dwIQF2qcETdUIZ61N2x+2+k5Eev8LsLtsGXfxLx/fh2FrGqdjy1/bx4I/7p7sAYgqY4dRjW/wUm3AM9BtutJq7x9buFrI161mnEUzq3zIeETjDuZmZO7Gku+Dbh23ec56of4Tt5/8RbUaLLVEfqs4vKyXptM1W1d5HsSyB7SoRvJlyCMQQNmLMkD8/y/+XJxDNcu3oiu3PMnaGd5KjxVLGX0PLfETmR/bW6yNyYGdC5Z8c/z9AufEXlZM3L4ax/PL6qsWR//DbeO9pe/bXhiC7ItFH9qKpV+EmgupbI/X5chjEEDZh5VSqsXwbDb2b3xlRTc8ZmMs8bQfLyvVT7a0lKSGx3SmfDC8Ulsp2/d9rPQv+lmG/YPnIKjgTmbgjVJPHxhu3cuerfQP0Uz9ZuxEIDw2mzFtadtz7farJ2V1NJEkmJ7f/9uB0TLG7I+r/AmXJ8o/RdiSk1YC/eEX3I/vL5PJnwL7LPW9Huu7mZM8ZSOPuWugvE3y8rgaSu3HLXg5GUa2gjmen9zs3dSBCm+3MofLRP3fcU/M7CHY0Hz9fgs3fvH8nu+hzJ1JiU4BYwhiCU2mpY9Ty+/p8j690TAKYtZQzgvWAM3eQs3t0vwpHdHfosX1E5gh/fxo06bTG5W4RUGtpDcO4GQPaDXryd98OGvza6oIdD0M0EgfN2RwFsmY/Xm0klycYItIAxBKFsfQsq9pEz6B5TaiAGCKYXps1ayEs1t3HGn8D8Xz/Z7vTC4IVCIWSdeALfwC9EWLGhPdTN3RidAuO/gG/zJrJeCbmgh2kMzrmZ9Hn75rsLobaKaz7T/adNscjmMTGCIErBirkw4DwyL76E5E1rOFvtN6UGbCTU7wvAB9u5c9ULMLl9JQLq+6MTyakeY9IJbaSplN3Jks51CeVU1dTS1gSBoJspeN5+sXoBjJ7BR19+2MKjcAfGEATJXwoHtsAdv8Ob1s+UGohFLvs2rP0DLP8/uHNes5s1l0HyxYyhJCd6qK6uJskDmWMGWSjW0BqNDD2AUvieu5PkwzWcVUltuhGrVyJk+km8n66BzPkWKHcfxjUUZMWvoccQuOhuwJQaiEm69YdLHoHN/4SDO5rdLBgYnv/YNADmPzaNwtm38OxdE8m+rpbvJP6D7JuNzzgmEcGbOZ3spJ/Sl4o234jpbRXe3S9A/7Gm90CYGEMAUOyDwk/h0seZs6ywzpcIxq8Yc0z7lg7wLp/d4maNAodF5VB1Cm/u97jVk4M30/SsjVkm3os3sZBfJb2Ed2iPsP4kNJ40RXZB6Qa+X3YZcz5sPDI0NEZUFBuER4KMjAy1bt26Dn1GQ9fB75J+zeWeLbyWuYgnbprcUYkGq1n6E/j0l9xQOZudani9VcF88xeW5fOrD3biV5AgcEl6P2YUPceDiYu5p/IHrFbn19veEBsEz80vJSzl50mv8kbNdGbVPMyT1zbhRmqOfzwA+R/Bf243WWEhiIhPKZXR5Lp4NARBfEXlfP3F93k1+Vd4r7wNrnsmIp9rsJjTR2HuREi/Cl/mb7nzxZXMf2xaPRdC3YzVaj+dkzxk39IF7/t3wNRvwM3P2ijeEDYf/RQ++QVcNQuufiq8vzmwFV66AjIfgxt+Zq0+h9GSIYhb11DwQlFBd7Kqvotv6FfslmQIl659IfNxfFt3kPWKbnzeMM2wXn76/RPxrn4S+ow0xt5JXP09mPRl7QZc98fWt9/2Drx6feD38Zj1+lxE3BqCYCqhwkM1SeSU+e2WZGgLmY+R45kcSDOsP98j6C++88WVAGz+05P4jxbxZupTxlXgJETgtl/D6Bmw8Duw872mt/PXwoc/gje/qttQfuMT6DU0qlKdTtymj2YOFpKpoRoxNUicSJfeZE6aSHJONWdJJikxoe47rJeWuOdT+PMHcMljfPGme2wUbGgXCUlw95/gz7fCPx6E+9+FYRefW3/6KMx/SDem9z4ANz0b0eY28YKlMQIRuRGYCyQA85RSsxus7wS8BniBI8A9SqnClj6zrTGCxjnlirsTlvP9xL+y3T+cX9Tcg0/pmYcmcOgc5izJ49WlG3k+6Tes949hhBykv1TQP2UYF44dC90HQfeB+k7RkwCProDkrnbLNrSXk4c49vx0/GeOcWvlzyhlACMo4y/JsxmScIzEW3+pDYGhWWwJFotIApAHzACKgbXAfUqpbSHbPA5MUEo9KiL3Ap9XSrV429aeYPG5wGEtnaWG7KSf4k0bALfNhQHmwu9oClfA5jfhxAE4GfLw1+j14oEHFsEI03fa8RzZje+lR8g68QRnSaQz1WT3fhnvl38KQ5u8vhlCaMkQWOkamgrkK6UKAiLeAO4AtoVscwfww8DrfwLPi4ioCFunnIIjVFUHpqwrDzlj/x/eL90NnrgNkbiHtMv0IxS/H86Ua4OQ3A36jLBHmyGy9BtFzvinqVpVAcHY3uTZeIdOsluZ47HySpgK7At5XxxY1uQ2SqkaoAJo5KwXkUdEZJ2IrDt06FCbRMxZkscvFu9EofDgpxYPv9jSnTlL89v0OQYH4fFAt34w6AJjBFzEnCV5/GLVSRSC4KcW4RfLSsxkzwhgpWvoLuBGpdRDgfdfAS5RSj0Rss2WwDbFgfe7A9scbu5z2zuPwFdUTk7BETLT+5nSAgaDgzHncvuwyzVUAgwLeT80sKypbYpFJBHohQ4aRxzviD7mR2MwuABzLkceK11Da4ExIjJSRJKBe4EFDbZZANwfeH0X8FGk4wMGg8FgaBnLRgRKqRoReQJYjE4f/YNSaquI/BhYp5RaALwK/EVE8oGjaGNhMBgMhihi6YQypdQiYFGDZU+HvD4L3G2lBoPBYDC0jMmfNBgMhjjHGAKDwWCIc4whMBgMhjjHGAKDwWCIcxzXmEZEDgFF7fzz/kCzk9Vcijnm+MAcc3zQkWMeoZQa0NQKxxmCjiAi65qbWedWzDHHB+aY4wOrjtm4hgwGgyHOMYbAYDAY4px4MwQv2y3ABswxxwfmmOMDS445rmIEBoPBYGhMvI0IDAaDwdAAYwgMBoMhznGlIRCRG0Vkp4jki8isJtZ3EpG/B9avFpE0G2RGlDCO+QEROSQiuYHHQ3bojBQi8gcRORhobtTUehGR3wT+H5tEZEq0NUaaMI55uohUhHzHTze1nZMQkWEiskxEtonIVhF5soltXPNdh3m8kf+elVKueqBLXu8G0oFkYCNwQYNtHgdeCry+F/i73bqjcMwPAM/brTWCx3wlMAXY0sz6m4H3AAEygdV2a47CMU8H/m23zggfcwowJfC6B5DXxG/bNd91mMcb8e/ZjSOCqUC+UqpAKVUFvAHc0WCbO4A/B17/E7hWRCSKGiNNOMfsKpRSn6B7WDTHHcBrSpMD9BaRlOios4Ywjtl1KKXKlFLrA69PANtp3PvcNd91mMcbcdxoCFKBfSHvi2n8j6zbRilVA1QA/aKizhrCOWaAOwND53+KyLAm1ruJcP8nbuNSEdkoIu+JyIV2i4kkARfuZGB1g1Wu/K5bOF6I8PfsRkNgaJp3gTSl1ARgCedGRAb3sB5dT2Yi8FvgbXvlRA4R6Q7MB76tlDputx6raeV4I/49u9EQlAChd7tDA8ua3EZEEoFewJGoqLOGVo9ZKXVEKVUZeDsP8EZJm12E8ztwFUqp40qpk4HXi4AkEelvs6wOIyJJ6ItitlLqX01s4qrvurXjteJ7dqMhWAuMEZGRIpKMDgYvaLDNAuD+wOu7gI9UIArjUFo95gY+09vRvkc3swD4aiCjJBOoUEqV2S3KSkRkcDDWJSJT0ee3k29wCBzPq8B2pdRzzWzmmu86nOO14nu2tGexHSilakTkCWAxOpvmD0qprSLyY2CdUmoB+h/9FxHJRwff7rVPcccJ85i/JSK3AzXoY37ANsERQET+hs6e6C8ixcAzQBKAUuoldK/sm4F84DTwoD1KI0cYx3wX8JiI1ABngHsdfoMDcBnwFWCziOQGln0XGA6u/K7DOd6If8+mxITBYDDEOW50DRkMBoOhDRhDYDAYDHGOMQQGg8EQ5xhDYDAYDHGOMQQGg8EQ5xhDYIgrRKRfSNXG/SJSEnh9UkR+Z9E+vy0iX23H3yWLyCeBSY8Gg2WY9FFD3CIiPwROKqV+aeE+EtElAaYE6lq19e+fQRcUzI64OIMhgBkRGAzU1Xj/d+D1D0XkzyLyqYgUicgXRORZEdksIu8HSgAgIl4RWS4iPhFZ3EzFy2uA9UEjICIfi0hG4HV/ESkMvL5QRNYERiebRGRM4O/fBrKsPXpDvGMMgcHQNKPQF/Hbgb8Cy5RSF6Fnct4SMAa/Be5SSnmBPwA/a+JzLgN8YezvUWCuUmoSkIGuoAmwBbi4A8dhMLSK8T0aDE3znlKqWkQ2o8t2vB9YvhlIA8YB44ElgbIvCUBT9W1SCK+u0yrgeyIyFPiXUmoXgFKqVkSqRKRHoD69wRBxjCEwGJqmEkAp5ReR6pBaLn70eSPAVqXUpa18zhmgc4NlwSZIScEFSqnXRWQ1cAuwSES+oZT6KLC6E3C2/YdiMLSMcQ0ZDO1jJzBARC4FXTq4mQYh24HRDZYFXT3T0SMJRCQdKFBK/QZ4B5gQWN4POKyUqo74ERgMAYwhMBjaQaAl6F3A/4nIRiAXmNbEpu+hew2Hcp2IrAWuA46KyLeALwJbAhUnxwOvBba9GlgY8QMwGEIw6aMGg8WIyFvAfyuldonIx8B/KaXWhfm3/wJmKaXyrNRoiG/MiMBgsJ5Z6KBxmwg0GXrbGAGD1ZgRgcFgMMQ5ZkRgMBgMcY4xBAaDwRDnGENgMBgMcY4xBAaDwRDnGENgMBgMcc7/B6dkFLVZ24STAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sim.set_config(config_spam)\n", + "sim.evaluation_times = 0.4\n", + "res_spam = sim.run()\n", + "res_spam.plot(obs)\n", + "sim.reset_config()\n", + "sim.eval_times = 'Full'\n", + "res_clean = sim.run()\n", + "res_clean.plot(obs)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now modify the *SPAM* dictionary, as below, allowing for more ($40$%) badly prepared atoms." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "config_spam_mod = SimConfig(noise=('SPAM'), eta=0.4, runs = 100)\n", + "sim.set_config(config_spam_mod)\n", + "sim.evaluation_times = 0.5\n", + "res_large_eta = sim.run()\n", + "res_large_eta.plot(obs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see here that the population doesn't go well above $0.6 = 1 - \\eta$, which is to be expected : badly prepared atoms don't reach state $\\Ket{r}$. We can expect this limit of $0.6$ in the Rydberg population to be more and more respected as the number of runs grows." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changing $\\eta$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first initialize all spam error values to $0$. Then, we do a sweep over the parameter $\\eta$, probability of badly prepared states, to notice its effects." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "res_clean.plot(obs)\n", + "for eta in np.linspace(0,0.99,4):\n", + " config_spam_eta = SimConfig(noise = 'SPAM', eta=eta, runs = 50, epsilon=0, epsilon_prime=0)\n", + " sim.set_config(config_spam_eta)\n", + " sim.run().plot(obs, label=f'eta = {eta}')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As $\\eta$ grows, more qubits are not well-prepared (i.e, pumped into a state different from $\\Ket{g}$) and we stop seeing occupations at all. You may increase the number of runs to smooth the curves." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changing $\\epsilon$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now run a sweep over $\\epsilon$." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "res_clean.plot(obs)\n", + "for eps in np.linspace(0,.99,4):\n", + " config_spam_eps = SimConfig(noise = 'SPAM', eta=0, runs = 50, epsilon=eps, epsilon_prime=0)\n", + " sim.set_config(config_spam_eps)\n", + " sim.run().plot(obs, label=f'epsilon = {eps}')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As more false positives appear, it looks like the system is never captured, so always in a Rydberg state. Note that when $\\eta=0$, the object we obtain is a `CoherentResults` rather than a `NoisyResults`, since in this case, the randomness comes from measurements and the simulation is entirely deterministic. This results in smooth curves rather than scattered dots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changing $\\epsilon'$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we run a sweep over $\\epsilon'$." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kyJAhzT7OnTt33fUFBQX4+jZ9m/Dx8aGgoKDZmLKysoiOjiYgIIDnnnuOPn36XH3tpptuwsvLC0dHR+bOnQtAY2k2AFaufq3+rsZgMfVvuFHB6TVvmLxvSWoXrQb33c9RrLgQMPefJu36hVsj2eR4J4qmlroDMo9S6uIyDiEOvs93msmEj5vbriLfXUn3WjzVw+tHX+d8yXmDttnfrT/PxT7X4us7d+7k+PHjV89SrK2txcvLCwCVSsWdd94JwMKFC5k9ezYA0dHR3HPPPcycOZOZM2e22HZ5eTllZWVXD+1etGgR8+bNu/r6lfaGDx9Oenr6dfe39wDvaymK0uI3i4CAABISEsjNzWXmzJnMnTsXb29vALZt20ZdXR333HMPu3btYtyYkdjrKqmy8sDBqnP1wDqif8wNxO8aTVT6V5SXPoOzq4fJY5AkfeTt+ICQxhS2D1jGVHtXk/Zta6Xmj3dNZ9vnXzPxyCcw8WmwcTZpDJKkFyEQPz1PgcqTL+weYtMNYeaOqMPkTJgBCCFYtGgR8fHxxMfHc+HCBV5++eVmr70yqNmyZQtPPPEEJ06cYMSIEWg0HavRc2VZUK1WN9tGe2fCvL29ycvLAyAvL+/qYLIlffr0YeDAgdcthdrY2DBjxgzWr1+PUp5DI2ps3cy3Xu94y8s4Uc251UvNFoMktaoiF9cjb7BfDCH2tt+ZJYTBAS6UDnsKO101iRvfMUsMktSmrCMoeSf5sP52np0+HDur7juf1H0jb0FrM1bGMmXKFGbMmMHixYvx8vKipKSEyspKgoKC0Ol0rF69mgULFvDdd98xbtw4dDodWVlZTJ48mXHjxrFixYoWjzdydnbG1dWV/fv3M378eL7++uurs2L6aO9M2PTp01m+fDlLlixh+fLlzJgx47prsrOzcXd3x9bWltLSUg4cOMDixYupqqqisrISX19fNBoNW7ZsIXb4UGyoo9q2D/ZGTjBuTeigURz/eTKDs7+juODPuHv7my0WSWpO3cZnQavhxKAXGW9nZbY45t9xG3HnRhBy7gvyCv+Ar6ecOZa6lrr9H1Iv7CnoO5ObBviYO5xOkTNhBhAVFcXSpUuZNm0a0dHRTJ069epskr29PUePHmXgwIHs2rWLl156Ca1Wy8KFCxk0aBBDhw7lqaeewsXFpcX2ly9fzrPPPkt0dDTx8fG89NJLRvtdlixZwvbt2wkPD2fHjh1Xd2vGxcXx0EMPAU05cCNHjmTw4MFMnDiRP//5zwwaNIjq6mqmT59OdHQ0Q4YMwdPTk8cX3EQd1ti5tD6jZgqe01/BikaS17xq7lAk6dcyD2OTspmPtLOYfcM4s4ZioVbhd8dfcaOSHd+8gU4nzBqPJP1KaQZWyVtZobuRF2fGdMtk/GspQnSvN1hMTIyIi4v71XOJiYlERkaaKaLWOTg49NpDvKuKcnBouEStY19sHV1+9Zq5/psdfe8uBpdup+zhI3j7h5q8f0lqjmbVg9Sc+5GXQn/gvXvNOwi7Iv+DG6E4hdNz9jE1OtDc4UgSAHmr/oTn2S/5z/ANPDZ9grnD0YuiKMeFEDHNvSZnwiSjaGxswLa+iBqV/XUDMHMKmPUyCjrS175s7lAkqUlVIUriBtZoxnPfxAHmjuYqz1tfxEcpJWfPf8wdiiQB0FhTjuO579mtHsOim7tXZfyWyEGYkfXWWbD60jwUdFi4dK3cK9+gfpz0nMmw4i1kp5wxdziShO7E/1ALDSe8ZjMs0LQ7IlujDp1EruMgphR/R1ZhubnDkSTi1n+MA9U4TnrK4GepmoschEkGJ3Q6bDRl1KodsDLi+ZAdFTr3ZTSoyd/4N3OHIvV2Oi31h//DL9oobp2s/4Ybk1AUrCc/S4BSSMJPX5g7GqmXu1Reg9+Fr0i2jmLk+GnmDsdg5CBMMri6qhIs0IGdu7lDaZaHTyDxfe5kWPlO0s4dM3c4Um+WsgPbmlx+tL2VaV1wl5f70OlkWoUy4OLnNDQ0mjscqRfbvOYrApUCnCc91e2T8a8lB2GS4dUU04AFtg5dZ2nltyLn/B/V2FC69e/mDkXqxcr3f8Il4ULo+DtRq7rgB4uiUBHzNMHkcnrHN+aORuqlymsbiUz/hjJLb7xi57V9QzciB2GSQWkb67DV1VBn4YKqK36oXObi4c1pn5kMqjxARXGeucOReqPSdByzdrNWmcLc2BBzR9OiyBvuIUPxw/3kB9DNdtNLPcOBA7sZrTpL7ZAHjX6gvanJQVgXsnHjRpYtWwbAyy+/zFtvvWXyGEpKSpg6dSrh4eFMnTr16mHhv5WZmcm0adOIjIwkKirq6pFJ9eWFvLDsI4aPvYHIyEg++OADE0bfPu5j7sdS0XJx51fmDkXqhSoOfI4QoBlyHw7WXfeDRW1hQXLEIwQ3ppIft97c4Ui9kPXxz6jDGp/Jj5o7FIOTg7AuZPr06VeLo5rLsmXLmDJlCsnJyUyZMuXqoPC37rvvPp599lkSExM5evRo0/FGQvDd11+RnlvM+fPnSUxMZMGCBSb+DfQXET2SC6oQXJJXmzsUqbfR1KM+9S27xHDmTB5p7mjaFH3L78gSnmj3vClnwySTSktPY3ztblL9ZqDYdd0Ul46SgzAD+eabb4iNjWXIkCE8+uijaLVaoKlY6+LFixkwYABTpkyhsLAQgA8++ICoqCiio6OvDlS++uornnzyyevajo+PZ9SoUURHRzNr1qyrs1OTJk3iueeeIzY2loiIiOvOb+yIDRs2sGjRIqDpsPD169dfd825c+fQaDRMnTr16u9oZ2dHY005n/1vJc8ueR61umn7cFtnT5qToijkBs2ib2MKxRdPmDscqRepSViHvaaUi4Hz8XW2NXc4bfJyceSA1z34VZ+lPvWAucORepGs7R9jrWjwmfZHc4diFHIQZgCJiYmsXLmSgwcPEh8fj1qt5ttvvwWgurqamJgYzp49y8SJE3nllVeAphmnkydPkpCQwCeffNJq+/fddx+vv/46CQkJDBo06GobABqNhqNHj/Lee+/96vkr2nuAd0FBAb6+TQdt+/j4UFBQcN01SUlJuLi4MHv2bIYOHcqzzz6LVqtFW1XExfRsNm39mZiYGG655RaSk5P1/xdpBkGT7qdBqMnd+19zhyL1IuV7/026zptxN883dyh6C57yEFXChrx9X5k7FKmX0NTXMiDnBxJsY3EL6jqFjA2p6yYidFD+P/9JfeJ5g7ZpHdkfnxdeaPH1nTt3cvz4cUaMGAFAbW3t1RkglUrFnXfeCcDChQuZPXs2ANHR0dxzzz3MnDmTmTNntth2eXk5ZWVlVw/tXrRoEfPm/f/dIVfaGz58+NW8rGu19wDvaymK0uxWYI1Gw/79+zl58iSBgYHceeed/PfLL/jdbSOob2jE3t6OuLg41q5dy4MPPmiQGTpjCQkK5JDVCPpnbQTtuz0u6VPqehpzT+NbHs83Lg+x0L/7LK+MjPBnu+Uoxmb+BJp6sLA2d0hSD5e8azmRlJEa85i5QzEaORNmAEIIFi1aRHx8PPHx8Vy4cIGXX3652WuvDGq2bNnCE088wYkTJxgxYgQajaZDfVtbN/1FqFarm22jvTNh3t7eVw8fz8vLa3Y50d/fnyFDhhASEoKFhQUzZ84k7sgvKEAfP7+rA8NZs2aRkJDQod/LlCr7z8dVlJF/cou5Q5F6geztH1MvLAmY/LC5Q2kXlUpBO2AuDqKK3GMbzB2O1NMJgUP856QQwOAJM80djdH0uK/9rc1YGcuUKVOYMWMGixcvxsvLi5KSEiorKwkKCkKn07F69WoWLFjAd999x7hx49DpdGRlZTF58mTGjRvHihUrWjzeyNnZGVdXV/bv38/48eP5+uuvr86K6aO9M2HTp09n+fLlLFmyhOXLlzNjxozrrhkxYgRlZWUUFhbi6enJrl27GBruT7WwZuasWezevZu+ffuyd+9eIiIi9O7bXAZNnktxwt+oPLQcn5jrf19JMhRRV4F3+nr2WI5j6uB+5g6n3UbdOIei+BcpO/ItfUZ3n6VUqfupvLCHgPoUNgYtIcyyZxxR1JweNwgzh6ioKJYuXcq0adPQ6XRYWlry8ccfExQUhL29PUePHmXp0qV4eXmxcuVKtFotCxcupLy8HCEETz31FC4uLi22v3z5ch577DFqamoICQnhv/81Xv7SkiVLmD9/Pl988QVBQUGsWrUKgLi4OD755BP+85//oFareeutt5gyZQpCCIYNieaxu6dTaenKC88/zz333MO7776Lg4MD//lP1z/819fNma0OU7ixeDOipgTFzs3cIUk9VPrur+grahExD3bpOnotcXO0Y6/bjYwq3UhNRQl2TvK9IhlHya4PaBQOhE150NyhGJUiutl245iYGBEXF/er5xITE4mMjDRTRK1zcHDo8Yd4NxSloa4vp8YtEkdb/fJEutp/s5+2b+Pmg/PJGbMUv2l/MHc4Uk8kBFmvDaO6QUfQ88ex7cK1wVpz9uguBmydxbHoVxkx+2lzhyP1RLVlNL4eykbr25nz/HJzR9NpiqIcF0LENPeazAmTOkenxaKhnArFAQcbK3NH02GjxkwiUQTCqe/MHYrUQ+We2UtAQyqZIXd12wEYQFTMJHJUvlglrjF3KFIPlXd0LZZosIyeY+5QjE4Owoysp8+CaWtKUCHQ2rh160NVXeytSXC/Fb/qc2gLDLu7VpIACnd9TKWwZeht3Ssh/7cUlYrC4OkMakjgfNIFc4cj9UDVJ34gW3gwdsI0c4didHIQJnWKrqqIWmGFvYOTuUPpNJeR96ARKvL2fWnuUKQepqwwl8iSXSS434Knu7u5w+m00CkPolIEF3d3/6UiqWtprCohuPwIZ11vwN3RxtzhGJ0chEkd11iLpa6OSpUTtlbdd3nliglDB7CfITheWAM6rbnDkXqQCzu/wkrR4Dvl9+YOxSAc/fqTadufvrlbqKxrNHc4Ug+StHcFFmhxibnT3KGYhByESR2mqSpCJxRU9j1jh5StlZp0/5k4a4poSN5p7nCkHsQxdSvpqgBCBsSaOxSDUUXPJ0pJ58Chg+YORepBxJl1ZOPFsFGTzR2KSchBmNQxOh2q2lIqsMPZvuuffaevvmNnUybsKT7wlblDkXqIkoJs+tWfIb9Pz8pv8Rt3D1pUaONXmTsUqYcoLcqnX81xMnxuwtKi59YGu5YchHUhGzduZNmyZQC8/PLLvPXWWyaPoaSkhKlTpxIeHs7UqVOvHhb+W3/58x+JvmE2o6ZMZ+3qH64+v2vXLoYNG8bAgQNZtGhRh08CMJex/fzYphqPe/Z2qC0zdzhSD5CyfxVqReA5smcVN1UcfUhzjGFw2XZq67vX+1zqms7u+g5LRUufsXeZOxSTkYOwLmT69OksWbLErDEsW7aMKVOmkJyczJQpU64OCq+1ZcsWThw/ztFtP7Bj3yHeeustKioq0Ol0LFq0iBUrVnDmzBmCgoJYvrx7Je5aqlWUhs/FSjRQd0puwZc6zyZlC9mKT49airxCN3AeAcolEg7/bO5QpB7ANmkjuSpf+g4cY+5QTEYOwgzkm2++ITY2liFDhvDoo4+i1TYldjs4OLB48WIGDBjAlClTKCwsBOCDDz4gKiqK6OhoFixYAMBXX33Fk08+eV3b8fHxjBo1iujoaGbNmnV1dmrSpEk899xzxMbGEhERYZCDsjds2MCiRYuApsPC169ff901586cZkLsYKosXPD1cCE6OpqffvqJ4uJirKysrh5VNHXqVNas6X4DmZjRN5Ck86P6yP/MHYrUzZWXFBJZe5Jsnykoqp73123f8XdShyXa+JXmDkXq5i6kpjO48RRFQbdCNy531F49728FM0hMTGTlypUcPHiQ+Ph41Go13377LQDV1dXExMRw9uxZJk6cyCuvvAI0zTidPHmShIQEPvnkk1bbv++++3j99ddJSEhg0KBBV9sA0Gg0HD16lPfee+9Xz1/R3gO8CwoK8PX1BcDHx4eCgoLrrhkUGcq2Pb9QrbWktKSY3bt3k5WVhYeHBxqNhisnGqxevZqsrCw9/y12HcOC3NhuNQX30ngoSjF3OFI3lrRvFZaKFtfhc80dilFY2jlzznEckaW70DY2mDscqRtL2vM9FoqO4PH3mDsUkzJqXQFFUW4G3gfUwH+EEMt+83ogsBxwuXzNEiHE1s70uX9VEkVZhi2Q6hHgwPj5LR9EvXPnTo4fP86IESMAqK2txcvLCwCVSsWddzZttV24cCGzZ88GIDo6mnvuuYeZM2cyc+bMFtsuLy+nrKzs6qHdixYtYt68eVdfv9Le8OHDSU9Pv+7+9h7gfS1FUZotwHrDqKEcvWE8M+64DW8vL0aPHo1arUZRFFasWMHixYupr69n2rRpqNXdL7lSpVIgej7a419TH/cNdje/bO6QpG7KImkzBbgTPnSiuUMxGs2Aubge3k3S4Y1EjO+Zg03JuBq1Ojwyt1Jg6Y9332HmDsekjDYTpiiKGvgYuAWIAu5SFCXqN5f9FVglhBgKLAD+Zax4jEkIwaJFi4iPjyc+Pp4LFy7w8ssvN3vtlUHNli1beOKJJzhx4gQjRozocAK7tXXTWY1qtbrZNto7E+bt7U1eXh4AeXl5VweTV+m0WGiqePLpxZyKj2f79u0IIa4uQY4ePZr9+/dz9OhRJkyYcPX57mZK7GD266LRxa+Ebna+qtQ1VFWUElV9jDTPG1B1wy8j+oocP4tS4UDjSbkkKXVM/PkURogzVIXe3quWIsG4M2GxQIoQIhVAUZQVwAzg2k9+AVwpte4M5Ha209ZmrIxlypQpzJgxg8WLF+Pl5UVJSQmVlZUEBQWh0+lYvXo1CxYs4LvvvmPcuHHodDqysrKYPHky48aNY8WKFS0eb+Ts7Iyrqyv79+9n/PjxfP3111dnxfTR3pmw6dOns3z5cpYsWcLy5cuZMWPGr17X1FagaDWU1GtxUxQSEhJISEhg2rSm7feXLl3Cy8uL+vp6Xn/9dV588UW9++5K+vs4sdVhApNqP4SCs+Az0NwhSd3Mhf1rGK404jSsZ59/52hvzy7HiYwu2YGor0SxdjR3SFI3U3h0NRaKDt+xd5s7FJMzZk6YH3BtQlD25eeu9TKwUFGUbGAr8AcjxmM0UVFRLF26lGnTphEdHc3UqVOvzibZ29tz9OhRBg4cyK5du3jppZfQarUsXLiQQYMGMXToUJ566ilcXFxabH/58uU8++yzREdHEx8fz0svvWS032XJkiVs376d8PBwduzYcXW3ZlxcHA899BCa6lJqG3TccccMoqKieOSRR/jmm2+wsGgaz7/55ptERkYSHR3NHXfcwQ033GC0WI3NZcgd6IRCefwGc4cidUNK4kaKcabfiKnmDsXoGqPmYks9BUe730Ycyfy8s38k18IfO/9oc4dicoow0lKLoihzgZuFEA9d/vleYKQQ4slrrnnmcgxvK4oyGvgCGCiE0P2mrUeARwACAwOHZ2Rk/KqvxMREIiMjjfJ7dJaDg0PPOcRb6NDmnaZascfJN6xTTXXl/2ZXZJXUUPjeePycrfD+0yFzhyN1I3U1VeheD+G0x82M/EPP32WbX1aD5t1BaN0iCHr6R3OHI3UjGZkZ+H8xmNMhDzNk0ZvmDscoFEU5LoSIae41Y86E5QAB1/zsf/m5a/0OWAUghDgE2AAev21ICPGZECJGCBHj6elppHClttTXVKJGBzbO5g7FJALc7DjnNA7vynNQ0emVcqkXSTywHjulHrvBs8wdikn4uNhx2G4y/qWHoarQ3OFI3Uj2LytRKwKfMb2nQOu1jDkIOwaEK4rSV1EUK5oS7zf+5ppMYAqAoiiRNA3CetQ7uMfMggGa6lJ0QsHO0dXcoZiM4+A7AMiPW2/eQKRuRXtmPeXY03/UreYOxWTqI+egRkflidXmDkXqRlzSNpOhCsAnbKi5QzELow3ChBAa4ElgG5BI0y7Is4qivKooyvTLl/0JeFhRlFPA98D9wljro1KnCCGw0lRSp7a7mv/VG4wdNY4M4UV1wiZzhyJ1Ew31dURUHCTJZQKWVtbmDsdkho8YQ5rOm8rTm80ditRNVBVl078ugSzfm3rdrsgrjPppernm19bfPPfSNX8+B4w1UF/N1rSSDKOmuhJ7NDTaeHe6re40zvZwtOG40zgmlW1CV1eJykbu/JJad/6XTURTg+WgmeYOxaT6+Tjxg1Usswp/goZqsLI3d0hSF5d+YCUDFYFzzLy2L+6hekTFfBsbG4qLi7vVh3t3o6kuQwA2Dp1bihRCUFxcjI2NjWECMwHbQbdjTSOpR+Q3fKltdQnrqRY2RI69w9yhmJSiKNT3nYoljdQl7TJ3OFI3YJO0kRQCiIzueeeq6qtHrCv5+/uTnZ199VxGybCEEGjK81AUFRblnT/Gx8bGBn9/fwNEZhrDxt1K+UF7Kk5thIm9M3lU0o+msYHw0n2cdxrDcJveNxMUPmIalUm2lMZtIHBg7xqESu2jLc8jpOYU2zzuJ0zdI+aDOqRHDMIsLS3p27evucPosY4ejyN222yShz5P+Kgl5g7H5BzsbDnqNJrwkgM0NjZiaWlp7pCkLur80W0MpAJV1PS2L+6BYkK92a0MJjZ7V9NJEzJFRGpB7qGVBCCwHtyzixm3pfcOPyW9lZ1cB0DA6PlmjsR8rAfchisVnD6yw9yhSF1Y9cm11Aor+o2fbe5QzMJCraKwz2ScNcVocuLNHY7UlZ1dx3ldAMOHjzJ3JGYlB2FSm3xyd5JpGYKNV4i5QzGb/uNm0Yia0pOyer7UPJ1WS0jRbs47xGLn0Dtq6TXHZ9jt6IRC3rH15g5F6qoq8/GrPMUpp0k42/XulQU5CJNalZGZwUDteUoDp5k7FLOydnAlzWEYwUV7qW3QmjscqQtKOr4LT0rR9uvduVAjB/XnFGGokreZOxSpiypN2IoKgTrqNnOHYnZyECa1KvPQGlSKwDu2d6/bA6j63UKokkt8/DFzhyJ1QWXH19Ag1ERM6L3b7QHsrS1Icx2HX00ioiLP3OFIXVBFwlbyhBtDho8zdyhmJwdhUqvs034iX/HCJ2KEuUMxO//RTQPR8vjfHvwg9XZCpyOoYAeJdsNxcnE3dzhmZzuwaYYj77gsciz9hrYRz0u/cNxyOKFeDuaOxuzkIExqUXVlGQNqT5DpNVnucgJsPILJtAzFJ3+3rEkn/UpKwkF8KaQh/HZzh9IlDI8dR65wo/bMFnOHInUxdWmHsBPVVAfeIAusIwdhUiuSf9mAtdKIXXTv3G7fnLKAKQzSJpKelWXuUKQupDhuHVqhEDa+9+4gvpaXky0JdqPoU3wYGuvMHY7UheTHbaRRqAmMucXcoXQJchAmtUgkbqZMONAvtncn5V/La8Qs1Iog8/B6c4cidSFu+ftJtuyPq6evuUPpMrRh07CljuJzsnq+9P9Zp+/iBP0ZHhFk7lC6BDkIk5olNA2Elh0k0WkslpZW5g6ny/DpN4oixQ3bNLnzS2pSXpRLWGMyJb7jzR1Kl9J/9G3UCisKj8uyLlITUZ6Nb91FcjzGYmUhhx8gB2FSCzJO/IwT1Wj73WruULoWlYp09wkMqDlGbU21uaORuoCLRzajUgSug+XyyrVCfD05aTEY95zdTdXzpV4vN67p/F37gfJz5Qo5CJOaVRG/gVphRcQYmQ/2W9YD78BeqefCka3mDkXqAkTyDkpwJHzIBHOH0qUoikJ5wBQ8tQVUZZ8xdzhSF1B77idyhDvDho82dyhdhhyESdcTgj75u4i3Go6Xm5u5o+lywkfeQrWwpuGs3PnV2wmdlr5lh0lxiMXCokccxWtQfUbMACDz8FozRyKZnaaBPiWHOW0bi6eTjbmj6TLkIEy6TnnGKTx0RVQGTTF3KF2Sja09ifYjCC7eh9DpzB2OZEZZ547gRjnaUPleac7AyEjO0xfr1O3mDkUys7IL+7ETtWhC5HvlWnIQJl0n61hTgcU+w+S6fUvqQm7CSxSTk3jY3KFIZnTpZNNsaNAIWR+sOWqVQrbnBIJrztBQUWTucCQzyj++iQahJnSkPKroWnIQJl3HIm0PqfgT1T/K3KF0WcGjZqIVCoVx68wdimRGjtl7SVKF0sdfbrdviWP07agVQaos69KrOWbt5pRqAP0DZRmXa8lBmPQr2vpq+tacIsttNCqVrGbcEn//QM5aROKWvdPcoUhmUldZSmjdWfI9x5o7lC4tOnYyRcKZhnM/mjsUyUzqizPwa0yn0HeCrJL/G3IQJv1KatzPWNOIdf+p5g6lyyvwmUxQ40XqCjPMHYpkBqlHN2Oh6LAfcJO5Q+nSbK0tOe84muCyQwhNg7nDkcwg80hTrTi3wTLF5bfkIEz6lfIzP1EvLIkcebO5Q+nyXAc35TakH5WHFPdG9ed/plLYEjlCJhq3Rel3E05Ukxa/29yhSGagvfAz2cKTIUNHmjuULkcOwqRf8So4yHnrQTg7O5s7lC5v4JCRFAhXNMlySbLXEQL/ol9ItB2Gna2tuaPp8vqPnUGDUFNyYqO5Q5FMTDTWEVh+jGSnUdhYyTIuvyUHYdJVl7IvEqjLojpgorlD6RZsrCxIdowloOwYQqsxdziSCRWlncJTFFETdIO5Q+kW3N3cSbSOxit/j7lDkUwsJ2E3dtShRMgziJsjB2HSVelHmpbVfIbLdXt96UIm40wlubJURa+Sc/n4Fd/hcru9vqqDbiRQl01e6llzhyKZUHH8ZuqFBf1Hy8+V5shBmHSVKnUXhbjRt3+MuUPpNkJimz6E80/KI4x6E+v0XVwkgPCw/uYOpdsIHDULkNXzexv33H2csRyEj4eHuUPpkvQehCmKYmfMQCTzqm9oILwqjkzXUSgqOTbXl79/IEmqUOyz95k7FMlEdHVVhNScItNtjCzj0g7+oQPIVPpgm7nX3KFIJlKRl4K/NpMK/0nmDqVFOmHeU0/azJJTFGUM8B/AAQhUFGUw8KgQ4nFjByeZzvnjexmsVGPV70ZzhwJAVUMVe7P3olapsbOww97SHjsLO+ws//+fbSxsUCnmHzBe8hrDyLzvqK0sw9bRxdzhSEaWcWIbfdFg2a/rlHHJqsziu8TvKK0vJcwljAjXCCJcI/C28+5SdZly3UcRXbiFhrparGzkhoaeLu3wBgYD3sPvMHcov5Jansq29G38nP4zDwx8gOmh080Wiz5bFd4FbgI2AgghTimKMsGoUUkmV3b6J3RCIXSUed8sOqFjc+pm3j3+LkW1rR9zYqWy4oGBD/Do4EexVFmaKMLrOQ6YhmX+1yQe/ZHoKXeZLQ7JNCrPbKNGWNN/pPkTjc8Vn+O/Z/7Lzxk/o1bUuNu6syX1/x8s72jpSLhreNPDJZxRfUYR5GS+6v5WEVOwK1rLuRO7iBoj8+l6OnXKdrLxpn/UUHOHQnp5Oj9n/My29G0klSahoDDMexgu1i5mjUuv/aJCiKzffJvSGiccyVw88veTZh1BqIuX2WI4V3yOfx75J6cKTzHIYxBvTHgDNxs3qhurqdHUNP2zsYZaTS3VjdWcLT7Lpwmfsj9nP6+Ne40QlxCzxN0v5kZqdlhTm7gd5CCsx/Ms2M8Zq2hiXcxTxkUIweG8w3x55ksO5x3GwdKBRQMWsTByIV52XlQ2VJJSlkJSSRLJZckklyazNXUrlY2VWKmseGn0S8wIm2GW2MNib0FzUEXF2Z9BDsJ6NE19DaHVxznudjv+avOsWGRWZF4deJ0vOQ/AUK+hLIldwtSgqXjZme/z7gp9BmFZl5ckhaIolsDTQKJxw5JMKTs3l/7aJE4HPWSW/kvrSvng5AesSVqDq40rr455lRlhM/Raarw5+GZePfQq8zbN44/D/8g9kfeYfInSxtaOBLvB+BYfMmm/kulV5V3AV5vLmYC7Td63RqdhR8YOvjzzJYkliXjaerJ4+GLmRczD0crx6nWOVo4M9RrKUK//P/sghCC7KptXfnmFvx78K4klifwp5k8mn0F2cnHnvFU/3Ap+MWm/kumlHPuZ/jRg09/0J0podVqWHlnK6qTVAAz2HMxfRvyFqUFT8bH3MXk8rdFnEPYY8D7gB+QAPwNPGDMoybTSj/2IvyLwGHKLSfvV6DSsurCKj+I/oqaxhnsi7+H3Q36Pk5WT3m3cGHQjQ7yG8PIvL/PGsTfYk7WHpWOX4utg2kNi6wMnEnjhTbJSLxAQ0s+kfUumk3V0M5GA+2DTvlfK6sp4ePvDnC85T7BTMC+Pfpk7Qu/ASm2l1/2KohDgGMAnUz/hnePv8PW5r0kuTebNiW/iZuNm5Oh/rdRnHCMz/0NF8SWc3M0/EyEZR+XprdQJS/qNNv3nyl8P/pUtqVtYGLmQ+6LuM/nnQXu0OWUghCgSQtwjhPAWQngJIRYKIYpNEZxkGkrqTqqxxW/AeJP1mVqeyp2b7+S1o68R5R7FmulreC72uXYNwK7wsPXgwxs+5JUxr3Cm6AyzN85m48WNCCGMEHnz/GKallYyjm02WZ+S6SkXd5IhfBg4aJjJ+qxprOGJnU+QWpbK6+NfZ8PMDcyJmKP3AOxaFioL/jLiL/xz3D+JvxTPXZvvIrHYtAsbLgOnoVIEF49uaftiqdvyKTzABZvBODqabtm+UdfIc/ueY0vqFp4a+hTPxT7XpQdgoMcgTFGU/yqK8uVvH6YITjI+nVZHSPkRUh2Ho1i0/y/1jiiuLebxHY9TVFvEO5Pe4fOpnxPqEtqpNhVFYXb4bFZPX02EawQvHniRZ/Y8Q2ldqYGibl2fsCEUKu5YZewxSX+SGWjqCaqII8VpJFYWplnybtA28Mfdf+RM8RnemPgGt4bcapDl9jtC7+B/t/wPrdBy34/3sTXVdHXuwoZOpErY0pi8y2R9SqaVm3qOAF0ONUGmO1e1QdvAn/b8iZ8zfubPMX/m4eiHTdZ3Z+jzbt4MbLn82Ak4AVXGDEoynYsXTuJLEZq+pjl+pUHbwOI9iymqLeLjKR8zNWiqQbfQBzgG8OVNX/LM8GfYm72XR7c/Sr223mDtt0hRyHYbRUT1cerqG4zfn2RyBad3YUs9ItQ0HyxanZbn9z/PobxDvDLmFaYEGrbfAR4DWHH7CqLco3hu/3O8E/cOWp3x91xZWlmTbD8U/9IjRu9LMo+suKZZTr8Rptl8Uaep4+ndT7M7azcvjHyBRQMWmaRfQ9BnOXLNNY9vgfmALKneQxRervTuP+J2o/clhODlX17m5KWT/GPcPxjoMdAo/ahVah4Y+ADvTHqHxJJEXjvymlH6+S2bflNxUao5e1wWo+yJik5tpV5YEBpr/BwXIQR/P/z3q9/qZ4bNNEo/HrYe/Gfaf1jQbwH/Pftfntr9FBqd8c9BbQicQB9RQG7qOaP3JZmeZcY+8vEgIHSQ0fuqaazhD7v+wMGcg/xt9N+4q3/32qHekXntcEBmU/YQ9ll7yVb1wTPA+Mnk/zn9HzalbuKJIU9wU7Dxd8xMCpjEw4MeZk3yGtYlrzN6f8Gxt6ETClVnfzZ6X5LpueTsI0E9gGBfT6P39f6J91mTvIaHBz1s9G/1lmpLXhz1Is/HPs++7H18fvpzo/YH4DusaSCbfVzmhfU0Wo2G0OrjZLrEGv30lerGah7f+ThH84+ydNxS5kbMNWp/xqBPTliloigVV/4JbAKeM35okrHV19UQUXuKHPcxRu9re8Z2Pjj5Abf2vZVHox81en9XPDHkCUb5jmLp4aWcKzbut25bFy/SrUJxLzho1H4k02ssycSvMZ1C73FGr0D/1Zmv+OLMF8yLmMcfhv7BqH1d6+7Iu7kj5A4+OfUJJy+dNGpfAWHR5OOBZbqcNe5pUk//gjPVqMImG7WfioYKHtn+CPGX4lk2fplZq953hj7LkY5CCKdr/hkhhFhjiuAk40qJ24Gt0oCVkY9fOVt8lhf2v8Bgz8G8OvZVkx6jolapeX3C67jZuvHMnmcory83an+lPuPo13ie4hK5gbgnybq869Vp4M1G7Wdd8jrePv42NwXfxIsjXzT5kUMvjHyBPvZ9WLJvCZUNlUbrR1GpyHSJJbT6BFqN8Zc/JdMpStgGQHCM8ZbthRD8ec+fOVd8jrcnvs0tfU1bBsOQWhyEKYoyrLWHKYOUjKP63DYahJrQWOMtDRZUF/DUzqdwtXHlvcnvYa22NlpfLXGzcePtiW9TUFPA8/ufN+qBrU4Dp2GpaLl47Cej9SGZnubCdvKEG9HDRhmtjx0ZO3j50MuM6TOG18a9hlqlNlpfLXGwcuD1Ca9TUFPA3w/93ahlXlThN+BENRdP7TdaH5LpOeYeIFUVjIdPgNH6WJeyjkN5h1gyYglTTLgD0xhamwl7u5XHW8YPTTI2z4KDXLAagJOTq1Hav5IwWdVYxUdTPsLD1sMo/egj2jOa50Y8x/6c/Xya8KnR+uk75AZqsEaTtMNofUgmptPSp/QI52xjcLI1ThmXCyUX+Mu+vzDQYyDvTnoXS7X5zkKN9ozmiSFP8GP6j2y8uNFo/YSMuBWAkgT5haWnqKupIrzuLJc8Rxutj4LqAt489iYx3jHM6zfPaP2YSouDMCHE5FYeetUzUBTlZkVRLiiKkqIoypIWrpmvKMo5RVHOKoryXUd/Eal9Kgqz6KtNo8zXOAVadULHiwde5ELpBd6c+CYRrhFG6ac97ux3J7eH3M6/4//NgZwDRunDwtqWFNsh+JccNkr7kumVpx3HQVTTEDTBKO3rhI5XD7+Ko5UjH9/wMXaWdkbppz0eHPggI3xG8I8j/yCzItMofbh5+ZGiDsUpT+ZQ9hTJcTuxVhqx7W+c2akru4Y1Og2vjHnF5EfUGYNev4GiKAMvD5buu/LQ4x418DFwCxAF3KUoStRvrgkHngfGCiEGAH9s7y8gdUzG0aYcF5dBxslx+fLMl+zI3MGfhv+JCf7G+fBqL0VReGn0S4S5hrFk/xJyqnKM0k9d4EQCRS45aReM0r5kWjnHfwTAf5hxlu3XJK8hoTCBP8f8GRcbF6P00V5qlZp/jvsnlipL/rLvLzRqG43ST6HXaMLqz1FdWWaU9iXTqkrcToNQExZjnDzjrWlb2Zu9lyeHPkmgU6BR+jA1fXZH/g348PJjMvAGoM82hFggRQiRKoRoAFYAM35zzcPAx0KIUgAhxKV2xC51gi5lJ0XCmX5DDL8zMr86n09PfcqUwCncG3WvwdvvDFsLW96d9C5anZZn9jxjlEKu3kObkkRz5Pb7HsEiYx9JBBIVHm7wtotri3nv+HuM8BnB7SHGr9XXHj72Prwy5hXOFp/lo/iPjNKHQ+Q0rBQtF49tM0r7kml5XDpEinUU9o4uBm+7uLaYZUeXEe0RzcLIhQZv31z0mQmbC0wB8oUQDwCDAX0Og/IDsq75Ofvyc9eKACIURTmoKMphRVGMu/VIaqLTEVR2hCSHGKws9TnDvX3eOf4OAsGzI541+e4ufQQ5BbF0XFPJirfj3jZ4+4ERQyjAHYv0PQZvWzIt0VBDUPUpslxiUasM///yO8ffoUZTw19H/rVLvlduDLqRuRFz+e+Z/3I4z/BL7OEjbqROWFJzXuZQdnelRfmEai5S7jvWKO2/dvQ1qhureXXsq2bZtGIs+gzCaoUQOkCjKIoTcAkw1LYHC5qKv04C7gI+VxTF5bcXKYryiKIocYqixBUWFhqo697rUkocLqKChqBJBm/7RMEJfkz7kfsH3I+fw2/H3F3HlMAp3NX/LlZeWElSaZJB21ZUKtJdYgmrikMnt993a1kJe7CmEUsj1DyKy49j48WN3D/gfkJcQgzevqE8G/Mswc7BvLj/RYOfxWpja0+yzSB8ig4ZtF3J9FKP/ohKEbgONPxS5M6MnWxL38Zjgx/r9DnDXY0+g7C4ywOjz4HjwAlAn3dMDr8erPlffu5a2cBGIUSjECINSKJpUPYrQojPhBAxQogYT0/jV6vu6fJONuW4+A0z7MSjVqdl2dFleNt58+DABw3atjE8MeQJ7C3teSfuHcM3HtK0/T79jHE2AEimUXx6O41CTcRIw+aDNWobWXp4KX4OfjwS/YhB2zY0O0s73pjwBqX1pfztl78ZvGxFtf94gnVZFOakGbRdybQ0KbuoEraEDTFsDnB5fTlLjyylv1t/Hhj4gEHb7gr0Kdb6uBCiTAjxCTAVWHR5WbItx4BwRVH6KopiBSwAfrvfeT1Ns2AoiuJB0/Jkqv7hSx1hlbmfVPwJDTFsjsu6lHUkliTyzPBnusQOr7Y4WzvzaPSjHMw9yMEcw+7QunKEUckpmevSnTnlHeS8RT98DPzl73/n/sfF8os8H/s8tha2Bm3bGPq79Wfx8MXsztrNT+mGLSnhObjpy2D6MZlD2Z35lx4h2X4oFpaGLePy5rE3Ka0r5dUxr2KpMl/pFmPRJzF/o6IodyuKYi+ESBdCJOjTsBBCAzwJbAMSgVVCiLOKoryqKMqVxP5tQLGiKOeA3cCzQghZatyIRGMdwdUJZLmMQGXAHJeKhgo+PPkhw7yGdavqxXf1vwt/B3/ePv42Wp3WYO16+/iRrA7BMVcWouyuasuLCa5PosTLsDWPcqpy+OTUJ9wQcAMTAyYatG1juifyHiJcI/jw5Ic06gy3W7LvgJGU4ISSusdgbUqmlZOaiJ8ooD7AsLNgB3IOsOHiBh4c+CCR7pEGbbur0Gc58m1gHHBOUZTViqLMVRTFRp/GhRBbLx9zFCqE+Mfl514SQmy8/GchhHhGCBElhBgkhFjR4d9E0kvW6X3YUo86dJJB2/3k1CeU1pWyJHZJl0wwbomV2oo/Dv8jyaXJbLi4waBtF3iOIaTuHA3VZQZtVzKNi3E/oVYETgNuNGi7y44sQ1EUlsQ2Wzqxy1IpKp4e9jRZlVmsS15nuHbValIdR9C34hhCZ7zTLCTjyTnRlOLia8AUl6qGKl459Ap9nfvy6GDTnTdsavosR+4VQjwOhACfAvNpSs6XuqHi09vRCoWQEYZ7s6SWpfJ94vfMDp/dLb+tTAuaxmDPwXx48kNqGmsM1q5t/6lYKlrS4uSSZHdUe34n1cKa/sMNl5S/K3MXe7L38Pjgx/F18DVYu6Yy3m88w7yG8e9T/6ZWU2uwdnUhk3CnjPTEYwZrUzIddfpeLuFGYPhgg7X5/on3Kagu4NUxr5rluDtT0bdYqy0wB3gMGAEsN2ZQkvE45B4kSR1GHx8fg7QnhOCNY29ga2HLU8OeMkibpqYoCn+O+TNFtUV8dfYrg7UbETOFWmFF9fmdBmtTMh3v4sMk2w7G1tYwOVs1jTUsO7qMMJcw7om6xyBtmpqiKDw97GmKaov4NvFbg7UbNOI2AAoubxqSug+dVktI1XEynGNRVIapYJ9VmcUPST8wv998hngNMUibXZU+OWGraMrpugH4CAgVQvzB2IFJhtdYU0bfukQueRjuEOK92Xs5mHuQxwY/hpuNm8HaNbUhXkO4Kfgmvjr7FZdqDDPR6+zowAWrAXhckkcYdTf5WRcJ1OVQ6z/OYG1+kvAJedV5vDT6pW6dYDzMexgT/Sfy5ZkvKa8vN0ib3v6hZKj8scuWOZTdTeqZw7hSiWLAFJfPEz5Hrai7/M5hQ9Bn2PoFTQOvx4QQuy/XDJO6ofTj27FQdNj20+vozzY1aBt449gb9HXuy12RdxmkTXN6etjTaHQaPjppuOrg5b5jCdRmUFWUbbA2JePLiGuakfEZaphl+9SyVL4++zWzwmYx1GuoQdo0pz8M/QNVDVX898x/DdZmvvsowmsTqKutNlibkvEVJTSlWwTH3GqQ9rIqs9h4cSPz+s3Dy87LIG12ZfrkhG0TQhhu25hkNpWJO6kTlvQbYZhiet8kfkNWZRbPjXiuW3+zvyLAMYC7+9/N+pT1XCgxzLmPLgObkrrTj8lllu5ESd1DCU4ER8YYpL1PEj7BSm3F4uGLDdKeufVz68etIbfybeK3Bps5tu53I7ZKAynH5fJ9d2KXc4B0VSAefYIM0t5nCZ9hobLgdwN/Z5D2urrufwS5pDf3S4e4YBWFs5Njp9sqrCnk01OfMsl/EmP9jHNMhTk8HP0wTtZOvBX3lkGKUvYbPJYyYU9jym4DRCeZgk6ro29lHOlOMSgGOB4lvTydbenbWNB/Aa42rgaIsGt4YsgTaHQaPj31qUHaC4u9mUahpvLcdoO0JxlfXW014bWnyTdQiktWRRabLm5iXsQ8PO16R2F2OQjrJapLcgnSpFPmY5gDu98/8T4NugaeHfGsQdrrKpytnXks+jEO5x3mQE7nq93bWFuRbDeUPiVHwcCVxiXjuJh4HE9KEX0nGaS9L858gaXKsssdZt9ZAY4BzI2Yy9rktWRWZHa6PQcnV1Ks+uNR8IsBopNMIeXELmyVBmz6TTFIe5+dbpoF6w4nrhiKvrsj/RRFGaMoyoQrD2MHJhnWleUwlwHTOt1WVkUWm1I3cXf/uwl0Cux0e13Nnf3uJNAxkLfj3kaj6/zZj3UB4/AWhRRnnjdAdJKxXTr1MwCBMZ0vOpxblcvmi5uZGzEXD1uPTrfX1Tw6+FEs1ZZ8FG+YPMoy37FNh0AXFxikPcm4Ks/tQCNUhI3o/LFemRWZvW4WDPTbHfk6cBD4K/Ds5cefjRyXZGD1SbspF/b0G9r53V5fnf0KtaJm0YBFBois67FUW7J4+GIull9kXUrni1J6D276CyrrhMwL6w5ss/eTo/jgGRDR6ba+PPMlKHD/gPs7H1gX5GHrwcLIhfyY9iPnSzr/JcN1wI2oFEFqnGGPRpKMw73gF5Kt+uPg1Pll9iu5YL1pFgz0mwmbCfQTQtwqhLjj8mN6WzdJXYgQ9Ck5QrLtYGysO3euV1FtEetT1jM9dHqP3rkyJXBKU1HK+H/ToG3oVFth/YdQgBtK2j4DRScZS0NDA+E18eS6jex0W5dqLrEueR0zw2biY2+Yunxd0f0D78fJyon3T7zf6bZCh06kRlhTnyxzKLu68pJCQhuTKfPpfE5wZkUmm1M397pZMNBvEJYKdP+tb71YcfYFfMQlg9Q8+jbxWxp1jT32m/0ViqLwaPSjFNYWsiW1cwcLq9Qq0hxjCKqIQxjwfErJ8JJP7sNRqcUivPNV8pefXY5WaHv8N3snKyceGvQQB3IOcCy/cxXvLa1sSLEbTJ/iowaKTjKWi8eajvVyGdj53fafJnzatCNyUO/YEXktfQZhNUC8oiifKorywZWHsQOTDCfzcs0j7yGdq3lU1VDFyvMruTHoRoKdgw0QWdc2us9o+rn2Y/nZ5Z3eKakNnoALleScl8eydGVlZ5t25oV2Mh+stK6UH5J+4Na+txLgGGCI0Lq0u/rfhZetF++feL/T75Vav7EEihzyslMNFJ1kDI1JO6gWNoQNndSpdjIrMtmSuoX5/eb3yLzJtugzCNsI/B34BTh+zUPqJpS0vVzCldDIzhWJ/CHpByobK3tN/RZFUVg0YBEXyy+yP6dzlbwDhzd9qBfEy3MkuzLnvF+4qA7FyaNzy4dfn/uaOk0dDw16yECRdW02FjY8NuQxThWeYl9255bdvYY05VBmxm01RGiSkfQpOUqK3WAsrTp3ruOnCZ9iqbLs8TPGLdGnWOty4Hv+/+Dru8vPSd2A0GkJqjhOmmMManXHK5I0aBv4+tzXjPQdyQCPAQaMsGu7ue/NeNt5d/pMSf+gUNIVP6yz5LEsXVV1VQURDeco8hrdqXYqGir4/vz3TA2aSohLiIGi6/pmhs3E19630++V4KhYSnFCSZU5lF1VXsYFAkQutQGdK5TQ22fBQL/dkZOAZOBj4F9AkixR0X1knz+GKxVogzv3n2zTxU0U1hb2mlmwK67UdzqWf4yzRWc73I6iKOS4xhJam4C2sd6AEUqGknxsO1aKBvv+nat59H3i91Q1VvFw9MMGiqx7sFRZcnf/u4kriONc8bkOt6Oo1KQ5Die44hhCJ0/J64qyjzftXvUe0rnSFFdmwR4Y+IAhwuqW9JkaeRuYJoSYKISYANwEvGvcsCRDKTjVtPzVmZpHWp2Wr85+RaRbJKN8DXf4d3cxJ3wODpYOnf6Gbxk2GVvqSYvfa5jAJIOqOb+TBmFBWMyNHW+jsYavE79mov9E+rv1N2B03cPsiNnYWdjx9bmvO9WOLngCXpSQnnTKQJFJhqSk7aMYF4L7D+9wGxkVGWxO3cyd/e7stbNgoN8gzFIIcfUgPSFEEnK3ZLdhnXmADMUPv8CwDrexK2sX6RXp/G7Q71AUxYDRdQ8OVg7Mi5jHzxk/k1OV0+F2QkbcjFYolJ6Rx7J0RZ5Fh0mxjsLG3qnDbfyQ9APl9eW9bhbsCicrJ2aFz+KntJ8oqO54wVW/YU2biPJPynphXY1OqyP46rFeHU9x+SzhM6xUVtw/8H7DBdcN6fNvME5RlP8oijLp8uNzIM7YgUmdp2moI7T2FLmusR0ePAkh+PL0lwQ6BnJjYMdnCLq7uyPvRoWqU9/wPTy9uWgRimPeQQNGJhlCyaVcQjWpVPh2/FivOk0dX539ilG+oxjsOdiA0XUv90Teg1ZoWXFhRYfb8A2OJF/xxDqr80eHSYaVfj4OD8oQfSd2uI3symw5C3aZPoOw3wPngKcuP85dfk7q4lLj92JHPRZhkzrcxtH8o5wpPsP9A+9HbYDDjLsrH3sfbg25lbXJaymvL+9wO4WeowmtP09ddcfbkAwvLe4nVIrAdVDHax6tS1lHUW0Rj0Q/YsDIup8AxwBuCLyBH5J+oKaxpmONKArZrrGEVp9E09ho2AClTimIbzrWK6ATKS4rzq9AharHnafaEfrsjqwXQrwjhJh9+fGuEEJmFncDZWe2oxMKobEdf7N8eeZLPGw9mB4qD0lYNGARtZpaVl1Y1eE27PrfgKWiJTXuZwNGJnWWJmUPVcKW0MEd28DSqG3kyzNfMtRrKDHeMQaOrvu5L+o+yuvL2XRxU4fbsAidhLNSTXKCPNC7K7HN3k+24ot3QHiH7q9prGFt8lqmBk3F297bwNF1Py0OwhRFWXX5n6cVRUn47cN0IUod5ZT/CykWYbh5dOx/9HPF5/gl9xcWRi7EWt25WjA9QYRrBGP7jOXbxG+p13bse0h4zI00CAuqzu8ycHRSZ/iVHiHFfggWlh071mtz6mbyq/N5JPqRXpk3+VtDvYYywH0A3yR+g050bIdj8OWZlpLT8gtLV9HQ0EBYzSny3GI73Mbm1M1UNlZyd+TdBoys+2ptJuzpy/+8HbijmYfUhdVUlRFaf54ir47vZvzyzJc4WDowv998A0bWvd0/8H6K64o7fJSRg4MTSVZReFw6ZODIpI7KTb+Av8inLmB8h+4XQvDd+e8IcwljbJ/On6PXEyiKwn1R95Fekc7+7I7VxnPxDiBDHYhDrpwJ6yqST+7FQanFKuKGDt0vhOC7xO+Ico/q1XmT12pxECaEyLv8x8eFEBnXPoDHTROe1FEXj23HUtHi0MGaR5kVmWzP2M6d/e7E0crRwNF1XyN9RtLfrT9fnf2qw9/wy33HEKJNo6Ior+2LJaPLPt50rJdPB4/1OlV4ivMl57mr/11yFuwaU4On4m3n3anNLJc8RhNRf4aamioDRiZ11JUUl5AOprgcyT/CxfKL3BN5j3yvXKZPYn5zmaqdO1hNMrqaCzupF5Ydrnn01dmvsFAsWBi10MCRdW+KonD/gPtJK0/r8Dd814HTAEg99qMhQ5M6SJ2+l0JcCerXsWO9vj//PQ6WDtwecruBI+veLFWW3B15N0fyj3C+5HyH2rDtdwO2SgPJx+XyfVfgnP8LaZYhOLp2LMXl28RvcbNx4+bgzp1j3JO0lhP2e0VRTgP9fpMPlgbInLAuzqPwMMnWUdjZt38Wq6yujA0pG5geNr3Xbx9uzrTgafjY+/Dfs//t0P1hQ8ZTKWxpTNlt4Mik9hI6LX07UfOoqLaInzN+ZmbYTOws7YwQYfc2J3wOtha2HZ4NCx1xExqhoipxp4Ejk9qroqKM8IZESjp4rFdWZRZ7s/YyN2IuVuqO5V72RK39rfMdTblfG/l1LthwIYScHunCSgqyCdWmUdmnYzWP1qesp0HXwD397zFwZD2DpcqSeyPv5XjBcU4Xnm73/VZWVqTYDaZPyVEjRCe1R0ZiHG5UoOs7qUP3r0lag0an4c5+dxo0rp7C2dqZmWEz2Zq2lcKawnbfb+voSqpVBG4FMofS3FLidmCtaHCI7NjqysrzK1ErauZHyBzja7WWE1YuhEgXQtx1OQ+sFhCAg6IogSaLUGq3tLimKtNXlr3aQyd0rLywkuHewwlz7XiV/Z5uTsQcHC0dO3yUUZ3/ePxEPgUZF9q+WDKagvjLx3oNb//ySKOukVVJqxjTZwzBzsEGjqznWBi5EK2u48Vby33GEK5JpqSk2MCRSe1x5VivkOHtzzO+UpbixqAbZVmK39DnAO87FEVJBtKAvUA6IJNZujBtym4qsCNs8Lh23/tL7i9kV2WzoN8CI0TWc9hb2jOv3zx2ZO4guzK73fd7D2kaIGcel28lc7LL3k+G4odvB4712p25m0s1l7ir/11GiKznCHQKZFLAJFZdWEWdpq7d9zsPvBELRUfKMXmEkTl5FR3hok0U1nbtP9brSlmKeyLl6spv6ZMEsRQYBSQJIfoCU4DDRo1K6jCh0xFQeoSLdkM7VPNo5fmVuNu4MyWwY7sqe5MrH76rk1a3+97g/jEU4YIqfZ+hw5L0pGmoI7TmFLluIzt0/4oLK+hj34fxfh0rbdGb3Bt1L2X1ZWxKbX/x1pAhk6kTljQmyxxKc8nPzyFMm0p1B0qwyLIUrdNnENYohCgGVIqiqIQQuwFZErqLyk1PxJdCGoLaX/k7tyqXfTn7mB0+G0u1PKO9LT72Pkz0n8i6lHU0att3tIpKrSLdcTjBFccRuo6VupA652L8XuyUeizCJ7f73uTSZI7lH+PO/nf26uO89BXjHUOkWyRfn/u63aVdLKztSLUdhG/xESNFJ7Ul7VjTsV7u0Te1+94rZSnu7n+3LEvRDH0GYWWKojgA+4BvFUV5H6g2blhSR12peeQ7tP05LldmdOZFzDNoTD3Z/H7zKakrYUfmjnbfqwuegDtlZJw/boTIpLaUn9mOViiExrT/vbLi/AqsVFbMCptlhMh6HkVRuDfqXtLK0ziY0/4D7Gv8xxMiMsnJSjd8cFKbxMU9VGFL0KD2p7hcLUvRV5alaI4+g7AZQA2wGPgJuEhTFX2pC7JM30sB7gSERbfrvgZtA2uS1zDRfyK+Dr5Giq7nGdNnDH4Ofh06T/LKAbj58TLXxRyc8g+SYhGOm4dXu+6rbKhkU+ombul7C642rkaKrue5OfhmPGw9OpSg7zX4cg5lnMyhNDUhBP5lR0m3H4LKon0rJNeWpZBH3zVPn0HYS0IInRBCI4RYLoT4AHjO2IFJ7afVaAitPk6Gy8h21zzakbGDkroSudW+nVSKinkR84griCO1LLVd9/oG9SNb8cU2q2NFX6WOq60sJaz+PEUdqHm08eJGajW13BUpE/Lbw1JtyaywWRzIOUBeVftOiwiIGkUF9iBzKE0u7eJ5AsmnsQMpLrIsRdtkxfweJPX0LzhTjSp0UrvvXXlhJQGOAYzu07FCfL3ZzLCZWKgs+CHph3bfm+M2kvCaeBrq279rTOq41LifsVB02LXzWC+d0LHi/AqiPaIZ4D7ASNH1XHMj5iKEYE3ymnbdp6gtSHccRlD5MXRamUNpSjnHm2bq+wxr33KiLEuhH30q5vdvpmJ++ytUSkZXlNBU8yh4xK3tui+pNIkTl05wZ787USntrxre27nbujM1cCobLm6gVlPbrnutIm7ATqkn5YTc+WVKNRd2UScsiYhp3yDscN5h0ivSWdBflnDpiD4OfRjrN5Z1yevQ6DTtulcbNIE+FJKafMZI0UnNsczcR4nigndo+471kmUp9KNPxfwNXF8xX/5b7YIccw+QpgrGwyegXfeturAKa7U1M0JnGCmynm9+v/lUNlTyU1r78rtCYm9DKxTKz/5spMik5ngWHuKC9UDs7R3add/357/HzcaNm4Lbv0tMajIvYh6Xai+xN3tvu+7zu1xQN+9ygV3J+Bo1WsKqjpPlMgLasbPxSlmKSLdIWZaiDW1WzAfeB0qEEBmXK+drFEXpWGEdyWhqqyuJqDtDgWf7lhOrG6vZdHETNwXfhIuNi3GC6wWGew8nxDmk3UuSzq4epFhG4Jb/i5Eik36rrCCLYG0Gle2seZRTlcO+7H3MCZ8jz77rhAn+E/Cy82r3e8UreBBFihvWmTKH0lQunD6Kh1KOKmRSu+47ln+sqSxFpCxL0RZ91p7+DVRd83PV5eekLiTl+A6sFA227cxx2XxxMzWaGlkhv5MURWF+v/mcLjrNueJz7bq3xGcsYY0XKC8tMlJ00rVSj20FaHfNoys7YOf3k0nGnWGhsmB2+Gx+yfmFnKoc/W9UFLJdYgmrPkFDY/uWMqWOKUpomqEPimlfisvqpNU4WTlxc7AsS9EWfQZhihBCXPlBCKEDLIwXktQRVYk7aBBqwmKa20fRPCEEKy6sIMo9ioEeA40YXe9wR+gd2Kht2l2uwnnANNSK4OJRuf3eFDQpeyjDgYho/Q+4r9fWszZ5LZMDJuNj72PE6HqHOeFzUBSFNUntS9BXhU3GTakkOUHOHJuCQ+5BclV9cPIN0fueK3UT7wi9AxsLGyNG1zPoMwhLVRTlKUVRLC8/ngbatxdfMjrPS4dIsY7C3tFF73tOXDpBSlkKd/a7U04ZG4CTlRO39L2FrWlbqWyo1Pu+sGGTqRHWNCa1v+Cr1D5CpyOw7ChpDsNQW+j/XfKntJ8oqy+T50QaiI+9D+P9xjedNqHT/7SJ4BG3AVCSIPPCjK2qto7+dQlc8mhf9tGmi5to1DUyJ3yOkSLrWfQZhD0GjAFygGxgJPCIMYOS2qe0MI8QTSrlvu3LcVl5YSWOlo7c0ldWHDGU+f3mU6upZXPqZr3vsbK2IdluMH4l8lgWY0tLOo0PRWiDJ7brvpUXVhLiHEKsT6yRIut95kXMo6i2iD1Ze/S+x8krgDR1X1zyZF6YsSXG7cFBqcW23w163yOEYHXSagZ7DibcNdyI0fUcbQ7ChBCXhBALhBBeQghvIcTdQohL+jSuKMrNiqJcUBQlRVGUJa1cN0dRFKEoijyTsgNSj21FpQhcB03T+56i2iK2Z2xnRtgMbC1sjRhd7zLAfQCRbpGsurCKa1bx21QbMAF/kUdu+gUjRiflnmha8g1sRxmXCyUXOF10mnkR8+SMsQGN8xuHj70PP1xoX4J+oddo+tWfpaqqwkiRSdCU4qITCkHtONbreMFx0ivSmRsx14iR9SxtDsIURYlQFGWnoihnLv8crSjKX/W4Tw18TFNh1yjgLkVRopq5zhF4GpDTAB2kTdlNpbAlbPB4ve+5UqdHJhkblqIo3NnvTlLKUogvjNf7Pp+hTbOR2ce3GikyCcA6az8FiidegZF637MmeQ2WKktuD5GntRmSWqVmdvhsDuUdIqsiS+/77COnYaVoSDkmlySNya3gEJlWodg463+s15rkNThaOsoSLu2gz3Lk58DzQCOAECIB0GcrXSyQIoRIFUI0ACtoOofyt/4OvA7IkuEd5F96lBT7oVhY6rdtXqvT8kPSD4z0HUlf575Gjq73uaXvLThYOrDywkq97wnqN4xLuKFO22O8wHq52roGImpOkus+Uu+aR3WaOjanbubGoBtlCRcjmB02G7WiZnXyar3vCY2ZSr2wpO68zKE0lkvFxfTXnKfMR/+SR+X15fyc/jO3htwqV1faQZ9BmJ0Q4uhvntNnf7AfcO3Xm+zLz12lKMowIEAIsUWP9qRm5KSepY8ooD5Q/xyXg7kHyavOk+d5GYmdpR23h9zOz+k/U1pXqtc9ikpFhvMIQqqOo9NqjRxh73TuxH6clWqsI/Qv47I9YzuVDZXMDZfLK8bgbe/NBP8JrE9ZT6NWvwR9GzsHkmwG4l14yMjR9V7JcduxVjS4DNA/xWVz6mYadA3Mi5hnxMh6Hn0GYUWKooQCAkBRlLlA+05fbYaiKCrgHeBPelz7iKIocYqixBUWFna26x4l+3hTjkt7zvVak7QGNxs3JgdMNlZYvd78fvNp1DWyIWWD/jeFTMaVSlJPy+33xlB2djsAISP034iyOmk1gY6BjPAZYaywer15EfMoqSthZ9ZOve+p9JtAX10GxXkZRoys92pI2kMjagKH6JeUfyUhf6D7QPq59TNydD2LPoOwJ4BPaTpDMgf4I007JtuSA1x7fo7/5eeucAQGAnsURUkHRgEbm0vOF0J8JoSIEULEeHp66tF172GZsZcC3AkIi9br+qLaIvZm72VG6Aws1ZZGjq73CncNZ5jXMH5I+gGd0O/A4b6Xk8WLTslcF2Nwzf+FTMu+2Lj66nV9ankqJy6dYHb4bJmQb0Rj+oyhj30fVl/Qf0nySqHdjGP670KW9COEwK/kEGm2A1HZ6Hes16nCU6SUpciE/A7QZ3dkqhDiRsAT6C+EGHf5+KK2HAPCFUXpqyiKFU15ZBuvabdcCOEhhAgWQgQDh4HpQoi4Dv0mvZBWoyGk6gSZLrEoKv0O3t6QsgGt0DI7fLaRo5Pm9ZtHZmUmR/L023Pi0SeINFUQDrly+72h5RaVMEBzjnIf/Qu0rkteh4ViwYwweaaqMalVauZEzOFI/hEyKvSb2QobNIpinOGiPPje0NIz0ogQ6dQGTNL7ntVJq7GzsJPljjpAn92R7oqifADsp2nW6n1FUdzbuk8IoQGeBLYBicAqIcRZRVFeVRRlemcDlyD1zCFcqEIJ1W9ZUQjB2uS1DPceTrBzsHGDk5gaNBVna2fWJOtfFbzAczThdWepq6lq+2JJbxeO7sBaacRNzzIujdpGNl7cyMSAiXjYehg5OmlW2CwsFAtWJ+k3G6ZWq0lxGEFw+VGETuZQGlJ2XFOKtvcw/cq4VDRUsC19G7eG3IqdpZ0xQ+uR9Jk+WQEUAnOAuZf/rNe2LyHEViFEhBAiVAjxj8vPvSSE2NjMtZPkLFj7FJ36CYBgPWsexRXEkVmZKSsZm4i12po7Qu5gZ+ZOvRP0bfvfiLXSSPKxn40cXe/SkLwLDWr6ROuX47IraxcldSXyvWIinnaeTAqYxIaUDTRoG/S6RxM8ETfKyUs6buToeher9N2U4IxPhH6FibembqVOWyeXIjtIn0GYrxDi70KItMuPpYC3sQOT2uaYe4A0VTAePgFtX0zTlLGjpSNTg/Q/X1LqnFnhs9DoNGy6uEmv68NHTKNBqKlOlNvvDUWj1eFXepQsuwEoNk563bMmaQ2+9r6M6aP/8qXUOfMi5lFaX8qODP3+3/e/fKh0/klZW89QNBoN4ZVHyXAZBXqkuAgh+CHpByLdIhngPsAEEfY8+gzCflYUZYGiKKrLj/k0LTFKZlRXU0V43VkKPEfpdX15fTk7MnZwW8ht8lBVE4pwjSDaI5q1yWv1qqBv5+BMsnUUnoVyh6ShnElJJ0qk0hioXzHj7MpsDuUdYlbYLNQqtZGjk64Y1WcU/g7+/JCkXwX9wKBQLioB2GbuM3JkvUdywi+4KZUoYfqVcTlTdIak0iQ5C9YJ+gzCHga+A+ovP1YAjyqKUqkoijw3wkyS45pyXGz76fdmuVLDRb5ZTG92+Gwull/kVOEpva6v6DOOUG0axQXZRo6sd8g53nSsl2+MfhXv1yavRaWomBU+y8iRSddSKSrmRMwhriCO9PL0Nq9XFIVst9GE1Cagra8xfoC9QNmpplnFoFj93itrktdga2HLrX31PwZM+jV9dkc6CiFUQgjLyw/V5ecchRD6ze1LBleVuJ0GoSZsRNuJxldquAxwHyBruJjBLX1vwc7CTu8EfffopppvacfkMosh2GXuokJxxDFkZJvXanQaNqRsYGyfsfjY+5ggOulaM0JnoFbUrEtZp9f1VhFTsKaR9BNy+d4QXPL2k6IOxdXLr81rqxur2Zq2lZuDb8bBSr9SFtL19Nkd+bvf/KxWFOVvxgtJ0ofnpUOkWEdh7+jS5rWni06TUpYiy1KYiZ1l09btbenbqGpoe9djaPQ4yrFHpMjt951VVl3HwNo4ct1Hgx5LiwdyDnCp9hJzImRCvjl42nkywX8CG1I20Khru4J+v5E3Uy8sKDvzkwmi69mqK0oIq0/kktdYva7fmraVWk2tXF3pJH2WI6coirJVURRfRVEG0lTPy9HIcUmtKC3MI0STSrmvfm+Wtclr5ZSxmc0On02tppYf039s81q1hQUX7YcTWHYEodOv0KvUvIS4/Xgq5VhH6leaYk3SGtxt3JngP8HIkUktmRM+h+K6YvZltZ3r5ebiwgWrAbgVyBzKzko99iOWihYHPY8qWp20mgjXCAZ5DDJyZD2bPsuRdwPLgdPAFuCPQog/GzswqWWpx35CpQhcB7a9y/HKlPFNwTfJKWMzGuQxiDCXMNYmrdXr+sagiXhTTFZKgpEj69mqzzbtIQqIuaPNawuqC9iXs4+ZYTOxVMnTJMxlrN9YvGy99F6+r/AbT19NGmUFWW1fLLWo4fx2qoQNETE3tnntueJznCs+x5zwOfI0iU7SZzkyHHgaWANkAPcqiiIrspmRNnk7lcKWsCFtf1v/Ke0najW1st6RmSmKwpzwOZwpPsOFkgttXu8f01R5Ou9E2zNnUvOEEPgWHiDTOhwL57bzu9anrEcndPK9YmYWqqZTCg7mHiS/Or/N6z0u51BePCqPMOowIehT/AvnbYdgY2Pb5uVrk9dirbbmtpDbTBBcz6bPcuQm4P+EEI8CE4Fkmo4kksxAp9URXHqIZMdYLCyt2rx+bfJaQp1DGew52ATRGZa2qgptVTW6hoYesSx3R+gdWKmsWJvc9myYX8gAchVvrOT2+w67mJXDQN0FKv0ntXmtTuhYl7KOkT4jCXDSr+5eVyKEoD41ldrTp2nMyUFXX2/ukDplVvgsdELH+pT1bV4bPngMpTiiS9ll/MB6qJKs8/jqCqj2n9jmtTWNNWxJ3cK0oGk4WzubIDrDE0LQmJND5Z491KelmTUWCz2uiRVCVACIpkJHbyuKol/lScngkk4fpj8lZIW1vRSZVJpEQlECz8Y82y2mjIVWS+2pU1Tt3kPVnt3UJ6f8+gILCxRLSxQrKxRLS1Q2NtiPGYPT7bdhFxOj9/mZ5uJs7cyUoClsSt3E4uGL26zXluU6kgHF29E01GNhZW2iKHuO9GNbCVN0eA1re7v94bzD5FTl8PSwp00QWeeJhgbqzp2j5vgJak6coPb4cbRlZb+6RuXggIW7O2oPDyzc3bHwcMc6oh9Ot9+G2qFrpyYEOAYwyncU65LX8Uj0I6iUlt/barWaVMcR9C0/ik6rQ6Xu2n8PdEXZcZtwA7yGtj2ztT1jO1WNVd1i84oQAk1hIfXJyU2PlBTqk5NpSE5BV9NU1sTzj09j/dhjZouxxUGYoih/EUK8IYSoUBRlnhDi2gp69wMvGD066TqFJzbRHwgd3fahwmuT12KpsuSO0LbzYcxFW1lJ9YEDVO3ZQ9XefU0fJBYW2MXE4Hn7HSiWlojGxqZHQ8Ov/qwtLaF80ybKVq3Cwtsbp9tuw+m2W7GJiuqyg8454XP4Me1HdmTu4PaQ1gcHluE34FCykfPxe+kfq1+yrPT/WaTupAp7PPuPa/PaNUlrcLF2YUqgfnX3zKHm5Emq9u6l9vgJahMSEJdnu6yCgnC44Qbshg9D7eqGprgIbXExmqLipj8XFVOfkkL1kSPovvuegjfewPn223FdcCc2UVFm/q1aNid8Ds/ue5bDeYfbPrkg9AY84neRdPYYEdFtlyKRfs0ybReZeBMRGd3mtWuS1xDsFMwwr2EmiKxjGjIzKfrsM6q270BbXn71ebW7O9ZhYTjPno11eDjW4WFYh4ebMdLWZ8IWAG9c/vPzwLWDsJuRgzCzcM3dQ6pFGCHega1eV6+tZ9PFTUwJnIKrjauJotNf1f79FH/5JTXH4kCjQe3igsPECThMmoT9uHGoHfXbgKurqaFy124qNm+m5H//o+TLL7Hq2xen22/D+bbbsAoONu4v0k4jfEbg7+DP2uS1bQ7CQkbciu7wYspO/wxyENYudQ0a+lcdJcM1lgHq1if8S+pK2JW1i7v634WVuu0lflOrT07m0tvvULVnD6jV2ERG4rrgTmyHD8du2DAsPPQ/YLz29BlKV3xP+caNlK1ahc3gaFwX3IXTLTejsulaJ2ncEHgDztbOrEla0+YgrO/I2yH+r1w6uVUOwtpJNNYRXHmCo843E6hq/ctralkqJy+d5E/D/9Qlv+jWp6VR/OlnlG/ahKJW43TrrdgMGHB1wGXh7m7uEK/T2t9OSgt/bu5nyQTy8vOIbEwkPvh3bV67I2MHFQ0VXW7KuDE3l4LXllG5fTuWfn64P3A/DpMmYTtkCIq6/UfEqOzscL79Npxvvw1NaSmVP2+nYssWij76mKIPP8Jh0iR8/vYSlr6+Rvht2u9KVfD3T7xPRkUGQU5BLV7r4uFNkmU4rnn7TRhhz3Am/jAxSgllEW0v229M2YhGp2FueNeqd9RYcImijz6kbM1aVPb2eP35T7gsuAu1g32H27QdNBDbQf/A+y9/oXzDRkpXrCDv+ecpWLYMl5kzcb1rQZf54mKltuKOkDtYcWEFJXUluNm4tXitm29fMtUBOOTIHMr2Sj+5i77Uow5ve1fkmuQ1WKgsutzqSv3FixR98ikVW7agWFnhtnAhbr97EEsvL3OH1qbWFs9FC39u7mfJBFIObUStCLyGtf0GWJO8Bj8HP2J9Yk0QWdtEQwNFn33Oxdtup2r/fjwXLybkx614/elP2A0f3qEB2G9ZuLrieud8gv63nLDdu/D849NUHzlC6u13ULpiZZdJ7p8eOr2pKnhy21XBS3wnEt54gaKCXBNE1nOUJVw+fmXk9FavE0KwJnkNQ72GEuISYorQ2qStqqbwgw+4ePPNlK3fgNu9Cwn9eRvuDz3UqQHYtdTOzrjddy8hWzYT+L/lOIwdQ8l333HxjukUff45Qqs1SD+dNTt8Nhqdhk0X205DLvIaS//605RXVJogsp6jJOFHGoSa/qNbzwdr0Daw6eImJgdMxt22a8wo1Scnk/PMn0i9/Q4qd+zA7YH7CduxHe/nl3SLARi0PggbrChKhaIolUD05T9f+VlWZzMDVcp2ynHEf2DrOS4ZFRkcyz/G7PDZrSa0mkr1oUOkzphJ4Tvv4DBuLKFbNuPx6COorIy39GPp44PHY48RsnEDNoMGkf/yy2Q+8CANmZlG61NfXnZejPcfz/qU9W1WBfeKmYFKEaT8ot8xLlIT97x9ZFr2xda99Z2OxwuOk16R3iXKUojGRkq//56LN91E0b/+jePkSYRu3YL3889j4WqclAJFUbCPjcXvnXcI37UTxxtuoPDtd8i8/wEac80/8A93DSfaM5q1yWtp2hfWMscB07BRGkk8us1E0fUMbvkHuGAVhUcbS3W7snZRWl/aJd4ruro68v7vJVLvmE7Vnj24P/wwYTt34P3ss+1anu8KWvyEFkKohRBOl8+ItLj85ys/y0qGJlZT30D/qiNkuI5GaSPHZW3yWtSKmhmhbSfvG1NjQQHZixeT+cCDCK2WgM8+xf/DD7H0a/tcMkOxCggg8L9f4vPqK9SdPUvq9BkUf/WV2b/pX60Knt368knfQWMoxgVVys8miqz7y7tUyADNOUp8266jtyZ5DY6WjkwLNm/OXV1iIqnTZ5D/yqtY9+1L8KqV+L3zDlYBpiuXYeHpid977+L7z382vVdmzKR88xaT9d+SOeFzSC1P5VThqVavC4m5iUbU1CbKcyT1VVKQSV9NKuV99HivJK2hj30fRvcZbYLIWtaQmUn6grso++EH3B54gNCdO/B6ZjEWbi0vV3dl5p8mkfRy+the3JUKrCNvbvW6Rm0j61PWM8F/At723iaK7npla9dx8ZZbqdq1G4+n/kDIpo04TDDPUTCKouA6fz4hmzdhP3Ikl5a9Tsbd91B/8aJZ4gEY5zcOL1uvNmuGKSo1Ge7j6F91lNraOhNF172lHNmClaLFbXDrx3SV15ezPWM7t4bciq1F2wUqjaVi28+k330Pupoa/P/1MYFf/w/b6LZ3qRmDoii4zJ5F3w3rsQ4NJffPfybn2b+gragwSzwANwffjJ2FXZsV9NU2DqTZDqJP8eE2Z82kJhcPNS3zeg1t/b2SXZnN4bzDzAyfadbVlcpdu0mbM5fGvDwCPv0E7+f+YrRZYlORg7BuovL0VnRCoe+o1nNcdmftpqSuxGyHqgohKPzgA/JeeAHbQYMI2bwJz8cfR2Vt/jpXlj4++H/yb/q8+QYNGRmkzZzVNCtmhr+wr1QFP5BzoM2q4LZRt+Kk1HD2yHYTRdfNpeykGhsCBk9u9bLNqZup19ab973yr3+R8/TT2ERE0Hf1DzjecEOX2HVmFRBA0Ddf4/HUH6jYupXUmTOpOWaeGt12lnbc0vcWtqVvo6qhqtVr64Mm0Y80ki4mmyi6bu7iDopxJmxQ67Nba5PXolJUzAqbZaLAfk1otVx69z2yH38cywB/+q5ZjcPEtgvLdgdyENYN6HQCn0v7SbeNxMrJs9Vr1ySvwdvOm7F99Dvc25BEQwN5S5ZQ9K9/4zxnNoH/+dykyyn6UBQF5zvuIGTLZuwnTuDSste5tOx1syTtX6kKvi6l9XyvsNG304ia6jPyWJa2aLU6QsoPkeYYg2LR8sD/SkL+APcB9Hfrb8IIm+hqa8l55hmKPvgQ5xnTCfzfciw8W39vm5piYYHn448T/N23KJaWZNy3iEtvv4PQaEwey5zwOdRqatmatrXV6/rENn1JzTm6wRRhdWuNGg2hlcdIdx6JqpWNURqdhg0pGxjbZyw+9m0f/2VompISMh96iOJPP8V57hyCv/8eK39/k8dhLHIQ1g2cv5hGlC6F2qDWv9lnV2bzS+4vzAmfg1rV+d2G7aGtqCDzkUcp37ARz6efwnfpUhTLrps6aOHujv8HH+B6772ULF9O7pIliMbWk+QNLcAxgNG+o1mbvBatruUcNUs7Zy7aDSGg6ABanVxmac35s8fxoxBtaOtFV08XnSa5NNksJVwa8/PJWHgvlT9tw+vPf8J32bIuMVPcEtvBgwlZuxaXuXMp/vxzchYvRtfQYNIYBnoMJNw1vM3le/e+wyhQeeGYIfPC2nL+xH7cqMQiovXSFAdyDnCp9pJZ3iu18fGkzZ5D7fET+C79O32WLu3S75WOkIOwbiDj6EZUisBvROuJ9lenjMNNO2XcmJtLxj33UHP8OH1eX4bH73/fJZZU2qKoVHi/8Dyef/wjFRs3kfX4E1ePsjCVuRFzya/O52DuwVavawydRgjZnDvXenJyb1d0simRPDi29WX7NclrsLWw5da+refCGFrtqVOkzZtHQ1oa/v/6GPeHHuoW7xWVvT2+f38V7xdfpHL7DrKfeBJdba3J+lcUhTnhczhbfJbzJedbu5Acr4kMrDtBRaX58ti6g5KEHwEIG932e8Xdxp0J/qbN6S1dtYr0e+9DUasJ+v47XOZ2rTp+hiIHYd2AXcYuylQuuISMaPGaRl0j61LWMd5vvEmnjOvOnSP9zgU05hcQ+PlnOM8w747M9lIUBY/HHsXn769SffAgGQ88gKa01GT9Tw6cjLuNOz8k/dDqdcGjmwbW+cc2miKsbsspZy9Zan+c+4S1eE11YzU/pv3ILX1vwd7SMHW39FG+aRMZ996HytqG4JUrcJzc+sx2V+R270J8l/6d6gMHyHr0MbRV1Sbr+/aQ27FSWbU5G2Y36A5slQaSDsnl+9a45u0nzTIMe7eWC1lfqrnE/uz9zAybiaXKdCsbJd9+S/5Lf8N+5Ej6rlmN7YABJuvb1OQgrIsrKKtmcH0ceZ7joJUDqvdl76OotsikNVyq9u4lfeG9YGlB8HffYj9qlMn6NjTXefPw/+B96hPPk7HwXhrz8kzSr6XKklnhs9iXva/VBH3HPv3ItfDHJXunSeLqjsrLy4mqP02B1/hWr9uatpVaTa1J3ysly5eT++xfsB08mOAfVpn9vLrOcJk7lz5vvknN8eNk/e53Jts56WztzI1BN7L54mZqNS3PwoWNuIkqbNEmtp4/1ptl5xUQqbnQZmmKDSkb0Aots8NnmygyKF2xgoK/L8VhyhQC/vUxahcXk/VtDnIQ1sUlHNmJi1KN86DWqxmvTlqNl21TEVBTKP3hB7IefwLr4GCCV6zo1h8qVzjeeCMB//kcTUEB6XfdbbISFrPDZzcl6LdRQb+kz2SiNWdIyykwSVzdTdKRH7FWGnEc2HoZlzVJawh3DWeQh2lqTpeuWkXBa8twnDaNwC/+0+231AM4334b/u+/R925c2Qsuh9NSYlJ+p0bMZfKxkq2pbdckNXCyoYLDrGElB5AtJJr2ZslH9mCpaJttTSFTuhYk7yGWJ9YAp1aP6vYUEpXriL/5VdwmDQJ/3ffQTFiQe+uQg7Curj6cz+iQYXvsFtavCa3KpeDOQeZFT4LC1XrhVwNoXzjRvL/7yXsx44h6Ov/dZvjIfRhHxtL0Nf/Q2g0ZNx9D7Xx8UbvM8AxgDF9xrAmeU2rCfreMTOwVjQkHW77CJfeqOHCz9QKK0JiWi68er7kPGeLzzInfI5JcrHKN20m/28vYz9hPH5vvdmjPlQcb7wR/3/9i4bUVDLuu4/GgktG7zPGO4a+zn354ULry/ea0JvwpJSMM63nWvZaKTuowYY+A1su83A0/yg5VTkmmwUrW72a/L/9rem98sH7Peq90ho5COvC6hq19C37hWz7QSh2LX97vpIjYYo3S+WePeS+8CJ2o0bh/+GHqOxNl1NjKjaRkQR//x0qZ2cyf/cQdefOGb3PeRHzKKgp4EDOgRav8RwwiWrssEiR9cJ+SwhBYMkvpNgPxdLarsXrVietxlptze0htxs9psodO8hdsgS7ESPw/+CDHvmh4jB+HAGff4YmN4+Me++lMSfHqP0pisL8iPkkFCWQWJzY4nXBo2ehFQpFx2UO5W/V1mvoV3mYTOcRYNHy/5NrktbgZOXEjUFtH+zdWWVr15H3fy9hP25c0+dKD3yvtEQOwrqwuDNnGaCkI8KmtniNRqdhXfI6xvqNpY9DH6PGU3P8ODlP/xGbfv3w/+ijHrdV+FpWAQEE/W85KicnMh95lIbsbKP2NzFgIh62Hq0n6KstyXYfw8Caw5RU1Rs1nu4mM+U0ASKPuuAbWrymVlPL1tStTA2airO1s1Hjqdp/gJzFz2A7cCD+//oXKhsbo/ZnTvaxsQR++QXasjLSF95LQ1aWUfu7I/QOrNXWrb5XvH36cM4iCo8cmUP5W6fj9tJHKUaJbPmLSGldKTszd179d21MZevXk/fii9iPHo3/Rx/26M+V5shBWBdWcLxpu71fbMs7Dq/UcDF21e+6CxfIeuz3WPr6EvD5Z6gdet4M2G9Z+vgQ+PlniMZGsn73kFHzXixVlswKm8X+nP2tJujbDbwVb6WME0f2Gi2W7ignrum94h9zR4vX/Jz+M5WNlUZPyK85dozsP/wBq7AwAj77tFe8V2yHDCHoq/8iamrIeuhho+4wdrZ25ubgm9mSuoXqxpZ3Zxb63UCwJpXqS+lGi6U7qj61Ho1QETym5ffBpoubaNQ1Gn11pXzTJvKefwG7kSPx//ijHv1lpSVyENZFCSFwzdlDqdoDqz4tJxCvTlqNh62HUWu4NGRlkfnQQ6js7Aj88otue1BqR1iHhRHw73/RmJ9P1mO/N2odsTkRcxBCtLoF33/EdHQo1Mjq+b9il7GbbMUX35CWt7KvSV5DsFMww72HGy2O2oSEpi8rfn4EfvEf1M7GnXHrSmyiovD/979ozMtrqiNWb7zZ2vn95lOjqWFLassHjLsOafrymnGo9TMnexMhBEGFu0mxjca6hdNXhBD8kPQD0Z7RRLhGGC2W8s1byH2uabk+4N//QmVrvvNbzUkOwrqos9nFjNCdosRvErSQQJxfnc/+nP3MCptltBoumsJCMh/8HTQ0EvjFf7DsY9wlz67Ibtgw/N5+i7ozZ8hevNholfX9HPwY49eUoK/RNX80jOLgSbZdFEElB6lrlDu/AOpqq4mojSfHo+Wjui6WXeTkpZNGTcivu3CBzIcfQe3m1uu+rFxhN2wYfd54ndoTJ8h9bonRjgMb5DGI/m79WXVhVYtnvw4cPJw04YtF8k9GiaE7Sr1wihCRRXVIyzuIj+QfIb0inQX9Fhgtjqr9B8h97jnshg0j4JN/99oBGMhBWJd14eh2HJVaPIa0XJpiXfI6dEJntCljbUUFmQ89jKa4mIDPPsU6rOUCmD2d44034vPSS1Tv3Ufe31422qHf88LnXS2Q2BJt2DQGKykcO9tK5fBeJPnoNuyUemwiW86dXJO8BguVBdPDWq8O3lH1aWlkPvg7VLa2BP73v1h6exuln+7A6eab8Xr2WSp/+olLb71tlD4URWFexDwulF7gdNHpZq+xVKtIdhlHcNUJRJ2sng9w6ehqAALHzGvxmpXnV+Ji7cK04JZ3GXdGfXIyOYsXYx0Whv8nn6Cya3kjTW8gB2FdlJKynUYscI5qfmeKVqdlbcpaxvQZg7+j4Q8z1dXWkvX7x6lPTcX/gw+wHTzY4H10N64L7sTjiScoX7uWwvffN0ofEwIm4Gnryerk1S1e4xc7E4CCOLnzC6A2YT01wpp+o5pPNK7X1rPp4iZuCLgBNxvDz05py8vJfuz3oNMR+OWXWPn7GbyP7sbtwQdwvftuSr78kpJvvzVKH7eF3IadhR2rLqxq8Rp15K1YoSE7ThZuBXDP2k6yOgxP/+a/UOdX57M7azezwmcZJSFfU1JC1mO/R7GxIeDf/+oV+ZJtkYOwLuhSZR0Dqo+Q7zIUbJyaveZg7kHyq/ONkpAvtFpynvkTtSdO4PfG6ziMa3mZp7fxePIJXObNo/iTTyn57juDt3+lgv6BnAPkVTVftd/KbzClFh645uxB18sP9NZqNIQW7+Gc0xhs7ByaveantJ8oqy9jfr/5Bu9faDTkLF5MQ24u/h99iHVIX4P30R0pioL3iy/gMHkyBf/4J5W7dhu8D3tLe24LuY2f0n+ivL682WuGjr2ZMmFPWfwGg/ff3ZQXZBLReJ4Cv5ZLTqxJXoNO6JgX0fJMWUfp6uvJfuJJNEVFBPzr416Z2tIcOQjrgo6cPEU/VTZW/W9q8ZrVSatxs3Fjkv8kg/d/6Z13qNq9G++/vojTLS0Xie2NFEXB528vNX24/H0pFT//bPA+5oQ3JeivSW4hoVhRKPObTKzuFAkZxi+Q2ZWdP/oz7pSjRDa/zCiE4Lvz3xHqHEqsT6zB+y9Y9jrVvxzC9+W/YTfceAn/3ZGiVuP39lvYREWR86c/UXu6+WXDzpgXMe/qTGdz3BztOOcwioCi/Qht83mWvUX6L00lPdyHN5++0qhrZHXSasb5jSPAMcCgfQshyPu//6P25En6LHsN2+hog7bfnclBWBdUntA0de41rPnt9gXVBezL3td0qKrasAn55Zs2UfLFl7jctQC3e+4xaNs9hWJhgd87b2M7eDC5f36W2lOnDNp+H4c+jPUby7rkdS0m6HsOn4GjUsuFY4YfBHYnVSdWUyus6D+++Q+WhKIEzhWf467+dxk8Ib90xUpKv/kGt/vvx2WO6c6h7E5UdnYEfPJvLNzcyHrs9wavtxfpHkm0RzSrklpO0Ff3vxUXKrlwvHfXDLNO3koGvvQbOKLZ13dm7qSotogF/Q2fkF/86adUbNyE59NPyS/2vyEHYV1MXaOWgMK9lFr5onj2a/aadSnr0Aqtwesd1Z4+Q95f/w+7mBh8XnjBoG33NCpbW/z//S8svLzI/sNTaAoLDdr+vIh5XKq9xL7sfc2+7tD/BhqwxDKl9w7CdFotIUW7SHQYib2jS7PXfH/+exwsHbgjtOX6YR1RfeQo+UuXYj9+PF7P/tmgbfc0Fh4eBHz+GUKjIevhR9CWlRm0/Xn95pFWnkZcQVyzrw+YMJsGoaYorvcuSWprSgmtOclF90mo1M1/7K88vxI/Bz/G9jFs+knFjz9S+N77OE2/A/fHHjNo2z2BHIR1MXGJFxlDApUhtzZbmqJR28iqC6sY02eMQQ9V1RQWkv3kk6jd3ZrO7bI0TsmLnsTC1RX/jz9CW1lJ9lNPIxoaDNb2BP8JeNl6tVwV3MqeAvdYhtQdJaO45YKVPVnS8V14Uoquf/MDrKLaIralb2NG2AzsLA23A6shK4ucp57CKjAQv3feRlGrDdZ2T2UdEkLARx/SmJ1NzjPPIDSGWxq8KfgmHK0cWzxP0sHZjYt2Q/C7tAeN1jglM7q6jEPrsESL1aDml+1TSlOIK4hjfr/5qFWG+/+5NiGB3CXPYztsGL5Ll5rkvNbuRg7CupjCY2uwVLR4j7672de3Z2ynsLaQeyINt1Soa2gg+6mn0VZUEPDxx72yvlFH2fTrR59//oPakyfJ/8c/DdauhcqCWeGzOJhzkNyq3GavsRt4KyGqfI7EHTVYv91J2fHVNAgL+k1oPol4TVJTvbU7+91psD61VVVk/f73CGja3eXoaLC2ezq7ESPweflvVP9yiML33jNYu7YWtswIncH2zO0U1xY3e43odzN9yeFk/HGD9dudNJ7dSIFwYVDslGZfX3lhJVYqK2aFzTJcn7m5ZD3+BBaenk3HEfWi8yDbQw7CuhAhBH45P3LJwhfrwOaTfL89/y1BTkGM8xtnsD7zX321KWHytX9iExlpkHZ7E6dbbsH94YcpW7mS0pUtb5dvryvLzS0l6LsPaZoBqjvb+7bfC52O4IKdnLMbgaPz9V8aGnWNrEpqmjHu62yYHYtCqyX3T3+mIS0d//ffwyooyCDt9iYuc+bgsuBOiv/zBRU/Ga6I6ryIeWh0GtanrG/29dCxTQP1/KPrDNZnt9FYS1DJL5yyH4uz3fVlJ6obq9mUuombgm/C1cbVIF3qqqvJevwJRF3d1ZxAqXlyENaFJKelM0x7mqKg25pdijxdeJqEwgTu6n8XKsUw/+lKv/2O8tVrcH/sUZxubrmKstQ6zz8+jf348eQvXUrNiZMGadPXwZcJ/hNYnbSaem0zR8C4BlFoG0J42QHKagy3FNodJMfvx4dCGvs1Xxtsd+ZuLtVc4u7+zc8od8Sld96hau9efP76IvajRhms3d7G54UXsB0yhNwXXqQuKckgbYa4hBDjHcMPST+gE9cvOVp79iXXOgSfgt3Ua3rXSROZxzZjQz2qFg7s3nxxM9WN1dzZ3zAzxld2QtYnJeH37ru9usi3Pow6CFMU5WZFUS4oipKiKMqSZl5/RlGUc4qiJCiKslNRlF791TL30EosFB0+Y5r/4Pj2/LfYW9ozI7TlA73bo/rwEQpeew2HyZPxfOopg7TZWylqNX5vvYmlry/ZTz9FY4FhSkfcE3kPJXUlbE1tfrZLG3ErsUoiv5w6Z5D+uoviY6toFGoiJjT/wfH9+e/xc/Az2Ixx2fr1lHzxJa5334XrXXcZpM3eSrGywu/991HZ25H9hz+grTBMNfv5/eaTU5XDodxDzb7eGHYTQ8V5Dp5ONkh/3UXx8XVUCDtiJl2fDyaEYMWFFUS6Ne0yNYTS//2Piq0/4rn4jziMN8z7rycz2iBMURQ18DFwCxAF3KUoStRvLjsJxAghooHVwBvGiqc7cE/fQrbaH7eQYde9VlhTyLb0bcwKm4WDVfNFKdujITubnD/+Eau+wfR58w0UlZwU7Sy1szP+H32IrrqGnKeeQmeARP1RvqMIdw3nm8Rvmt2C7zVmIWpFUHWihQT+HkjodATmb+ec7TCc3a4/hPhCyQXiCuJY0G+BQZKMa8+eJf+lv2E3ciTezz/f6fYksPT2wv+992jMySX3L88Z5IzJKYFTcLNxa7GCvt/I2VgoOjKP9J5dko2NDfQt3kei4xhcHK//3Dhx6QQpZSks6L/AIEnzNceOUfDGmzhOvRH3hx7qdHu9gTE/eWOBFCFEqhCiAVgB/GoKRwixWwhRc/nHw4Dhz9/pJooLMolqOE2O3y3NLkWuSlqFVqflrv6d/xauq6kh+4knETodAR9/jNqh84M6qYlNRAR9XnuN2lOnyH/11U6fMakoCvdG3ktSaRJH869PwFd5R5JrE0bEpW29Zpkl9cxh/EQB9eHNn6u64sIKrNXWzArvfJKxtqyMnKeeRu3mht+778hdwwZkN3w43s8voWrPHoo+/len27NSWzEzbCZ7s/c2e9qEhX8MlRZueObuprq+dxRuTTj4Ey5UYhvd/OrJyvMrcbR05Ja+na/d1VhwiezFz2AVEIDvP/8pd0LqyZiDMD8g65qfsy8/15LfAT8aMZ4uLWPf96gVgVvs9csrDdoGVl1YxQT/CZ0uSyGEIO/ll5vW699+WyYXG4HTTdNwf+xRylevoWzFik63d2vIrbjZuPH1ua+bfb2m3yyGKMmcPGWYXLSu7tKRVWiEivAJ1xeVLK8vZ0vqFm4LuQ1na+dO9SN0OnKee47GS5fwf/89mVxsBK53343zzJkUffyxQY42WtCv6f+JbxK/uf5FlYq6vjcyXolnx5ms61/vgcpPrqMeSyLHz7zutaLaIrZnbmdG2AxsLWw71Y9oaCDnj39EV1OD/4cfyF3D7dAl1qAURVkIxABvtvD6I4qixCmKEldo4KKYXYXjxU2kKIGEDYi57rWf0n+ipK6EuyM7n2RctmJFU+Xip/4g1+uNyPMPf8B+4gTy//FPak6c6FRb1mpr5vebz97svaSXp1/3uv/4ewGojFvZqX66A6HT4Zf3M+dtonH19L3u9Q0pG6jV1F79MO6Mok8+oXrvPryfXyIPsDcSRVHweflv2ERFkfuXv1Cfltap9nwdfJkWPI01yWuobKi87nX3YTNwUmpJPrqtU/10B2XV9fQr20e680gsba8/g/hKCRdDnKla8OZbTTvsl/4d6/DwTrfXmxhzEJYDXHsAlf/l535FUZQbgReB6UKIZraAgRDiMyFEjBAixtPz+hyQ7q6+JJPwutOkeU+7bgpXCMG3id8S4hzCaN/RneqnNiGB/H++hv3ECbg/+min2pJap6jV+L35JpZ9+pDzx8VoipuvX6SvO/vdiaXKkm8Tv73uNRuPIJJtBhGa/6NBcmu6sowLJwjU5VAdev1SpE7oWHFhBUO9hhLp3rlSK1X7D1D04Uc4Tb9DJuIbmcrG5v+1d9/hURRvAMe/cy2FhPSENEroBJAmRRQRsYAFCyiKigWwUUQQUKTYsAFCaIqoYAcbYAF/Kk2K9NB7T++9Xe7m98ddeoC0yxGYz/Pw3N62vMfe3L47OztD0LwwhF5vaaifUb3Oh4eGDiXTmMlPx8t27aJp2odcjTONotaQnHl1P1G8ZfM6AkUCLh3uK7Ms35zPD8d/oLt/92p34ZL6628kf/UVnkOHUr9//2rt61pkyyRsJ9BcCNFECGEABgOri68ghOgIfIIlAbtmRyKO2PwdAC6dynY6uS9+H4cTDzOk9ZBq3WPPT04mYsxL6H19CXz/fdUQvxZo69cnKGwuptRUIl8eV61ewr2dvOnXpB+rTq0iNTe1zPK05vcRIi9w8sDV3XFr9LblmKWgaTm3IrdEbuFC+oVqt5s0RkYSNX48Ds2a4T99umrbUgv0gYEEfjSbvNNniJ48uVptKUO9QunaoCtfHfkKo8lYcqHBmaxmd3OnZjv/21e9WrcrXca+lZjQENi17LiqGyM2EpsVW+1xInOOHSd66lScunTGd/y4au3rWmWzM7GUMh8YCfwJHAFWSCkPCSHeFEIUPCv7IeAC/CCECBdCrL7I7q5qhqOrOCwb07Fj2YFVvz7yNa4GV+4OKb+Pl4qQJhNR41/BlJhI4Ny5aN3dqxGtUhmOrVrRYNo0srZvJz5sXrX29USbJ8jOzy6389YmNz2KUWpJ3lG2puxq0iDiT446tMW7Qdm2kd8d/Q5vJ2/6Nuxb5f2b8/KIeGks0mQiaF4YGueaG+5IubR63bvjO+5l0v/8k6Rly6q1r6GhQ4nLimPt2bIdwrr3eBxXkU3sjvI7Qb4anIzLoEPGZmLdO0E9rzLLvzv6HX7OftwcdHOV/4YpLY2I0aPQurgQ9NFH6qGVKrJpdYiU8g8pZQspZVMp5TvWeVOllKut032llH5Syg7Wf+UPbHUVMyaeJTjrEMe8+uKoL/k4fUxmDH+f+5sHmz9YrbHvEhYsJHPLFvymvI5T29DqhqxUkvsD9+M+aBCJixdXq/FxS8+WdG3QlW+PfIvRXPIK39M3gP2OnWkUvQau0luS54+H08R8jrRynuQ6n3aezZGbGdRiEHpt1U8Gse/MIOfAAQLeexdD48bViFapCs+nn8b1tr7EzZxF1u6qDzF0Y+CNhLiF8OXhL8vUqolGN5Lm0IDrktYQk5pT3ZCvSOu2bKWlJgLXjmWfijyUcIjt0dt5pNUj6DS6Ku1fms1ETZyEMTKKwDkfobsKmwnVFnVPys7ObLQ8xePbo2y18PJjy5HIalUZZ2zcSMLChbg98ADuAwdWeT9K9fi9PtnS+HjiRPIuVP3JrMdaP0ZsViz/nPunzLKUpgPwM8cTe3hjdUK9YkVttfT/1OSmsuXh+2PfoxVaBrUofxzJikj5ZSUpy5fjNXwYrn2rXpumVJ0QAv8ZM9AHWttSJiRUaT8aoWFo6FCOJh1le8z2Ugs1mNo+xI3iAOt2hlc/6CuMySzJPfgrAK7X3Vdm+WcHP8NV71qtMVUTP11Cxvr1+E2YgHPn8ofYUypGJWF2Zji6ikOiGd06lfwi5+Tn8OPxH+kd1JtAl0v17HFxeRGRRE6YiEOrVjSYOkW1bbEjjYMDgWFzQQgixozBnFO1K/Cbg2+moWvDcrurCOk5iGxpIPm/q/OWpPeFtRzVtcYvqGmJ+VnGLFaeWMltjW7Dx7lqV+Q5R48SM306zt264TNmTE2Eq1SR1tWVoLAwTOnpRI4bX+W2lHeF3IWXoxdLDy0ts8yjx1C0QpK75+p7onjrqQRuMG4jxb0NuJe8bX8m9Qx/n/ubwa0GV7nT78xt24ifO5f6/fvj8fhjNRHyNU0lYXaUeP4IjfOOExvcH5225KFYc2YNKbkpPNamal9yc24ukWPGgNlMUNhcNI6ONRGyUg2GoCACPnif3MNHiHn77SrtQyM0DGk9hP0J+wmPCy+xrEmgH9v03QiM+hNKN0iu4yJPH6GZ6RQp5dyKXHlyJenGdB5pXbUG+Za2LWPQurkROGsmQle1WzRKzXFs2bLabSkdtA480uoRtkRu4URyqaGKvJsRW78dPTL+4mx8Rg1EfOX4d9s2OmtOUK9j2VrhLw5+gUFrYEjrIVXatzEmhshx4zGENMH/rTfVhX0NUEmYHZ3aYKnNCOldMtGSUvL1ka9p7tGcLn5l+w2riNh3ZpBz6BAB77+HoWH1OnhVao5r795FHbn+9HOV9nFfs/tw1buWWxuW0nQAruY0kg5cXf0gXdhieYK4Yc+StyLzTHl8fvBzOvl2ooNPh0rvt7BtS1QUgXPmoPP2rolwlRrgfv99xdpSrqvSPh5u+TCOWke+PPxlmWUOnYfQSnOBbVur30nslSIjNx+/kyswoUXfqWSiFZMZw6+nf+X+Zvfj5VS2sf7lWDpkHYvMySEoLAxNvXo1FfY1TSVhdiKlxOvs7xzVt6FxSMsSy3bF7uJ48nGGtKpatxQpP/9CyooVeA0fjmufPjUVslJDfEaNwrlHd2LefJOcI0cqvb2z3pkHWzzI3+f/JiojqsSyzrcOJEXWI25LOT2G12Ge59ZwQtecgMYly8qqU6uIzYrl2fbPVqmsJC7+1NK2ZeJEnDt1rKlwlRpS1JZyUpXaUro7unNfs/v47fRvxGeV7Ojb/fqHMaLHcGhFtYcXu1Ks3XeeAWIjaY1uA1e/EssKHlJ4su2TVdp37IczyQ4Px3/GOziEhNRAtAqoJMxujhzYRVPzWbKa31Nm2bJDy3BzcOOukPLHxruU7EOHirVtGV0ToSo1TGi1BM6cidbdnYjRYzClpVV6H4+2ehSB4Luj35WY38jXgz0uN9Mwfj2mnKvjNkvM+RO0yD9OQsOStyKNZiOfHfiMdt7t6BFQ+Y6MM7duJT4sjPp33YXHY1W7PaPYVk20pXy8zeOYzKYyZQVnT6L8bqZX7gaORCbVUMT2dWHrD3iLNNxvLDl4dkpOCj8e/5F+TfpVqY1x6u+/F3XIeuedNRWugkrC7CZyy3eYpaBln8dLzD+YcJCNERt5os0TOOoq144rPzm5aLDh2bNU25YrmM7Li8A5H2GMjiZqwsRK93Tv7+JP30Z9+en4T2QZs0osc+48GGdyOLpxRU2GbDenrbftg28o+TTX76d/JzIjskq1YMaoKCLHjcehaYhq23KFq25byob1G3Jrw1tZfmx5mbLi0eMJfEQahzb9UlPh2s2FpCw6J/5KmkMDRNOSd0C+O/od2fnZPN326UrvN/fkSaKnTMWpUyfVIasNqCTMDrLzTDSJ+ZMz9dpTzzu4xLKF4Qtxc3Dj0VaVGydSmkxEvTKB/Lg4gsLmovOq/D1/pXY5d+yI36uTyNiwgYSFiyq9/WOtHyPdmM4vJ0ueQDrddBexeGLcV/eTsDxjPg1Pf88RQzuCmrUtnG8ym1hyYAmtPVvTK6hXpfZpzssjYsxLyLw8AsNUh6x1gWvv3ng9W/W2lENDh5KWl1amrNRv2480jRueJ3+q87ck/966g17aA9DxcdAU9TmZZczim6Pf0DuoN809Kjeuoykjk4jRY9A4ORGoOmS1CZWE2cGWrZtoJiLQtC05nMS++H38G/kvT4Y+WenHhxMWLCBz82b8Jk/GqX37mgxXsSGPRx/FbcAAEubPJ3195RoId/DtQHvv9nx1+KsSnbca9DrO+vcjNHMHsTFRl9jDlW/HXysIIhZ5fcnbK2vPruVc2jlGtB9R6Vqs2BmWDln9352BQ5PqjZun1B6f0da2lG+8QfbBQ5XatoNvB67zuY6vDn+FyWwqWqAzENfobm407WTfibM1G3AtklKi3fc1ZjTU7/FUiWU/nfiJ1NxUnmn3TKX3GT3ldfLOniVw9mz0fr41GbJipZIwO0jbtQITGhrfVPKR+kXhi/Bw8Kh0LVj6uvUkLFxk6ZD14YdqMlTFxoQQNHhjOg5tWhM1YSJ5585Vavtnr3uWyIxIfjlR8go/uNcT6IWJQ3+XfYKyrpBSot/zGUnCg9a3FJUVszTz6f5PaebejD4NK/fgScrKlaR8vxzPZ56m/u2313TIig0JrZbAWbPQensRMWoU+YmJldr+ydAnicyI5J/zJTs69r/5KRyEkQv/1t3+9XadieeOvL+I9b0J3IrafBlNRpYdWkZnv8508O1QqX0mf/UV6WvW4vvyWOp161rDESsFVBJWy84nZNIhbR1R7l0QxZ5e2Ru3ly1RW3iq7VOVGqIo79w5oiZOxLFNG9Uhax2lcXQkKGweQqMhYuQozJmZFd72psCb6ODTgY/3fUxOflGj5YBW3YjUBeN+ahUmc928zbJ9z26uN+4mpvlghM6hcP7f5/7mVOopRrQfgUZU/Ccs5+hRYqZNx7lrV3zHjrVFyIqN6Tw9CZo3D1NSkqW7BGPF+8O7JfgWgl2DWXpoaYlbj/UadSHK0IiGF1aTb6qbQ34d2fgDfiIFj5tK1hj/dvo3YrNiGdZu2EW2LF/Wnj3EfvAhLn1vxfOZytWgKZWjkrBatnPdj4RoYnDtVrJvsAXhC/B09KzUUBLmrCwiRo1GaDSWti2qQ9Y6yxAUSMDsWeSeOkX0lCkVbp8ihGB0p9HEZ8fz/dHviy8go/n9dDAfZnv4fhtFbVsJ6xdhFoJmd44snCelZPH+xTSu35jbG1W8JqtEh6zqoZU6zSk0FP833yBr505iP/ywwttpNVqeaPMEBxIOsDu22LiUQpDeYiDXcYy9+6o+XqW95BhNNDr7I6k6Lxzb9C+cbzKb+Pzg57TybEXPgJ4V3p8xJoaI0WPQBwYQ8O676sLexlQSVotMZknQkc9J1nrifn3R7ZVdMbvYHr2dZ9o+U+FaMCkl0VOnkXviBAEzZ2IIqtrQRsqVw6VnT3zGvkTaH2tI+mJphbe7vsH19AzoyZKDS8jIK+qWosktQ9EISeTmuneb5dC5WHqmr+WcTx8MnkGF8zdc2MCx5GMMazcMrUZ78R0Uozpkvfq4DRiAxxOPk/zlV6SuWlXh7QY0G4C3kzdz98wtcaHT6JanMEtByra617/exl3h3Mhe0ls9BNqii4t1F9ZxNu0sz7R9psKJlDknh4gXRyKzswlesACtq6utwlasVBJWi8J3b6WbDCe25ROgMxTOX7hvId5O3jzUsuLtuZK//oa0337DZ/QoXG660RbhKnbgNWwYrrffTtzMmWT+91+FtxvVaRSpuaklegY3+DYjql4b2iSsJTY12xbh2kz4ms/wEBk06FuyFuyT/Z8Q6BJI/5D+l9i6pPi5YapD1quQ3yuv4Ny1K9FTp1W4ob6TzokXOrxAeHw4684X9cLv6BXMCZfOtI7/ndxK3OK8EmT8txStkATc8mzhPCklnx34jGDXYG5rdFuF9iOlJHry6+QcPkzAhx/i0KyZrUJWilFJWC3K2zyfbBxo0m9U4bwd0TvYGbOTYe2GVbhfsKzdu4l9/31cbrkFr2efvfwGSp0hhMB/xgwMTZoQOfZljFEVe7ox1CuU2xrdxrJDy0jKKep40qHL44RqzrHln9W2CrnGRSRl0j5qBXFOIdRr0btw/taorRxKPMSwdsPQayr2qHzqr7+S+MknuA8apDpkvcoIvZ7AOR+h9fKsVEP9+5vdT4hbCB/t+ajEU8XmdoMJIp59W9baKuQaF5uSSfeUPzjr1hWNV9GTvv9F/8ehxEM81fapCtcYJy5ZQtrvv+MzZgyufW6xVchKKSoJqyVp8ZF0TvkfB7zvwsHVcjtESsmC8AX4OvkysMXACu0n78IFIkaOstyvf/89hEYdwquN1qUeQfPmIfPyiBg9BnNuboW2G9lhJDmmHD478FnhPK+eT5KqccP/4KI600D/z//9QTvNGQzdh4P1NkpBLViDeg0Y0HRAhfaTHR5O9OTXcb7+ehpMeV21bbkKVaWhvk6jY2znsZxLO8ePx38snN/s5sFk4ohxz7e2DLlG7Vr3E4EiAafuRZ2wSilZcmAJPk4+FS4r6Rs2ED/7I+r374fXsyNsFa5SDnUGryVn1s5Fhwn3W4qGEvov+j/2xO1hWPthOGgdLrG1hSktjQvPPY80mwn++GO09evbMmTFjhxCmhDwwfvkHDxIzLTpFWqoH+Iewj0h9/D90e+JyYyxzDQ4E93qKXqY97J3+wbbBl0DUrOMeB9ZRo7GGffuRaNJ7IzZyd64vTzd9mn02svXghmjorgwchQ6Pz8Cw+YiDIbLbqPUTVVpqH9z0M108evCx/s+LmxHqXdy5ZhnH65LXU9mRuWHEqttUkrqH/6WVOGG3/UPFs7fcGEDO2J28HTbpzFoL/+9zz11iqhx43Fs3Rr/d95RFyu1TCVhtcGYTePT37Hd0JUWoZY2KVJKFoYvxM/ZjwebP3iZHYA0Gol86SXyzp0jKCxMdTJ5DXC99Va8X3yR1JUrSfz44wpt83yH5zFj5pP9nxTOC+n/Ehk4weY5Noq05vy0OZw72UZWq0HgUNQo+JP9n+Dj5MMDzR+4xNYW5qwsLrw4EpmTQ/Ciheg8PGwZsnIFqGxDfSEE47uMJyknic8Pfl4436nLY7iIbA6vu/Jrw46eOEV34w4iG99X2MY415TLBzs/oKlbUx5udfkn7U2pqVx44QWEoyNBC+ajcXKycdRKaSoJqwXRm5biJtNIaT+8cN7WqK2Ex4czov2Iy16tSCmJefsdMrduw/+NN1THedcQ75Ev4jbgXuLnhpG6+vLtugJdAhnUYhC/nPiF82nnATC4eLDffxAdMzaSeO6wrUOustx8E5n/LcVB5OPZ+4XC+Xti97AjZgdPhj552Rpjy5OQE8k9dozA2bNU4+JrSGFD/SlTydp9+a4mQr1D6dekH18d/orYzFgAWna7kwsiAO/wRZhNpsvswb4iNixBL0wE93mucN7Sg0uJyIhgUrdJl203KfPzre1OowmaF4be39/WISvlUEmYrZnN6HYu4qBsQvdb7gWKasEC6gVwf7P7L7uLpKXLSFm+HK/hw3B/8PI1AcrVQwiB/1tv4dy1K1GTXydzx47LblOQ2M8Pn184L7DfOIzoiFnzvi3DrZbVey9wv2ktKX7dwLcVAPnmfN7d8S6+Tr4MajnosvuIDwsj/a+/8Zs4AZdelRtTUqnbhF5P4Nw56P39ufDCi+SeOnXZbUZ3HI1JmgrLikarJbbTSzQxn2Xvn19eZmv7yTPm0zLqZ044tcc1uA0A0RnRLDmwhNsa3UZ3/+6X3UfchzPJ3LqVBlOn4Nypk61DVi5CJWE2ln3kT3xyzrGzwaN4uFiu4v+N/Jf9CfsZ0X7EZdu3pK9bR9wHH+B62234qF6+r0nCYCBoXhiG4GAiRo667MnF28mbIa2HsObMGo4lHQOgUcPGbHK5kxYxv2FOiaiNsCvFbJbsX/cDQSIBt15FtWDfHPmGo0lHmdRtEk66S98qSf31NxI//gT3QQPxeOIJW4esXIF0Hh4EL/kUoddzfvhwjLFxl1w/yDWIR1o9wqqTqziefByAjv2e4awmGK9dszDl59dG2JW2b/OvNCSWvOuK2k3O3DUTgPFdxl92+5SffyFp2TI8HnsMj0GXv7hRbEclYTYWvXYm0dKTDv2eBCy9GC8IX0CgSyD3Nrv3ktvmHD5M5PhXcAwNJeCD99WTkNcwrZsbwYs/Qej1XBjxLPkJCZdc/8nQJ3HVuzJ/b1FtmKbnGIQ0E7Vmpq3DrbSNx+Ppm7GabEdfRKu7AMuV/YLwBdwcdDN9G/a95PbZ+/YRPXmy9UlINXzXtcwQHEzwJx9jTknlwogRmNLTL7n+iPYjcDG4MHv3bAC0Oh2JXcbR2HyBvWs+u+S29iJ3LSONerTsbel2ZXv0dv537n883e5pAlwCLrltxsaNRE+dinP37vhNnFAb4SqXoM7qNnRy/zZC0nexL+BhOja2jED/zZFvOJx4mFEdR13ynr0xNpYLz7+Atn59ghYuUA0mFQxBQQR/vIj8xEQuvPAi5uyLd8Dq5uDGU22fYkPEBsLjwgG4qWsn/tTciPfx7yAr6aLb2sOqdZu4WbsfQ7dnQKtHSsmM7TMAeK3ba5dMqixPQo5UT0IqhZxCQwkMCyP31CkiRo1G5uVddF03BzdGtBvBlsgtbIvaBkDHO57gtKYxvnvmkG+8+Lb2kBQXSYeMfznq2x+dYz2MZiPv7XiPQJdAngp96pLbZu3eTcToMTi2aEHQvDCEvmL97Sm2o5IwGzGZJed//5AsHOkxaBwA59LOEbY3jN5Bvenf5OI9fpuzsoh4/gXM6ekEf7wIva9vbYWtXOGc2rUjcNZMcg4cIGrCBOQlGg8PaT0ET0fPwiFaHHRaokKfw1HmEPf33FqM+tL2R6TQNupHzEKHtsuTAKw7v44NERt44boXLnllb4yN49yTTyFzcgleuEA9CakUcrmxJ/5vv0XWf/8R9dpkpPnig3M/0voRAuoFMHv3bMzSjEarJbX7KzSUUez9fXEtRn15x1dMQYsJvz4vArDi2ApOppzkletfuWSH3zlHjnDhuefRBwQQvORTNSTRFUIlYTayctMubszZQHSTB3Hz9MFkNjFlyxQMWgNTe0y96JW9OS+PiJdeIufoUQJmz8KxVatajly50rneeit+r75K+l9/E/fBxftFctY782KHF9kVu4sVx1YAMKj/bWwU1+O0dwm5Wam1FfIlfbHhMA9pN2FqdTe4NiDTmMmMHTNo6dGSIW0u3st9flIS559+GlNCAg0/XYxD8+a1GLVSF7jfdx8+Y8eS9ttvxM2addH1HLQOjO40mqNJR/n99O8AdOj7KCe1TQnYF4Yxr2IdJtvamcM76RL/C7t87qdRq44kZieyYO8Cevj3oE9wn4tul3f2LOeHDUdTrx4NP1uCztOzFqNWLkUlYTYQl55D4oYF6ISZkLstjSS/Pfote+P28mrXV/Fx9il3O3NeHhGjRpG56V8aTJuGa+/etRi1Upd4PvE4Hk88TtKyZSR9+dVF1xvUYhA9A3syc9dMTqeext3ZQL1bX8FVZrBl+cVPSrXlQlIWTkd+pL7IRN/dMgTX/L3zic+KZ2qPqRe9ZW9KSeH8089gjIwk6ONFOHXoUItRK3WJ14jheDz6CEmffU7Slxd/4rFfk3608WpD2N4wcvJzEBoNGTdMIFDGsvfXRbUYcfmk2UzmqvFkCidaDn4XgLC9YWTnZzOp26SLXtgbY2M5//QzYDbT8PPP0Adcus2YUrtUEmYDH/66h0HyL7JC+iG8Qiy3IfeEcXPQzdwdcne525jz8ogcNZrMjZtoMH06Hg9XfDBv5drkN3Eirrf1JXbGDJK+/qbcdYQQvHXDWzjpnJi0aRJGk5EuN97BSeeOtDn7JeFnYmo56pK+3RDOON0P5DXoBA17cCjxEN8e/ZaHWj5Ee5/25W5jysjg/PAR5J06RdD8+dTrqvrNUy5OCIHf5Mm49L2V2HffI21t+WNDaoSGcZ3HEZMZw6cHPgXgulse4riuBcEH5pOXm1ObYZcR/ve3tM0N50jLkbh7N+BgwkF+OfELQ1oPIcQtpNxt8pOTOf/MM5hSUwn+9FMcQspfT7EflYTVsM0nEnA4tAIPkYFL7zGYpZmpW6ai1+ovehuyIAHL2LjRkoANvnxPx4oitFoCZs3C5dZbiX37bRI/+7zc9XycfZh+w3SOJB1hQfgCAPzveY0GIpm/vw8jx2ifTilTsvJose893EUmhvvmYZJm3tz2Jp6OnozpNKbcbcxZWVx49jlyjhwhcO5cXG7sWctRK3WR0GoJnDkTpw4diHplAhmbt5S7Xlf/rtzb9F4+3f8pW6O2IjQacm6chD/x7Fk1r5ajLpKbk4XPtrc4qwmm84PjMEszM7bPwMvJi+eue67cbUwZmVx49jmM5y8QtHAhTm1DazlqpSJUElaDcowmpq3cx7OGPzEHdILgbnx75Fv2xO1hUtdJ+DqXbWBvzssjcvQYawI2TSVgSqVoDAaC5lgG3o378EMSFpV/26RPwz482PxBPj/4OTtjdlKv1W1keIbyQPZPzFxrn170N679gfvFRpI7PgcN2vL9se85nHiYiV0n4moo22jYnJvLhRdfJHvvXgJnfohrn1vsELVSV2kcHQletBBD06ZceP75i9aITe42mabuTXn131eJzYylXa/7OapvQ8jhheRkZ9Zy1BZ7VrxLkIwhvfdb6A0OrDq5igMJBxjbeSwuBpcy61uatowk59AhAud8pEZZuYKpJKwGfbzxFHelfEuwjEJz0zjOpZ9n7p659ArqxT0h95RZvzAB27DBmoANtkPUSl0n9HoCPvwQtwEDiJ8bRtycOeUO+D3h+gkEuwbz2ubXSDOm43LrBEI0McT8t4IdZ2q3y4qcrHQ673+DGF0A3v2nEJMZQ9ieMHoG9uSORneUWV9ay0rWf9sJeHcG9e+8s1bjVa4OWnd3Gn25DKf27Ykc+zLJ3y8vs46z3plZN88iOz+bCZsmkI+J/JtfxZckwlfW/lPFCdHnaX9qMXudb6Bdr/tJzU1lzp45XOdzXbnNW8w5OUSOfZmsbf8RMOMdXPtcvMG+Yn8qCashZxIy2bphDWP0P0P7hzG36m+5DanRM7V72duQ5rw8Ise8ZEnApk1VCZhSLUKrxf/dGbgPGkTix58Q9/4HZRIxZ70z7930HvFZ8bzz3zvQ+h7Mnk0ZbfiV8SvCycytvd7Bz/w4hSBiSej9AeideG/He5ilmde7vV6mrMj8fCLHjS+8Xe82YECtxalcfbT169Nwyae49OpFzPTpJHz8SZmyEuIewvQe09kTt4d5e+YResPdHDK0o9mxT8jOzKjVeE8vn4geI94PfECeKY8x68eQnpfO5G6T0YiSp/D8xETOD32SjHXr8JvyuiordYBKwmqAlJIZP29ntm4+1A+E/h8W3oac2HUifvX8Sq5fkICtX29JwB55xE6RK1cTodHQ4M038HjsMZKWLiX2rbfK9I3Uzqcdz1/3PH+c+YPfz65Fc+NYWsgzXJ/2J++uOVIrcZojw2lxehl/Gm4jtOfd/H3ub/45/w/PXvcsQa5BJdY1paZy4fkXSP/rL/xenaQeWFFqhMbJiaD586h/zz3Ez5ljuWgpVVb6h/Tn4ZYP88WhL9gQsRFxy2S8SWHfytp7qvj4nk10SV7DHv/BBDVty/St09kdu5u3e75Na6/WJdbNPX2asw8PJufYMQLnzsFzyMW7d1GuHCoJqwGr90Vx54XZBJCAduASzuelMnfPXG4KvIl7m5YcmsiUkUHEqNFkrF+P39QpKgFTapTlSbDX8HzmaZK//Y7oqVPLdOg6rN0wOvp25O3/3iaqaS9odCPvGT5n7/aNbD5x6eGQqs2UT8aPL5IkXTD1fYPdsbuZ9O8k2ni1YWjo0BKr5p4+zdmHHibzv/9o8OYbeA4depGdKkrlCb2egPffK7xoiX5tMrLUWJGvXP8KrT1bM3nLZOq3bccBh460OLGEzHTb97EnzWbMayaQLOoT+sjbLNq3iF9P/8qojqPoH1Kys+/M7Ts4O/gRzNnZNPpyGfVvv93m8Sk1QyVh1ZSabWT76k95UPsv9HoFY2BnpmyZgl6jZ1qPaSVureQcO8bZBweSsXkzDaZPw/PRR+0YuXK1EkLgO3483i+8QOqPPxE1YSLmrKzC5VqNlhk3zkAieW3bNEwDP0Pn6sPnjnOY8cMm0nKMNostdeM86icfZL7DcPwb5TNy3UgCXQJZ1HdRiT7B0jds4OxDD2NKT6fR0i/weEjVgCk1T2g0+E1+De9RI0lduZKI0WMw5xR1ReGgdWBW71kgYfzG8dBnEp6ksfvbaeW2u6xJu/9YQivjEU63H8f6hM0s2reIAU0HMLzd8BLrpaxcyflhw9D5+NB4+XKc2pfftYtyZVJJWDV9+utGJpk+Icu3E3k9RzNm/Rj2xO3h1W6vlrgNmfLTT5x96GHMWVk0WrZUtQFTbEoIgc/oUfiMe5m0P/7gzMBB5Bwput0Y5BrEa91eY3fsbr44+xti8Df4aNKYkvMh76zeb5OY0qJOYtj0LhtkJ3o8eBcj172Au4M7i29bjKejpQdvKSUJiz8l4vkX0DcMpsmPP+DcubNN4lEUsJaVF1/Eb8rrZKxfz4XhJQf9DnYN5u0b3+ZQ4iFWy93sdb+dXtFfsHzxDHLzbdO9S1ZGKsG73uOEthnmbt2YtnUa3Rp0K3FhL6UkPmwe0ZNexblzZxp/9y2GoECbxKPYjkrCqiH8XCK9Dk7GQQvmB+fz/PpRbI7czJTuU7inqeVpSHN2NlGvTSZ68us4dexIk19+xrlLFztHrlwrvIcPp+Hnn2HOyODsQw+TuHRpYduXe0Lu4Y7Gd7Bg7wI2mdPR3BtGD81hWu7/gH+OxNZoHDl5+ZxeOgKTFGTcNYl3w1/GUefIktuXFF6smLOziRo3nvjZs6nfrx+Nv/lG9e6t1BrPIUMI+PBDsvbu5fSAAWRsKepLrE/DPgxtM5Tvj31PzN1PcM69O4OiPiRs3mwSM2p+SKN9y9/Cj0TO3fwSYze9TEPXhsy+ZTZ6raW22JyXR9SEiSQsXIjbAw/QcPEnaOvXr/E4FNtTSVgV5ZvMhH83na6aoyTf/ibDdr7F3ri9vHfTezzU0nLrJPfMGc4+PJjUX37B+4XnLWN2eXvbOXLlWlOvRw+arFpJvV69iHvvfS6MeJb8+HiEEEzpPoXmHs0Z+c9IlulyMV4/gqd1a9n04zySM/Nq5O+bzJLvPptFh7zd7Gj3LHPOWca7/PT2Twsb4hujojg7ZAhpa9bg8/LLBMyaicbJqUb+vqJUlNvdd9H466/QODhy4ZlhRE+dhinD8jTkmM5j6Ojbkenb38b02GxSva5jTMp7vBW2kOOx6ZfZc8UdObCLjueXsr5+L2bGf4Neo2fBrQuob7AkWcbISM4//TRpv/6Kz0sv4f/O2wiDocb+vlK7VBJWRb+v/Y0h2d9wIOg2nov5nRPJJ5hzy5zCBpNpa9dyduAg8uPiCF78CT6jRyO0WjtHrVyrdB4eBM2fR4Pp08jauZPTA+4jY+NG3BzcWHrnUvo26svMXTN5282RFP/uvJb/MUtW/FTtvyulZNbKrdwbM4/DbqHM1OwiKz+LxbctJsQtBCkl6evXc2bgIEvP3osW4j1i+EXHwVMUW3Pq0IEmv/yM59NPk/Ljj5y+914yt25Fr9HzQa8PcNQ58vhfI9jTbywmz2bMyHuPaQuXseFYXLX+bnJmHp9+uRT/H+8lRePEoiYGErITmNdnHkGuQZhzcoifv4BT/e8i5+AhAmbOxPu5Z1VZqeOErRsX1rQuXbrIXbt22TWGmPgEcuf3JM1BMqFFE5JyU5jXZx5d/buSn5xMwrz5JH/7LU7XXUfgnI/Q+/vbNV5FKS735Ekix40n99gxPB5/HN/x48CgZ2H4Qj7Z/wmdvdvz5sFd6LLyOHL3r9x6fdsq/60l6w4StP4luhjCGdbmeqJzklh8+2Ku87mOrD17iZs9i+xduzGEhBA0f54a2065omTt3Uv0a5PJO3MG94cewnfCK0SYE5m4aSKHEg8xsPFdvLznD0hLZFDuFAbfdQdDb2hcqcTIZJZ8t+M85/4MY6L5cxKcGvFuh26sj93GrN6z6NuwL+l//03ce+9jjIzEtd+d+E2YoM4rdYgQYreUstx2SCoJqyRpNrP1o0fxzf6b4SGtMArJor6LaG1uQNIXX5C8YgUyKwvPoU/gO26cqiZWrkjm3FziZs0i+cuvcGjRAp+XxuBy8838cW4tU7dMxdfBndknDpFlbEKjsX/h7VZ2aJTL2fznDzTc8ioe2gSeadGB06YMFvVdRPt0d+I+mkPGunVovb3xefEF3AcOROj1l9+potQyc04O8WHzSFq6FF0DPwLefhtDt+uZFz6PLw5+QROXIN4/fxr/DCN3Z02hd7cuTLsnFL328jeadp9L5o1V+3gwbj5DdX/xb8MbWODtwqHko4zrPI5HnG4i9p13yNy6DYfmzfGbPJl63bvVwqdWapJKwmqANJvZv/EnDFtnYxYnGBbQEAen+nwS+ib1f1hH6s8/I81m6t/VH+/hw3Fo3rzWY1SUysrYuJHo6W+QHx2NPjAQj0cfIbJ3a0bveo3s3HRmRUeQZ+jHLS99UfGr+6wkYn4cj+fpn1juGsRPDQM5lxPPvDZTCflhB6mrVqGpVw+vYcPwfOJxNM7Otv2QilIDiteKufTpg9t9AzjUwpHJO6aTkpPM2NQM7srQc0fqa7Rq3pz5j3bCzan8C4uEjFzeX3OUP3cfZYnTfHy1h/moWSfW5cbg5+zHS62epcvvp0j++hs0zs74jBqFxyODETpdLX9qpSbYLQkTQtwJzAW0wBIp5XulljsAXwKdgUTgYSnl2Uvts7aTMFN+PuH/+4p6u+cSb4jm13qerHN1pE2GO6+fDMX0vw0IjQa3Bx7Aa9gzGIKDay02W5BSYjKayc8zk280Y8o3WV6N5sLXwul8yz9zvhlTvix8b5knMZnMSJPEZJZIk8RskpjNErPJjNkkkWaJlFhfi09b4pDmsvGVzgOERiCE9bX4tLBMa7QCjVZjfS05rbW+1+oEGp0GbeE/UWxag9agQafToDNoLe/1GnQGy7KC17rcLkPm55P+zzqSv/6arJ07EY6O6O+8lY+aHGWTwzkmJCXTLL0Juxxv4KDrjUhXfzycDbg563F3MuDhrMfdWY+bo56Gsf9DbJnCz4Z8vq7vRabWSKgmiFeONqXe6k0gBB5DhuA1Yjg6Dw97f/RqkWZJfr4ZU56ZfGNROTHlX6SsWF/NpqKyUjBdUF4Kyogsp6yYzcXKjARkUVmh4LWQ5fsoROGk9b1AaCyvGq0oLCsajWW+RmMtI7qislJQTjQ663SJsqJBqxfodFrLcn2xsqLXoNNrCl91ess6dbmsmHNySFy8mOQffsAUn4DGzQ2H2/vwdaMIluv2cGOOkQlZLgxMnIi7py+fDb2ext71CrfPN5n56r9zzP7rOA2MEXxcfw4rnDNZUd+VetKB0Zq+3HDekaw1/8OUlIT7wAfxGTsWnaenHT919UmzJN9oLSd5BeXChMkoi84x+bKwjJiKnV8Kykm5ZcVkLlVeLGXCUmbMWB4ELzqXFJaRgnOMhHa9g2jZrYFNP79dkjAhhBY4DtwGRAA7gUeklIeLrfMC0F5K+ZwQYjBwv5Ty4Uvtt7aSMGNeLv/9toCI00vY55DDIaMTXkkQkmSge4IbwftjEc7OeDz8MJ5PPonez9dmsZT5sbcmSIXTeQXzir03Fpt/uffGonkmYzmZT2UJLD/O1h9vYf0hFxprImT9oS/40UcINNYTA6Jo3uV+qwtORJaTU0HiVjyJk4WFt3iBNZskpoL3+TXw/RcUnnh0hoIkTWs58Ri06A0atHrLa8E6ha/6i7wvnG+ZLp742fIklnPsGMnffEvq6tXInByimrmzPDSNmAYQrM+lTX4eHkYvonM78k92dw6bAgCBH0mMcFnMKbdIDmY50zRackOSD61jBNrzMaDR4Hb/ffi8+KJNu50o/WNf/Htd5vteplyYMRpNlnKWZ8JYpqwU35fl5FBtpcpK0cWCtbxoii4eSlxgFJQXKDktrOWi4P+jcFpa/3+KlQ0zJZM7c9GJrLC8FJz4rPNq4vMWJGQlv/cly0zhsoq+15fal40TPpmfT+a2baSuXEX6P/8gc3LICfDkt2ZphLfK51G9huQMb7JM3vRp34nQZu04mFmft/5NZ0ecYHjwWfzz5/BrvgMtzsAdsX74H0+E3DyEgwP1unfHe+RInNpVvT3mZT9DQVkp/h23fr+NeZZyYMwzFZadsuWhgucWo6nGfme1uoILA+uFQqnyUbrcFJxTipeP0u9b9wygeRe/S/zhGgjdTklYD2C6lPIO6/tXAaSU7xZb50/rOtuEEDogBvCRlwjK1knY2X372fj1UvISIiFb4J4u8MwQ6MwgsSYMbu44X98VpxtuQDg5l7lSLfwhyzdbTvYFr4XTZusVQMmsP99YUKtUrCbKWPUfe6ERhT9WZRIBfbGr1RIn/5JXsZYrWW3hFW3h1W3hVbC1UFinC04mdYW0JnEmo/UKK7/4FVjBlVnZ2sCi17KJbMEPkKnUsoIftIIfM6pY9ErXMGj12sLau4Jjo9EVP07FTvC68mo3NGVqR2RONtm7d5O1bSvmhHiENdhMgyTDCTIdIc9RoncQOLjWIyMlHecUDf4poDcJJALh6oqhYSP0DRvhEBqK1tunqKyYipUX6wnfVF6ZKX4sSpWVwpqm/KJjUdUf+0uWlWKJsN6gQVtQfkqXFUOxcqIrVVaKl5mCmterpKwUrxUvPD55xcuJtVyUSYZLJ8UXv5CsEuvFUYnyUlCbba3BK5jWaEu+15ZKiMurTS9IimVeLrkHD5KzZzd5p04ipJkEV0meTmLUCYxaSb4W0ILQSIQWzPnQIF7gnqVBItD6+uLQshUOLVuhb9wEdLqSZcUkMZuLakqLn1+K340oPq90WbHUPBWVmaqWFY1WWP4/DWXPJyWS42LzCs4hJS4qi59TdCXLTfHzS0Htq0ZTd2tQ7ZWEDQTulFIOs75/HOgmpRxZbJ2D1nUirO9PWddJKLWvEcAIgIYNG3Y+d+6cTWIG+PrV10hN7ltj+yu89aXTlHkt/mXT6UudOAu/xBf/Al/uClGrqzs/8NcaKWVRsl36pFPOlWVhwmcsqBEtOrkVr7ovrNYvVaVvLnYRUPBDfqXRaIQlKbQmkYW3vqyvBd/pi/14X7SslK5xLF1WDJaTrnJlKmwicbFkrbyLoHIulsprSmEqXl6KN60wFZUZWRfKivViqvhFcfELgqIyoy1KTAtq7fUlaxC1xaZ1Dhr0hoILEMsyVVYq71JJWJ1o5SelXAwsBktNmC3/1u3PjmDbH7/Q9dZH0NerBxTdFiuq2qSwyrPMq/UKqSDZqsvtHxTbEUJYfuT0Wqh3+fVrmpRFV9imfHNhG7zSNboFr5S5xWVte5GXR0ZsJPV8/dE5Opao/hfFqv0LbxNoi5UXbVGZ0QjLPEUpTQhhTZa1UK/2n6AteZu2WLu9Erd2LWWkeLs969ZICVl5+Tgbik63Uko0Gk1hGSmoiRbFmmVYaqhFyRo5TVE7PuXqYMskLBIo3ko9yDqvvHUirLcj3bA00Lcb38aNGfDCWHuGoCg2J4Sw3sIEvUP1OhH2a+pVQ1EpypVHaARajaWsWJ4xU5SaY8t6xZ1AcyFEEyGEARgMrC61zmpgqHV6ILDuUu3BFEVRFEVRrhY2qwmTUuYLIUYCf2K5fPhcSnlICPEmsEtKuRr4DPhKCHESSMKSqCmKoiiKolz1bNomTEr5B/BHqXlTi03nAINsGYOiKIqiKMqVSD3moCiKoiiKYgcqCVMURVEURbEDlYQpiqIoiqLYgUrCFEVRFEVR7EAlYYqiKIqiKHagkjBFURRFURQ7UEmYoiiKoiiKHdhsAG9bEULEA7YbwdvCG0i47FpKbVPH5cqjjsmVSR2XK486Jlem2jgujaSUPuUtqHNJWG0QQuy62Ijniv2o43LlUcfkyqSOy5VHHZMrk72Pi7odqSiKoiiKYgcqCVMURVEURbEDlYSVb7G9A1DKpY7LlUcdkyuTOi5XHnVMrkx2PS6qTZiiKIqiKIodqJowRVEURVEUO7imkzAhxJ1CiGNCiJNCiEnlLHcQQiy3Lt8uhGhshzCvORU4Lk8KIeKFEOHWf8PsEee1RAjxuRAiTghx8CLLhRAizHrM9gshOtV2jNeaChyT3kKI1GLlZGptx3itEUIECyHWCyEOCyEOCSHGlLOOKiu1qILHxG5lRVdbf+hKI4TQAguA24AIYKcQYrWU8nCx1Z4BkqWUzYQQg4H3gYdrP9prRwWPC8ByKeXIWg/w2rUUmA98eZHl/YDm1n/dgEXWV8V2lnLpYwLwr5Ty7toJRwHygXFSyj1CCFdgtxDir1K/X6qs1K6KHBOwU1m5lmvCugInpZSnpZR5wPfAgFLrDACWWad/BG4VQohajPFaVJHjotQyKeUmIOkSqwwAvpQW/wHuQgj/2onu2lSBY6LUMilltJRyj3U6HTgCBJZaTZWVWlTBY2I313ISFghcKPY+grIHpnAdKWU+kAp41Up0166KHBeAB61V+T8KIYJrJzTlEip63JTa1UMIsU8IsUYIEWrvYK4l1uYrHYHtpRapsmInlzgmYKeyci0nYUrd9SvQWErZHviLotpKRVGK7MEyXMp1wDxgpX3DuXYIIVyAn4CXpJRp9o5HuewxsVtZuZaTsEigeA1KkHVeuesIIXSAG5BYK9Fduy57XKSUiVLKXOvbJUDnWopNubiKlCelFkkp06SUGdbpPwC9EMLbzmFd9YQQeiwn+2+klD+Xs4oqK7XscsfEnmXlWk7CdgLNhRBNhBAGYDCwutQ6q4Gh1umBwDqpOlaztcsel1LtJ+7Fco9fsa/VwBPWJ7+6A6lSymh7B3UtE0I0KGjDKoToiuX3Xl1E2pD1//sz4IiUcvZFVlNlpRZV5JjYs6xcs09HSinzhRAjgT8BLfC5lPKQEOJNYJeUcjWWA/eVEOIklgawg+0X8bWhgsdltBDiXixPvSQBT9ot4GuEEOI7oDfgLYSIAKYBegAp5cfAH0B/4CSQBTxln0ivHRU4JgOB54UQ+UA2MFhdRNpcT+Bx4IAQItw67zWgIaiyYicVOSZ2Kyuqx3xFURRFURQ7uJZvRyqKoiiKotiNSsIURVEURVHsQCVhiqIoiqIodqCSMEVRFEVRFDtQSZiiKIqiKIodqCRMUZQ6QwjhJYQIt/6LEUJEWqczhBALbfQ3XxJCPFGF7QxCiE3Wjp4VRVHKUF1UKIpSJwkhpgMZUsqZNvwbOixDmnSyjh9b2e2nYRmQ/psaD05RlDpP1YQpilLnCSF6CyF+s05PF0IsE0L8K4Q4J4R4QAjxgRDigBBirXUIE4QQnYUQG4UQu4UQf5YaiaFAH2BPQQImhNgghOhinfYWQpy1TocKIXZYa+X2CyGaW7dfCQyx7adXFKWuUkmYoihXo6ZYEqh7ga+B9VLKdlh6w77LmojNAwZKKTsDnwPvlLOfnsDuCvy954C5UsoOQBcgwjr/IHB9NT6HoihXMdVWQVGUq9EaKaVRCHEAy/BXa63zDwCNgZZAW+Av65BxWqC88fv8qdjYpNuAyUKIIOBnKeUJACmlSQiRJ4RwlVKmV+cDKYpy9VFJmKIoV6NcACmlWQhhLDYOnBnL754ADkkpe1xmP9mAY6l5wvqqL5ghpfxWCLEduAv4QwjxrJRynXWxA5BT9Y+iKMrVSt2OVBTlWnQM8BFC9AAQQuiFEKHlrHcEaFZqXsHtxd5YatAQQoQAp6WUYcAqoL11vheQIKU01vgnUBSlzlNJmKIo1xwpZR4wEHhfCLEPCAduKGfVNUCvUvP6CiF2An2BJCHEaOAh4KAQIhzLbc4vreveAvxe4x9AUZSrguqiQlEU5RKEEL8AE6SUJ4QQG4DxUspdFdz2Z2CSlPK4LWNUFKVuUjVhiqIolzYJSwP9ShFCGICVKgFTFOViVE2YoiiKoiiKHaiaMEVRFEVRFDtQSZiiKIqiKIodqCRMURRFURTFDlQSpiiKoiiKYgcqCVMURVEURbEDlYQpiqIoiqLYwf8BnTO2pAiF9hsAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "res_clean.plot(obs)\n", + "for eps_p in np.linspace(0,.99,4):\n", + " config_spam_eps_p = SimConfig(noise = 'SPAM', eta=0, runs = 50, epsilon=0, epsilon_prime=eps_p)\n", + " sim.set_config(config_spam_eps_p)\n", + " sim.run().plot(obs, label=f'epsilon = {eps_p}')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As there are more false negatives, all atoms seem to be recaptured, until no Rydberg occupation is detected." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Doppler Noise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As for any noise, Doppler noise is set via a `SimConfig` object. When averaging over several runs, it has the effect of damping the oscillations. Let's increase the number of runs in order to see this and get smoother curves." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that you may change the standard deviation of the doppler noise, which is $k \\times \\sqrt{k_B T / m}$, where $k$ is the norm of the effective wavevector of the lasers, by changing the temperature field, setting it in $\\mu K$. We'll exaggerate the temperature field here to emphasize the effects of Doppler damping; the default value for temperature is 50$\\mu K$." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Options:\n", + "----------\n", + "Number of runs: 100\n", + "Samples per run: 1\n", + "Noise types: doppler\n", + "Temperature: 5000.0µK\n" + ] + } + ], + "source": [ + "config_doppler = SimConfig(noise='doppler', runs=100, temperature = 5000, samples_per_run=1)\n", + "sim.set_config(config_doppler)\n", + "sim.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now simulate the entire sequence with Doppler noise, much like what we did in the SPAM case. We should see damped oscillations if the standard deviation is high enough. This is the case here, as we exaggerated the temperature field." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "res_clean.plot(obs)\n", + "res_doppler = sim.run()\n", + "res_doppler.plot(obs)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple Atoms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now run the AFM preparation sequence from the Pulser tutorial with our noise models, and compare the results to the clean case. \n", + "\n", + "Note: We will not include dephasing / phase-damping, as the number of qubits ($9$ here) is too large and slows down the simulation, since the solver has to work with $2^9 \\times 2^9$-dimensional matrices instead of $2^9$-dimensional kets." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters in rad/µs and ns\n", + "Omega_max = 2.3 * 2*np.pi \n", + "U = Omega_max / 2.3\n", + "delta_0 = -6 * U\n", + "delta_f = 2 * U\n", + "t_rise = 252\n", + "t_fall = 500\n", + "t_sweep = (delta_f - delta_0)/(2 * np.pi * 10) * 1000\n", + "R_interatomic = Chadoq2.rydberg_blockade_radius(U)\n", + "\n", + "N_side = 3\n", + "reg = Register.rectangle(N_side, N_side, R_interatomic, prefix='q')\n", + "\n", + "rise = Pulse.ConstantDetuning(RampWaveform(t_rise, 0., Omega_max), delta_0, 0.)\n", + "sweep = Pulse.ConstantAmplitude(Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.)\n", + "fall = Pulse.ConstantDetuning(RampWaveform(t_fall, Omega_max, 0.), delta_f, 0.)\n", + "\n", + "seq = Sequence(reg, Chadoq2)\n", + "seq.declare_channel('ising', 'rydberg_global')\n", + "\n", + "seq.add(rise, 'ising')\n", + "seq.add(sweep, 'ising')\n", + "seq.add(fall, 'ising')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "config_all_noise = SimConfig(noise=('SPAM', 'doppler', 'amplitude'),\n", + " runs=100, samples_per_run=10)\n", + "simul = Simulation(seq, sampling_rate=0.05, evaluation_times=0.2, config=config_all_noise)\n", + "spam_results = simul.run()\n", + "simul.reset_config()\n", + "clean_results = simul.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now plot the simulation results by sampling the final states." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,5))\n", + "spam_count = spam_results.sample_final_state(N_samples=1e5)\n", + "clean_count = clean_results.sample_final_state(N_samples=1e5)\n", + "\n", + "clean_most_freq = {k:v for k,v in clean_count.items() if v>500}\n", + "spam_most_freq = {k:v for k,v in spam_count.items() if v>500}\n", + "\n", + "plt.bar(list(clean_most_freq.keys()), list(clean_most_freq.values()), width=0.9)\n", + "plt.bar(list(spam_most_freq.keys()), list(spam_most_freq.values()), width=0.5)\n", + "\n", + "plt.xticks(rotation='vertical')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The bars represent the simulation results as populations of bitstrings. They're colored blue for the noiseless simulation, and orange for the noisy one. We clearly identify the antiferromagnetic state as the most populated one in both cases, but it is slightly less populated in the noisy case, while some other bitstrings, not present in the noiseless case, appear." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb b/tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb deleted file mode 100644 index bd6fededf..000000000 --- a/tutorials/applications/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb +++ /dev/null @@ -1,718 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation of XYZ spin models using Floquet engineering in XY mode" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib\n", - "matplotlib.rcParams['text.usetex'] = True\n", - "import matplotlib.pyplot as plt\n", - "import qutip\n", - "\n", - "import pulser\n", - "from pulser import Pulse, Sequence, Register\n", - "from pulser.simulation import Simulation\n", - "from pulser.devices import MockDevice, Chadoq2\n", - "from pulser.waveforms import BlackmanWaveform" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, we will reproduce some results of \"Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms\", P. Scholl, et. al., https://arxiv.org/pdf/2107.14459.pdf." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Floquet Engineering on two atoms\n", - "\n", - "We start by considering the dynamics of two interacting atoms under $H_{XXZ}$. To demonstrate the dynamically tunable aspect of the microwave engineering, we change the Hamiltonian during the evolution of the system. More specifically, we start from $|\\rightarrow \\rightarrow \\rangle_y $, let the atoms evolve under $H_{XX}$ and apply a microwave pulse sequence between $0.9\\mu s$ and $1.2\\mu s$ only.\n", - "\n", - "Let us first define our $\\pm X$ and $\\pm Y$ pulses. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Times are in ns\n", - "t_pulse = 26\n", - "\n", - "X_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, 0)\n", - "Y_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, -np.pi/2)\n", - "mX_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, np.pi)\n", - "mY_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, np.pi/2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also define a function to add the pulses during one cycle." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def Floquet_XXZ_cycles(n_cycles, tau_1, tau_2, t_pulse):\n", - " t_half = t_pulse/2.\n", - " tau_3 = tau_2 \n", - " tc = 4*tau_2 + 2*tau_1\n", - " for _ in range(n_cycles):\n", - " seq.delay(tau_1-t_half, 'MW')\n", - " seq.add(X_pulse, 'MW')\n", - " seq.delay(tau_2-2*t_half, 'MW')\n", - " seq.add(mY_pulse, 'MW')\n", - " seq.delay(2*tau_3-2*t_half, 'MW')\n", - " seq.add(Y_pulse, 'MW')\n", - " seq.delay(tau_2-2*t_half, 'MW')\n", - " seq.add(mX_pulse, 'MW')\n", - " seq.delay(tau_1-t_half, 'MW')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are ready to start building our sequence." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# We take two atoms distant by 10 ums.\n", - "coords = np.array([[0, 0], [10, 0]])\n", - "qubits = dict(enumerate(coords))\n", - "reg = Register(qubits)\n", - "\n", - "seq = Sequence(reg, MockDevice)\n", - "seq.declare_channel('MW', 'mw_global')\n", - "seq.set_magnetic_field(0., 0., 1.)\n", - "\n", - "tc = 300\n", - "seq.delay(3 * tc, 'MW')\n", - "Floquet_XXZ_cycles(4, tc/6., tc/6., t_pulse)\n", - "seq.delay(6 * tc, 'MW') \n", - "\n", - "# Here are our evaluation times\n", - "t_list= []\n", - "for p in range(13):\n", - " t_list.append(tc/1000.*p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's draw the sequence, to see that the microwave engineering only happens between $900 ns$ and $2100 ns$, which corresponds to $H_{XX} \\to H_{XXX}$. During that period, the total y-magnetization $\\langle \\sigma^y_1 + \\sigma^y_2 \\rangle$ is expected to be frozen, as this quantity commutes with $H_{XXX}$." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "seq.draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "sim = Simulation(seq, sampling_rate=1.0, config=None, evaluation_times=t_list)\n", - "psi_y = (qutip.basis(2, 0)+1j*qutip.basis(2, 1)).unit()\n", - "sim.initial_state = qutip.tensor(psi_y, psi_y)\n", - "res = sim.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "sy = qutip.sigmay()\n", - "Id = qutip.qeye(2)\n", - "Sigma_y = (qutip.tensor(sy, Id)+qutip.tensor(Id, sy))/2.\n", - "Sigma_y_res = res.expect([Sigma_y])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "\n", - "# Showing the Hamiltonian engineering period.\n", - "line1 = 0.9\n", - "line2 = 2.1 \n", - "plt.axvspan(line1, line2, alpha=.1, color='grey')\n", - "plt.text(1., 0.5, r\"$H_{XX} \\to H_{XXX}$\", fontsize=14)\n", - "\n", - "plt.plot(sim._eval_times_array, Sigma_y_res[0], 'o')\n", - "plt.xlabel(r\"Time [$\\mu$ s]\", fontsize=16)\n", - "plt.ylabel(fr'$ \\langle \\sigma_1^y + \\sigma_2^y \\rangle$', fontsize=16)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Domain-wall dynamics\n", - "\n", - "Now, we will look at the dynamics of the system under $H_{XX2Z}$ when starting in a Domain-Wall (DW) state $|\\psi_0\\rangle = |\\uparrow \\uparrow \\uparrow \\uparrow \\uparrow \\downarrow \\downarrow \\downarrow \\downarrow \\downarrow\\rangle $, for two distinct geometries : open boundary conditions (OBC) and periodic boundary conditions (PBC). In the case of $H_{XX2Z}$, only 2 pulses per Floquet cycle are required, as the $X$ and $-X$ pulses cancel out." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def Floquet_XX2Z_cycles(n_cycles, t_pulse):\n", - " t_half = t_pulse/2.\n", - " tau_3 = tau_2 = tc/4.\n", - " for _ in range(n_cycles):\n", - " seq.delay(tau_2-t_half, 'MW')\n", - " seq.add(mY_pulse, 'MW')\n", - " seq.delay(2*tau_3-2*t_half, 'MW')\n", - " seq.add(Y_pulse, 'MW')\n", - " seq.delay(tau_2-t_half, 'MW')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "N_at = 10\n", - "# Number of Floquet cycles \n", - "N_cycles = 20 \n", - "# In the following, we will take 1000 projective measurements of the system at the final time.\n", - "N_samples = 1000 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also create the initial state of the system using QuTiP." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Creation of the initial DW state\n", - "initial_DW_state=[]\n", - "for m in range(N_at):\n", - " if m" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1,1)\n", - "img = ax.imshow(magnetizations_obc, cmap=plt.get_cmap('RdBu'))\n", - "plt.title('OBC',fontsize=16)\n", - "ax.set_xlabel('Cycle',fontsize=16)\n", - "ax.set_ylabel('Atom number',fontsize=16)\n", - "cbar = fig.colorbar(img, shrink=0.7)\n", - "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)\n", - "\n", - "\n", - "fig, ax = plt.subplots(1,1)\n", - "img = ax.imshow(magnetizations_pbc, cmap=plt.get_cmap('RdBu'))\n", - "plt.title('PBC',fontsize=16)\n", - "ax.set_xlabel('Cycle',fontsize=16)\n", - "ax.set_ylabel('Atom number',fontsize=16)\n", - "cbar = fig.colorbar(img, shrink=0.7)\n", - "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the magnetization profiles look rather different for OBC and PBC. It seems that the initial DW melts in the case of PBC. In fact, the decrease of $|\\langle \\sigma^z_j \\rangle|$ for all sites is due to a delocalization of the DW along the circle. This delocalization can be more apparent when looking at correlations. More specifically, we see on the plot below that the number of spin flips between consecutive atoms along the circle, $\\langle N_{flip} \\rangle=1/2\\sum_j(1-\\langle \\sigma_j^z \\sigma_{j+1}^z\\rangle)$, remains quite low during the dynamics for both OBC (red) and PBC (blue), while it should tend to $N_{at}/2=5$ for randomly distributed spins. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1,1)\n", - "plt.title(r'Evolution of $\\langle N_{flip} \\rangle$ in time for OBC (red) and PBC (blue).', fontsize=16)\n", - "ax.set_xlabel('Cycle',fontsize=16)\n", - "ax.set_ylabel(r'$\\langle N_{flip} \\rangle$',fontsize=14)\n", - "ax.plot(correl_pbc,'--o',color='blue')\n", - "ax.plot(correl_obc,'--o',color='red')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To investigate even more this delocalization effect, let's consider a smaller region of only 3 spins prepared in $|\\uparrow \\rangle$. The delocalization timescale will then be shorter, and we will see it more clearly happening in the system" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Creation of the initial DW state with only 3 spins up.\n", - "initial_DW_state=[]\n", - "for m in range(N_at):\n", - " if m < 3:\n", - " initial_DW_state.append(qutip.basis(2, 0))\n", - " else:\n", - " initial_DW_state.append(qutip.basis(2, 1))\n", - " \n", - "initial_DW_state = qutip.tensor(initial_DW_state)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "N_cycles=26\n", - "\n", - "magnetizations_pbc = np.zeros((N_at, N_cycles), dtype=float)\n", - "samples_evol = []\n", - "for m in range(N_cycles):\n", - " seq = Sequence(reg, MockDevice)\n", - " seq.set_magnetic_field(0., 0., 1.)\n", - " seq.declare_channel('MW', 'mw_global')\n", - " seq.set_magnetic_field(0., 0., 1.)\n", - " seq.add(X_pulse, 'MW')\n", - " Floquet_XX2Z_cycles(m, t_pulse)\n", - " seq.add(mX_pulse, 'MW')\n", - " sim = Simulation(seq)\n", - " sim.initial_state = initial_DW_state\n", - " res = sim.run()\n", - " samples = res.sample_final_state(N_samples)\n", - " samples_evol.append(samples)\n", - " correl = 0.\n", - " for key, value in samples.items():\n", - " for j in range(N_at):\n", - " magnetizations_pbc[j][m] += (2*float(key[j])-1)*value/N_samples" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1,1)\n", - "img = ax.imshow(magnetizations_pbc, cmap=plt.get_cmap('RdBu'))\n", - "ax.set_xlabel('Cycle', fontsize=16)\n", - "ax.set_ylabel('Atom number', fontsize=16)\n", - "cbar = fig.colorbar(img)\n", - "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see above that the magnetization profile tends to average. But if we look at the histogram of sampled states in time, we will remark that domain-wall configurations are dominant (in red in the histograms below). As time increases, the delocalization mechanism populates more and more domain-wall states distinct from the initial state." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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MMQsAAEB3FLMAAAB0RzELAABAdxSzAAAAdEcxCwAAQHcUswAAAHRHMQsAAEB3FLMAAAB0RzELAABAdxSzAAAAdEcxCwAAQHcUswAAAHRHMQsAAEB3FLMAAAB0RzELAABAdxSzAAAAdEcxCwAAQHcUswAAAHRHMQsAAEB3FLMAAAB0RzELAABAdxSzAAAAdEcxCwAAQHcUswAAAHRHMQsAAEB3FLMAAAB05625GwAAAMwjM9d+bFWN2BJYnSOzAAAAdEcxCwAAQHcUswAAAHRHMQsAAEB3FLMAAAB0RzELAABAdxSzAAAAdEcxCwAAQHfemrsBAFO4yY/CR/hheACA1jgyCwAAQHcUswAAAHRnsmI2M+9n5rNzth1k5oPLtgEAAMCyyYrZqnq6/Hdm3h+2Hw5/H5y3bar2AQAA0I85TzP+OCKOh+vHEbF3wTYAAAA4Zc5idufM3+9esO2UzHyQmUeZefTy5ctbahoAAAAtm7OYfR0Rd66x7ZSqelxV+1W1f/fu3VtqGgAAAC2b83dmv4s3R2J3I+LZ8PfZbQAAAHDKlKsZH0TE/tIiT08jYnfYvlNVh+dtm6p9AAAA9COrau42rG1/f7+Ojo7mbgbQgcy80eN7/qwEgIvc5PvxJ9+NN/mu9T3LBTLzeVXtn3fbnHNmAQAAYC2KWQAAALqjmAUAAKA7ilkAAAC6o5gFAACgO4pZAAAAuqOYBQAAoDuKWQAAALqjmAUAAKA7ilkAAAC6o5gFAACgO4pZAAAAuqOYBQAAoDuKWQAAALqjmAUAAKA7ilkAAAC6o5gFAACgO4pZAAAAuqOYBQAAoDuKWQAAALqjmAUAAKA7ilkAAAC6o5gFAACgO4pZAAAAuqOYBQAAoDuKWQAAALqjmAUAAKA7b83dAGB9mbn2Y6tqxJYAAMC0HJkFAACgO4pZAAAAuuM0YwAAuGWmBsH4HJkFAACgO47MAqzoJv93PcL/YYcxOdoFsL0UswAAW0LxD2wSxeyW8mUGAAD0zJxZAAAAuuPILAA0xJkzAHA9jswCAADQHUdmAQBY3U1WdncWATCC5orZzLwfEa8jYreqHs/cHNgaTm2kC/7jGQAYNHWa8VDIRlUdDn8fzNuitmTm2hfa4XWENngvAkDfWjsy+3FEfD1cP46IvYg4nK85N7cNR7vG3MdtyNoGY/XXTYuGHvp+zH1sJWsb3z+t7mOrr6P+mi+rVa32V6t932q7xtTif0tsQ1ZvWitmd878/e7ZO2Tmg4h4MPz5/zLzf992o27RLyLiTxfduOLAlCVLlqwmstb4Up0ka41TlLvbR1ndZHX3vh47a8X3Y5f7KGu1LGOim6w5/NuLbmitmH0dEXcuu8Mwj3Yj5tJm5lFV7cuSJUuWLFmytierxTbJkiVLVo+amjMbEd/Fm6OzuxHxbL6mAAAA0KqmitmqehoRu7lY+GnnZCEoAAAAWNbaacZRVV8OV7ehkB3zdGlZsmTJkiVLVh9ZLbZJlixZsrqTPa9eBQAAwHZq6jRjAAAAuI7mTjPedJn5YUQcxOJnh/4cES+q6n+OmP/zqvrrnFlj7uM2ZF2Q73WcIOuC/JX7q9X9k3VzLbwXx8xqte9bfR3t42a0axuyLsjfqL6/IH9j9rHFNvXAacYTyszfRsSriDiOxc8Q7UTExxFRVfUPYz3HnFlj7uM2ZF32HF7H28267DlWyWp1/2RtxntxzKxW+77V19E+bka7tiHrsuewj7fbrrGyWmxTN6rKZaJLRHx9wfbfr5H1TUT8ayx+zujkchQRf545a8x93IYsr2Pnfd/w/smaYTw0ntVq37f6OtrHzWjXNmRtQ99v9D622KZeLk4zntYPmfmbiHgRi/9jcici9mLxf01W9euIeFBV/7i8MTN/N3PWmPu4DVlex/myxuqvVvdP1mpafS96X69mG/qr1X1stV3bkLUNfb/p+9him7rgNOOJZeYnEXEvIt6OxaB6VuufD/92Vf1lpHaNmXWyjzvxZh+/vWHWGP11G1k7cfN99DrOlzVKf21JX21DVqvvRe/r1bK2ob9uYx9bbddOtNX3rWa1OibsY8dt6oEjs9N7FYuJ2LH077rez8yxJnePljV82Zz6wsn1Fz4Zs79Gyxp5H72O82WN0l9b0lfbkNXke3HMrC0Zq9vQX2OOrybb1XDfN5l1XpG3gf3V6rgfK6vFNjXPkdkJDROy/xwRf4yGJnc3PkF/zP4aJeuy5/A6Xtiu5l7H2+6vDeyrbchq7r3ofa2/Lsgac3w1167LnqOBfWwu67Ln2JR9bHzc3zirxTZ1oxqYuLstl2h0cvfIWcsT9I9iwybo28eN2sexFm3QV7J6yTJW9VcvWfp+/f7auMWRtiGrxTb1cnGa8bRandxtgv5q7ON87WoxS1/J6iXLWF2N/povS9+vRn/1n9Vim7rgNOOJtTq5u4MJ+jtL7WphAYjb2Eev42pZzfSXvpLVUZaxulpW65/1O6HvV2nXTuivqdvV+rhvJqvVfm+dI7PTa3Vyd5MT9KvdBSBG28eR29XkPjb8Oo6Vpa9k9ZJlrK6myc96fb8a/TVru5oc941mNdnvrXNkdkKtTu5udYL+Zc+xKfvodZx9H8datEFfyeoly1hdLau5z/rLnqOB/mqu7y97jgb2sbn+anUfL3uOTcjqrd+bUg1M3N2WSzQ6ubvhLAtAbEbWRr+O+krWlmYZq/q+lyz9tRlZY76OzWW12u89XJxmPK1WJ3e3mmVBg83I2vTXUV/J2sYsY3W+LH2/Gv21GVljvo4tZrXa781zmvHEWp3c3XDW27U9CxpsctZGv44d9NXOUrs2bdETWfNlbfT7uvEsfb9alv7ajKwxX8fmslrt99Y5Mju9Vid3t5r1fm74ggZbkrXpr2OTfVXbseiJrPmyNv193XKWvl+N/tqMrDG/H39SfN7g+3GsdrXa701zZHZCrU7ubjzrVWzwggZblLWxr2OrfXXZc2xCv8tqImtj39cdZOn71bL012Zktfr9eON2tdrvXagGJu5uyyUandwtS5as9bNabNPwGIueyJIlS5YsWT99zPL348ll1u/HVvuqh4vTjKfV6uRuWbJktbEAVKuLZbS6j7JkyZIlS9aqWvx+bLWvmuc044m1Orl7y7J2lrJuuhjOmFmt9pesDts0ZL1dFpOSJUuWrNb/W6LVfdzkrOa+H1sdW61zZHZ6r6LNyd0bn1UjLoYzZlY02l+yZskZO+v9tJiULFmyZDX53xJjtkvWypr7fmx4bDXNkdkJtTq5exuyLnuOObNa7S9Z/S/a0PhiGa32lyxZsmStzGdhl1nNfT+OlTPFmG9KNTBxd1su0ejk7i3JGnMxHAvryGq6TbeQZczLkiVLls9CWbc0JlodWz1cnGY8rVYnd29D1piT/VtcOEDWfFkttmnsLGNelixZsnwWyvqpscZEq2OreU4znphFCGbNervGm+w/ZpbXcb2snWikvzroq9beP829hrfYrjGzWhsTrff9NuzjmFmt9ldrWa1/Fo6Z1Vrft5o1yphodWy1zpHZ6VmEYL6s93Okyf5jZnkdV9NofzXZVyNnbfyYH7Ndre5jq1nb0F/G18ZkNflZaEzMmjXWmGhybLXOkdkJWYRg9qxXMcJk/1YXDmi87zd23LfaV62+fy57jk0ZW2NmNT4mmuv7bdjHMbNa7a/Gs5r7LBwzq/G+bzXrxmOit7HVlGpg4u62XKLBCee30C5ZXset6q+G+6rVrOZew1toV6v72GrWNvSX8SXLmJDV3djq4eI042m1OOF87HbJWo3XcTUt9lerfdVqVouv4djtanUfW83ahv4yvmSdZUzIuq02jTkemuc044m1OOH8TLt2ltrV0iIErWZ5HafPuo3+sgDUtFmtjvkx29XqPraa1dz7+haytml8tdb3rWYZE7LOy9mJhj5TW+fI7PRanHAe1egiBK1mhddxzqyffDg38Dq2uihFq1mtjvkxx1ar+9hqVovv67GzRhsTY2Y1/N24DVnGhKwfNfyZ2jRHZifU4oTzq55D1vmPCa9j91ljvY63MB62IWujx/w27GOrWdsw7lsdX433fatZxoSsK/Nb/HxuSjUwcXdbLtHghPPhMa0uQtBqltexjazvbpjV4qINsoytqfax1ayW+n4bsrbhu1GWMdF8VqufqT1cnGY8rRYnnEe0uwhBq1lex83IanHRBlmr2fSxNXa7tiGr1bHaapa+l3WWMTFPVqufqc1zmvHEbmlS/RhZb1ebixC0mtXcwgFDVqv91WrWKO+hht/X25DV+tjyvp4+q9Wx2mpWq+Ne1nxZ3o8zZLX6mdo6xWwDcsQJ2bKmycrMv4mIk4UW/hQ3WGhh5KwPl7JutADENmRdkD/K+GphnMqaL8v7er6sC/JnHxPbkNXwd6Os1bI+DO/HJrJabFNrfjZ3A4iIiDEnY8u65axcTPb/JBYLLTyLxaT/vx22z511b8g4lLW2scbXxox5Wavxvp4v6xIbM75azWr8u1HWalnej+1ktdimpjgyO6HM/CYi/iYWpyBkRNTw7/tV9W4jWT9ulnVh1tdV9atztv++qv5OVjdZo7yHOnlfb0PWj5sbyGp1zG9DVg9jtdWsHzevmdXqmJA1X1YP476JrFa/z3pgAahptTq5W9ZqWlw4QNbqxhoTrY5TWfNltTrmtyGr1TGxDVmtjglZ82W1OlZbzGqxTV1wZHZi2ejkblkrZzW3cICstbJGGRMNj1NZ82W1urDLNmS1Oia2IavVz3pZ82W1Olaby2qxTT1QzE4sG10wQ9bNZaOT9GXNk9Vim2RNl5XtLuyyDVkfRoPfG9uQdUH+7O9HWbJ6zWqxTa2xANSEstEFM2SNptVJ+rLmyWqxTbImyMq2F3bZhqzmvje2IesSG/PeliVrhqwW29QUR2YnlO1O0Je1WlZzCwfImm+BhLFyZG1UVqufXbJk3WZWD5/1subL+nGzrD7a1AsLQE2r1Qn6slbT6iR9WfNktdgmWfNmtfrZJUvWbWa1+n6UJauHrBbb1AVHZieW7S6YIWu1rLerwUn6subJarFNsmbPanUxFlmybjOr1fejLFnNZ7XYph4oZieW7S6YIWu1rA+jwcU3ZM2T1WKbZM2bdUF+kwt5yJIlS5asNrNabFNrLAA1oWx7wQxZq2U1t/iGrHmyWmyTLAvhyJIlS5asjchqsU1NcWR2Qtnuog2yZMlaM6vFNsmaPauHxVhkybrNrB83y5Ilq8829cICUNNqddEGWbJkrZ/VYptkzZvV6kIesmTJkiWrzawW29QFR2Ynlu0u2iBLlqw1s1psk6zZs96uBhfykCVLlixZbWa12KYeKGYBAADojgWgAAAA6I5iFgAAgO4oZgEAAOiOYhYA1pCZzzPzwSW3f5+ZjyZqy2TPBQCt8NM8AHA7voiI4w18LgBogiOzAHALquppVb04+TszH2Tms+s+fpX7n30uANgGilkAAAC6o5gFgPV9kJlPMvOHYd7q3skNy3NqM/NJRHwVEQcn912631fDthoes3vF/Z9l5v3MfHSy/ez83eE+D4Z/fzjJXbp9d+m2Z8M+fJ+Zn1/UptvrQgBYj2IWANZ3EBFfVNU7EXEYEd+ed6eq+jQiHkbEYVW9U1UfRERk5v2I2B+2ZUT8OiJeXXT/wZ2I+ENE7ETEvQvadSciHg0Z7w/bvli6/UlEPBnafRwRO1X1QVV9eVGbVugTAJiEYhYA1vd1VR1HRFTVw4jYycyDFTN2M/MgM3eq6kVVvb7GY46q6uHJc1/gm6o6HvK+jojlo6t7sSi+IxZHgPdHaBMATEoxCwDjOY7TReOlquppRPw2FgXlySm/O9d46HUWhnp+yW0vIuL+cP0g3hS2N2kTAExKMQsA49mNiKNVHlBVXw6nEb8Ti9ODL/zt2iWvV2/aKa8i4leZ+UMsTlX+9QhtAoBJKWYBYH33MnNnuDyJiONLfiLnVUTsD/c9iIgYTuU9OHOfC+8/ov1YFLAfRcSny6cRX9EmAGiGYhYA1nMci9N1n0TED7FYkOmjS+5/GIvC8I+xWJjpxBfDEdI/RsTrqvryivuP4TgWpyF/H4tTiSszH12jTQDQjKyqudsAAExk+AmfT6vq3tK2vVgUtx9dcmQZAJriyCwAbJ875/x27Ou4+VxcAJjMW3M3AACYTlU9zsyIiCdLBe1RLI7WXvZTPwDQFKcZAwAA0B2nGQMAANAdxSwAAADdUcwCAADQHcUsAAAA3VHMAgAA0B3FLAAAAN35//RnpVO9gW6AAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dw_preserved =['1110000000', '0111000000', '0011100000', '0001110000', \n", - " '0000111000', '0000011100', '0000001110', '0000000111', '1000000011', '1100000001']\n", - "\n", - "for n_cycle in [2*k for k in range(int(N_cycles/2))]:\n", - " color_dict = {key: 'red' if key in dw_preserved else 'black' for key in samples_evol[n_cycle]}\n", - " plt.figure(figsize=(16, 5))\n", - " plt.title(r'Cycle $= {}$'.format(n_cycle), fontsize=18)\n", - " plt.bar(samples_evol[n_cycle].keys(), samples_evol[n_cycle].values(), color=color_dict.values())\n", - " plt.xlabel(\"bitstrings\", fontsize=16)\n", - " plt.ylabel(\"counts\", fontsize=16)\n", - " plt.xticks(rotation=90)\n", - " plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env_xyz_quantum_sim", - "language": "python", - "name": "env_xyz_quantum_sim" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/applications/QAOA and Parametrized Sequences.ipynb b/tutorials/applications/QAOA and Parametrized Sequences.ipynb deleted file mode 100644 index 54ff74ea6..000000000 --- a/tutorials/applications/QAOA and Parametrized Sequences.ipynb +++ /dev/null @@ -1,386 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# QAOA for the UD-MIS problem: Using a Parametrized Sequence" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import igraph\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from itertools import combinations\n", - "from collections import Counter\n", - "\n", - "from pulser import Pulse, Sequence, Register\n", - "from pulser.simulation import Simulation\n", - "from pulser.devices import Chadoq2\n", - "from pulser.waveforms import CustomWaveform\n", - "\n", - "from scipy.optimize import minimize" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction\n", - "\n", - "**Parametrized** Sequences are useful when we want to construct several `Sequence` instances while changing some of their defining parameters (for example the amplitude, the detuning or the duration of the pulses). \n", - "\n", - "This tutorial is based on the \"Using QAOA to solve a MIS problem\" tutorial. The construction process is not too different from the usual `Sequence` design, but it applies the parametrization of the sequences with a set of time variables, and then proceeds to build the actual sequences with each desired value." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Register and Graph" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use a parametrized Sequence for the QAOA solution of the MIS problem of the tutorials. We restrict the number of atoms to 5, just to show a proof-of-concept.\n", - "\n", - "A link in the graph corresponds to two atoms that are within the Rydberg Blockade Radius of each other. The radius is obtained using `Chadoq2.rydberg_blockade_radius()`. In this notebook, $\\Omega$ is fixed to a frequency of 1 rad/µs." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "Rb = Chadoq2.rydberg_blockade_radius(1.*2*np.pi)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def make_graph(coords):\n", - " N = len(coords)\n", - " g = igraph.Graph()\n", - " edges = [[m,n] for m,n in combinations(range(N), r=2) \n", - " if np.linalg.norm(coords[m] - coords[n]) < Rb] \n", - " g.add_vertices(N)\n", - " g.add_edges(edges)\n", - " return g" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we create an atomic register with 5 atoms and the corresponding graph." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "coords = np.array([(0., 0.), (-4, -8), (4, -8), (7, 5), (-7, 5)])\n", - "G = make_graph(coords)\n", - "reg = Register.from_coordinates(coords)\n", - "reg.draw(blockade_radius=Rb, draw_graph=True, draw_half_radius=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph has two maximal independent sets: $(1,3,4)$ and $(2,3,4)$, respectively `010111` and `00111` in binary. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Build parametrized sequences" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "layers = 2\n", - "guess = {'t': np.random.uniform(8, 10, layers),\n", - " 's': np.random.uniform(1, 3, layers)}" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'t': array([9.41459098, 9.64685539]), 's': array([1.61492496, 2.59529682])}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "guess" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice next how we construct the sequences with parametrized variables. This is done using the. `.declare_variable()` method. We will need two time values for each layer of the QAOA sequence (the \"Mixer\" and \"Cost\" parts), so we store these variables in two lists. Next, we construct the sequence as usual, where the methods are stored until the actual building takes place." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Parametrized sequence\n", - "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ch0','rydberg_global')\n", - "\n", - "t_list = seq.declare_variable('t_list', size=layers)\n", - "s_list = seq.declare_variable('s_list', size=layers)\n", - "\n", - "if layers == 1:\n", - " t_list = [t_list]\n", - " s_list = [s_list]\n", - " \n", - "for t, s in zip(t_list, s_list): \n", - " pulse_1 = Pulse.ConstantPulse(1000*t, 1., 0., 0) \n", - " pulse_2 = Pulse.ConstantPulse(1000*s, 1., 1., 0)\n", - "\n", - " seq.add(pulse_1, 'ch0')\n", - " seq.add(pulse_2, 'ch0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once we have the parameters that we want to apply, we use the `.build()` method to assign these values into a `assigned_sequence` sequence. It is this sequence which is simulated every time the quantum loop is called:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def quantum_loop(parameters):\n", - " t_params, s_params = np.reshape(parameters.astype(int), (2, layers))\n", - " assigned_sequence = seq.build(t_list=t_params, s_list=s_params)\n", - " assigned_sequence.measure('ground-rydberg')\n", - " \n", - " simul = Simulation(assigned_sequence, sampling_rate=.01)\n", - " results = simul.run()\n", - " return results.sample_final_state()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "example_dict = quantum_loop(np.r_[guess['t'], guess['s']])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then plot the distribution of the samples, to see the most frequent bitstrings sampled." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_distribution(results):\n", - " C = dict(Counter(results).most_common())\n", - " indexes = ['01011', '00111'] # MIS indexes\n", - " color_dict = {key:'coral' if key in indexes else 'royalblue' for key in C}\n", - " \n", - " plt.figure(figsize=(12,6))\n", - " plt.xlabel(\"bitstrings\")\n", - " plt.ylabel(\"counts\")\n", - " plt.bar(C.keys(), C.values(), width=0.5, color = color_dict.values())\n", - " plt.xticks(rotation='vertical')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_distribution(example_dict);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Optimization " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The rest of the optimization takes place just like in the related tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def get_cost_colouring(bitstring, G, penalty=10):\n", - " z = np.array(list(bitstring), dtype=int)\n", - " A = np.array(G.get_adjacency().data)\n", - " # Add penalty and bias:\n", - " cost = penalty*(z.T @ np.triu(A) @ z) - np.sum(z)\n", - " return cost \n", - "\n", - "def get_cost(res,G):\n", - " counter = dict(res)\n", - " cost = sum(counter[key] * get_cost_colouring(key,G) for key in counter) \n", - " return cost / sum(counter.values()) # Divide by total samples" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def func(param,*args):\n", - " G = args[0]\n", - " C = quantum_loop(param)\n", - " cost = get_cost(C,G)\n", - " return cost" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "res = minimize(func, \n", - " args=G,\n", - " x0=np.r_[guess['t'], guess['s']],\n", - " method='Nelder-Mead',\n", - " tol=1e-5,\n", - " options = {'maxiter': 60}\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now plot the sample that we woud obtain using the variational parameters `res.x`. Each time the Quantum Loop is called, a new instance of a Sequence is built and simulated:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "count_dict = quantum_loop(res.x)\n", - "plot_distribution(count_dict)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/applications/Simulating Sequences with Errors and Noise.ipynb b/tutorials/applications/Simulating Sequences with Errors and Noise.ipynb deleted file mode 100644 index 6c977241d..000000000 --- a/tutorials/applications/Simulating Sequences with Errors and Noise.ipynb +++ /dev/null @@ -1,873 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulating different noises in Pulser" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "$\\newcommand{\\Ket}[1]{\\left|#1\\right>} \\newcommand{\\Bra}[1]{\\left<#1\\right|}$\n", - "This notebook gives an overview of how to simulate several kinds of noise in Pulser. Quantum computers aren't perfect and are susceptible to various sources of noise. In order to realistically simulate these quantum computations, we need to take them into account.\n", - "\n", - "As of now, the types of noise implemented in Pulser are:\n", - "\n", - "- SPAM (State Preparation And Measurement) errors : There are three types of such errors, one (with probability $\\eta$) related to bad initial state preparation of the all-ground state $\\Ket{g}^{\\otimes n}$, and two (with probabilities $\\epsilon, \\epsilon '$) linked to detection errors. During the imaging process, excited Rydberg atoms in $\\Ket{r}$ might decay to the state $\\Ket{g}$, allowing them to be trapped in the tweezers : those are the false negatives modeled by $\\epsilon'$. On the contrary, some atoms in $\\Ket{g}$ might get excited due to various causes (collisions...) and tweezer recapture might fail, inferring them incorrectly as atoms in $\\Ket{r}$ : those are the false positives modeled by $\\epsilon$.\n", - "\n", - "- Doppler damping : The atoms in the register are cooled to a temperature $T \\sim 50\\mu K$, which is low but still non-zero. Therefore, the laser frequency they observe is shifted by Doppler shifting due to thermal motion. This corresponds to a shift in the detuning frequency of the laser, and leads to a damping in the Rydberg population.\n", - "\n", - "- Waist of the laser : For global pulses, the laser amplitude has a Gaussian profile and atoms at the border of the waist feel a slightly lower amplitude than those at the focus.\n", - "\n", - "- Dephasing / phase-damping: Each qubit interacts with its environment, and we can model this interaction with random $Z$-rotations on each qubit. Given a dephasing probability $p$, this noise model adds two collapse operators $M_0 = \\sqrt{1-\\frac{p}{2}} \\times \\mathbb{1}$, $M_1 = \\sqrt{\\frac{p}{2}} \\sigma_z = \\sqrt{\\frac{p}{2}} (\\Ket{r}\\Bra{r} - \\Ket{g}\\Bra{g})$ and forces the solver to adopt a density matrix formalism. See [here](https://ocw.mit.edu/courses/nuclear-engineering/22-51-quantum-theory-of-radiation-interactions-fall-2012/lecture-notes/MIT22_51F12_Ch8.pdf) for a more thorough explanation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import qutip\n", - "\n", - "from pulser import Register, Pulse, Sequence, Simulation\n", - "from pulser.simulation import SimConfig\n", - "from pulser.devices import Chadoq2\n", - "from pulser.waveforms import ConstantWaveform, RampWaveform" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single atom noisy simulations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sequence preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Prepare a single atom:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "reg = Register.from_coordinates([(0,0)], prefix='q')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Act on this atom with a Constant Pulse, such that it oscillates towards the excited Rydberg state and back to the original state (Rabi oscillations):" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ch0', 'rydberg_global')\n", - "duration = 2500\n", - "pulse = Pulse.ConstantPulse(duration, 2*np.pi, 0., 0.)\n", - "seq.add(pulse, 'ch0')\n", - "seq.draw()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now run the noiseless simulation, to obtain a `CoherentResults` object in `clean_res`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "sim = Simulation(seq, sampling_rate=0.05)\n", - "clean_res = sim.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we obtain the excited population using the projector onto the Rydberg state." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "obs = qutip.basis(2,0).proj()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(clean_res._sim_times, clean_res.expect([obs])[0])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The SimConfig object" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each simulation has an associated `SimConfig` object, which encapsulates parameters such as noise types, the temperature of the register... You may view it at any time using the following command." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Options:\n", - "----------\n", - "Number of runs: 15\n", - "Samples per run: 5\n" - ] - } - ], - "source": [ - "sim.show_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When creating a new `SimConfig`, you may choose several parameters. `'runs'` indicates the number of times a noisy simulation is run to obtain the average result of several simulations, `'samples_per_run'` is the number of delivered samples per run - this has no physical interpretation, this is used simply to cut down on calculation time." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also add `SPAM` noise to the simulation by creating a new `SimConfig` object, and assigning it to the `config` field of `sim` via the `Simulation.set_config` setter. We pass noise types as a tuple of strings to a SimConfig object. Possible strings are : `'SPAM', 'dephasing', 'doppler', 'amplitude'`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "config_spam = SimConfig(noise=('SPAM'), runs = 30, samples_per_run = 5)\n", - "sim.set_config(config_spam)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now show the new configuration to have an overview of the changes we made." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Options:\n", - "----------\n", - "Number of runs: 30\n", - "Samples per run: 5\n", - "Noise types: SPAM\n", - "SPAM dictionary: {'eta': 0.005, 'epsilon': 0.01, 'epsilon_prime': 0.05}\n" - ] - } - ], - "source": [ - "sim.show_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that `SimConfig.spam_dict` is the spam parameters dictionary. `eta` is the probability of a badly prepared state, `epsilon` the false positive probability, `epsilon_prime` the false negative one." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When dealing with a `SimConfig` object with different noise parameters from the config in `Simulation.config`, you may \"add\" both configurations together, obtaining a single `SimConfig` with all noises from both configurations. This adds simulation parameters to noises that weren't available in the former `Simulation.config`. Noises specified in both `SimConfigs` will keep the noise parameters in `Simulation.config`. Try it out with `Simulation.add_config`:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Options:\n", - "----------\n", - "Number of runs: 30\n", - "Samples per run: 5\n", - "Noise types: SPAM, dephasing, doppler\n", - "SPAM dictionary: {'eta': 0.005, 'epsilon': 0.01, 'epsilon_prime': 0.05}\n", - "Temperature: 1000.0µK\n", - "Dephasing probability: 0.05\n" - ] - } - ], - "source": [ - "cfg2 = SimConfig(noise=('SPAM', 'dephasing', 'doppler'), eta=0.8, temperature=1000, runs=10000)\n", - "sim.add_config(cfg2)\n", - "sim.show_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that we set the temperature in $\\mu K$. We also observe that the `eta` parameter wasn't changed, since both `SimConfig` objects had `'SPAM'` as a noise model already. This feature might be useful when running several simulations with distinct noise parameters to observe the influence of each noise independtly, then wanting to combine noises together without losing your tailored noise parameters." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting evaluation times" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a `Simulation` field, `eval_times` refers to the times at which the result have to be returned. Choose `'Full'` for all the times the Hamiltonian has been sampled in the sequence, a list of times of your choice (has to be a subset of all times in the simulation), or a real number between $0$ and $1$ to sample the full return times array. Here, we choose to keep $\\frac{8}{10}$ of the Hamiltonian sample times for our evaluation times." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "sim.evaluation_times = .8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now obtain a `NoisyResults` object from our noisy simulation. This object represents the final result as a probability distribution over the sampled bitstrings, rather than a quantum state `QObj` in the `CleanResults` case." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "res = sim.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting noisy and clean results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The new `res` instance has similar methods to the usual `SimResults` object. For example, we can calculate expectation values. Observe how different the Rydberg population in the clean case and noisy case are : we clearly see a damping due to all the noises we added." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(res._sim_times, res.expect([obs])[0])\n", - "plt.plot(clean_res._sim_times, clean_res.expect([obs])[0])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also use the `SimResults.plot(obs)` method to plot expectation values of a given observable. Here we compute the `sigma_z` local operator expectation values. You may choose to add error bars using the argument `error_bars = True` (`True` by default for `NoisyResults`.) Be wary that computing the expectation value of non-diagonal operators will raise an error, as `NoisyResults` bitstrings are already projected on the $Z$ basis." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "res.plot(obs, error_bars=False, fmt='.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SPAM effects" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare both clean and noisy simulations for the default SPAM parameters (taken from [De Léséleuc, et al., 2018](https://arxiv.org/abs/1802.10424))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sim.set_config(config_spam)\n", - "sim.evaluation_times = 0.4\n", - "res_spam = sim.run()\n", - "res_spam.plot(obs)\n", - "sim.reset_config()\n", - "sim.eval_times = 'Full'\n", - "res_clean = sim.run()\n", - "res_clean.plot(obs)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now modify the *SPAM* dictionary, as below, allowing for more ($40$%) badly prepared atoms." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "config_spam_mod = SimConfig(noise=('SPAM'), eta=0.4, runs = 100)\n", - "sim.set_config(config_spam_mod)\n", - "sim.evaluation_times = 0.5\n", - "res_large_eta = sim.run()\n", - "res_large_eta.plot(obs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see here that the population doesn't go well above $0.6 = 1 - \\eta$, which is to be expected : badly prepared atoms don't reach state $\\Ket{r}$. We can expect this limit of $0.6$ in the Rydberg population to be more and more respected as the number of runs grows." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Changing $\\eta$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first initialize all spam error values to $0$. Then, we do a sweep over the parameter $\\eta$, probability of badly prepared states, to notice its effects." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,5))\n", - "res_clean.plot(obs)\n", - "for eta in np.linspace(0,0.99,4):\n", - " config_spam_eta = SimConfig(noise = 'SPAM', eta=eta, runs = 50, epsilon=0, epsilon_prime=0)\n", - " sim.set_config(config_spam_eta)\n", - " sim.run().plot(obs, label=f'eta = {eta}')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As $\\eta$ grows, more qubits are not well-prepared (i.e, pumped into a state different from $\\Ket{g}$) and we stop seeing occupations at all. You may increase the number of runs to smooth the curves." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Changing $\\epsilon$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now run a sweep over $\\epsilon$." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,5))\n", - "res_clean.plot(obs)\n", - "for eps in np.linspace(0,.99,4):\n", - " config_spam_eps = SimConfig(noise = 'SPAM', eta=0, runs = 50, epsilon=eps, epsilon_prime=0)\n", - " sim.set_config(config_spam_eps)\n", - " sim.run().plot(obs, label=f'epsilon = {eps}')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As more false positives appear, it looks like the system is never captured, so always in a Rydberg state. Note that when $\\eta=0$, the object we obtain is a `CoherentResults` rather than a `NoisyResults`, since in this case, the randomness comes from measurements and the simulation is entirely deterministic. This results in smooth curves rather than scattered dots." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Changing $\\epsilon'$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we run a sweep over $\\epsilon'$." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,5))\n", - "res_clean.plot(obs)\n", - "for eps_p in np.linspace(0,.99,4):\n", - " config_spam_eps_p = SimConfig(noise = 'SPAM', eta=0, runs = 50, epsilon=0, epsilon_prime=eps_p)\n", - " sim.set_config(config_spam_eps_p)\n", - " sim.run().plot(obs, label=f'epsilon = {eps_p}')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As there are more false negatives, all atoms seem to be recaptured, until no Rydberg occupation is detected." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Doppler Noise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As for any noise, Doppler noise is set via a `SimConfig` object. When averaging over several runs, it has the effect of damping the oscillations. Let's increase the number of runs in order to see this and get smoother curves." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that you may change the standard deviation of the doppler noise, which is $k \\times \\sqrt{k_B T / m}$, where $k$ is the norm of the effective wavevector of the lasers, by changing the temperature field, setting it in $\\mu K$. We'll exaggerate the temperature field here to emphasize the effects of Doppler damping; the default value for temperature is 50$\\mu K$." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Options:\n", - "----------\n", - "Number of runs: 100\n", - "Samples per run: 1\n", - "Noise types: doppler\n", - "Temperature: 5000.0µK\n" - ] - } - ], - "source": [ - "config_doppler = SimConfig(noise='doppler', runs=100, temperature = 5000, samples_per_run=1)\n", - "sim.set_config(config_doppler)\n", - "sim.show_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now simulate the entire sequence with Doppler noise, much like what we did in the SPAM case. We should see damped oscillations if the standard deviation is high enough. This is the case here, as we exaggerated the temperature field." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "res_clean.plot(obs)\n", - "res_doppler = sim.run()\n", - "res_doppler.plot(obs)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Multiple Atoms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now run the AFM preparation sequence from the Pulser tutorial with our noise models, and compare the results to the clean case. \n", - "\n", - "Note: We will not include dephasing / phase-damping, as the number of qubits ($9$ here) is too large and slows down the simulation, since the solver has to work with $2^9 \\times 2^9$-dimensional matrices instead of $2^9$-dimensional kets." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters in rad/µs and ns\n", - "Omega_max = 2.3 * 2*np.pi \n", - "U = Omega_max / 2.3\n", - "delta_0 = -6 * U\n", - "delta_f = 2 * U\n", - "t_rise = 252\n", - "t_fall = 500\n", - "t_sweep = (delta_f - delta_0)/(2 * np.pi * 10) * 1000\n", - "R_interatomic = Chadoq2.rydberg_blockade_radius(U)\n", - "\n", - "N_side = 3\n", - "reg = Register.rectangle(N_side, N_side, R_interatomic, prefix='q')\n", - "\n", - "rise = Pulse.ConstantDetuning(RampWaveform(t_rise, 0., Omega_max), delta_0, 0.)\n", - "sweep = Pulse.ConstantAmplitude(Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.)\n", - "fall = Pulse.ConstantDetuning(RampWaveform(t_fall, Omega_max, 0.), delta_f, 0.)\n", - "\n", - "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ising', 'rydberg_global')\n", - "\n", - "seq.add(rise, 'ising')\n", - "seq.add(sweep, 'ising')\n", - "seq.add(fall, 'ising')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "config_all_noise = SimConfig(noise=('SPAM', 'doppler', 'amplitude'),\n", - " runs=100, samples_per_run=10)\n", - "simul = Simulation(seq, sampling_rate=0.05, evaluation_times=0.2, config=config_all_noise)\n", - "spam_results = simul.run()\n", - "simul.reset_config()\n", - "clean_results = simul.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now plot the simulation results by sampling the final states." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20,5))\n", - "spam_count = spam_results.sample_final_state(N_samples=1e5)\n", - "clean_count = clean_results.sample_final_state(N_samples=1e5)\n", - "\n", - "clean_most_freq = {k:v for k,v in clean_count.items() if v>500}\n", - "spam_most_freq = {k:v for k,v in spam_count.items() if v>500}\n", - "\n", - "plt.bar(list(clean_most_freq.keys()), list(clean_most_freq.values()), width=0.9)\n", - "plt.bar(list(spam_most_freq.keys()), list(spam_most_freq.values()), width=0.5)\n", - "\n", - "plt.xticks(rotation='vertical')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bars represent the simulation results as populations of bitstrings. They're colored blue for the noiseless simulation, and orange for the noisy one. We clearly identify the antiferromagnetic state as the most populated one in both cases, but it is slightly less populated in the noisy case, while some other bitstrings, not present in the noiseless case, appear." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb index 32f2adc15..abc08e98d 100644 --- a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb +++ b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Using QAOA to solve a MIS problem" + "# Using QAOA to solve a UD-MIS problem" ] }, { @@ -149,9 +149,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": { @@ -188,9 +188,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we must build the quantum part of the QAOA. All atoms are initially in the groundstate $|00\\dots0\\rangle$ of the `ground-rydberg`basis. We then apply $p$ layers of alternating non-commutative Hamiltonians. The first one, called the mixing Hamiltonian $H_M$, is realized by taking $\\Omega = 1$ rad/µs, and $\\delta = 0 $ rad/µs in the Hamiltonian equation. The second Hamiltonian $H_c$ is realized with $\\Omega = \\delta = 1$ rad/µs. $H_M$ and $H_c$ are applied turn in turn with parameters $\\tau$ and $t$ respectively. A classical optimizer is then used to estimate the optimal parameters. \n", + "Now, we must build the quantum part of the QAOA. All atoms are initially in the groundstate $|00\\dots0\\rangle$ of the `ground-rydberg`basis. We then apply $p$ layers of alternating non-commutative Hamiltonians. The first one, called the mixing Hamiltonian $H_M$, is realized by taking $\\Omega = 1$ rad/µs, and $\\delta = 0$ rad/µs in the Hamiltonian equation. The second Hamiltonian $H_c$ is realized with $\\Omega = \\delta = 1$ rad/µs. $H_M$ and $H_c$ are applied turn in turn with parameters $\\tau$ and $t$ respectively. A classical optimizer is then used to estimate the optimal parameters. \n", "\n", - "Experimentally, we don't have access to the state vector $|\\psi\\rangle$. We therefore make it more realistic by taking samples from the state vector that results from running the simulation with `simul.run()`. This is done with the built-in method `results.sample_final_state()`, in which we add the measurement basis which was declared at the end of the sequence, and the number of samples desired. Currently, the repetition rate of the machine is $5$Hz." + "Instead of creating a new `Sequence` everytime the quantum loop is called, we are going to create a parametrized `Sequence` and give that to the quantum loop." ] }, { @@ -199,20 +199,54 @@ "metadata": {}, "outputs": [], "source": [ - "def quantum_loop(param, r=reg):\n", - " seq = Sequence(r, Chadoq2)\n", - " seq.declare_channel('ch0','rydberg_global')\n", - " middle = len(param)//2\n", - " params = np.array(param)\n", - " params += 4 - params % 4 # Adjust to Chadoq2's clock period (4ns)\n", - " for tau, t in zip(params[middle:], params[:middle]):\n", - " pulse_1 = Pulse.ConstantPulse(tau, 1., 0, 0) # H_M \n", - " pulse_2 = Pulse.ConstantPulse(t, 1., 1., 0) # H_M + H_c \n", - " seq.add(pulse_1, 'ch0')\n", - " seq.add(pulse_2, 'ch0')\n", + "LAYERS = 2\n", + "\n", + "# Parametrized sequence\n", + "seq = Sequence(reg, Chadoq2)\n", + "seq.declare_channel('ch0','rydberg_global')\n", + "\n", + "t_list = seq.declare_variable('t_list', size=LAYERS)\n", + "s_list = seq.declare_variable('s_list', size=LAYERS)\n", + "\n", + "if LAYERS == 1:\n", + " t_list = [t_list]\n", + " s_list = [s_list]\n", " \n", - " seq.measure('ground-rydberg')\n", - " simul = Simulation(seq, sampling_rate=.001)\n", + "for t, s in zip(t_list, s_list): \n", + " pulse_1 = Pulse.ConstantPulse(1000*t, 1., 0., 0) \n", + " pulse_2 = Pulse.ConstantPulse(1000*s, 1., 1., 0)\n", + "\n", + " seq.add(pulse_1, 'ch0')\n", + " seq.add(pulse_2, 'ch0')\n", + " \n", + "seq.measure('ground-rydberg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have the parameters that we want to apply, we use the `.build()` method to assign these values into a `assigned_seq` sequence. It is this sequence which is simulated every time the quantum loop is called. Here's an example of a sequence for some arbitrary parameters:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Experimentally, we don't have access to the state vector $|\\psi\\rangle$. We therefore make it more realistic by taking samples from the state vector that results from running the simulation with `simul.run()`. This is done with the built-in method `results.sample_final_state()`, in which we add the measurement basis which was declared at the end of the sequence, and the number of samples desired. Currently, the repetition rate of the machine is $5$Hz." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def quantum_loop(parameters):\n", + " params = np.array(parameters)\n", + " t_params, s_params = np.reshape(params.astype(int), (2, LAYERS))\n", + " assigned_seq = seq.build(t_list=t_params, s_list=s_params)\n", + " simul = Simulation(assigned_seq, sampling_rate=.01)\n", " results = simul.run()\n", " count_dict = results.sample_final_state() #sample from the state vector \n", " return count_dict " @@ -220,11 +254,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "example_dict = quantum_loop([2000,5000], r = reg)" + "guess = {'t': np.random.uniform(8, 10, LAYERS),\n", + " 's': np.random.uniform(1, 3, LAYERS)}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "example_dict = quantum_loop(np.r_[guess['t'], guess['s']])" ] }, { @@ -236,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -254,12 +298,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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q/7F1Znd3Ve1427SpfJ+bJI985CNXnxQAAHaxkSPT3X3T9N9bkvx2kscnubmqTkmS6b+37PKzF3X3ke4+cujQoXVFBgCAu1h7ma6qB1TVg44+TvLkJFcnuTTJOdPTzkny9nVnAwCAu2MTp3mcnOS3q+ro+7+pu3+vqi5L8taqemGS65M8ewPZAABgaWsv0939ySSP3WH6Z5M8ad15AABgrpGGxgMAgANFmQYAgJk2NTQewN1SF9S+vE6fv+OomwAwiyPTAAAwkzINAAAzKdMAADCTMg0AADMp0wAAMJPRPABmMsIIAI5MAwDATMo0AADMpEwDAMBMyjQAAMykTAMAwEzKNAAAzKRMAwDATMo0AADMpEwDAMBMyjQAAMykTAMAwEwnbDoAAPunLqh9eZ0+v/fldQDu6RyZBgCAmZRpAACYSZkGAICZlGkAAJhJmQYAgJmUaQAAmEmZBgCAmZRpAACYSZkGAICZ3AERgJVyV0bgnsyRaQAAmEmZBgCAmZRpAACYSZkGAICZlGkAAJhJmQYAgJmUaQAAmEmZBgCAmZRpAACYSZkGAICZlGkAAJhJmQYAgJmUaQAAmOmETQcAgHWrC2pfXqfP7315HeDgUqYBYMP2q9wnCj6sm9M8AABgJmUaAABmcpoHAHAXTj2B5TgyDQAAMzkyDQAcDLV/R8vTjpazP5RpAIC5FPx7veFO86iqp1bVx6rquqo6b9N5AABgN0OV6aq6b5J/luRpSc5M8tyqOnOzqQAAYGdDlekkj09yXXd/srv/T5K3JDl7w5kAAGBHo50zfWqSG7Z8f2OS791QFgCAA2e/hjXczyENR8y0X6oHOtm9qp6Z5Knd/fem738iyfd294u3POfcJOdO335bko+tPejyHpbkTzYdYhuZljNaptHyJDItS6blyLQcmY5ttDyJTMsaMdNW39Ldh7ZPHO3I9E1JTt/y/WnTtK/r7ouSXLTOUHNV1eXdfWTTObaSaTmjZRotTyLTsmRajkzLkenYRsuTyLSsETMtY7Rzpi9LckZVfWtV3T/Jc5JcuuFMAACwo6GOTHf3V6vqxUnemeS+SV7b3ddsOBYAAOxoqDKdJN39jiTv2HSOfTLi6SgyLWe0TKPlSWRalkzLkWk5Mh3baHkSmZY1YqZjGuoCRAAAOEhGO2caAAAODGUaAABmUqYBAGAmZRoAAGYabjQP9k9VfXuSs7O4TXuyuAHOpd197YbynJDkhUl+OMkjtmR6e5KLu/srm8i1m6r6+e7+xxt676ckeUbuvO7e3t2/t4k8e6mq53f36zb03sMtpxEz7WZT2/hB+l2wyd8DHJ+quqi7zz32M9dnxEwcP6N57KORdqJV9dIkz03yliQ3TpNPy+JGOG/p7gs3kOnNSW5Lcsm2TOckOam7f3TdmfZSVX/c3Y/cwPu+Ksljkrwhd15Oz0vyie7+6XVn2ovlNHamvWxw3R2Y3wWbWkbH4g+hr+c5abdZST7c3aetM08ybKaHJHlZFh3l4Uk6yS1ZrLcLu/u2DWQaals6Hsr0PhltJ1pVH0/ynds3xunOktd09xnrzHM0U3c/5u7OW3GmL+w2K8lf7O61/+vNbsuiqirJxze07q7abVaSx3T3N6wzTzLschox04HZxo81b4V5hltGx+IPoa/nuT3J9Vmsq6N6+v7U7r7/OvMMnOmdSf5zkku6+zPTtG/OYr09qbufvIFMQ21Lx2O4XxAH2NN32Yn+VpKPJ1n3EamvZfGX3vXbpp8yzduEz1XVs5K8rbu/liRVdZ8kz0ry+Q1lui3J93T3zdtnVNUN64+TJPlyVX1Pd1+2bfr3JPnyJgIlOTnJU3LX9VRJ/nD9cZKMuZxGzHRbxtvGR/tdcFvGW0bHLPnrzLLFX9lhX3djkvdPB3HW7ZNZlME/3j5jg+tuxEyHu/uVWydMpfqVVfWCDWUabVuaTZneP6PtRF+S5D1V9YkkRz+8j0zy6CQv3kCeZHGKySuTvLqqju4wH5rkvdO8TXhDkm9JcpedaJI3rTnLUT+Z5DVV9aDc8df66Un+dJq3Cb+T5IHdfeX2GVX1X9aeZuEnM95yGjHTiNv4aL8LRlxGyZglf7Q/hF6V5MQkdymuSX5xvVG+7lUZL9P1VfWzWRyZvjlJqurkLH4v2ZaOk9M89klVPS7Ja5LstBN9UXdfsYFM90ny+Nz5HO7Luvv2dWfZrqq+KUm6+7ObzjKq6Z/gvr7ujv7THHc24nIaMdOo/C7YXVX9QhYXjX9wh3mv7O6XbiDT4Sz+EHpi7ig8D83iD6HzuvuP1p2JY6uqE5Ocl8WgBA+fJt+c5NIkr+zuz20g0+HceVuqLLal/5wDti0p0/tspJ3odJ7m9jL9wR5wpVfVD3X3uzf03g9J8tTceTm9cxMXZBxLVX17d/+PDb33cNvTiOtOpuOzyd8FO9nkZ250o/8hNNq2lIyZaQSjb0vHokzvo5F2WFX15CSvTvKJKUeyOLH/0Un+fne/a92Z9rLBi2mel+T8JO/KnZfTDyW5oLvfsO5Me9ngchpuexpx3cl0/EYbPWPTeUbarxzLaEVx0+tuJ4Nm2uTwpjsN4fv2g/YHrDK9T0bbYVXVtUme1t2f2jb9W5O8o7u/Y515pve+dLdZSZ7Y3Q9YZ54kqaqPJfne7Tum6Z/EPrChEUZ+fbdZSc7p7gevM08y7PY04rqTablMQ/0uGPEzl4y3XzmWTRTF0balZMxMe9ngQZrhhvCdywWI++cfZXFl6m1bJx7dYWVxgcs6nZA7Ns6tbkpyvzVnOer7k/x4kj/bNv3o6QObUFkMWbTd13LnYY3W6flJfibJn+8w77lrznLUiNvTiOtOpuWM9rtgxM9cMt5+5VhF8ZvWmWUy2raUDJjpGMObnrzOLFu8MDsP4fsrSa5JokzfC422w3ptksuq6i2540rd07P4i+/iDeRJkvcn+VJ3//72GdPRs014RZIPVdW7cudRT34oyf+zoUyXJbm6u+8y5FxVvXz9cZKMuT2NuO5kWs5ovwtG/Mwl4+1XkvGK4mjbUjJmphGHNx1xCN9ZnOaxT6rqnCQ/n8U/x91lh9Xdr99ApjOT/O3c9XbiH113lpFNR3mekruek7iRoXlqcfesL3f3lzbx/rsZcXsabd3JdDAN/Jkbcb/yu0l+sbvfu8O893X331h3Jo6tqi5O8rru/oMd5r2pu39sA5memuQ3srgW5y5D+PYG7h49lzK9j0bdYU07imxi6JuDYhpvc+soLDuNN7t2I667ETONZtTtaTQjLqfRtu9R9yujGXFb4thGHsL37nCaxz7q7s9X1Xtz5w/0po5uPjKLweGfmMVY11VVD84d4zd+ahO5dlNVH+nu79rA+56V5J8neUgW5wRXktOq6rYsRqn40AYyHV13T8ripg0bX3e2p6Xf96wMtj3tZYPL6buzGJf/IdlyYd2mltOIn7mjRtqvbDVKeR1tWzqWTX3m9lJVD+zu7aftrEtv+Tr6/YE6xSNRpvfNgDvR38riLkx/9+hfeFV13yzuLPSWJE9Yc55U1Y/sNivJN68zyxavT/JT3f2BrROr6glJXpfksRvINNy6GzGT7Wk5gy6n12Ws5TTc9j1lOCtj7VdGLK+jbUujfub28tEsTq9Yq72GXK2q4Ybw3YvTPPZJVV2Z3T/Q/6K71/qBrqpPdPcZd3feijN9Jckbs/MFNc/s7getOdKxltN13f3owTJtat2NmMn2tFwmy+n48mxk+57e+8oMtF8ZMdNo29L0viN+5v7hbrOS/KPuPmmdeZIMOeTqXI5M758HbP/lkiTd/f6q2sSYkldU1auTXJI7j75wTpL/voE8SXJVkl/q7qu3z6iqv7mBPEnyu1X1H7MYYmrrcnpekk1d/DDiuhsxk+1pOZbTsY24fSfj7VeS8TKNti0lY37m/t8k/yTJV3eYd581ZzlqxCFXZ3Fkep/UYtD/R2XnD/QfdfeL15zn/lmM4bj9zkKXJrm4u3caT3XVmb4/yfXd/cc7zDvS3ZevO9P03k/LDsupu9+xoTwjrrsRM9melstjOR07y3Db95RrqP3KwJmG2ZamPMN95qrqD5P8g+6+Yod5N3T36RvI9LIkz87iVKrtQ66+tbv/v3VnmkuZ3kejfaABONhG3K+MmIm9VdW3Jflcd9+6w7yTN3gB6Xdk523pQA3hq0zfQ1XVCVkcaXlGtt3zPosjLV/Z5UfXkemHsxiofeOZ9lJVF3X3uRt435HX3YiZbE97v6/ldOz3HG775vj5zLEuyvQ+qaqHJHlZFn9hnZzFhQe3ZPHhubC33Q52DXnenMUQT5fkzve8PyfJSd39o+vMM3Cm3S66qCQf7u7T1pknGXY5ybRcJtvTcpmGWk4jLqMp11D7lREzjbYtJWNuT1vW2zOSPDwDbEt7qarf7e6nbTrHspTpfVJV78xiTNJLuvsz07RvTvKTSZ7Y3U9ec56Pd/dj7u68e2Gm27O4lenWW/P29P2p3X3/DWQacTnJtFwm29NymYZaTiMuo+m9h9qvjJhptG1pyjTc9rTHejsnyZM2tC09brdZSX6nu09ZZ57jYTSP/XO4u1+5dcK0wV5YVc/fQJ7PVdWzkrytu7+WJLW409CzkmxqwP8RM30yi18kO10ocsMOz1+HEZeTTMuxPS1ntOU04jJKxtuvjJhptG0pGXN72m29vbKqXrChTJcl+f3c+Q+hox663ijHZ1PDodwTXV9VP1uLu0IlWZzUX1UvzR1Xqa7Tc5I8M8lnqurjVfXxJJ9J8iPTvE0YMdOrkpy4y7xfXGOOrUZcTjIt51WxPS3jVRlrOY24jJLx9isjZnpVxtqWkjG3p9HWW5Jcm8WY5T+4/SvJn2wo0yxO89gnVXVikvOyOI/s4dPkm7MYWunC3sDtX3e5Svbt3X3turMMnunbd8h0qeUk08xMtqflMg21nAZdRiPuV0bMNNS2NGUaansadL09M8lHuvtjO8x7Rnf/+3VnmsuR6X3S3Z/v7pd297d390nT13d090uzOOF/raa/Nt+UxbljH5i+kuTNVXXeuvMMnOlnsxjjspJ8cPqqDWcacTnJtFwm29NymYZaTiMuo2S8/cqImUbblqZMw21Po623KdO/3alIT3b714YhOTK9BlX1x9291vveT/+s9J29bQieWtyc4JrezO2fZZJJJpmGyzRanmVsYr9yLPZ142bai23p+LkAcZ9U1VW7zcpi+KB1+1oW41tev236KdO8TZBpOTItR6blyHRso+VJMuR+ZcRMI6674TINuN6GzDSXMr1/Tk7ylNz1St1K8ofrj5OXJHlPVX0id1xc8Mgkj06y9tu9yiSTTDINnGm0PEeNtl9Jxsv0koy37kbMNNp6S8bMNIvTPPZJVV2c5HXd/Qc7zHtTd//YBjLdJ8njc+cLIC7r7tvXnUUmmWSSaeRMo+WZMo24Xxkx04jrbqhMg6634TLNpUwDAMBMRvMAAICZlGkAAJhJmQYYUFUdrqqrd5j+L6vqzOnxzy3xOi+pqm/cY/7XXw+Au8850wADqqrDSX6nu//SHs/5s+5+4DFe51NJjnT3XW7PW1X33eRFWgD3BI5MA4zrhKp6Y1VdW1X/tqq+sar+S1UdqaoLk/zFqrpyes4Dquo/VtWHq+rqqvrRqvq/sxjv9r1V9d5kUcCr6per6sNJvu/o622Z94rpNd5fVSdP0x81ff+RqvqFqvqzafopVfW+KcPVVfX9m1lMAJujTAOM69uSvLq7vyPJF5L8/aMzuvu8JP+7u8/q7r+b5KlJ/md3P3Y6mv173f3rSf5nkh/s7h+cfvQBST4wPW/7kFQPSPL+7n5skvcl+b+m6b+W5Ne6+7uS3Ljl+T+W5J3dfVaSxya5cr/+xwEOCmUaYFw3dPd/nR7/6yR/fY/nfiTJD1XVK6vq+7v7T3d53u1J3rbLvP+T5Hemx1ckOTw9/r4k/2Z6/KYtz78syfOr6uVJvqu7v7hHPoB7JGUaYFzbL2rZ9SKX7v54ksdlUap/oap+fpenfnmP86S/0ndcSHN7jnGX3O5+X5K/kcUNKV5fVc/b6/kA90TKNMC4HllV3zc9/rEk20/L+EpV3S9JquoRSb7U3f86yT/JolgnyReTPOg4c7w/yd+ZHj/n6MSq+pYkN3f3byb5l1veE+BeQ5kGGNfHkryoqq5NcmKS12ybf1GSq6rqjUm+K8kHq+rKJOcn+YUtz/m9oxcgzvSSJP+wqq5K8ugkR08h+YEkH66q/57kR7M4txrgXsXQeADsaRqn+n93d1fVc5I8t7vP3nQugBHseT4cACT5K0l+o6oqyW1JXrDZOADjcGQaAABmcs40AADMpEwDAMBMyjQAAMykTAMAwEzKNAAAzKRMAwDATP8/lXj5SevQ1f0AAAAASUVORK5CYII=\n", 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" ] @@ -305,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -323,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -334,7 +378,7 @@ "-3" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -345,16 +389,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-1.073" + "-0.981" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -365,13 +409,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def func(param,*args):\n", " G = args[0]\n", - " C = quantum_loop(param, r=reg)\n", + " C = quantum_loop(param)\n", " cost = get_cost(C,G)\n", " return cost" ] @@ -380,23 +424,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### QAOA for depth $p = 1$" + "### QAOA for depth $p = 2$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We now use a classical optimizer `minimize` in order to find the best variational parameters. This function takes as arguments `func`, the graph `G`and an initial `x0` point for the simplex in Nelder-Mead minimization. The initial point `x0` was estimated beforehand using the best of many initial points, greatly facilitating the optimization process. " + "We now use a classical optimizer `minimize` in order to find the best variational parameters. This function takes as arguments `func`, the graph `G`and an initial `x0` point for the simplex in Nelder-Mead minimization." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "res = minimize(func, args=G, x0=np.array([1000,9000]),method='Nelder-Mead', tol=1e-5,options = {'maxiter': 15})" + "res = minimize(func, \n", + " args=G,\n", + " x0=np.r_[guess['t'], guess['s']],\n", + " method='Nelder-Mead',\n", + " tol=1e-5,\n", + " options = {'maxiter': 100}\n", + " )" ] }, { @@ -408,12 +458,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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m8n1hkpx55pnrTwoAANuY5cx0d988/fcDSX4vyUOTvL+qTk2S6b8f2OZ7L+vug9198MCBA3sVGQAAjrHnZbqq7lVV9zl8P8mjklyb5MokF0wPuyDJK/Y6GwAAnIg5pnmckuT3qurwn/+S7n5VVb05ycuq6qlJbkzyxBmyAQDAju15me7udyd5yBbjH0zyiL3OAwAAo5a0NB4AAOwryjQAAAyaa2k8AJLUJbVrz9UXb7miKABr5Mw0AAAMUqYBAGCQMg0AAIOUaQAAGKRMAwDAIGUaAAAGWRoPgC1Ztg/g9jkzDQAAg5RpAAAYpEwDAMAgZRoAAAYp0wAAMEiZBgCAQco0AAAMUqYBAGCQMg0AAIOUaQAAGKRMAwDAIGUaAAAGKdMAADBImQYAgEHKNAAADFKmAQBgkDINAACDlGkAABh00twBAOBE1SW1q8/XF/euPh9w16FMA8AuUvThrsU0DwAAGKRMAwDAIGUaAAAGKdMAADBImQYAgEHKNAAADFKmAQBgkDINAACDlGkAABikTAMAwCCXEweAu5DdvNy5S52DM9MAADBMmQYAgEHKNAAADFKmAQBgkDINAACDlGkAABikTAMAwCBlGgAABinTAAAwSJkGAIBByjQAAAxSpgEAYJAyDQAAg06aO8BmVfWYJM9Jcvckv9Hdl84cCQDYA3VJ7erz9cW9q88HW1nUmemqunuS/5rksUnOSfKkqjpn3lQAALC1RZXpJA9NckN3v7u7/znJFUnOmzkTAABsaWnTPE5L8t4NX9+U5KtnygIAkGTZU1CWnC3Z3XxLnLpT3csJVVXfluQx3f1909ffneSru/vpGx5zYZILpy+/KMk79zzozj0gyd/OHeI4lpxPtnFLzifbmCVnS5adT7YxS86WLDufbOOWnu/zu/vA5sGlnZm+OckZG74+fRr7tO6+LMllexlqVFUd6u6Dc+fYzpLzyTZuyflkG7PkbMmy88k2ZsnZkmXnk23c0vNtZ2lzpt+c5Oyq+oKqumeS85NcOXMmAADY0qLOTHf3J6vq6UlendXSeM/v7rfPHAsAALa0qDKdJN39yiSvnDvHLln6dJQl55Nt3JLzyTZmydmSZeeTbcySsyXLzifbuKXn29KiPoAIAAD7ydLmTAMAwL6hTAMAwCBlGgAABinTAAAwaHGredwZVdVl3X3h7T9y7y0hW1WdlOSpSb4lyQOn4ZuTvCLJ87r7E3NlO56q+snu/qmZMzw6yeOTnDYN3ZzkFd39qtlCTZac7Xgc1zFV9ZTufsECcnxxkvNy9L67sruvmy/V8c39d26//gxegv2676rqj7r7sXPnuLOwmscuqar7b7cpyVu7+/S9zHNUgAVnS5KqemmSDye5PMlN0/DpSS5Icv/u/vaZoh1XVf11d58545//7CQPTvKiHL3fnpzk+u7+oZmiLTrb7XFcx8y936YMP5bkSUmuyNH77vwkV3T3pXNlO565991++Bm81DeYS953VfUV221K8gfdfepe5jkRSzjRdyKU6V1SVbcluTGrv6SH9fT1ad19z1mCZdnZkqSq3tXdDz7RbXuhqj6y3aYkn9nds/12Z7t9U1WV5F3dffYMsQ5nWGy2KYfjOqCqrtluU5IHd/dn7GWeY0JUvSvJl24+GzhdUfftM++7ffd37va27ZUlv8Fc8r6b/u3/0xz9b/9hD+vuz9zjSEdZ+om+E2Gax+55d5JHdPdfb95QVe+dIc9GS86WJB+qqickeXl3fypJqupuSZ6Q5O9mTbY64/BV3f3+zRsWsO8+XlVf1d1v3jT+VUk+PkegDZacLXFcR52S5NE59nVZSf587+Mc41NZ/ar9xk3jp07b5vThLPfv3JJ/BifJ47Z5g/nbSd6VZM7f1ix5312X5Pu7+/rNGxbwdy5Jbs32J/o+d5ZEg5Tp3fPsJCcnOaawJvnZvY1yjGdnudmS1a9gfybJr1XV4R8+90vyumnbnF6U5POTHPMPYJKX7HGWzb4nyXOr6j45crbmjCR/P22b0/dkudkSx3XUHyS5d3dfvXlDVf3Jnqc51kVJXltV1yc5XBbOTPKFSZ4+V6jJkv/OLflncLLsN5ib911lte/+d+bfd8/M9gtN/OAe5tjO0k/07ZhpHixKVX1OknT3B+fOsl9U1edlwzzC7n7fnHk2WnK2pbPvxkxnBR+ao+fWvrm7b5sv1f6xxJ/B09zf5ybZ6g3m07r7qrmybbTEfbdkVfW0JH/W3W/dYtsPdvevzBBriDK9B6rqkd39mrlzbGXJ2ZJl5Kuqz07ymBz9j/Oru/vDs4WayLb7quqLu/sv586xlSVkm+Zuby6rb+qF/GOy5Hz78TWxhJ/Bhy31DeY2K8i8Yu7X6vEsZfWdOwvrTO+N580d4DiWnC2ZOV9VPTnJW5I8PMlnTbdvTHLVtG02sq3NH88d4DhmzVZVj0pyfVa/Pn7cdLskyfXTtlktOd8+fk0s4t+I6Y3Iv9l4q6r7zRoqn15B5oqspne8abpVkiuq6hlzZrsdl8wd4Hiq6pFzZzgRzkzvkqq6crtNSb6pu++1l3mOCrDgbMmy81XVO5N89eYzR1V1cpI3zvxJbdkGVdUvb7cpyQXdfd+9zHNUgGVnuy7JY7v7PZvGvyDJK7v7S2YJdiTHYvMt+TWx5J/ByaffiFyc1ZvJm6fh05M8Mskl3f2iGbMteQWZRa++czxzLxd5onwAcfd8fZLvSvIPm8YP/8pxTkvOliw7X2X16eLNPpWtlxvaS7KNe0qSH0nyT1tse9IeZ9lsydlOypE5qxvdnOQee5xlK0vOt+TXxJJ/BifJf07yldu9Ecnqw51zWfIKMotefed23sR9zl5muaOU6d3zhiQf6+4/3bxhOiMxpyVnS5ad71lJ3lJVf5yjVwd4ZJL/MluqFdnGvTnJtd19zD8oVfXMvY9zlCVne36SN1fVFTlyXM/IatWCJUwHWHK+Jb8mlvwzOFn2G5GLstwVZJa++s7S38TtmGkecDumsx+PzrEfGpp7DVHZBk0XC/h4d39s7iybLTlbklTVOUm+Ocdervsd86U6Ysn5lvyaWLKquiDJT2Y1zeOYNyLd/cKZoiWxgsyoqvqjJD/b3a/bYtvru/sbZog1RJm+C6mqU3L0J6G3Wu90NkvPt1T22x1z+Cpc3f2hubNsJtu4pebzeh2zH9+IVNW9u3vzWddFWHK2/UiZ3gNV9bbu/rIZ//wvz2qNzs/O0R/e+HCSH+jut8wULcny821nAcf13CT/Lav9dlNWvxqz33aW4cysLlj0iKz2VyW5b1YXWnjG5g+wyXZMtm/Kao3fxWRLlp3P6/WO229vRJb8IbolZdtvx3Ur5kzvkqr61u02Jfm8vcyyhRdkdUnRN24crKqHTdseMkuqIxabb+HH9YWx30b9dlZXBv3Ow7+Kraq7Z3UJ4CuSPGy+aLLdAUvO98J4vQ7Z7o1IVX04M78Rqaof3m5TknvvZZZjAiw4W7L9ibQlHNcT5cz0LqmqTyR5cbb+kMS3dfd99jjSp1XV9dstz1NVN3T3F+51pk0ZFpvPcR2z5P2W3O6+23bbXpBt3JLzeb2Oq6qrs/0bkf/e3XO+Efl4kp9L8sktNv+n7r7f3iY6YsnZkmUf1xPlzPTuuSbJz3f3tZs3VNW/nSHPRn9UVX+Y1fJBGz/h/uQkr5ot1RFLzue4jlnyfktWF8r4tSSX5+h9d0GSv5gt1Yps45acz+t13L02F64k6e43VNWsa2BndSGe3+8tLmleVd83Q56NlpwtWfZxPSHOTO+Sqvr6JDd2919vse1gdx+aIdbGDI/NsZc7vbK7XzlfqiOWms9xHbMP9ts9kzw1W+y7JM/r7q3WeJZtwdmSfZHP63VArS5k9KBs/Ubkr7p7tiXoquqLknywu/92i22nzDn/d8nZpgyLPa4nSpkGABZtqW9EuGPuLMdVmd4lVXVSVmdEviWrqyElq78Ur8jqjMgntvveOVXVZd194dw5tjN3Psd1+M9f9H7bkO/xOfqH+Oz5ZBu39Hzb8Xq9c5r7uB7PkrPtR8r0Lqmql2a1xNHlOXI529Ozmqt3/+7+9pmifXq91a02JXlrd5++l3mOCbHgfI7rmCXvt2TZ+WQbt+R8Xq/jquqzk/x4VmcwT8nqg5IfyKrsX9qbLjO+x9mWfFwXmy1Z9nE9Ucr0Lqmqd3X3g090216oqtuS3JgcddnVnr4+rbvvOUuwyZLzOa5jlrzfbi/D3PlkG7fkfF6v46rq1VmtFX55d79vGvu8JN+T5Ju6+1EzZlvycV1stmTZx/VEWc1j93yoqp6Q5OXd/akkqdUlRp+QZO4rNL07ySO2+XDJe7d4/F5bcj7HdcyS91uy7HyyjVtyPq/XcWd1989sHJjK16VV9ZSZMh225OO65GzJso/rCbnb3AHuRM5P8m1J3ldV76qqdyV5X5JvnbbN6dlJTt5m28/uYY7tPDvLzee4jlnyfkuWnU+2cUvO9+x4vY66sap+tFZXykuyWo2iqn4sR1aBmMuzs9zj+uwsN1uy7ON6Qkzz2EVV9SU59lOpr+ju6+ZLtVJVX5ytPzE7e7Zk2fkc1zFL3m/JsvPJNm7J+bxex1TVyUmekVW+z52G35/VkoeXdvesZ88XflyXnG3Rx/VEODO9S6Z3Ui/Jaj7SG6dbkry0qp4xW7AkVfWjWV1Kt5K8abrVErIly87nuA5nW+x+S5adT7ZxS87n9Tquu/+uu3+su7+4u+8/3b6ku38sq5VbZrPw47rYbMmyj+uJcmZ6l0y/FvvSzUsI1eoiAm/veS9ju9hsU47F5pNtzJKzTTkWm0+2cUvOJ9t6VNVfd/eZM/75i913S852e+Y+rifKBxB3z6eyWp/zxk3jp07b5rTkbMmy88k2ZsnZkmXnk23ckvPJNqiqrtluU1ZLqs1pyftuydmWflxPiDK9ey5K8tqquj5HJs6fmeQLk8x9ScyLstxsybLzXRTZRlyU5WZLlp3vosg26qIsN99FkW3UKUkenWNXFqkkf773cY5yUZa77y7KcrMlyz6uJ8Q0j100LSX00Bw90f/N3X3bfKlWlpwtWXY+2cYsOVuy7HyyjVtyPtnGVNXzkrygu/9si20v6e7vmCHWxgxL3ndLzrbo43oilGkAABhkNQ8AABikTAMAwCBlGmCBquqsqrp2i/HfqKpzpvs/sYPnuaiqPus42z/9fACcOHOmARaoqs5K8gfd/S+P85h/6O57387zvCfJwe7+2y223X0JH0QC2M+cmQZYrpOq6sVVdV1V/Y+q+qyq+pOqOlhVlyb5zKq6enrMvarqD6vqrVV1bVV9e1X9x6zWmX1dVb0uWRXwqvqFqnprkq85/Hwbtj1reo43VNUp0/iDpq/fVlU/XVX/MI2fWlWvnzJcW1VfP89uApiPMg2wXF+U5Ne6+0uSfCTJDxze0N3PSPKP3X1ud39nksck+Zvufsh0NvtV3f3LSf4myTd29zdO33qvJG+cHrd5Sap7JXlDdz8kyeuT/Idp/DlJntPdX5bkpg2P/44kr+7uc5M8JMnVu/U/DrBfKNMAy/Xe7v4/0/3fSvJ1x3ns25I8sqp+pqq+vrv/fpvH3Zbk5dts++ckfzDdvyrJWdP9r0nyO9P9l2x4/JuTPKWqnpnky7r7o8fJB3CnpEwDLNfmD7Vs+yGX7n5Xkq/IqlT/dFX95DYP/fhx5kl/oo98kOa23M5Vcrv79Um+IasLQbywqp58vMcD3Bkp0wDLdWZVfc10/zuSbJ6W8YmqukeSVNUDk3ysu38ryc9lVayT5KNJ7nMHc7whyb+f7p9/eLCqPj/J+7v715P8xoY/E+AuQ5kGWK53JnlaVV2X5OQkz920/bIk11TVi5N8WZI3VdXVSS5O8tMbHvOqwx9AHHRRkh+uqmuSfGGSw1NIHp7krVX1F0m+Pau51QB3KZbGA+C4pnWq/7G7u6rOT/Kk7j5v7lwAS3Dc+XAAkOQrk/xqVVWSDyf53nnjACyHM9MAADDInGkAABikTAMAwCBlGgAABinTAAAwSJkGAIBByjQAAAz6/4ClxreTbW4fAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -438,6 +488,7 @@ } ], "metadata": { + "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", @@ -453,7 +504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/tutorials/applications/Bayesian Optimisation for antiferromagnetic state preparation.ipynb b/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb similarity index 99% rename from tutorials/applications/Bayesian Optimisation for antiferromagnetic state preparation.ipynb rename to tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb index 9cea1f9e5..d187eae1d 100644 --- a/tutorials/applications/Bayesian Optimisation for antiferromagnetic state preparation.ipynb +++ b/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Optimal control for an antiferromagnetic state" + "# Optimal Control for AFM State Preparation" ] }, { diff --git a/tutorials/applications/Building 1D Rydberg Crystals.ipynb b/tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb similarity index 99% rename from tutorials/applications/Building 1D Rydberg Crystals.ipynb rename to tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb index faf388314..c3cecfa19 100644 --- a/tutorials/applications/Building 1D Rydberg Crystals.ipynb +++ b/tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb @@ -88,7 +88,7 @@ " \n", " atom_coords = [((R_blockade/N)*x+40*group,0) for group in range(groups) for x in range(1,N+1)]\n", " reg = Register.from_coordinates(atom_coords, prefix='q')\n", - " reg.draw(blockade_radius=R_blockade, draw_half_radius=True)\n", + " reg.draw(blockade_radius=R_blockade, draw_half_radius=True, draw_graph=False)\n", " \n", " resonant_pulse = Pulse.ConstantPulse(1500, Omega_max, 0., 0.)\n", " \n", diff --git a/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb new file mode 100644 index 000000000..6496185e2 --- /dev/null +++ b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb @@ -0,0 +1,717 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation of XYZ spin models using Floquet engineering in XY mode" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import qutip\n", + "\n", + "import pulser\n", + "from pulser import Pulse, Sequence, Register\n", + "from pulser.simulation import Simulation\n", + "from pulser.devices import MockDevice, Chadoq2\n", + "from pulser.waveforms import BlackmanWaveform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we will reproduce some results of \"Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms\", P. Scholl, et. al., https://arxiv.org/pdf/2107.14459.pdf." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Floquet Engineering on two atoms\n", + "\n", + "We start by considering the dynamics of two interacting atoms under $H_{XXZ}$. To demonstrate the dynamically tunable aspect of the microwave engineering, we change the Hamiltonian during the evolution of the system. More specifically, we start from $|\\rightarrow \\rightarrow \\rangle_y $, let the atoms evolve under $H_{XX}$ and apply a microwave pulse sequence between $0.9\\mu s$ and $1.2\\mu s$ only.\n", + "\n", + "Let us first define our $\\pm X$ and $\\pm Y$ pulses. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Times are in ns\n", + "t_pulse = 26\n", + "\n", + "X_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, 0)\n", + "Y_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, -np.pi/2)\n", + "mX_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, np.pi)\n", + "mY_pulse = Pulse.ConstantDetuning(BlackmanWaveform(t_pulse, np.pi/2.), 0, np.pi/2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also define a function to add the pulses during one cycle." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def Floquet_XXZ_cycles(n_cycles, tau_1, tau_2, t_pulse):\n", + " t_half = t_pulse/2.\n", + " tau_3 = tau_2 \n", + " tc = 4*tau_2 + 2*tau_1\n", + " for _ in range(n_cycles):\n", + " seq.delay(tau_1-t_half, 'MW')\n", + " seq.add(X_pulse, 'MW')\n", + " seq.delay(tau_2-2*t_half, 'MW')\n", + " seq.add(mY_pulse, 'MW')\n", + " seq.delay(2*tau_3-2*t_half, 'MW')\n", + " seq.add(Y_pulse, 'MW')\n", + " seq.delay(tau_2-2*t_half, 'MW')\n", + " seq.add(mX_pulse, 'MW')\n", + " seq.delay(tau_1-t_half, 'MW')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are ready to start building our sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# We take two atoms distant by 10 ums.\n", + "coords = np.array([[0, 0], [10, 0]])\n", + "qubits = dict(enumerate(coords))\n", + "reg = Register(qubits)\n", + "\n", + "seq = Sequence(reg, MockDevice)\n", + "seq.declare_channel('MW', 'mw_global')\n", + "seq.set_magnetic_field(0., 0., 1.)\n", + "\n", + "tc = 300\n", + "seq.delay(3 * tc, 'MW')\n", + "Floquet_XXZ_cycles(4, tc/6., tc/6., t_pulse)\n", + "seq.delay(6 * tc, 'MW') \n", + "\n", + "# Here are our evaluation times\n", + "t_list= []\n", + "for p in range(13):\n", + " t_list.append(tc/1000.*p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's draw the sequence, to see that the microwave engineering only happens between $900 ns$ and $2100 ns$, which corresponds to $H_{XX} \\to H_{XXX}$. During that period, the total y-magnetization $\\langle \\sigma^y_1 + \\sigma^y_2 \\rangle$ is expected to be frozen, as this quantity commutes with $H_{XXX}$." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OpqpF1Tnl0uLBUVT9cnj68MDCsCyVYVGT9STblJW7g6WrzulUrjXz7SsVhTnTzenUDfSxwVUlppluGw+oBhbEpgiO4pVheY/PSX/sdcLExuB6momIiIhkUaieYT2uA54GYGanA58BTgLeAPzeEMfVF7fsgDf+6Ny3vf7S4PalmpiYWPqTiEihRdUvaU6OW+1WML0q5YluFtFUtSyBRKvdyr86p5m+MiweSLRy/HeZaDU/w2i0U67GmTJkySKqAkpz7MW3aaDTJBOOL3o/tdvtgawmmTa0zPJ+yiwMzNMuQDBaGaXWUmWYiIiIFFuRw7CnAN8Iv38J8HV3vwz4eYKVJpe1ZhtWVOe+bUUVWn34G3fr1q1LfxIRKbRGq0G1XKXdbicOjhqtBuVSeTBT1VozU9XSjK9arg6kb1MUvKWqzolNVcszMOnsJ7NUgUmz1RxIGBY1gE8TWjZbzc425d3gv3dKYRLRNjV9AIs3tBuMVtOFYb3TjvMSHdflUjn9AgQpt0lERERkGIochpWB6K+pHwFuDL+/HzhlKCPqo3M3wL9+c+7b/vWbsPkHlv4a4+PjS38SESm0+Ml70tXlopAl7YlupvHFpklGDbmTjC8KWfJeMa+zcmWaaYitOmPVMVqt5NuUdWzlUplKqZIqtBxUIBGtHJh235Yt2Kbcw7r4sZfivTGofnVZQstGq8FYZSz38cWPvTQ9zQYVxIqIiIgsVZHDsDuB15nZxQRh2GfD608D9g5tVH3yphfCL10HH/sKNMN/sG624O+/Cq+9Lrh9qdauXbv0JxGRQutUUaWcCla2MtVydSDVOamnqrXqnTAs70Ci7e3g55BiWle9GY4v58q1eHCUpqdZy1tUy9Xcq/4arQbVUjXVqp+NVoNSqUS1XM29wX9Xb7cUU3TLpTLuPrCqxFRBZzMM61r5BsWdMKxcSRWqdhZVaOZ77ImIiIgsVWEb6ANvBf4ZeDPwEXffHl5/OXDrsAbVLy+/GO5/HF55LbzqWli3GvYeDm77jZ+AVzx36a+xevXqpT+JiBRavVmnUq7Q9naqE/5KuUK5VE4VsmQdX9TgP23lmpN/IAFkO+Gvhg3+c5wmGe3bSrnCdD3dfqqUKrlX/dWb9WDxACNV0FkpVVJXu2UaXxjEHpo+lGp80YIIeVclunvq0LLWCnqGPTH9RK7vjejnUClVUgXmUbic974VERERWarChmHu/mUzWw8c7+4HYjf9NXBkSMPqq999CbzqefC57bDnMKxfDZdeCJvWw633w0VnLe35d+7cyaZNm/oyVhEpplqzlvrkvdFqULJSEJgMaMXBNFVetUaNUikoXM47kIAgOEq1Yl6GqZ9ZLGU/VcqV3Ktz6s16sBADpJsmGVYc5V0ZFlVRNVspphA3g585JA/4MrP0oWXn2GsNaJpkuZKqatLMBhJ0ioiIiCxVYcMwAHdvAQd6rm4Bbwm/lr0nrYfX/Pfu62oNeNbvQOtjS3vu8847b2lPICKFF02lg+Qn79GJbrVczb0yrNFqsGp0VeqpapVS8Osp70DCcaqVauom4ceNHJd7E/OuQCJFDyZ3D/rI5bxSaFTlBaSaJtmZfpd3v7p22F8rxXTWLOFyZp4hiA2rEpvt5AFfFo3mzHTReiP5fnL3gexbERERkaUqbBhmZjfMc9NG4GyOkjBsPv34E3fnzp2ccsqyX2tARBZQb84EEqkrw8qVVCe6WXQqw1L0OOpMv2MA1TlAtZRuWle9VefE6omDWU0yQ88wJwwk2nXcvVO91W/x/ZRmtcaSlaiW8u9X12g1OH7seJqt5KFlvVXvhMtJtymraD81Wg3a7XanGnKx8R0/dvxgGuhbGStb6mMvmiaZdJtEREREhqGwYRiwr+dyGXgy8FTglYMfzmD149Rlair5ClAisjzVm/XOCWfSk/foMQPpKxX2bWp68kCi1gqqcwzLNWyCIGyLKq+SnrzHp37mPk0ybDafZspjVJ0zXZ/ONQyLqocMS12VCORfGdbKsJpkc4BViWHPsHqznvg4araawdTPvCvDwmOv5KVUQXHn2GtM5/reEBEREVmqwoZh7j5n4GVmv0IQhv3dYEe0/GzdunXYQxCRnNVaNSqlSupAolKq0C63U02/yyJL36Z4xVGzne9UPzyoDDvSPpKq79VYZSz3aZLxn0Pa/VQpVTrVQ6WcFo6OTylMup+ilUytbLkfe/VmvTOlMMs0ybyPvagyLFqNM3rdhdSbdcaqY7lXhkUN9EuU0k3RxYOpn2HVZJJtEhERERmGwoZhC7gBePewB7FUV/3N/Le1+vT37fj4OJdcckl/nkxECilLcBSfJpl7GBb2OEpz8h4FJpD/VDUIms23WskDiUarQbVSTTX1M4toqlqpXEpdRVUtV3OvHopPKUzTM6xUKlGmPJjFG8ojqSrDohDIzAZy7FVL4X5KEcRG+3YQ0yTLpXLqz4hquZp7WCciIiKyVMuxmcNWYDyn534RcA+wA7h6jtvPAL4IfBP4NnBZ1hdqtOb/aju8/OKszzzj5JNPXvqTiEihxaedpe7blHL6XRaNdqMzrSvLVLpBNNCvlCupx5d26mcW8f2UJjiKpt/lXj3UrFMuh0FswibwWbcpi2g6a5p929UHrZVzGObMVIalOfaqo6kqLbPo+oxIszJkuE1pAj4RERGRYShsZZiZvW+Oq08BfgK4MX67u7+pDy9ZBt4PXArsAm4jqEK7K3af3wI+AXwAOB+4EdiU5cU+9EtLGGlCIyMj+b+IiAxVVMkCyU/e6806JStRKpWotQZQGVZO17ep1qxRKVcoWbopWmlF46mWqqn6f9Wb9c5qjYPo21QpVRIHEu7eNVUt755mUfVQw5ONrysMG1DPsDQVclEQm6UiKq2o2Xyq8bVn+tXlHsSWMgSx4TZFlZYiIiIiRVXYMAy4cJ7rbwXWhV/Qn4UXAS4iqAh7ILx8PXAF3WGYA8eH358APJL1xb50F3z6dvizlweXj3sl1GLnEl+8Bp57XtZnD+zatYuzzz57aU8iIoUWNfyulJOvOBid8FfKlfwrw2KBRKrKsHAVxTyrh5qtZufnkGZlyGar2WnMPqjKsKTBTKvd6jwm6kWVl3qrTqVcCVa7rKc79gZSlRhbyTTNapKVUrBNeYZh7XYbw7p6uyURhcuDaKBftnKqyrBoPIMIYkVERESWqrBhmLs/f8AveRrwUOzyLuCHeu7zduBzwC8DK4EXZH2x998MP/fDM5dHKnBzODHzlh3wvpuWHoZdcMEFS3sCESm8erPOSGUkCCRShGElG8xqko1m0Lcpzcl7FLLkXZ0TVdVFU9XS9G0aKY/Q8vybmEe93ZKGgtHiCOVSeTAN/sPKsOlGsmMvqkqslAdw7IX7qe3t5GFYIzwmcg7D4qFgqjAsXMHT8dz3bfQZkfTn0AnZS5Xc3xsiIiIiS1W4nmFm9l4zu9jMCjc24KXAh4GNBP3CPkrGn+H4g/Cjsdo3A559bvD1+kvhm99b8li59957l/4kIlJoURPzcqmcuNKm0WpgZun7AWXgOKVS8DGZ6oQ/3KY8K8PiFWhpTt7b3u5s06D6NqVZHCFLwJd1fFH40WinmyZZKSef+rmU8ZWsBJZ8Pw1qmmT8ddJMIY4eZ9jgjr0U/eA6x17Oi0uIiIiILFURA6cVBFMUHzezD5vZT5rZijxeyMyuM7PdZnYn8DBwupmdZGY3r1279k82b978QjM7MbyvvfrVr76mWq3+spl928xqwBgz0zVT2XsYVo7NXP671818v2IEdh/Kvl2R3Jv/isjQNVoNyuUwOEpYaTPIQAKCQAxPH0gMqjon7cm7h/9Bvg3+s/QMi//s2u32QFaTTBNadkKWUv5BLICZASTvB5dhm7LoqqJKEYY5TslKuVeGdY69ciVdGJYhXBYREREZhsKFYe7+Onc/DfhxgoDq94C9ZnaDmb3KzNb38eU+TLCCJAQN88/ZsGHDH4yMjHxp3759D5fL5X9hZlXJH7vrrrva09PTbweuOu644z5EEIbtyfLCK0Zg176Zyy9++sz3u/YFty/VhRfO13ZNRI4W8RPQWiNZcFRr1jon4klPdLNqe9AbKfo+iWhFv3KpnGtg0pmqVqqmOnl3PNimFAFfpvE106/oF/3sor5NuQYmzZkwMW0YliZkySpaTABSVCWG4XLeU4i7pkmmOfZi25R31V/n2EtR9afKMBEREVkuCheGRdz9Vne/xt0vAJ4K/CfwC8AuM/uKmb3ZzE5b4mt8GdgfXmwCbxwbG3vljh07XgN84rvf/e4fr1+//tXA5cAVlUrlD8vl8mvc/a9PPvnkc//93//918jYwP/ic4O+YXN5/83B7Ut1xx13LP1JRKTQ6s166pP3enOmSXju1TkeVudY8pP3RntmUYCkAV8WneAo7cm7p684ymK6OT3T4D/LVLUUFUdZxBvoJ63gqzVrwfFaTj61MqtOaEny0DIexOYZhsVfp9lK3mw+yzZlUWvWOqtJJj324p8rLc93iq6IiIjIUhW2gX6cu+8A3gO8J6wMezFBQAXwf/r4Ujc++OCDR04//fRN4WXbs2dPBbgBuOorX/nKR6PX27lz5+cvu+yyHVn/GH3bFfCc34X9k3DlM+G0k2DXfrj+a/DRr8BXfmfpG7Nhw4alP4mIFFpUGZbm5D1qHF8tV5mqT+U7QIsFR0mnSTZnqt0GURm2lJP3PFdrjJrhV0tVDrWSzZ3vrAKYstot6/g6FXwJ+9VFTferpeQhS1aGdY69VJVhA1jtMr5aY5pVP7NsUxbRfor6wbl753XnE02tHMRKpiIiIiJLtSzCsDh33wNcF37l/VpuZqnPjszsKuAqCAKpVeXZ99l6Jtzw6/D6D8EHvxg00HfgySfDP/9acPt8Wm0oled4UhE55sSnSSZuoB+b3pa0MXtW7p56mmQ8ZMl9qprN9G1KevLu7jON2XOsfumsbBir8koaSAyiMiweJqYNYqNjL8k2ZeUEz21Y4n2bJVzOoquCL0UQG9+mVju/vqCd92C53Ok9l+TYG9TiDSIiIiJLVagwzMwSB1zu/qqchvG4mW1w90fNbAOwO7z+YeD02P02htfNNbZrgWsBtmzZ4g/u2EetAaPV7vtdeiHc91647zHYcwjWrYbNCYq5vrevxClnnL7o/R599FHOPbcP8y1FpLA6PY7KyQOJWrPWCYHyDiSA1Cfv9Vb6qZ9ZZD15dzyoeMs5kIg3c297skAiesxAVpOMrfqZuMF/cyboTBqyZBUPYluebD9lCZez6K2iSjVFN9qmPI+9WO+5pMfRrHBZDfRFRESkwIrWM2x9z9dPAz8FnB1+/STwP8i4gmNCNwCvCL9/BfCZ2PUvt8AzgSfc/dHFnqxSqXDhfzuXT902/33O+QH44c3JgrDpOlx/2wp+4vL/seh9t2zZsvgTisiyFjW6TjVVrVWnVCp1Aok8T1q7AomEJ+9ZtimLTjP3DCfvpfDXZ+7VOeV04UInZClVcw8kGu1YA/20lWEDCEyiKipId+xFq7PmPkU3DBLT/BzatDtBbK7TJFszPc2SVk3OqnZTA30REREpsEJVhrn7i6PvzextwBTwSnefDK9bCfwtsL0fr2dm/whcAqwzs13A7wDvBj5hZq8Gvgf8bHj3G4HLgB3AEeCVSV/n3e/5S37q8heyc+80P76lzbrVkPbfwetNuON78N7PHceF257PxRdfvOhjtm/fnuh+IrJ8xZuYp6nO6Wp0neNJazyQSL2iX4qQJYuo+iVa0S/pzyFezZRnGBZV8EWBRJLxxafA5t5AvxmEqmmOvVqzRqVUCbYp78AkXkXVSlGVaAM49npCwTSVYSWCKbp59lyL9lOafnr1Zj1YgXIAQayIiIjIUhUqDOvxJuBHoiAMwN0nzeydwOeBdy31Bdz9pfPc9CNz3NeBN2R5nec85zn8xxf/i796/5/yMx+8iUOHJxd/UI9qpcLZZ53J/3zdK7nql36JUmnxor6y+oqJHPWarWbq/lqdqo9yuqqUtKIT/Cg4SrUiYjSNM8cT/k5lWDl5lVJ8m8ws155rWSvDOtMkB9FA39I10O+EdQOaSmdmlKyU+L0R/5nnXRlWslIniE3zczAzypZz1WRsOmuqY29AQayIiIjIUhU5DFsFnArc1XP9BuC4wQ9naZ761KfygWs/NNDX3Lx580BfT0QGr6vHUcKT93pzpposz5PWKKgzLF1gkmGbsug0m0/xc2i1W5RKpWCbrEy9kXP1UFQZlrQ6p1VPvU1ZNVvNTnCUNBTs2qYce5q5e6cqMU3vueiYHXQQm2Q/xYPYcqlMvT2AfnopjqNBLt4gIiIislRF6xkW90/Ah8zsSjPbFH5dSTBN8lNDHtuycOeddw57CCKSs3glUNKT90EFEtEJddqT9/hKdnk30E8bSEQ/b4Byqcx0czq/8YXTWaMKvsSBhM00Zh9EZVilVKHeTF95lWYFzyxjq5QqnSC21qglH58NbvGGsgWLIyTZT81WcyaILZWZbuR37M0KYlMce2Ur47imSYqIiEihFbky7HXAe4APA1WCNlsNgjDszcMb1vKxcePGYQ9BRHLW9nanEijNVLCR0ZFOP6BBBRJJT95b7Vansf1AqnNKFdokCyQarQaVcqUTouUdmFTL1fQN9K1EyYJ/68qzOifeQD9NZVjVqkHAl7DaLdPYeoLYRjtdVWK5XB7IsRe1PEi6WmOlVJnZppyrJjvVo610U3SjadF5rmQqIiIislSFDcPcfQp4vZm9BTgrvPr+eA8xWVi9nt9JmogUS5rgqHclu7xOWqPeZBippjwa1pnelmdPrmh8aU7eoxADg7LlG5jUmjXGqmPpGuiH4xtEIOHunVAwzRTdsbGxIIBst3ML6+rNelcQm3Q6a5ZwOev4oiovSBZado5XgvdGrZms2i3r+MYqwbHX9naqKbrRNuUVsouIiIj0Q2HDMAAzqwBPBc4ARoAtnT/w3f9uiENbFnbv3s35558/7GGISM4cT1WlFO8H1G4nq4jKInqdkpWCqWoJT96jE+9KOfn0u0zjazY6jfAh2cl7VJ0TbVOu0yRjU/aSNlmP+jal2aalSlOllKUxexbxyrAswVFUjZeX3iqqNA3qseBnnud7o96qs3J0ZeopxIMKYkVERESWqrBhmJk9BfgX4EyCKZItgvE2gBqgMGwRW7duHfYQRCRnneAoxcl7vdnTMyyn6pzOCX9YyZLm5N3x3CvDOifvGHiyk/d6s7s6J9cV/cL+Wmn2U71Z72yTh//lyXGqpWryqsR2d8CX67EXTZMsp5tSGIXLee/beBCbuF9dLFzOOyiOf0YkCuti4bJ6homIiEjRFbmB/p8C48AJwBHgPGAbcAfw00Mb1TIyPj4+7CGISM4yTVVr1WeamA8qkEixmqS7B2FTim3KImoSnjaQiPo8lcvJq92y6KoMSzidtROGpag4yir6eaWpDIsHsc1WM//KMIyKJa8Mi7apUqok7jOWdXydIJYUlWHxcHkADf5TryZp6aZ+ioiIiAxLYSvDgGcAz3P3STNrAxV3/4aZ/Qbw58APDnd4xbdixYphD0FEchY/eT9SP5LoMc1Wk4pVcp+q1ltFVWslD446PcNy7slVKc/8GkzcQD+cJpmmD1oW9WY9aGJeTt5Av94KemVF8gwkHO+sXJm0gq9rNckcG+h3jj2zVCutRtuU5jFZ1Jq17n51Cav+ogb6eVeuRT+/NFN0s2yTiIiIyLAUuTLMCCrCAPYAp4Xf7wLOHsqIlplNmzYNewgikjMzS33yHlWGlcv5NtDv6tuU4uTd8cE00A+rlACwFJVhUQP9Aazo11UZlmIqXWQQgUTqMGzAU3TLpXLqKqpohczcxtec2U+GpesHh1G29NuUanzt7t5uSRv8x4PYPHuuiYiIiCxVkcOwOwma5wPcCrzVzJ4H/C6wY2ijWkbuvvvuYQ9BRHLm7sFqjeXkwVHUDygKWfJqsh5vqJ3m5D0+TTLXQKInOEpy8h6t6BdVhuXat6mVvtl8rVFLvU1LEfWvSnocdVWG5dxAP1pIIO2UwugxeYZ1URVVJMl+ijfQT7NgRhadILacbopuFC4nDfhEREREhqXI0yTfBawMv/8t4N+ALwJ7gZ8d1qCWE1WGiRwbommIUSAR9bSaT1fIkuNUtU4VFelP3qNAot1uB+FYOPWqn6KpYJCiOqcZ26acp0l29W1K2NstCjEg/0CiE8Sm6GnWG/DlXhmWdppkeKx1xpdn1aTNhGGJK8Os1Akgc23w3+z+jEi12mUoz6pOERERkaUqbBjm7jfFvn8AOM/MTgIOuBpRJHL48OFhD0FEcuTunQb6aU7eo5PqvKtfele/SxocOd6Z3jaIaZyRNNU5JStRLuc8VS3cT2l+DtEU2Phz5MXdKVGamW6bYhpnmj5oWXRNKSwlX+igU5UY9cpqt7uOkX7pDWKT7KfecDn3IDaq4Guln6JrZrmGdSIiIiJLVchpkmZWNbOvm9m58evdfb+CsOT27ds37CGISI6arWbn5DN+8r6YeD+gZju/Ff06KxuG4UKSk+NWu9WZWhn1DMtrGmd8qppZ8kAivihAnmFY16qfSfs2NetULPh3rpKVcgtMoqqwtFMK46tJ5hnERv2rovEl+TlEY0kbQC5lfBD0nksy3TZqoB8F2bn202vF9lOaBvrlmc+jPFfjFBEREVmqQoZh7t4AzoSc/go9RmzdunXYQxCRHPVWXiU9eW+2mpTLZarl6kAqwzpT1RKcvEerNUaPGVRlWNJgq9FqBFM2w+mBuU5V62k2n3iqWiyQqDWSr+CZZWxA+vGlXKUw6/jMrBNaJpkm2RUu51y5Fq8MSxqG9W5T7v3qwiA2ad++eM+wcqnMdGM6t/GJiIiILFUhw7DQR4DXDHsQy9n4+PiwhyAiOeoNc5IGW81WM5jeliLEyDq+eJVX0uqXqPIqTVVKFlGlDSQ/eY8qZtKu4JlFFM50ersl2Le1Zq0zla5cLlNv5xOYNFoNKuWZKqWkveea7Zltiqb55jW+eM+wpEFnfHGEQQTFEB57zcWPvXi4XC1X8z/2wp8fJF9ptStczjGsExEREVmqwvYMI2ie/7/M7FJgHJiM3+jubxrKqJaRVatWDXsIIpKj+Mlx2mDLzChZCSffQKITzJTKiU7eu6rJBhBIjFXHOuNLWp3T1UA/x6lgUT+4KJBIWnm1cjRYe6Zs+TVZ7w1zkq4m2W63Oz8/INeqPzObmW6b8NiLh6NJA8gs6q06I+URIHmFYdfqrKWc+9W10y/E0Bsu5zk+ERERkaUqchh2HvCN8Psn99ym6ZMJnHrqqcMegojkqGtKYYoVB3sDiDwrwzqVNimqcyqlSlcD/VzHF5vWlbiJeSldH7SsnO5VNJNW50Q9wyrlSuLG8WlF+6mriirBr+ZBH3vRcZR0im48iG2327mO77iR44Dw2EsQqvZWXuW6WqMzUxUW/pdkfGnDZREREZFhKWwY5u7PH/YYlrt7771XgZjIUaze6p5S2G63M1Xa5NYkvFnvnFAnPXkfZGVYvVnv6q+VNKzrTP1M2AdtKeLbnjSQ6FRsWX6BRKfnlQXBR9vbmfZTnvs2vp8STdGN3k+W/0qms/prJZgmGW1TFC7nHcTGJan6i2+TpkmKiIhI0RWqZ5iZnZnivmZmp+c5nuXurLPOGvYQRCRHvQ3q01RRxU92854mmWZFv3qzHlRehYFE27MFfElE/b8g3Yp+g5qq1ittIFEul3OtDCvbzKqaWSv48u5XBxmCWAYUxMaqvJJOk4yHy4NardGw5EFsOfZ+0jRJERERKbBChWHA18zsb83sWfPdwcxONLPXAXcBVwxuaMvPvn37hj0EEclRJ2yKTSks2jTJTiVLwjCsK2QpV2i1kvWiyjq+tA2/e7cpzybmENtXnqwyrN7qrnbLa3xzrmSaMDiKtmMQ/eo6VVRJj714ZVieq122YxV85XT96jpVk61WLmPr5XiynmGt7sqwPCvXRERERJaqaNMknwJcA/ybmbUJGuc/AkwDJwLnE/QSuxX4VXe/aVgDXQ4OHjw47CGISI56pxQ2281kJ+9OdxPzQVS/lCs0a+mmSXaamOfYZD3eBD5JFVVUuQYkDlmycneMmZ5hSRvop12lMIuodxoEYU7SyrAs25RFvVnHSukb6EfhcvSY3MKwZqN7SmGKKbppevBl1dsnLO1qknlWJYqIiIj0Q6Eqw9z9oLu/BTgNeC1wN7AGOBNoAh8Bnubuz1YQtritW7cOewgikqO5+mslCiQGVBlWb82sLlctV9NV5yxx+l0S8dUDk4YL8W2KpqbmKXUD/WZ3tVteYV28Mqxaqqbqr5V2m7KOL37sJZ0mGS1IsZQ+aEnH19WvLuEU3a5wOecG+vHAPGkQWymH740cjz0RERGRfihaZRgA7j4FfDL8kozGx8e55JJLhj0MEclJ1B8qmqqW+OR9QJVhtWYt9cl7b0+uovVtivfkqpaSBXxZda0mackCiagJPOTbxLzzcwgb6Lfayaaz9q6QmVeYWGvUZiqvwmPPvfu1e8X71aXtwZdW15TChBWGtVZtJoiNguJ2u1Oh10/xYNOwTA30ay1VhomIiEhxFaoyTPprzZo1wx6CiOSoq0l4ipN3x4nNVMstkOjtyZUkMOn0DIuq3fLs29TbMyxJZVhPgNby/HqaxcMbwxLtp+jnB0FIlWdlWKk00zst6XTW+DRJwwZSlVgpVYKVVhcJVaOfXZY+aGn1HntJF5eIT4HNc7VL6A7M01R1RuPLu5+eiIiIyFIoDDuKrV27dthDEJEcZWli3mq3MDNK4ce/YbQ8nzAsqvKC5CfvUciSdlGALOJhYtLgKN6gvlwuByFLDoFEtM3x/lpJwoVmq9lVjZdXX6nen12SILazTbHqrLzGV2/N9HYrl8qJ+un1rkCZ9xTdeHCUtIF+2nA5K/eZwLxkpcQ9zQZRlSgiIiLSDwrD5mFmO81su5ndYWa3h9edZGY3m9l94f9PHPY4F3L//fcPewgikqNGq4GZpeqvFe8zBkEwkedUurmmdS02vvhqjXkGEs1WM1vfJss/kGi2mp2fA4TN8BuLN8PvDXRyrQyLNXNP8nNotVtd21QpVag38gvr4sFMu91O/N7ohMueXxAb7d9ofFkqw9qeTxAbBbxRYJ4prMt5cQkRERGRpVIYtrDnu/sWd98WXr4a+Ly7nwN8PrxcWJs3bx72EEQkR9HJcZr+WvEm4ZB8FcVM42ulP3nvnFBb8oqjJY0v3lcq4YqDncqwHMdXb9WplLv3U9KpalEAWS6VE/VByzq+WcfeIvs2Ho5G48vr2Ks1al0/hyTBVqdf3QAWb6g366mbzc+aopvT+BqtBtVStXPsVUqVRKuSxlcYVQN9ERERKbplF4aZWcnMzhjSy19BsKIl4f9/ckjjSOSRRx4Z9hBEJEedyrAU/bWiQCKafpdndU4U6ESvk+Tkvd6qd6qH8q7OcfeuyqvEPcOsu2dYHtU58VU1o9dKEki0vEWpHGxT0lUUs44vCo5KVsLxZP3gyuXUP/Os40sbHHWm6IbvKcfzm6LbTl9F1RUul/ObQtxbPZo0VO1anVVhmIiIiBRcIcMwMxs1s98xs++a2ZSZPW5m/2RmW4D1wIMDGIYDnzOzcTO7KrzuFHd/NPz+MeCUAYwjs4mJiWEPQURy1Gk2T/KT9/gJP4TT7xKELJnG14w1c0948h4FfDDTWyqv6py4aqmaqDIs3pg9zSqKac1VwZdoOquHX+Tbt6kThsX6fyWq+guPh2h8tcZgqhKThKrxcDmS27HndAWdaav+8lxcYlYQW64kquBzvOsxCsNERESkyCqL32WwzGwM+CJwLkH11b3AScCLgVuBawY0lOe4+8NmdjJws5l9N36ju7uZzfmXdRieXQVwxhnDKmKDrVu3Du21RSR/WU7e5wpZ8uwrNWvlxYRNzOMr2eVWGRYLb8rlZFVK8UAnqjjKqzIsqryC5GGY4137NrfKsGb3+CDZsRf15IrG12jneOzF91MrYT89K3dtU16rNXYde0kXb2jWKa/oWU1yEJVhljyITRvwiYiIiAxLESvDriao/jrX3f+3u3/A3d/l7s8kCJjeOYhBuPvD4f93A58GLgIeN7MNAOH/d8/z2GvdfZu7b1u/fv0ghjun8fHxob22iOSvt8oLElbn9Ey/y6tvU++KeUkrw+JBGF6syrBGs9GZ+hn1QcurOifeMyxNlVf0M66U860MiwcmSasSK6VKZ5XCvKsS4/spyXTWuard8lqtMS5pFVWWqZ9ZdALzKIgtJ+vt5uF/kLwHn4iIiMiwFDEMeylwtbvv6b3B3T8MvA1i/2ybAzNbaWaro++BHwXuBG4AXhHe7RXAZ/Icx1KtXbt22EMQkRzVm3VK9FTnLHLyHvW8GkTfplqzlvrkvXd8kGNlmM+cvJfLCatzWt1NzJutZi7ji34OaRc6iI+lUqrkVhlWb9U703MjifrBlbpXk8yrwX+tVetaHKHVWnw6a71ZTx0uZ9W7n7KsJplXZVjvzyHp+Ny989dZ9N4QERERKaoihmFPAr45343u/ifunve4TwG+YmbfIpia+W/u/lng3cClZnYf8ILwcmGtXr162EMQkRxFYVOak/d4mAM5N9CPrZiX9OS91qzNCiRyqwyzmWld1VI10Ql/rdkTsuTUt6mzUmjaCj6bOQaq5WTblHV88WALEoRhvYs35Fi5Vm/Wu1f9TLK4RKueeupnVlGPP0jXQL/TM6ycX2VY9Dppg1gz665KbOezb0VERET6oXA9w4DDwAbg/rluDJvov8ndX5XXANz9AeCpc1y/D/iRvF6333bu3MmmTZuGPQwRycl0Y5pquTpzRYIphbVGjUqp0lWdk9c0yVqzRsXSrSYZja/Dcm5ibjPBTJL+VfVmnUqlp4F+DuOrNWtUyjP7KWlPM5yZfZvjVLXpxjTVUrXruiRBZ9apn2nVmrXOeyNpb7fpxvSscDmvaZLxlUyTrvoZf2/k2UC/1pz9GZG0Miwe3qoyTERERIqsiJVhXwTeMNcNZvYDwPXMTFWUBZx33nnDHoKI5GiuQGKxk/das0a5XO4KgfKaJjlXZdhiJ+/TjenOYyJ5NtCPn/AnOXmvNWudn3knZMlhfL1VfxVLFhzFG+hXS1XqrXou45trPyUJOrsas+fYMywexCZd9XOu91Or3cplfPFgLul02yikgpyD2DB0y9JXsHPshQFfXu9dERERkaUqYhj2DuDFZvYxM7vQzMbM7FQz+yXgNmBvjq/9IuAeYAdBI/+5/CxwF/Ad4B9yHMuS7dy5c9hDEJEcTTenO1PBIoudvPeGLElXKcwiqgSC5FMKp5vdgYRhufS96gQjYRFQdPKeJExMG/BlEQUfXaHlIvvJPWhi39m35TLtdju/MCxWwWfYomHirIqjcn49w+qNmSA2aRVVb6WlYbQ8nzCst4IvyUIM8TAs9yC2JzBPsp/i4XK5FBx7g1j8QkRERCSLwk2TdPc7zexFwHXAHbGbmsCfAX8OfC+Hly4D7wcuBXYRBG83EARfkXMIGvg/GzgAnJzDOPpmampq2EMQkRz1Tik0Wzw4yjoFKtP45jh5z1IZlkd1TtQXqcTsk/fSAv9O1NUzLM+palEVFckr+OrNOtVytTP++PgW2qYsppvTnemikSTHXm8ftDwXb+gKYhMee/F+ekAuYV2z1cTMuqaLRpVrva8fV2vWqFaCsC7vIDa+eEOlXKHWWrgyrNVudS2okHSbRERERIalcGEYgLt/xcyeAjwDOJOgj9jX3H1/uLrj7+bwshcRVIQ9EF6+HriC7jDsNQSB2YHw8u4cxtE3W7duHfYQRCRH081pVoys6Lpu0eqcnqlqlXJ+PcOa7WZXpUiSSpbpxjRjlbGu6/IIJGqNoKdU78n7YuNrtBrdjdkTrFKYaXxhcBQPLRerDOutJst9Kp3N/AlRslKi8XUdezkHsV37KUm/uliAFj0uj7Au6mfW+95IMr6uIHZA/eqSrPpZa/T0uEu4aIGIiIjIsBRxmiQA7t5296+7+/Xu/m/uvj+8ftLd8wjDTgMeil3eFV4Xtzn8+ipwC8G0ysIaHx8f9hBEJEe1Rq1rSmGSKY/1Vp2ylQdSGQbMrhRJsuJgLJDIq8H/rAb1KU7e40Fink3M0wZHncqw2L5NMv0ui+nm9KzgaLH+X/Vmz7GX42qSvf3gkq5kGp8mmXQVxbSi15kVxC7S4L/tbUqlngAtjyC20VMZlqCCb9bPrpxfECsiIiLSD4WsDCuwCsFUyUuAjcCXgQuBg8Mb0vxOPrnQszhFZInizdwhPHlfZDpTrVGjVCp1T1XLKZCIn9snbmLenJ4VSEw3+t9kfVYVVZqTd58ZW659mzIEEr0BVZLG7JnG16hRXTmznyqlCrXGIsdeMzz2UmxTZk5nP5WslLgn14nHndi5nNuxF1ZRRe/BThC7yHsjun/0mDb5BJ2zqhIT9AzrrXbLs3JNREREpB8KWxk2BA8Dp8cubwyvi9tF0EesATwI3EsQjhXSyMjIsIcgIjnq7a9VKVUWPXmfqx9QXpVhjned8Cc5eY83Po8et1jAl8V81TlpTt5LVsLxXHuGxQOJ1A3qc+xpNt3sbqBfLpUXPY46AWQsiE2ygmcW8VU1o/8nWe0yPvUzr6B4zv2U4Nibq3JssWqyrOPrDcwT71vrDvi0mqSIiIgUlcKwGbcRBFtnAiPAlQTBV9w/E1SFAawjmDL5AAW1a9euYQ9BRHIUb1APYXCUoTontzDMfdbJepLVJOOBRF6rXUaLD6SdJjnXyX1e1TklK6VaTTKqDIsHEnmt6BdVN0Uqpcqi0yRrjfTblFW0smZckqrE+KIAeU6TrJQrnZVMk+6nOY+9vPrV9VQlNtrJeoZ1hcstVYaJiIhIcSkMm9EE3gjcBNwNfAL4DvAO4PLwPjcB+wia6n8ReEt4uZAuuOCCYQ9BRHIUTWeKJDl5j0KWrsbseU1Vo6dyxRNU5zRnhyx5BRK9Kxu2WhkqWRJsU7/Gt2h1TmOeiqOcApPeILbeSNBAv6dn2GIhy1LEj70kFXy976eBVYZl7T2X17HX6AnME1SP9k6tVAN9ERERKTr1DOt2Y/gV99ux7x34tfCr8O69917WrVs37GGISE6ihumRkfIIU42pBR8zXe/uyTVSGRlIE/NI2jBspDLCdL3/fZvqzTrl8kz1y0h5hGa7SavdWvBx7jPT7yKDqAwbqYwwVV943/Y23a9WqjRajVzG17vQQbVS5UjjyIKPmW5Md4UsI5URjtQWfkxW8Sm6kSTTJOM9+Krl6qLvpyzqzXpX0FktV2k0F99PTvpKyyx6A/ORygiHjhxa8DH1Vr1rCuxIeYRmq6kwTERERApLlWFHsVZr4ZM6EVne5gqOFgtMphpTXRU9o9XRXJqERxVWXWGYLT6tK6puioyURxYNWbKoNYP+UPET/nqrvuDJezTtsysMS7BNWUw3ukPL0cpooqq/eBg2UhnJLQyLfn7x8S127PUujjBWGWO6MZ1bX6m0oWVvtdtIZYTJ2mTfx9VbGTZaGaXeri9+HPns91MePddqjdqs/VRr1RbcT7097qL302LhsoiIiMiwKAw7il144YXDHoKI5Kj35H20MrpocNQboI1Vxqg1a30PdOrNeldDbQhWw2u2EzSBL3eHdXkFEvEqpdHK6KLVOY1WoytsgmCb8ui5Nt3oblA/Vh0LxrfAfuqdqjZaGc0tkKg1a1Qr3WHdYvtpuj4963jNI6xrtVsYRqk08yeOYYl7rkWisK7fOsFRaWY/NZoNWj7/fmq327MqLZO8n7KYakx1VciNVkapNxc+jnqD2CTvJxEREZFhUhh2FLvjjjuGPQQRyVGj1eg64U8y7aw3ZBmp5DOdqbf6JbJYIBEFTvHxHannUxmWtpKl1gym0ZWt3HV9HtNMe1cKTTS+xhwVR82Fq92yqjfrXT+H0cro4lN0m7O3qdFaOODLImrmXor9iWOWLAzrOvaqOR57NjNNMpqqvFjQWS1Xu8Mws0UXzMg8vnL3e3Cx0LK3H1wUxOZRNSkiIiLSDwrDjmIbNmwY9hBEJGfxKqWx6tiigcSsyqucApO5Tt6r5eqilTa90xCTTL/LOr7ek/fFTvijPlnx8VXL1VwCk+nG9KzqnEXH16p39xkrByFGHpVhLW9RKs/s2yT7ac5jr1VfsCIqi86xFw+KyyNM1CYWfFyr3eoKw8Yqi7+fso6vVCp1xpckbJqr0nK0MspEfeFtyqJ3KnUnrFskDCuVZvqMRf3qNE1SREREikphmIjIMuXuxPtpj1ZGFw1mekOWvHr7RFPBeoOjQ9MLN+IGurYpz6lq8eCoUq4s2kA/CnPiAd9IZfGQJdP4eqYhRoHEYpVhXSuFliu0vZ3PVDVn1rG3aBDb06A+SUVUFlFVYryBfpL+X70N9wcVxJasRJv24lWJPeHySHmEyekcphD39AwbrYzSaC8cbNUaYbVb+H4qWSnRCp4iIiIiw6Iw7Cj26KOPDnsIIjJAo5XFm+HPVZ2TR9+muYKjROFCT4/u0eooU82cAonYNMnIgtUvjaBpfG91Tl6VYb39tRZr8N+7CiCQWyDRu6rhWHXx0LJ36meux15p9rG32H7q3abR6uIBX6bxNcJ+dSlWu5xrm0YqI7lUhk03Z0+lXqz/13zvJ1WGiYiISFElCsPM7M/M7AozOzHvAUn/bNmyZdhDEJGcxU/gk5y8967WGAUSfa8Mm+fk/XDt8IKP8/C/+Pjyqs4xs1Qn73NWhpVzXHEwZXDUuyhAJK9AIr6fRiojyabozhGy5FGVWClXZvXTW3Q/9QaxCcLlTOOLQstSumOvt9Iyt/dGY57prIuML15pGVFlmIiIiBRV0sqwXwY+Bewxs2+a2XvN7HIzOyHHsckSbd++fdhDEJEctdvtrmBmrDK2aEPt6eZ09/S76siiFUdZRFVAaU/e3btXzEtScZTFVH1qVg8mWPjkPdqmroqj6mgu0ySnGlOzpqotNk1yqjE1sOqctre7KpvGRhJWhs1R7dbvnmFRZVN8fKOVUSbrC4dhbdpdAV9ux948+2mxY693muRoJZ9jb85+dYtUhk03plUZJiIiIstKZfG7APAs4Pnh1w8DTwV+BWib2beBL4ZfX3b3hf/ZXwYmvhqUiBxd3IMKqt5gZqoxhbvPCnkik7VJRsujM48pLx6yZDFZm5zz5H2h6pw5tymszllom7I4PH2Y0crorOubrea8j5msz96mkXI+Kw4eqR1hpDIy8zqVxZvhT0xPzLlNeQUS8cqmqLfbYsfeWHWsc7lT7dbnnmGTtUmqle4G+knCMJxZDfRrzVoQOpf611ViojZ7PxlGo9WY9zFzbVNeK61O1ie7xpekr+Dh6cNUy9Wufb/YNomIiIgMU6IwzN2/DnwdeLeZlYFnAJcwE449DfjfBOHYN4EvuvtbcxmxJLZ58+ZhD0FEcjLdmGakMtJ18h5fGbJsc4fhk7XJOUOWZnv+ECiLyXoQusXHt1iz+XqzTrVc7d6m6sw0zvjUraWaqE0wWp0dSCw01W+yNslIeWTW+BYNWTKYrAevFRkpB4HEQvvp8PThrrAJgm3qd2BSb9YpW7nrGBupjNBsNWm2ml2Vh3GT9cmun3nUQL/vx160n3rGt1AQ22w1Z02bjS9a0M8w7PD04VnHXslKTEzP/96Itqk3/M5jiu5kbZLRkVhgHn6uLBQUz3XslUvlXMYnIiIi0g+p/7pz95a73+Lu73b3FwJrCAKxawgCs23Am/s6SsnkzjvvHPYQRCQnUXVJbxVVo9VY8KS10W7MGaD1u3poojZBuVyeNeVxoWBmvm2qNWt9H9/h6cNdFXIQBiYLrM43UZuYNU1yrDLGkVr/q3N6e4Yl6a91uHa4K0CDcCrdAiFLFpO1INTqrbyqtWoLTnmMpgdGklS7ZTFRmwgq+Erdx95CU3Qna0E1VO97o9Fq9H0a5+Hpw12BdPRaCzXDn6hNUClVZlWu5VUZFp/OWi1XF11Ncq5Ky9HKKIenNVlAREREimlJ/9RpZiuA/w68GLgcuCi86eDShiX9sHHjxmEPQURyMlmbZKwyNiuQWCxccO/uEl4pV2i1WwsGaFnHN6vKa5EV/eYKw0YqIzTbzb5XD03UJmZVsoxWRjlUOzT/Y6YnZq9SWM1nNcne3mnlUpk27YUDianDjFW6tynJogVpdYKjnsqrxcI6d+/q41WyEm3auezbarnaNb7FpujOd+wtFi5ncXh6nv00Nf9+6gR8KVfIzMLdu352ZgbOgvtprvfTSGVEYZiIiIgUVqo5J2Y2BjybmSmS24ARYC/wZeDXgf8E1Lm9AOr1+rCHICI5iXoI9Z7wLzaVbj559AzrDY4WqwyLpnAOonJtsjbJSLW7OmexQCKqmJlVndPofyABczdUX2jfTtZnb1Me1TmT9WA/zRXEZgmO8ti35VJ5VmXYQvtp3mNvkV5ZWcfXW5W4WDP8ydok5XK5a3wrRlbksppk1Luv10I/hyj8jhutjPY9iBURERHpl0RhmJm9gyAAewYwCjxOEHr9PfCf7n5XXgOU7Hbv3s35558/7GGISA4OTR+aVckyVg1Wk5wvMHH3YBXAORqc97s65+DUQUYro12vddzIcRw8cnDexxyamr1NK6orFtymrA5NH5pVnbOiuoJD0/NXhh08Msc2jR7HgckDfR0bzK4MiywUNh2aOjSrOmdFdQWHpubfpizm3U/NhffTfM31+1159cSRJ2b11zpu5Dh2H94972PmfD+NjC26TVkcmj7U1ZMLgvfuE1NPzPuYJ6Zmb9OK6goem3qs74tLzBWEwcKfEYemD7GiuqLrurHqmCrDREREpLCSTpP8LeCHCMKvC9x9g7tf6e4fUBBWXFu3bh32EEQkJ/sn93PcyHFdfaVWj61msj45b7hwpH6E0cpoVz+gSL8rTPYe3stxo8d1XXf82PFM1CbmrTDZf2Q/K6oruqpfVo2uYrI+2fdV6aKfX9zqsdXsndg772P2Tuxl5djKWY+ZqE30NdCZqk9RKXdXoMHizfD3H9nPypHu8a0aW8Xeyfm3KYv9k3Psp7GF99N0Y5pyqTznNvV7AYI9E3tYNbqq67rVY6uZrE3SaM49vuh4iI9v9ehqjtSO5HLs9e6n1WOr2Te5b97H7Dk8xzatCLapn8devVmnZKVZq1GXrLRg1eT+yf2sHJ29TQu9n0RERESGKWkY9jmgDrwK+JqZ3WhmbzGzi8LVJY8ZZvYiM7vHzHaY2dXDHs9CxsfHhz0EEcnJvol9s1Y2XDm6kiO1I9Sbc0+R3jexj5UjK2etyjhWHWP/kf19Hd9cgcSqsVVM1iYXHN9IZaRrfCtHV3KkfmTeECOrRqsxK5hZLJDYO7GXVSM9gcRoGLL0MTCJgoXe0HLFyAr2Hp4/XKg367O26YSxE9g/0d99u29yH6OV0a5pcceNHBfsp3l+Dvsn97NqdNWsbTpu5LgFtynr+OYKZhbaT/sm9jFanWObGv0Pw6Yb07OmFB6/4vgF99Peib2zA7TR1X0Pig8cOcCq0VVUS93jO270uAWDrSP1I7NWET1+7Hj2Tcz/fhIREREZpkRhmLu/iJlVI98dPu63gVuAg2Z2k5n9ppk928zmXlP9KBAGf+8Hfgw4H3ipmRV2HuI3v/nNYQ9BCubaa68d9hCkT/ZO7GWkOtIVLpRLZdrenv+Ef3Ifx40e13nMZz/5WSAIqR594tG+jm/fxD5WVucIJBY4ed87sXdW5Vq5VMbdqbf62wOxdyEBCIOjyYUDid5qtyjg62cgsW8yCC17A5PVo6t5/NDj8z5url5Pq1csHPD1SvIZsW9i36yVF0tWAmfBsKm3khGCyr+FtimLvRN7Z4dhi4SW+yZnh8tR4/j5wtus5jz2Vix+7PVuUydc7uN7o3c/RZ8Rq8dW89ihx+Z9nLvTO7vyhBUncOBI/6cQy3Dp7wiJ0/EgvXRMSFzRj4fEq0m6e8vdb3H3P4iFY88Gfp/gT6C3ETTRP2hm/5HHYAvgImCHuz/g7nXgeuCKIY9pXjfccMOwhyAFU/QPJEnu+/u/z5oVa2b3CjLmbcS968Aujh87vnOi+7lPfQ6AtSvXsmv/rr6O77EnHuP4447vum7tyrUcPHKQWrM252O+v//7nLDihDm3aaGVANOamA5XG+yZCrZu9Tr2Ht5Luz27cT3Aowcf5YSxE7quW7tyLQemDsy7TVn07qfISStP4vsHvj/nY6IG5r2VV+tXr2fvxPzb1CvJZ8T39n+PE1acMKunmZnNO5Vu14FdHL/i+FnjW7tqLQ8deCjR2JJ65MAjs/bTSatO4sCR+ffT9/bNvU0lK/W179VUfWrO6aLrVq1j7+TeeacQP3LwEdYct6bruuj91M+wbteBXZyw4oTOfur6jDgw92dENAU2vmABBO+nfRP7+r4AgQyX/o6QOB0P0kvHhMQV/XhIHIb1CsOxr7n7HwD/A7gS+H/ACoKVJo9GpwHxv9p3hdcV0oYNG4Y9BBHJyT2P3cP61etnXb925VruefyeOR9z3+P3sXpsNaOV7ubdp605jR27d/RtbO12m6nG1KzgY/XYag5PH5432Lr3sXvn3abvPvbdvo1vx+4drF+9ftbPYcMJG9h9ePecfbk621SevU0T0xN9DesW2k/377l/zsfcv+d+1q+aZ5sOzb1NWd3z2D2sW71u1vXrVq3jrkfnbiO6Y/eOObfp1DWnct/u+/o2Nnefc8reqtFVHKkfmf/Ye/xe1q+afeytWz3/NmVx/5775z329hzeM+d+cncmahOzKgWjKcQT0/OvQpnWfbvnOfZOnP8z4oE9D7Bu1brZ23R88H7q53tDREREpF9srnL9RR9ktoKgKuwSguBrG8HKlAZMAf/l7pf2b5jFYGYvAV7k7r8YXv554Ifc/Y0997sKuArghS984dbPfvazAx8rwM6dO9m0adNQXltEREREREREJC9mttfdZ/+LZgKzlxSb+wXGCPqFXUIQfj0DqBKEXzXgv4Avhl+3uHt/u80Wx8PA6bHLG8Prurj7tcC1ANu2bUufNvZJrda/aTsiIiIiIiIiIgXyvawPTBSGAQeZCb8awK3MhF//5e7HSupyG3COmZ1JEIJdCfzccIckIiIiIiIiIiJJJQ3D7gC+QBB+fdXd+9d8ZBlx96aZvRG4CSgD17n7d4Y8rHk9+uijnHvuucMehoiIiIiIiIhIYSQKw9z9mXkPZLlw9xuBG4c9jiS2bNky7CGIiIiIiIiIiBRK0p5hX0j5vO7uP5JhPNJH27dv5+KLLx72MERERERERERECiPpNMlLCHqF1RPef2hN42VGuVwe9hBERERERERERAolaRjWJGie/x/Ah4B/dfd2bqOSvti8efOwhyAiIiIiIiIiUiilhPc7DXgbcDbwaeBhM/tDM1N39gK78847hz0EEREREREREZFCSRSGufsed3+Pu18IPAv4DHAVcJeZfc3MftHMVuU5UElv48aNwx6CiIiIiIiIiEihJK0M63D3W939tcAG4OXAJPDXwKNm9rI+j0+WoF5P2uJNREREREREROTYkDoMi7j7tLv/PfA7wOeBlcCT+zUwWbrdu3cPewgiIiIiIiIiIoWSKQwzsw1mdrWZfRf4MnAe8AcEzfWXJTO7zsx2m9mcjbYs8D4z22Fm3zazpw96jGlt3bp12EMQERERERERESmUxGGYmVXN7CVm9m/A94G3A98GfgJ4krtf4+4P5TPMgfgw8KIFbv8x4Jzw6yrgAwMY05KMj48PewgiIiIiIiIiIoVSSXInM3sf8HPAicB24NeBj7n7/hzHNlDu/mUz27TAXa4A/s7dHbjFzNaY2QZ3f3QwI0xvxYoVwx6CiIiIiIiIiEihJArDgDcCU8A/At8IH/cLZjbf/d3d/2TpwyuU04B45duu8LrChmGbNm0a9hBERERERERERAolaRgGsIKgOuznEtzXgaMtDEvMzK4imErJGWecMbRx3H333ZxyyilDe30RERERERERkaJJGoY9P9dRLA8PA6fHLm8Mr5vF3a8FrgXYtm2b5z+0uakyTERERERERESkW6IwzN3/M++BLAM3AG80s+uBHwKeKHK/MIDDhw8PewgiIiIiIiIiIoWSZprkUc3M/hG4BFhnZruA3wGqAO7+V8CNwGXADuAI8MrhjDS5ffv2DXsIIiIiIiIiIiKFojAs5O4vXeR2B94woOH0xdatW4c9BBERERERERGRQikNewAF8yLgHoLqr6sXuN9PEywSsG0Qg8pqfHx82EMQERERERERESkUhWEzysD7gR8DzgdeGv6/12rgV4CvD25o2axatWrYQxARERERERERKRSFYTMuIqgIewCoA9cDV8xxv3cCfwhMD25o2Zx66qnDHoKIiIiIiIiISKEoDJtxGvBQ7PKu8Lq4pwOnA/82qEEtxb333jvsIYiIiIiIiIiIFIoa6CdXAt4L/MKQx5HYWWedNewhiIiIiIiIiIgUiirDZjxMUPUV2RheF1kNXAB8CdgJPBO4gQI30d+3b9+whyAiIiIiIiIiUigKw2bcBpwDnAmMAFcShF2RJ4B1wKbw6xbgcuD2QQ4yjYMHDw57CCIiIiIiIiIihaIwbEYTeCNwE3A38AngO8A7CEKvZWfr1q3DHoKIiIiIiIiISKEUvmeYmY0CpwIrgD3uvifHl7sx/Ir77Xnue0mO4+iL8fFxLrnkkmEPQ0RERERERESkMApZGWZmq83sdWb2ZYLpiTuAO4HHzOz7ZvZBM3vGcEdZfGvWrBn2EERERERERERECqVwYZiZ/RpBg/pXATcDVwBbgM3As4C3E1S03WxmnzWzc4Yy0GVg7dq1wx6CiIiIiIiIiEihFHGa5DOB57n7nfPcfitwnZm9Fng18DzgvkENbjm5//77Of300xe/o4iIiIiIiIjIMaJwYZi7/2zC+9WAv8x5OMva5s2bhz0EEREREREREZFCKdw0yTgzO9/Mzo1dvtTMPmZmbzOz8jDHthw88sgjwx6CiIiIiIiIiEihFDoMA64DngZgZqcDnwFOAt4A/N4Qx7UsTExMDHsIIiIiIiIiIiKFUvQw7CnAN8LvXwJ83d0vA34eeOnQRrVMbN26ddhDEBEREREREREplKKHYWWgHn7/I8CN4ff3A6f0+8XM7EVmdo+Z7TCzq+e4/RfMbI+Z3RF+/WK/x9BP4+Pjwx6CiIiIiIiIiEihFK6Bfo87gdeZ2b8ShGFvC68/DdjbzxcKe5C9H7gU2AXcZmY3uPtdPXf9uLu/sZ+vnZe1a9cOewgiIiIiIiIiIoVS9MqwtwKvAb4E/KO7bw+vvxy4tc+vdRGww90fcPc6cD1wRZ9fY6BWr1497CGIiIiIiIiIiBRKocMwd/8ysB5Y5+6vit3018Dr+vxypwEPxS7vCq/r9dNm9m0z+2TY1L+wdu7cOewhiIiIiIiIiIgUSqHDMAB3b7n7gZ6rW8BbhjCcfwE2ufsPAjcDH5nrTmZ2lZndbma379mzZ6ADjDvvvPOG9toiIiIiIiIiIkVU6J5hZnbDPDdtBM6mv4HYw0C80mtjeF2Hu++LXfwb4I/meiJ3vxa4FmDbtm3exzGmsnPnTk45pe/rDIiIiIiIiIiILFuFDsOAfT2Xy8CTgacCr+zza90GnGNmZxKEYFcCPxe/g5ltcPdHw4uXA3f3eQx9NTU1NewhiIiIiIiIiIgUSqHDMHefM/Ays18hCMP+ro+v1TSzNwI3EYRu17n7d8zsHcDt7n4D8CYzuxxoAvuBX+jX6+dh69atwx6CiIiIiIiIiEihmPvQZvFlFlZv3eXuK4Y9lsVs27bNb7/99qG89pe+9CUuueSSoby2iIiIiIiIiEhezGzc3bdleWzhG+jPYyswPuxBFN3JJ5887CGIiIiIiIiIiBRKoadJmtn75rj6FOAngBvjt7v7mwY2sGViZGRk2EMQERERERERESmUQodhwIXzXH8rsC78Alh+cz0HYNeuXZx99tnDHoaIiIiIiIiISGEUOgxz9+cPewzL2QUXXDDsIYiIiIiIiIiIFEohe4aZ2XvN7GIzK+T4lot777132EMQERERERERESmUooZNK4DrgcfN7MNm9pNmVviVI4um1WoNewgiIiIiIiIiIoVSyDDM3V/n7qcBPw48DPwesNfMbjCzV5nZ+uGOcHm48ML5Wq6JiIiIiIiIiBybChmGRdz9Vne/xt0vAJ4K/CfwC8AuM/uKmb3ZzE4b6iAL7I477hj2EERERERERERECqXQYVicu+9w9/e4+3OBjcB1wHOAlw53ZMW1YcOGYQ9BRERERERERKRQCr2a5HzcfQ9BGHbdsMciIiIiIiIiIiLLR+HCMDNLHHC5+6vyHMty9+ijj3LuuecOexgiIiIiIiIiIoVRuDAM6G2O/1ygDWwPL19AML3zy4Mc1HK0ZcuWYQ9BRERERERERKRQCheGufuLo+/N7G3AFPBKd58Mr1sJ/C0z4ZjMY/v27Vx88cXDHoaIiIiIiIiISGEUvYH+m4C3R0EYQPj9O4FfHtqololyuTzsIYiIiIiIiIiIFErRw7BVwKlzXL8BOG7AY1l2Nm/ePOwhiIiIiIiIiIgUStHDsH8CPmRmV5rZpvDrSoJpkp8a8tgK78477xz2EERERERERERECqVwPcN6vA54D/BhoAoY0CAIw948vGEtDxs3bhz2EERERERERERECqXQlWHuPuXurwfWAk8DtgAnufvr3f1Iv1/PzF5kZveY2Q4zu3qO20fN7OPh7V83s039HkM/1ev1YQ9BRERERERERKRQCh2GAZhZBXgqcD5BGPbTZvZyM3t5n1+nDLwf+LHwtV5qZuf33O3VwAF3Pxv4E+AP+zmGftu9e/ewhyAiIiIiIiIiUiiFniZpZk8B/gU4k2CKZItgzA2gBvxdH1/uImCHuz8Qvvb1wBXAXbH7XAG8Pfz+k8BfmJm5u/dxHH2zdevWYQ9BRERERERERKRQil4Z9qfAOHACcAQ4D9gG3AH8dJ9f6zTgodjlXeF1c97H3ZvAEwRTOAtpfHx82EMQERERERERESmUQleGAc8Anufuk2bWBiru/g0z+w3gz4EfHO7w5mZmVwFXAZx++ulMTEwwNTXF5OQkAGvXrqVWqzExMQHAiSeeSKvV4tChQwCsWbMGgIMHDwJw/PHHUy6XOXDgAACrVq1idHSUffv2AbBy5UpWrFjB3r17AVixYgWrVq3ik2+/nU82v0m7brSmSlRWtbAytBtG60iJysoWVgFvQnOyTPm4NqWq4y1oTpQpr2hTGnG8Dc1DZUor2pRHgyK4xhNlSqNtymPh5UNlSlWnvKINQPNwCUpQWRlenghy18qq8PJkCdpQWR1cbk2VaDeM6vGt4PK00a6VqJ4QXq4Z7akSleNbWAltk7ZJ26Rt0jZpm7RN2iZtk7ZJ26Rt0jZpm7RNKbZpdEWVt3/qFQPJI/bu3Yu7MzY2xqpVqzhw4ACtVouRkRGOP/54Dh48SLPZpFKpsGbNGg4dOkS9XqdcLnPiiScyMTHB9PQ0Zsa6des6uQrAunXrOt9nZQWd4QeAme0Htrn7A2a2A7jK3b9gZmcB2939uD6+1rOAt7v7C8PLbwNw9z+I3eem8D5fC3uZPQasX2ia5LZt2/z222/v1zBTefzxxznllFOG8toiIiIiIiIiInkxs3F335blsUWfJnknQfN8gFuBt5rZ84DfBXb0+bVuA84xszPNbAS4Erih5z43AK8Iv38J8IWi9gsDuPvuu4c9BBERERERERGRQil6GPYugsb5AL8FnAF8EfhR4E39fCF3b772ta/90KZNm+4544wzJn/mZ37mMXf/jpm9w8wuB9i3b9/Kyy677Oee9KQn1c4///y//tVf/dX39XMM/bZp06ZhD0FEREREREREpFAKPU1yLmZ2EnAgh4qsMnAvcClB8/zbgJfSvZrk84GvEzTzfx1wCfA/F3rSYU6T3L59OxdeeOFQXltEREREREREJC9H5TRJM6ua2dfN7Nz49e6+P6epiRcRTL18AKgD1wNX9NzniwRBGMAtwMYcxtE3UUM7EREREREREREJFDYMc/cGcCYwqNK104CHYpd3hdfN59XAv+c6oiXaunXrsIcgIiIiIiIiIlIohQ3DQh8BXjPsQczhZcA24I+HPZCFjI+PD3sIIiIiIiIiIiKFUhn2ABaxEvhfZnYpMA5Mxm9093420X8YOD12eWN4Xa8XANcAzwNqfXz9vlu1atWwhyAiIiIiIiIiUihFD8POA74Rfv/kntv6PX3yNuAcgqmZDwNXAj/Xc5+nAX8NvAjY3efX77tTTz112EMQERERERERESmUQodh7v78Ab5cE3gjcBPBypLXAd8B3gHcDtxAMC1yFfB/w8d8H7h8gGNM5d5771UgJiIiIiIiIiISU7gwzMzOdPcHE97XgI3u/tCid07mxvAr7rdj37+gT68zEGedddawhyAiIiIiIiIiUihFbKD/NTP7WzN71nx3MLMTzex1wF3AFYMb2vKyb9++YQ9BRERERERERKRQClcZBjyFoEH9v5lZm6Bx/iPANHAicD5BL7FbgV9195uGNdCiO3jw4LCHICIiIiIiIiJSKIWrDHP3g+7+FuA04LXA3cAagsb2TeAjwNPc/dkKwha2devWYQ9BRERERERERKRQilgZBoC7TwGfDL8kg/HxcS655JJhD0NEREREREREpDAKVxkm/bNmzZphD0FEREREREREpFAUhh3F1q5dO+whiIiIiIiIiIgUisKwo9j9998/7CGIiIiIiIiIiBSKwrCj2ObNm4c9BBERERERERGRQlEYdhR75JFHhj0EEREREREREZFCKWQYZma/bWaXht+faGbvMrMPmdmbzWxjDq93kpndbGb3hf8/cZ77tczsjvDrhn6Po98mJiaGPQQRERERERERkUIpZBgGvBZ4LPz+/wKXA2cD1wAPmNmv9vn1rgY+7+7nAJ8PL89lyt23hF+X93kMfbd169ZhD0FEREREREREpFCKGoadBOw1s7OAr7n7he5+MbAeeAPwe2b2k318vSuAj4TffwTo53MPzfj4+LCHICIiIiIiIiJSKEUNw/YTBGI/AvxVdKW7N939g8Cbgd/o4+ud4u6Pht8/Bpwyz/3GzOx2M7ulz2FcLtauXTvsIYiIiIiIiIiIFEpl2AOYx38AfwI8CbgdeLjn9s8Df5jmCc3sP4AfmOOma+IX3N3NzOd5mie5+8Nm9mTgC2a23d3vn+O1rgKuAjjjjDPSDLOvVq9ePbTXFhEREREREREpoqJWhv06cAi4B/hhM7vSzKqx268A9qR5Qnd/gbtfMMfXZ4DHzWwDQPj/3fM8x8Ph/x8AvgQ8bZ77Xevu29x92/r169MMs6927tw5tNcWERERERERESmiQoZh7r7H3V8SNqn/S+CZwD4zGzez7xJUhV3bx5e8AXhF+P0rgM/03iFc1XI0/H4d8Gzgrj6Ooe/OO++8YQ9BRERERERERKRQChmGxbl7291/lSB8+gxwE/Ayd/+jPr7Mu4FLzew+4AXhZcxsm5n9TXif84DbzexbwBeBd7t7ocMwVYaJiIiIiIiIiHQras+wWdx9O7A9p+feR9Csv/f624FfDL//L+DCPF4/L1NTU8MegoiIiIiIiIhIoRS+Mkyy27p167CHICIiIiIiIiJSKArDjmLj4+PDHoKIiIiIiIiISKEoDDuKnXzyycMegoiIiIiIiIhIoSgMO4qNjIwMewgiIiIiIiIiIoWiMOwotmvXrmEPQURERERERESkUBSGHcUuuOCCYQ9BRERERERERKRQFIYdxe69995hD0FEREREREREpFAUhh3FWq3WsIcgIiIiIiIiIlIoCsOOYhdeeOGwhyAiIiIiIiIiUigKw45id9xxx7CHICIiIiIiIiJSKArDjmIbNmwY9hBERERERERERApFYZiIiIiIiIiIiBwzFIYdxR599NFhD0FEREREREREpFAUhh3FtmzZMuwhiIiIiIiIiIgUisKwo9j27duHPQQRERERERERkUJRGHYUK5fLwx6CiIiIiIiIiEihKAw7im3evHnYQxARERERERERKRSFYYCZ/YyZfcfM2ma2bYH7vcjM7jGzHWZ29SDHmMWdd9457CGIiIiIiIiIiBSKwrDAncD/AL483x3MrAy8H/gx4HzgpWZ2/mCGl83GjRuHPQQRERERERERkUKpDHsAReDudwOY2UJ3uwjY4e4PhPe9HrgCuCv3AWZUr9eHPQQRERERERERkUJRZVhypwEPxS7vCq8rrN27dw97CCIiIiIiIiIihXLMVIaZ2X8APzDHTde4+2f6/FpXAVcBnHHGGf186lS2bt06tNcWERERERERESmiY6YyzN1f4O4XzPHVCcLWr19/0le+8pVPAjuA3gb5D5dKpTOAjwM73vrWt77rSU960uQ8r3Wtu29z923r16/PbZsWMz4+PrTXFhEREREREREpomMmDEugfNZZZ53z53/+579C2CA//H/kttWrVz99fHy8aWbnf/CDH/SPfexjPzicoSazYsWKYQ9BRERERERERKRQFIYBZvZTo6Ojj46Pj49+/OMfv9bM/gW4/hvf+MbLzOxGAHdv/sVf/MX3Lr744ouBuw8dOvQ3z3nOc54BLNh1f5g2bdo07CGIiIiIiIiIiBSKwjDA3T9dq9VeX6/Xr3P3U9z9hcCupz/96ce7+2XR/V72speNHjly5Ifd/axGo/FO4Alg7dAGvoi777572EMQERERERERESmUY6aB/rCMj4/vNbPvDenl1wF7h/TaUkw6JiROx4P00jEhcToepJeOCYnT8SC9dExI3CCOhydlfaDCsBkPA6fHLm8Mr5vrPrsIfnYnAPsWelJ3H1oHfTO73d23Dev1pXh0TEicjgfppWNC4nQ8SC8dExKn40F66ZiQuKIfD5omOeM24BzgTGAEuBK4oec+NwCvCL9/CfAFwAc1QBERERERERERWRpVhs1oAm8EbgLKwHXAd4B3ALcTBGF/C3wU2AHsJwjMRERERERERERkmVAY1u3G8Cvut2PfTwM/M7jhLNm1wx6AFI6OCYnT8SC9dExInI4H6aVjQuJ0PEgvHRMSV+jjwdw1y09ERERERERERI4N6hkmIiIiIiIiIiLHDIVh/fMi4B6CfmJXz3H7KPDx8PavA5vyHIyZvcjM7jGzHWY213jkKGRmO81su5ndYWa3h9edZGY3m9l94f9PDK83M3tfeIx828yePtzRZ7bYe+/XgLuAbwOfZwnL7y4HZnadme02sztj16U+BszsFeH97zOzV8z1WlJ88xwPbzezh8PPiTvM7LLYbW8Lj4d7zOyFsevn+p2y2Hsv8tMEi80UdjWhY4WZnW5mXzSzu8zsO2b2K+H1+oxYXpK8936W4Hffd4B/mO+JFjgm+vU5IcuImY2Z2a1m9q3wePjd8Pozzezr4b79uJmNhNePhpd3hLdvij3XnMfJUWCx998ZwBeBbxL87XnZHPdZNhY4Jj5sZg/GPiO2hNfr98YxwMzKZvZNM/vX8HLenxHXAbuBO+e53YD3Ebwvvw0kO691d30t/avs7ve7+5PdfcTdv+Xu5/fc5/Xu/lfh91e6+8fzGg/BAgD3A08mWBnzW0DvePR1FH4BO4F1Pdf9EXB1+P3VwB+G318G/Hv44fFM4OvDHn+GryTvvee7+3Hh96/zHN97RfgCnhv+Argz6zEAnAQ8EP7/xPD7E4e9bfrq2/HwduDNc9z3/PD3xSjBysr3h79PZv1OOf744y/wxd97uPtqd/+yu9/i7tuG/fM41r+ADcDTw+9XA/eG+12fEcvnK8nvvXPc/ZvuHu2TkzMcE0v+nNDfnsvvK3yvrwq/rxL8A/4zgU8AV4bX/xXwuvD71wN/FX5/JfDxhY6TYW9fH76SvP+u9eDvTcLbdhZg3HkcEx8GXjLH/fV74xj4Iig2+AfgX8PLeX9GPNfdn+7ud85z+2Xu/u/ubu7+THdPdF6ryrD+uIgghXwAqAPXA1f03OcK4CPh958EfoTgQyK38bj7A+4+33jk2BE/9j4C/GTs+r/zwC3AGjPbMITxLUWS994XgSPh97cAGwc2uiFw9y8TrHYbl/YYeCFws7vvd/cDwM0E/xIqy8w8x8N8rgCud/eauz9I8N66iDl+p5x99tlvYPH3HsA7gT8kWIBGhszdH3X3b4TfHwbuBk5DnxHLSZLfe68B3g8cCC/vnu/JFjgm5pP4c2KOcUnBhe/1ifBiNfxy4L8TnL/A7M+IrvMbMzPmP06WuyTvPweOD78/AXhkYKPLwQLHxHz0e+MoZ2YbgR8H/ia8bOT/GbHY37NXAH9HcGzeAqwh+MeeBSkM64/TgIdil3cx+w+J+H2awBPA2iGOR45ODnzOzMbN7KrwulPc/dHw+8eAU8Lvj4bjJO02vJrgX6uONWmPgaPh2JCFvTGcvnBdNCWOFMeDu2+a575xTwdOB/6tnwOX/ginKjyN4F/59RmxfCT52W8Ov75KcFKQ6ISz55iAJX5OzDEuWQbC6U93EISoNxNUbBx092Z4l/i+7ez38Pbo/OZoPR6SbNfbgZeFt90I/PJARpaj3mPC3aPPiHeFnxF/Ymaj4XX6jDj6/SnwG0A7vLyW4X9GZHo+hWEiR5fnuPvTgR8D3mBmz43f6EFd6rG6hOzLCHoW/fGwBzJMx/gxIIEPAGcBW4BHgffk8Bol4L3Ar+fw3LJEZrYK+CfgV939UPw2fUYcFSrAOcAlwEuBDxL8K/m85jgmBvE5IQXk7i1330JQSX8R8JThjmjZeSnBFMKNBFMGP8oyP+fuPSbM7ALgbQTHxjMIpj6+dXgjlEExs58Adrv7+LDH0g/L+o1ZIA8T/Ot3ZGN43Xz3qRCUze4b4njkKOTuD4f/3w18muCPmMej6Y/h/6PpEkfDcZJ0G14AXANcDtQGMK6iSXsMHA3HhszD3R8P/7BtE5wkR2XpiY8HM9s5z30jq4ELgC8R9DJ8JnADaqI/dGZWJQg9/t7dPxVerc+I5SPJz34XwfutATxI0AfsnPmecK5joh+fE3OMS5YRdz9I0GriWQRT3SrhTfF929nv4e3R+c3Rejwk2a5XE/RPAvgaMAasy39o+YsdEy8Kp1i7u9eAD6HPiGPFs4HLw78DryeYHvlnDP8zItPzKQzrj9sI/sg4k6Bp6JUEf4TE3QBEq2a8BPgC+f3L623AOeGqDvONR44yZrbSzFZH3wM/SrDiRvzYewXwmfD7G4CXh6u+PBN4IjZNZrlI8t57GvDXBEHYvH1TjnJpj4GbgB81sxPDqTE/Gl4nR4Ge3oA/xczKPDcAV4Yr/5xJ8N66lTl+p+zcufMDLPzee4Lgj/9N4dctBO/B23PaLEkg7NPxt8Dd7v7e2E36jFg+kvze+2eCqjAI3oebCXoczTLfMdGPz4k5xiUFZ2brzWxN+P0K4FKCPnJfJDh/gdmfEV3nN2F16XzHyXKX5P33fYLe0ADnEYRhewY1wH6b55j4buwfUIygP1T8M0K/N45S7v42d98Ytsu4kuA9/78Y/mfEDcDLmVm44QmCquYFVRa7gyTSBN5I8IYuEyz9+R3gHQR/+N9A8IfGRwmaw+0nOHhy4e5NM+saj7t/J6/Xk8I4Bfh08DuJCvAP7v5ZM7sN+ISZvRr4HsFy6xD0MbiM4Jg8Arxy8ENesiTvvT8GVgH/N3zM9wlOyo9KZvaPBCdB68xsF/A7wLtJcQy4+34zeyfBH30A73D3pE3YpUDmOR4usWAJdCeo2volAHf/jpl9AriL4L31Bndvhc/T9T7bt2/ft1n8vSfF82zg54HtYf8XgN9EnxHLSZLfezcRnGjeBbSAtzD/bIT5jomXLvVzQn97LksbgI+YWZmgaOIT7v6vZnYXcL2Z/R7wTYLzGsL/f9TMus5vFjpOlrkk779fJ6im/N8E759fYHlPPZ/vmPiCma0nCB/uAF4b3l+/N45NbyXfz4jO37ME1c+/Q7CYAwSrV2Y6r7UgmBMRERERERERETn6aZqkiIiIiIiIiIgcMxSGiYiIiIiIiIjIMUNhmIiIiIiIiIiIHDMUhomIiIiIiIiIyDFDYZiIiIiIiIiIiBwzFIaJiIiIHGPMbNTM7jKzDUt8nveY2ev6NS4RERGRQVAYJiIiInIUMbOdZvaCRe52FfBld390iS/3f4DfNLORJT6PiIiIyMAoDBMRERE59rwW+OhSnyQM074LXL7kEYmIiIgMiMIwERERkaOEmX0UOAP4FzObMLPfmOM+ZwBPBr4eu+7DZvZ+M/s3MztsZl83s7PC28zM/sTMdpvZITPbbmYXxJ7yS8CP57phIiIiIn2kMExERETkKOHuPw98H3ixu69y9z+a424XAg+4e7Pn+iuB3wVOBHYA7wqv/1HgucBm4ATgZ4F9scfdDTy1bxshIiIikjOFYSIiIiLHljXA4Tmu/7S73xqGZH8PbAmvbwCrgacA5u539/QaOxw+p4iIiMiyoDBMRERE5NhygCDc6vVY7PsjwCoAd/8C8BfA+4HdZnatmR0fu+9q4GA+QxURERHpP4VhIiIiIkcXX+T2bwNnmlkl8RO6v8/dtwLnE0yXfEvs5vOAb6UepYiIiMiQKAwTERERObo8TtAgf07uvougJ9hFSZ7MzJ5hZj9kZlVgEpgG2rG7PA/49+zDFRERERkshWEiIiIiR5c/AH7LzA6a2Zvnuc9fAz+f8PmOBz5IML3yewTN8/8YwMw2EFSL/fNSBiwiIiIySOa+WCW9iIiIiBxNzGwU+CbwIz3N8NM+z3uA+939L/s2OBEREZGcKQwTEREREREREZFjhqZJioiIiIiIiIjIMUNhmIiIiIiIiIiIHDMUhomIiIiIiIiIyDFDYZiIiIiIiIiIiBwzFIaJiIiIiIiIiMgxQ2GYiIiIiIiIiIgcMxSGiYiIiIiIiIjIMUNhmIiIiIiIiIiIHDP+f0yEdAQQhDRqAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seq.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "sim = Simulation(seq, sampling_rate=1.0, config=None, evaluation_times=t_list)\n", + "psi_y = (qutip.basis(2, 0)+1j*qutip.basis(2, 1)).unit()\n", + "sim.initial_state = qutip.tensor(psi_y, psi_y)\n", + "res = sim.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "sy = qutip.sigmay()\n", + "Id = qutip.qeye(2)\n", + "Sigma_y = (qutip.tensor(sy, Id)+qutip.tensor(Id, sy))/2.\n", + "Sigma_y_res = res.expect([Sigma_y])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "\n", + "# Showing the Hamiltonian engineering period.\n", + "line1 = 0.9\n", + "line2 = 2.1 \n", + "plt.axvspan(line1, line2, alpha=.1, color='grey')\n", + "plt.text(1., 0.5, r\"$H_{XX} \\to H_{XXX}$\", fontsize=14)\n", + "\n", + "plt.plot(sim.evaluation_times, Sigma_y_res[0], 'o')\n", + "plt.xlabel(r\"Time [µs]\", fontsize=16)\n", + "plt.ylabel(fr'$ \\langle \\sigma_1^y + \\sigma_2^y \\rangle$', fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Domain-wall dynamics\n", + "\n", + "Now, we will look at the dynamics of the system under $H_{XX2Z}$ when starting in a Domain-Wall (DW) state $|\\psi_0\\rangle = |\\uparrow \\uparrow \\uparrow \\uparrow \\uparrow \\downarrow \\downarrow \\downarrow \\downarrow \\downarrow\\rangle $, for two distinct geometries : open boundary conditions (OBC) and periodic boundary conditions (PBC). In the case of $H_{XX2Z}$, only 2 pulses per Floquet cycle are required, as the $X$ and $-X$ pulses cancel out." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def Floquet_XX2Z_cycles(n_cycles, t_pulse):\n", + " t_half = t_pulse/2.\n", + " tau_3 = tau_2 = tc/4.\n", + " for _ in range(n_cycles):\n", + " seq.delay(tau_2-t_half, 'MW')\n", + " seq.add(mY_pulse, 'MW')\n", + " seq.delay(2*tau_3-2*t_half, 'MW')\n", + " seq.add(Y_pulse, 'MW')\n", + " seq.delay(tau_2-t_half, 'MW')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "N_at = 10\n", + "# Number of Floquet cycles \n", + "N_cycles = 20 \n", + "# In the following, we will take 1000 projective measurements of the system at the final time.\n", + "N_samples = 1000 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also create the initial state of the system using QuTiP." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Creation of the initial DW state\n", + "initial_DW_state=[]\n", + "for m in range(N_at):\n", + " if m" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "img = ax.imshow(magnetizations_obc, cmap=plt.get_cmap('RdBu'))\n", + "plt.title('OBC',fontsize=16)\n", + "ax.set_xlabel('Cycle',fontsize=16)\n", + "ax.set_ylabel('Atom number',fontsize=16)\n", + "cbar = fig.colorbar(img, shrink=0.7)\n", + "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)\n", + "\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "img = ax.imshow(magnetizations_pbc, cmap=plt.get_cmap('RdBu'))\n", + "plt.title('PBC',fontsize=16)\n", + "ax.set_xlabel('Cycle',fontsize=16)\n", + "ax.set_ylabel('Atom number',fontsize=16)\n", + "cbar = fig.colorbar(img, shrink=0.7)\n", + "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the magnetization profiles look rather different for OBC and PBC. It seems that the initial DW melts in the case of PBC. In fact, the decrease of $|\\langle \\sigma^z_j \\rangle|$ for all sites is due to a delocalization of the DW along the circle. This delocalization can be more apparent when looking at correlations. More specifically, we see on the plot below that the number of spin flips between consecutive atoms along the circle, $\\langle N_{flip} \\rangle=1/2\\sum_j(1-\\langle \\sigma_j^z \\sigma_{j+1}^z\\rangle)$, remains quite low during the dynamics for both OBC (red) and PBC (blue), while it should tend to $N_{at}/2=5$ for randomly distributed spins. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "plt.title(r'Evolution of $\\langle N_{flip} \\rangle$ in time for OBC (red) and PBC (blue).', fontsize=16)\n", + "ax.set_xlabel('Cycle',fontsize=16)\n", + "ax.set_ylabel(r'$\\langle N_{flip} \\rangle$',fontsize=14)\n", + "ax.plot(correl_pbc,'--o',color='blue')\n", + "ax.plot(correl_obc,'--o',color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To investigate even more this delocalization effect, let's consider a smaller region of only 3 spins prepared in $|\\uparrow \\rangle$. The delocalization timescale will then be shorter, and we will see it more clearly happening in the system" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Creation of the initial DW state with only 3 spins up.\n", + "initial_DW_state=[]\n", + "for m in range(N_at):\n", + " if m < 3:\n", + " initial_DW_state.append(qutip.basis(2, 0))\n", + " else:\n", + " initial_DW_state.append(qutip.basis(2, 1))\n", + " \n", + "initial_DW_state = qutip.tensor(initial_DW_state)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "N_cycles=26 # Runtime close to 4 min !\n", + "\n", + "magnetizations_pbc = np.zeros((N_at, N_cycles), dtype=float)\n", + "samples_evol = []\n", + "for m in range(N_cycles):\n", + " seq = Sequence(reg, MockDevice)\n", + " seq.set_magnetic_field(0., 0., 1.)\n", + " seq.declare_channel('MW', 'mw_global')\n", + " seq.set_magnetic_field(0., 0., 1.)\n", + " seq.add(X_pulse, 'MW')\n", + " Floquet_XX2Z_cycles(m, t_pulse)\n", + " seq.add(mX_pulse, 'MW')\n", + " sim = Simulation(seq)\n", + " sim.initial_state = initial_DW_state\n", + " res = sim.run()\n", + " samples = res.sample_final_state(N_samples)\n", + " samples_evol.append(samples)\n", + " correl = 0.\n", + " for key, value in samples.items():\n", + " for j in range(N_at):\n", + " magnetizations_pbc[j][m] += (2*float(key[j])-1)*value/N_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "img = ax.imshow(magnetizations_pbc, cmap=plt.get_cmap('RdBu'))\n", + "ax.set_xlabel('Cycle', fontsize=16)\n", + "ax.set_ylabel('Atom number', fontsize=16)\n", + "cbar = fig.colorbar(img)\n", + "cbar.set_label(r'$\\langle \\sigma^z \\rangle$', fontsize=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see above that the magnetization profile tends to average. But if we look at the histogram of sampled states in time, we will remark that domain-wall configurations are dominant (in red in the histograms below). As time increases, the delocalization mechanism populates more and more domain-wall states distinct from the initial state." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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cs/r994EfZebF1fOtgZuXsmGSJEmSJC3GFot47fHAmyLi/sCzgPcPxB4AnL+E7ZIkSZIkaVEW08E9HLgd8FhKZ/cNA7EnAF9awnZJkiRJkrQoY3dwM/OXwJ/WxB6xZC2SJEmSJGkCi7kP7vkR8cCa2P0jwlOUJUmSJElTs5hJpnYHblsTux1wt86tkSRJkiRpQovp4AJkzfLVwLXdmiJJkiRJ0uQar8GNiJcCL62eJvCZiLhx6GVbAdsBxy598yRJkiRJGk/bJFPnA1+pfj8UOAVYO/SaG4CzgQ8sbdMkSZIkSRpfYwc3Mz8NfBogIgD+NjMvWIZ2SZIkSZK0KIu5TdCz+2yIJEmSJEldjN3BBYiIuwNPAXajzJw8KDPzOUvVMEmSJEmSFmPsDm5EHAQcR5l5+UrKtbeD6mZYHqxjV+AYYFX1+iMz8x8jYjvg45RbEf0YeEpmXhPlvOh/BA4AfgU8KzO/N26bJUmSJEmbjsXcJujvgK8BO2XmXTNzj6HH3ceo42bg5Zl5X+DhwAsj4r7A4cBXMnNPyqRWh1ev3x/Ys3ocBrx3Ee2VJEmSJG1CFtPBvTvw1swcnkV5bJl52cI3sJl5HfADYGfgQODo6mVHAwdVvx8IHJPFt4FtI2KnSfNLkiRJkubXYjq4PwTuvFSJI2J34MHAd4BVmXlZFbqccgozlM7vxQPFLqmWSZIkSZK0gcV0cF8BvLqaaKqTiNga+ATwksz8+WAsM5Mxrucdqu+wiDglIk5Zu3biL5glSZIkSTNsMbMov47yDe4PImINcPVQPDPz99oqiYjbUDq3H83MT1aLr4iInTLzsuoU5Cur5ZcCuw4U36VaNpz4SOBIgNWrVy+qcyxJkiRJmg+L+Qb3FuAc4FvA2ur54GNdWwXVrMgfBH6QmW8fCB0PHFr9fijw6YHlh0TxcOBnA6cyS5IkSZJ0q7G/wc3MfZYg3+8AzwTOiIjTqmWvBo4AjouI5wAXUu61C/B5yi2CzqPcJujZS9AGSZIkSdIcWswpyp1l5jeBqAk/esTrE3hhr42SJEmSJM2FsTu4EfG7ba/JzG90a44kSZIkSZNZzDe4X6N9duPNJ2+KJEmSJEmTW0wH91Ejlt0ZeDzwe8CLlqRFkiRJkiRNYDGTTH29JvTJiHgH8IfACUvSKkmSJEmSFmkxtwlq8jnWz3wsSZIkSdKyW6oO7r0Y4z64kiRJkiT1ZTGzKB8yYvGWwP2B5wCfXKpGSZIkSZK0WIuZZOrDNctvAD4O/EXn1kiSJEmSNKHFdHD3GLHs+sy8YqkaI0mSJEnSpBYzi/KFfTZEkiRJkqQuFvMNLgARsXDf2+2Aq4GvZebnlrphkiRJkiQtxmImmboD8FngkcDNwFXAnYGXRcR/AY/PzF/00kpJkiRJklos5jZBbwQeAjwT2CozdwK2Ag6plr9x6ZsnSZIkSdJ4FtPB/WPgtZn50cy8BSAzb8nMjwL/r4pLkiRJkjQVi+ng3hk4uyZ2dhWXJEmSJGkqFtPBvQB4fE3sgCouSZIkSdJULGYW5fcDb4uIrYGPApcBOwIHA88FXrb0zZMkSZIkaTyLuQ/uOyJiB0pH9lnV4gBuBI7IzH9c+uZJkiRJkjSeRd0HNzNfHRH/ADyc9ffB/XZmXtNH4yRJkiRJGtdi7oP7SmCXzHwxcMJQ7F3AxZn5D0vcPkmSJEmSxrKYSaaeDZxeE/t+FZckSZIkaSoW08HdDVhTE/sRcLfuzZEkSZIkaTKL6eD+Cti5JrYLcEP35kiSJEmSNJnFdHD/C/iriLjt4MLq+curuCRJkiRJU7GYWZRfB3wLODciPgJcSvlG9xnAnVl/6yBJkiRJkpbdYu6D+/2IeBTwVuCVlG9/1wHfBP44M7/fTxMlSZIkSWq3mFOUyczvZubvAnegXHd7h8zcJzNPGad8RHwoIq6MiDMHlr0uIi6NiNOqxwEDsVdFxHkRcU5EPHYxbZUkSZIkbVoWc4ryrTLz18CvJyj6YeDdwDFDy9+RmW8dXBAR9wUOBu4H3BX4ckTslZm3TJBXkiRJkjTnFvUNbleZ+Q3g6jFffiBwbGbekJkXAOcBe/fWOEmSJEnSTFvWDm6DF0XE6dUpzHeqlu0MXDzwmkuouU1RRBwWEadExClr167tu62SJEmSpBVoJXRw3wvcA3gQcBnwtsVWkJlHZubqzFy9ww47LHHzJEmSJEmzYOod3My8IjNvycx1wL+w/jTkS4FdB166S7VMkiRJkqSNTL2DGxE7DTx9IrAww/LxwMERcduI2APYE/jucrdPkiRJkjQbJppFeVIR8W/APsD2EXEJ8DfAPhHxICCBHwPPA8jMsyLiOOBs4Gbghc6gLEmSJEmqE5k57TYsqdWrV+cpp4x1W15JkiRJm7qI0cvnrJ80TyLi1MxcPSo29VOUJUmSJElaCnZwJUmSJElzwQ6uJEmSJGku2MGVJEmSJM0FO7iSJEmSpLlgB1eSJEmSNBfs4EqSJEmS5oIdXEmSJEnSXLCDK0mSJEmaC3ZwJUmSJElzwQ6uJEmSJGku2MGVJEmSJM2FLabdAEmSpLkXMXp55vK2Q5LmnN/gSpIkSZLmgh1cSZIkSdJcsIMrSZIkSZoLdnAlSZIkSXPBDq4kSZIkaS7YwZUkSZIkzQU7uJIkSZKkuWAHV5IkSZI0F+zgSpIkSZLmwhbTboAkSZKkZRIxennm8rZD6onf4EqSJEmS5oIdXEmSJEnSXLCDK0mSJEmaC8vawY2ID0XElRFx5sCy7SLiSxGxpvp5p2p5RMS7IuK8iDg9Ih6ynG2VJEmSJM2W5f4G98PA44aWHQ58JTP3BL5SPQfYH9izehwGvHeZ2ihJkiRJmkHL2sHNzG8AVw8tPhA4uvr9aOCggeXHZPFtYNuI2GlZGipJkiRJmjkr4RrcVZl5WfX75cCq6vedgYsHXndJtWwjEXFYRJwSEaesXbu2v5ZKkiRJklasldDBvVVmJrDom3Bl5pGZuTozV++www49tEySJEmStNKthA7uFQunHlc/r6yWXwrsOvC6XaplkiRJkiRtZCV0cI8HDq1+PxT49MDyQ6rZlB8O/GzgVGZJkiRJkjawxXImi4h/A/YBto+IS4C/AY4AjouI5wAXAk+pXv554ADgPOBXwLOXs62SJEmSpNmyrB3czHxaTejRI16bwAv7bZEkSZIkaV6shFOUJUmSJEnqzA6uJEmSJGku2MGVJEmSJM0FO7iSJEmSpLlgB1eSJEmSNBfs4EqSJEmS5oIdXEmSJEnSXLCDK0mSJEmaC3ZwJUmSJElzwQ6uJEmSJGku2MGVJEmSJM0FO7iSJEmSpLlgB1eSJEmSNBfs4EqSJEmS5oIdXEmSJEnSXLCDK0mSJEmaC3ZwJUmSJElzwQ6uJEmSJGku2MGVJEmSJM0FO7iSJEmSpLlgB1eSJEmSNBfs4EqSJEmS5oIdXEmSJEnSXLCDK0mSJEmaC3ZwJUmSJElzwQ6uJEmSJGkubDHtBiyIiB8D1wG3ADdn5uqI2A74OLA78GPgKZl5zbTaKEmSJElauVbaN7iPyswHZebq6vnhwFcyc0/gK9VzSZIkSZI2stI6uMMOBI6ufj8aOGh6TZEkSZIkrWQrqYObwIkRcWpEHFYtW5WZl1W/Xw6sGlUwIg6LiFMi4pS1a9cuR1slSZIkSSvMirkGF/g/mXlpRNwF+FJE/HAwmJkZETmqYGYeCRwJsHr16pGvkSRJkiTNtxXzDW5mXlr9vBL4FLA3cEVE7ARQ/bxyei2UJEmSJK1kK6KDGxG/ERF3WPgd+H3gTOB44NDqZYcCn55OCyVJkiRJK91KOUV5FfCpiIDSpo9l5hci4mTguIh4DnAh8JQptlGSJEmStIKtiA5uZp4PPHDE8quARy9/iyRJkiRJs2ZFnKIsSZIkSVJXdnAlSZIkSXPBDq4kSZIkaS7YwZUkSZIkzQU7uJIkSZKkuWAHV5IkSZI0F+zgSpIkSZLmgh1cSZIkSdJc2GLaDZAkSVoOETFyeWYuc0skSX2xgytJkiRprjnAtenwFGVJkiRJ0lywgytJkiRJmgt2cCVJkiRJc8EOriRJkiRpLtjBlSRJkiTNBWdRliR1VzM7Jc5OKUmSlpEdXEmSpFnmAJMk3cpTlCVJkiRJc8FvcCVJkjQVUfPtc/rts6QJ+Q2uJEmSJGku+A2uJEmSpNpv1MFv1TU7/AZXkiRJkjQX/AZXkuZE07VsjsprqbgvSZJWMju4GpsTQUiStGlxQEPSrLGDK0mSpBXHzrWkSdjBlTZRm+w/Dg3rzTyvd0eewaFZMM39dCUfIyu5bZK01GZikqmIeFxEnBMR50XE4dNuj6TpioiRj3Hjmi2+n1oJ6vZD90VtSjwGlp7bdOmt+G9wI2Jz4J+B/YBLgJMj4vjMPHu6Leugbqd1JHUqNtlvMlv4TcimZV7f77a6Z3lfm+W2N5nXfXElm9X1ntV2w2y3vck8f+bOok31f9wV38EF9gbOy8zzASLiWOBAYHY7uC0mmQl1FnbSxrb3fNqo/zAtv1ld767t7lLefwwmM6vH97y+3/O6Xpo/zjw/W2b1s17LbxY6uDsDFw88vwR42OALIuIw4LDq6S8i4pxlattS2B74KVDXybs1PuLgaoptWHdb7sXHlyx3a/mG9W6ru2vupm3ed+4udffQtj7f7z7fr8Xl3kTXu8v73efx2+d6d16vxbd9Rax3W+6u8T63eZfPzL5zd/rMXPq/313+r2iLj/3Z0dq2lnifn1t95m6ru2vuLnW3xqf4t6LPz8wJyi/ZMdSWe5nrbqu/rexKc7faSGau6AfwJOADA8+fCbx72u1awvU7ZdJ4l7LmNvdKyT2v62Vuc5vb3POWe17Xy9zmNnd72Vl6zMIkU5cCuw4836VaJkmSJEnSrWahg3sysGdE7BERWwIHA8dPuU2SJEmSpBVmxV+Dm5k3R8SLgC8CmwMfysyzptyspXRkh3iXsuY290rJPa/rZW5zm9vc85Z7XtfL3OY2d3vZmRHVOdeSJEmSJM20WThFWZIkSZKkVnZwJUmSJElzwQ6uJEmSJGku2MGVJEmSJM2FFT+LspZORKwCdq6eXpqZV0yzPYMmaVtE3DszfziN3OOW77Puaefuq2zX+rtss2nn7nOb9xlviW0DPG4wDnwxM68dd926WMnHwab62eExNF7blupvnCRtapxFeYWIiLOAzwAHAXcBErgS+DRwRNM/gxFxRmY+oCG+BrgK2IbyzyXALsC1wAuAHwGv6il3bTwizgCeCbyvrm2Z+b2Gui8CHjBJ25cgd1v5dwIv6anuFwDrppW7pXzjvtaWu21famnbO2lYb5q3WetxAOzeY+7GeJdt3jV3S7xtve8P/A1w4lB8P+D1lG275J89y3B815Zf6cfvrOZuKTvLx1DTvnRRZu5W1+6lNM2BgS5W8oBmTX1jD1rM6kBuTX23rneX8i3tCmBvNhxM/W4OdHT6Wq9JB3Kb9odx6+4ar8n97Mw8qqntK53f4C6jiPijuhCwJ3ANsE9mXl69fkfgUOC4iHhfQ9kdW+reA3hGZn5nqD0PB44CLge+2lPu3WriAewIfBh4Xl3bIuLrDXVvCxzX0PavRcTfdsj9+obcbeVPqtrUR91HUToCU8ndUr5xX2vL3bIvddrmNG+z1uOA0gHrK3djvMs275q7Jd623rcDfmv4D2lE3An4DvB0Jv/s6frZ0uU4+GREvGzS3Ezx+J3V3C1lZ/kY+mJE/FtNu7atXrcNEw7kTjpoGBHXMuHAwEJZajr2Y9Y90cD9UuWmebu8kxGDMePUTRnsqx206Jp7Ja93RDxh0rYBJ0XEhQ3rvT3wHmDNYBy4Z0S8gHK89LVer2XjgdxHAW+MiNdn5jFN5anfH1rrrp5PHG9o2+spn10zy29wl1FE3AR8lPLHadihmTnymuiIOAe4e0PZJ1H+kZyk7vOAWzLzXj3lfhZwdEPZyzNzz4a2rQJeDtww4iVvA65qaHt2zH03mte7qfyNmbllT3WfB+QUczeVb9vX2nI37Utdt3nTNhvnONisx9xt8S7bvGvuLvvaOuChmfmzodg2wCnAug6fPc9iesd318+WaR6/s5p7Xo+hdcCfUfM3LjO3j4gvUgaCjh4xEPQ0oG4g933A80blHYhfSn3n+/2Uf3Tryn8c+J2Gsk0d+7a629red+6m7dI0GPN+oGlg/lDKZ1dfuVfyep/f0ram8i8AHtFQdktg/8z88VB8D+DzlOOrr/W6DHhYw0DuFxrKH1blmLTu7BC/HDinJvdemXnbmnbPBDu4yygiTqX8IT1zROx64K8pf8CuqJatonwQ7gfcqaHsxZTRqbr4dcA3gGOAi6vFuwKHABcAewFf7in3jcBDGsp+CrhHQ9vuC7w2M781ovwFlNG6ura/hvKBOGnu325Z76byuwCX9FT3BZSOw7RyN5Vv29facjftS123edM2G+c4OLvH3G3xLtu8a+4u+9rJlM+1Ewfiu1Xb8++AP2Hyz56uny1djoOHAvvO6PE7q7nn9Rh6IPCUur9xmblHRJzTMBDUNtjSZdCw74GBLgOefefuMpjaNjC/TY+5V/J6X9PStqbyH8rMzRvKJnCfzLx5KLYl5W93l8G1tvW6muaB3B0byh9FwwDXGHVnh/hVwGrK2aMbhIFvZeZdR7RpZtjBXUYR8Ujgwsy8aETsUZRz5A+kHExQRleOB94M3K+h7Gpgq5b4DlXdg+fgH5+Zn69Gcg7vKfdzgRPrymbmKRGxf0PbtgOuz8xfDZev6mhq+9eBMzvkbnq/xinfW93V66aSu608zftaW9nG/bjLNh9jmzUeB5l5dV+5x2jbxNu8a+4x2tZW9k7AY9n4+p9rOn72dP1smfg4AK6btOy0j99Zzd1Wdoy6V+QxBHybhr9xVdkTmXwgt8ugYd8DA10GPPvO3WUwtW1g/uoec6/k9f5MS9uayv8M+GZD2UuBpwDHDsUPplxitFOP6/U6mgdyD2ko/2vg0R3qzg7xS4FXZ+Y3R+T+WGb+yfDyWWIHV5LUq1jBM7hLK0U1oEtmXj20fOKBXLoPGvY2MNB1wLPP3B0HsNsG5nvLvZLXe4y2tW23tvW+L/CEEfGzx8jddb2aBnJry3eteyni88oO7goREX9NOV/+IDbcCT+dmXXn7w+WfSPwHOCJwMJpBZdSJqH4YGbeVFP2yMw8LCIe21PuD1OuIxjZLuD2lMkzFv5wJ+PPonpCZu4/Sdsj4q8zc9R1SwvxIynXfCx6m1bljwKuqFsv4Bcd6j4S+Cvqt9t7qrZPsk27bpd/r9o20XpXr+9rmyct+1qH4+DIzDysLkbz+9Xatrbt1nR8L0HupnjjvsaGM09fQjn1aYPZYzts89dRrk+a9Bjqcnz/C+X0r2U/fmk/hho/U+n2fk7tc6ul3V2PobbPra65m+IfAV4LPJpyXARwR8o1t4fn0DWFkprVDRStBE1t69LutkHkunhE+8zTs8oO7goRET9n/ekXl1SLd6Gc2rAmM/+ioexFwH9T/jgePVT+UMr5/88ZVRT4PvAflOsP+8h9AOUC/1Gx7SizRI6aPONZwL6UUeuRqYHPUiaaWHTbq3Y/qKHu7wP/1bBe2wHPbyh/KWXmurr1uqpD3d8HzqJ+u/0l8A91uTPz92vqXortchBlRsJJ13tdS7zLNk+a97WzadiXaJ6c43TKLatGxdrer3Ha1rTd2o7vrrmb4o37Gs0zT7+fMrnHpJ89v6zKTXoMdTm+L67KLfvxS/sxtG1D3V3fz6l9brW0u+sxdBDNn1tdczfFD6dMpvQfmXlLFdsceDLwksx8eLVsooFcJhz8rspPPBA05uBaLwOeS5S7aaCoyyDyCcAfTrJeVflOA7lt690l9xjrfXBT21rKfwk4tWG97gi8hXK8/YyhgSLKdaa9vJ+ZuX9D/AzgD6q2jRrEehfw5zWxxgGu2HjW640GkdlwVu3h+DHAyxgx8zRlAPrEutyzwA7uMqo6sSNDwNaZGSPKBHAu609LGlV2K+D8zNyrJm9SriMYrD+r5zsDPx5VdolyN128fy7lwv+mWVTvSTkFa6NtAzwcuLih7TcDv2xodwAXssjtMtD2ezSU333U+zmwXtGh7p2BCxq2W9M2P4dyLcrIMN23S1vutvWmx21Oy742sm0Dx8Hdm3LTfIw1vV8Tt62Ktx3fXXM3xdve76aZp8+jzKI86WfPyM/Mqvw4x1Bfx3ffx2/bMdT2mdrl/Zzm51aX/bSt7X3nnnSbr8nMPSPinUw+kNs0AN11IKjr4FqXAc++c2/bUL5tMKZtYP5rLbn7HMjtMkjVlrttvU9vaVtT+W+xfiLWUWXvQBmkGjlQROn09vV+vrAh/j7gvIa2/Qvw3IZ2v6Wl7ktpHkTOhvjXgHsPd6Kjmnk6M+9Tk3s2ZKaPZXoAFwGramI3UmY6G16+N3BGS9mLKRNVPJnyD+XC8s2ApwLXA7s1lD29x9zXNcS+Q7nw/RWD9VP+qX0lZVKNM4E9J2z7jS3tXtOyXZrW6zst5a9vWa8udV/cst2uasnd9n522S7XdlzvPrd5277Wdhw05b6pw/s1Ttu6HN9dc3fZ194FfK5q5yOqx1OrZe8eY5s37as3dNhXuh7fXXN32aZt7e7z/Zzm51afx9C1Peduil9O+QbpYZRv1O5a/f4e4LjqtefWbNMAbgF+PuJxHWWQd2TZhXqr8udTBskWHgvPb2wpny1lz2koe84YbZtm7qbyN7bUfQulM3XSiMevO74n2ZK763p3yd223m1tayq/rqXsmob4mp7fz5sol+MdNeJxXUvbmnKv6Vj3eW25gS1GLN8SOK+u3Kw8tkDL6RjKFO2jJlj5GPDuiLgD60fVdqWMOj2LcjpJU9n3UmYdfU9EXFMt35ZyAL6RcruNjSYVoIwO/Tfw3p5y7w+8eCAWrB8ZPZjyj8XhwNcj4i5V2Ssok2c8hTJ6ttmIvFT1XtTQ9rbtfRHN2+UzQ+s13PYDGsr/NXDnhvW6Y4e630K5bqtuu62mTDv/9SjXXcD6CUmeQjklpa/t8nuUW79Mut7ZYbu0bfMc2GajtsvuNB8HD2/I/amWbdb0fo3Ttqbt1nZ8d83dFG/c17J+5ul/zjJ5x0OY/LPnQ5TbWoz63BnnGOpyfL9xKPdyHr9tx9C1DXV3ej/HyN3nenfZT9vaPvi51UfupvgDgT+iXAIxPMPyB6vn10fEQzPz5KFt+lDKP9975oiJ26LMint1RDwZ+ERmrquWb0bp7F9D6bw8OkdPOtRW/gbK/UPryl4YEa9g9OzPFwNbt7Qtp5j7Zw3lr2up+ybKt2ZrenhPrm/Jva7jenfJ3bbebe9JU/m23D+NiPdQvpkenAn5UOB/gW17fD+vBN6ao2emfgxwakPbLm9p989b6j4hIj7H6Bmgv0DZH+ripwInR8SxQ7GDWf/ZM7M8RXmFiXJaxOCF4JdPUMedATLzqpWUe9J2jZm3c9tb6u+z7b3V3bcubW8rO63t0ve+1NUs7y91um7zrtukz/24T5tq7q5mre0LA0GU0zCHB4LOBI7MzO+OKPdm1g9A78v6+11uSxkIOpwyKPHNzPz+iPIvZv1A0EL5wYGBsyjXAdeVXRjQOJByPT6sH9B4M+sHHUbVfTjrBzy75h4eVBgn97UNbX8fZTCmru59gTMy85wRbTsIOK0ld9N78grKQG5d7oXBlEnXe9zco97PtvX+ekvbmso/HfjNhrIL1xYPD6YuDBTdviF31/fzKppn+z69oW3HUK6jrWv33k11Z/dbs92nJnb2cL5ZYwd3hYiIe1NmBH0cG0/lfW31mm2a4g1175eZX2qKtdXdJTdlZGj4APp0Zv6wpeyzM/OotnhT27pus+p9qW17Tfz4zPzBGO1e8roX6gd+QsOEJH1vlx7We6m2+WO7bJem3F3erzHbVrtdliF3bbwltg3tM1cv+WdP02feYLzLvjbl43fRx99C3W3vd5f9cBnWe+L9tK3tS1D3RHHKKczPqYltMOnPLA8Eda17XgeZN9XcUt/s4K4QEbFw4f+JbDib2X6sn731b+rimXlMQ90XZeZudTHKLQpq6+6Y+xrKpCPHsuGEBgcDx2bmEZO0e8y2fxl4zITtvgj4Z+BpdW2n/LNeG69btz7rrur/OQ0zclNOS5l4X2pre5/rPel2qXJ/kuZZkhu3C2Vyrrrcl1XxifbzMdr2kynmzob4TlX+urL3pnlSkY/Qw2fPmJ8dXfbFxm3eUrbr8du0L3R9v9vez8bcPa930344Tryp7b+gnArYV+6m+D2ojpGh2KHAdpn51Gr79DaQ2xbveXBt4gHPttxdB2ua2k73wZgug2u9DfS05W7aJmN+IdHXYOoWtAwUdRzYGyf3E2meDXxU2z7M+pncN2r3QNm6uptm+z6Cllm16z4jomV26FlgB3cZRcS76kKU6bzvPLyzRblB83coO+XDGuJ1H8hBuQ728zWxfSl/VJvq7pL7D4Db5tDU8xGxJeUUo183lN2LcoF/U/zHDW27ArjLhO3el/Ihcr+GtmdD/Lqati+0+8IOdbdtt/tl5mYbBeLWmWnXMfn72bZd+l7vTts8m2fsbdsuNOT+BfAbHfbztrY1rXfvuRviN+SIGWAHt2m2zC7L5J89F1M6BqPWa1/gK6PysjTHd9s27/P47VJ32/vd9n52OT47r3eH/bT1GGpb7665F7vNq/i5mblXRBxCTwO5HQeCug6u9TnItCOTD3j0PRjTZaCo62BML4NUY+5LfQ6m3oXm2aEv61B32zZty900G/gBNN9Ks6nsdnS7LdyBjJ4BOoDPZuZOI2Izw0mmltezgZdTJkcYZdRowzq49ZYLTfFHAs+gfPgNWuhkvr8mtnf1s6/c+1NGnS4ciu1UlV8FPJb11wYNlv3WGPGmtndp997V65rang3xzSkffnXt7lJ323ZbE/UTklwP3Jb+tkvf691lmzdN1DLOdrmlIXeX92uctt1mirmzIX5LS9nLonnyjt2Y/LNnK5o/1/o8vrvsp12P36Z9oev73fZ+tuXuc72b9sNx4o1t7zl3U/zmaJ70B+A1wG9NMpAbEU9itADuHBHHN8Up3x6NGhh4O/UDPW8HzoqIP2moe1VL3U0DJq25qR9U+DjNAx7jtH3rzDygqe4uudvWu2YwZjlyX1ezXQJYFRGnj4jdGgcOaGtbQ/yGzHzwIsteAnw7yi3MHrzEdS8mNw3xGzPz+ROWXcj95sFg1ZE9IsqZBjTE3wS8lfV/awdtO2LZTLGDu7xOBs7MzG8NByLircD3IuJE1s9mthtlFPbvKB86TfGnAr/KzK+PqPvahtg5lNMc+sr9A+ArEbFmqOw9gRdR/ohvnZmnjSj7NcqIf1P8iw1tO7pDu8+hzJTa1HYa4l9uafcxHepu225fpnlG7vv3uF36Xu+meFvut9I8Y2/bdrm8IffftrS7bT9va9sOU8ydDfHn0ryvnU/zjL5/yOSfPWfRvJ/+siXe5fhu2+ZNZbsev037Qtf3u+39bMvd53o37YfjxJva/vKeczfFn0KZZKZuZmrodyC3y0BQ18G1PgeZbuhxoKjrYEyngaIp5m4bRJ7mYOovWwaKtupQd9s2bcvdOBt4h7LX0Dzbd9us2r+keXbomeYpyssoIrYDrs/MX9XE70T5cBi+juaaceId29Zb7upg3Huo7MlZ3dS6z7Z33WZtbe+ybn3WXZXfcbBsDkxI0vd26VJ2JW+XptxLsZ+3tG1qucdoW2PZlrzT/NybeF+b8n7a9742ce4+17trfIy295Z7zPjISX8i4lDKbdBGDfR8lXIqYtOA5Vsy8ySGRMQ3KP/kNsXfSLln9aiBgaMoZ6Y1DWgclZnfHFH3xygDnnV1LwyYTJr7Supnnn4hZcCjKXdT20+gnB5aV3d2zN203u+gdDKnkfs84O8b3s9fN2yzj1ENrk3Y9n8C/ryh7FU0zw59pw51t23TttzZEP8nyi0vJyl7OM2zfb+5Kr8QH54h+gfA/2TN7NCZ+Z/Dy2eJHdwpqTq7ZObV027LgmpUZ/CP7xVLGR+Rb+vMHB41nijelHup29217QuxiFgYIR/8Z+u7WR2UXeM1ue+d6yeSWNbtshzrPUbZbWiZjKVlX+qSe9HvV1Xu3lkmVJlm7trtNs42ran71klJuuyL0zq+KR2DqRy/fb7ftM/mP7XPrbZ9bYz4NHM3HUOtkx01DfT0OQhU5e5tcK3PQaaqfG8DRT0PtvQ2GNP3IFWbjm1vHUytGyjqWnfX3GO0bUXeOnFW2cFdRhGxG+Vm94+mjLoE5Z5kXwUOz8wf15Q7IzMf0FDvxPGIOINyetT7gG0oo1NBuYj9WsrkV+ta4gujW9uw4SQX1wIvyMzv1eRunZSgLQ48oaFt7wRestTt7tr2qt3PBd5DGSUdzH3Pqm10iWfmiQ25m7ZZb9tlGdb7A1X9dWV3pHnG3tNbtsv2HXI3rlfd+zXmdus792vpb6bytn2x6bPnH4G/oGY/pd/j+0rKRC/TOH47HUMt73fbbP6Xd8ndcb2b9sPXV8+b4o1t7zl3U/wU4F6MMalPnwM9fQ1otpWd5oDmlAdjZjJ334OpTXHaB98aB4q6DNSOEW/LXRvvUrZue1XlOt8WbpbZwV1GEfE/lE7XfwyMlG1OORXm9ZSpvDcqRvkH73l11Y4R/xDlVJ66spdSzsP/zgbBiIdTJnHJDvFPAG+ryf0a4O8b2j1O/KKG3CcB+3RYr6M7tO3vKP8U1ZW9Ath/eFAjIvZg/YzXk8ZPpdx+ZVTuQynXRXZ5v5u2yzTX+4fAvRrKbkbzjL2/onm9t+yQu229vsRoC+/ZpVPMfRmTz1R+fUPde1FOkZp0X5zm8f0W4B5TOn6b9oWu73fbbP63dMzdFG9b76b98Du0z/bf1Pa+czfFr6BhRu7M3DMiHkRPA7lt8S4DmmMMUHcZrOk6oNlpsKbjgMis5u51MLVlf2kbfNuJ5tmhf9KwXRoHaqvnTdu0LXc2xPu85Vyn28Jl5l/U1T0Ltph2AzYx22fmxwcXVB3dYyPi3yjfZowacbgd8HHgoxPG79hS928M/xNYte3bEfEb5deJ4ztRrn24eUTuzSjX9/xDh3hT2zfvuF5d2rYVzeu9Bes/UAZdSpkEIjvEtwXOZPRs3U+j+/u9Utc7WsouTKAxbGEyltbt0iF323o9m/oZ1p9G83brO3cw+QQ3bZOOdNkXp3l8w/SO367HUNP7Dc3v5zQ/t5r2w+jY9r5zN8WhedIfKPfKnGSg5yiaB3pa4xHRNBC0fUS8rCa2dU3s1jjlLIzHtAyI1MXHGdCsm3l6YcBj0tynRkTdgMi2NM96Pcu5xxlMra07IpoG17aN5ltpbksZVKxbL2ieHXodE85ETjlGmrZpW+5siP+CltnAm+qO9pnKm2aubps92g6uxnZqRLyH8s3BwsX7u1JGiK8B3pqZZw4XiojHUCZMmDT+9JayJ0TE5yijOIPtOgT4AmUWtknjVwD/mZmnjsj9XODsjvGmtv+g43o9pEPbXt1S9kPAyRFx7FDugymzWtMhfgH1s3W/rmWbdd0u01zvr7aU/QnNM/Y+pGW9L+2Qu2299qP5PWvabn3nfkPDdju6ZZs+kuYZfX/aYV+c5vH9UqZ3/HY9hpre77fS/H7uNMX1btoPx5ntv6ntfeduiv8T7TNT9zmQ2+eAZtsgUpcBk64Dml1yb0u3AZFZzd33YGqXwbdbaB4o6jJQ2zV3NsSbYq0zatPvbeFmmqcoL6Mopxw9h43PpT+eMjJ2XmZeNKLcasofkgsnjD8XOLGubGaeEhH7j2pXZn6+et1EceBHwFWZ+dMRuVdRPqwnjmfmFU1t67JeEXEv4OrMXNvQtrr4IyineIwsW7X7PjW5z65eN1GccgpS7WzdbevdcbssxXrfl3LGwaLjY5S9E80z9rZtl9q2d2x34wzrS7DeXXPXbre2bdqm4744reN7FWUW1Wkdv72932McIxPnboqPud5tbZuo7cuUu+kYapv0513APRg9WLMLpVMxKnYB5Z/gurLjxB8CvLhmoOcG4BE1sYur+urKXkw5nfUplNMvhwcdjque18WvoPxzX1f2J9TPPL0w4DFp7jsCz6wZELkAeN2c5m7b5k1lj6va8NqGtl/QEF9Lmfeg6TZ+76Z+duhVDdvlqzTPRJ4NZcfJTUP8KJpnA28qO85M5U0zVzfOHj3quJ0ldnAlrUjRMtN4U7ytbJ+6tLtrvO/cdaKHmYrVblrvd9+5O7ardV+cVu66eET7xDx9DfSMUffEA5q0DBJ1HfBcggHNaQ7GzFzuvgdTl2DwrW2gaOKB2iXIPfFs4G3xcURPt/lb0TLTxzI9KKeGPA84gTJj6+nV739GuRb2eZRT54ZjtxkoO0m8se6WNh/ZV3wp6qZMjHEEZbKaqyn3I/tBtWy3hti2LWUH4z+cIL5bU9mW9Tqhr3j1vo+73pNsl07rXZU/lnLK/RrKffeurJbt3hJ/RFPZltxntK13S/mTOrR7ML52qde7a+6W9V4DfLvaTl+iTNbxw2rZQ1rKXtS2zVvi0zy+a/cH+j9+uxwjE7/flGNkqXI37ceT7IdnAA+iYV+ccu6m+AuqfCdQJvH5AOVv9XnA7zfl3pQelDMmtltsrGvdXXJTvjV8SPVYtank7jM+znqNKLP1OOXb6u6Sm3LK8MOAP6oeD2P9l4y1sSWKb0O5H/bLqsdTqf6GNcVm/eE1uMvrXymzwL2eDWcsOxQ4h3KR/utGxD5COY3o2gnjjXVHxPNr2hvAAQsj2hPG/6AmvhR1HwDcjXJ6yaOyGnWqRqOeBXyPcv3PqNhxlNNO6soOxvcZih86Rnwh98iyEXF4w3o9KCIe0iG+uiYelH+0jhtzvSfZLp3WmzJZ2juBp+fGM40fW+Wui3+RMsPjyLIR8ZaG3Du2bZeWtv8O5XZbk7S7a7xxvbvmbtluewDPyMkmqNmabvviNI/vEyLihTXr1ffx2+UYad2XIuJVNbl37Ds33Y7fD9M8mdKvppg7G+JfA+6d9RP33CfKLUpeRfmmc1VV35XApymn+b6gJnZE9byu7GLiBwF3qck9KtZW9ohsuFd2RJxAGZh/C+X00Z+VxXFHynH5LsrplaNitbddrOo+A/iDhroPp/xP1Zb70VS3fBwquy0jZr2OiGur7fXTGc89SdnBeF3bm+LHUE4THrle2TDbN3B2RDyhYbu8kxEzkQ9ss3VN23SM3M+lZnbpiKidmToiXlA9rys7TnxHNp4B+lHAGyNi1OzRC7HG2/zNAk9RXkYRcW6OmM2sit2YmVvWlQNoKNsWb6v7HpQL2GMglNXznYHNO8R3p1xX0UfdOwMXZOa9JljvcwAaynaNt+W+J/B1NlyvBQ+n3JJm0vg+lG8U68petILX+5LM3LOm/Joqd128Kfcayr74UUZPFvEk4Cct693U9t/LzM0mbHfXeNt6d829O/Xb7dCG9T6P8of2Hxg9ycxLgSt63Nf6PL6T5mOsz+O3yzHSFk/KxGF1x8jlU9yPd6f5+G1q23kl9crMDdw+M28eWr4lcHZm3jMivkj5J//oEYM1f0k5xkbF9mX9QM1Sxw8F/qom96GUTkpT2UdTOjUjNwvwWeDH1N9a8V8oHYNRsZdQOkp1db+P8g15Xd0vYf1gzCS5b0fzLcp+1VD3LOduKts1/kFg34b1ahpMfQ3TvaXkFfR3m7+2+GZMeJu/uj7FrLCDu4wi4tuUe8J+IjPXVcs2oxy8HwD+b03sZZQDrK5sW7yt7u2AR+foSagupsymNmn8JsrU7n3UfTHllK8vU/6ALlzPtIryx/kVwJtrYvtRRuXqynaNt+XeEXhiZq6pWa+fdYjfBNy3oWzTNpv2ev835XTQo9l4pvHtq9x18cdT/imqK3sPSods1Gzi42yXprb/ivItziTt7hpvW++uuZu223XAN5hsgpqu++I0j+//Bzx4Ssdvl2OkLX4w8LsNx0ifubsev5+iebKk7aeYe11DfDtKp+TYodjBwHGZ+aaIOGeFDvR0zd1lwLPPQYk+BxXbBjzmNXefA2DjDKau7VB30zYbKzdwn5pBrF/QMMBF2X/ryo4TXwc8NDN/NhTfpmrXDjWxU+rWeVbYwV1GEbE75R+yfSlTegfldJKvUmYze3FN7HDKTlxXti3eVvcBwDcz8/sj2vxiygEyafw44A091f1iyunZh1NOr7pLFbqCMhnD+yjXGY+KvZmyzerKdo235d4XOCMzzxmxXgdRrqmeNP5m4EMNZb8+0O5VVejymvVabHxwvUeVbVvvz7PxTOOXAJ+hjOBmQ/wYymnCdWX3pnkm8h+1rHdT258E7DBhuyeNL0w6Mmq9F2JtZceJt2234fVemFRkYYKaplnSb6S/fa3LftwW/wHwP1M6frscI23xH9I8m//pI8ou1b7Wth837ofZcjeAaL6LQa+5q9c1ta1toqUTWZkDPV1zdxnw7Doo0edg6uVMPtgyy7n7HMjdj3Jv1kkHU5sGobrORN6Wu2m28K4zU7fFm2YT/yoNs0dn5oeH12eW2MGdkoi4M0BmXrWYWNd4W1lJkrRyVKcMrsSBnq65uwx4dh3Q7DJY0zggkpk3dBhsmdncLWWXIr5vw3o1DqbmFG8pmS2zhUdPt17LMWYTb4oNr8ussYO7zCLi3my8k386M3/YFGsr21Pdx2fmD7rG+6y7ZVs/OzOPWmys7/hKyB0Rj6VM/DG8P3yhet3E8a51N6zXX2fm304SH7dsH23v0u6FOPBGyh/+jXJTTo0+tCY2+E9D1/gTKTec3yCemTfVtPvIzDysYb2OzMzDprmvrdTc0zhG2uJj7Idd97UPM95+vKj9sGr7kZTJYqaV+69omMgpayZbiogTMnP/urolzY+Y4m3f5pEd3GUUEa8EnkY5lWBwNuODgcsoN+AeFTuW8gexrmxbvM+6p5o7M4+gRkRclJm7LTbWd3zauYFPAntRRmMHt+khlJn4skN8J8p7PlHdmfkXfa13W1k6bpe6ti/F+0k5ne5ayqlbwzOhH0D5pmNUbDvWz7DeR3xHyj/+GzUb+D7wm3WrVcX/gyntax3jfeZurLuvY6QtPsZ+2HVf6rofP79utSj72n9NMfdZ1E/kdCDwwpqyn83MnarXb3IDPSO2yfqN03FAk26DNR+mYUCk42BL14Gg29M8K/Yvesy9VIOpI+NdBlNpHmRaqpnID2Lxs4WfRDlNeV+qmaeBO9IwM/Vi4tkym3hmPmCxsVlhB3cZRZmx+H7DB2msv9D8N2piZ1EOmLqybfE+65527l8zWgD3Aza6/qaK7UW5fVJd2a7xlZz7whwxO15EBOUal+wQvyFHTNawiLpXDccG2r4VZQbIuvjWwHUTlt0KOL/Det8M/LJD7onaVuXvcwb2tnjSbZb0H09xX5vmft6l7i7HSNdjqGk/7Hu2/7a678EE+9oy5b4gJ5yROzO3ioh3sukN9Ew8mLMEg4ZdB2O6DLZ0zb0tzbNiX9Vj7pU8mNo0yPSX9DMT+aG0zxb+P5TTvvuambppNvEPAc+uib0vM3eoKTsbcgXcjHdTeVAm8LjbiOV3A25oiJ3TUrYt3mfd0859BeXekHcbeuwO3NIQ+0lL2a7xlZz7dMqsesPbdG/gjI7xX3es+yLqb0p/cUv85g5lL+643jd2zN0W/zblD9ZmA8s3o9yU/bqG2HdaynaNXw/s1tDuNS3xae5rKzV3W91d96Uux1Cf+1LX/bhtX5tm7hMpEzKtGli+CnglZRB4z7qy1c9za+IB3NgQW9NStmu879w/r3lcR/nHvi52c0vZm+tyL2zvlvjI9R4oewtwPmXwb+Gx8PzGnnOf0xA/p+fcXepui2fLNm3b5k3bpWm9zhljm7bFb6F0gE8a8VjXUHYNZaCnS/wmyjfvR414ZEPsurp6Z+WxBVpOLwG+EmU69MEZy+4J/G1D7EXV80njfdY97dxPBrbOzNMYEhE/boh9jfKPZF/xlZz7rcB7I+IOrB8l3ZVyesuzKB96k8afC7y7Q91PpHTGrxhuO/Axyod1Xfy7Hcp+DPh4h/U+pmPutvh7KZOyvCcirqmWb0v5A7k/ZZb0UbGDWT/Deh/xNwJ3onSahr2FMiLfFP9vprevTXM/71J3l2Ok6zHUtB923Ze67scH0LyvfWaofLD+267h3IOxUbmH4225P0L5FufrEXGXavkVlMlzXkTpLI/y4urn9RHx0Mw8eSj+UOCWhtj1QPYY7zv3tZTBno32x4i4mTIwMCp2MeU9qyt7MXB1RDyZ0bdPvKZqW138hpay59N8e8M+c/8sIl7B6JmrLwa27jF3U9mu8Rso96qt26bXt2zzCxu2y3Ut22xdx/hNlPvorhnRtl9FxHsYPXP0/1Z1d4n/HHhrjp5N/OkNsccML5s1nqK8zKqDdW82vL7g5My8pSnWVnaadU87tyZTnUJz6zbN6tSapYh3rXuaVnrbY0ozsI8Tn9Q097WVmnva+1mbPvelvvfDaeaeREQ8hDK4MGrQ45+AP6+JvZBqMKWneN+5n0iZUPK7I7bJt4CX1MTeTOlU1JV9M+sHa/aldK5g/YDJ4awf1BgV/yc2vPXicNm2Wy8uDLYslB8cMBmVezA+nHu47LU0397wjj3mbirbNX4W5drsum26rmWbLwwyjdou76Pfmcj3ZXq3GNyb+tnEnwucWBNbnZmnDC+fJXZwl1lEBBt31r6bmdkUays7zbrNPXu560TEvbOaWXup433WvVS5o9zg/HFsPGX+tdXrauNdyo4Tb2j7fpn5pcXG+o6PW3Ylb/Np5V7J61VnJexLbfFY/rsYLOls/5viQM9y6HMwZlPN3XdcamMHdxlFxO9TZmtbQ/njB+UC+nsCH6CcmjYq9oLqeV3ZtnifdZt7xnJn5onUiDmePbqtLPBa4G8o18sNbrf9gNdXz+viXwYeM2HZ1nhmHtPXervNV1zuFbtek+6HXeNLUTfwz8zwbP+zOiCyknPXWaaBvYkHVNrKNuReuE1gb7n7jLcNInUZZKLMT3LQiLxfqF6z7LOFR4+3fWuLt5WdBXZwl1FE/ADYP4em7Y6IPSiTKd2rJvb56mld2bZ4n3Wbe/Zy1/3xDcp1Gx/uED8MeH9Pdfed+zLgYcP//ES5Efp3KP/E1sWvAO4yYdlx4nX/uATl+sHP18T2Bb7SULZrvGvuS1i523xauVfyejXth9Pel9rilzKDs/1n5m0j4hBmcEBkJefua7BmCQZb2gZMGgdj2gZT+szdUrZrvFPulu3yc+CbTGcm8l5vMdjXfjwLnGRqeW3B+p170KWUP2Z1sdtQDpBJ433Wbe7Zy/1s4OWUSRuGPa1j/IWUf9b6qLvv3EHZrsPWVTEa4k2xtrLjxB8JPIPyj/igAP6A0rEfFdu7pWzXeNfcK3mbTyt3n3V3zb2S96W2+DrgrpTb+QzaibLOdbF1SxBfBTyW9ddrDrbtR5R/dEfFvlX9/hrgtzoMiNSV7Rqf2dxRrn0cJYA7R8TxDfG71sTHKXtnyjWTowZE3k7zgMnbqR+MeTtwVkT8SUPuVX3mbinbNd4pd8t22TozD9goEPFxmm/rthTxm6tvkEe1a6uq813X7q7xrWviQbkt3Eyzg7u8PgScHBHHsuFsZwdTLqKvi32wej5pvM+6zT17ufcDzszMhX+ebhURr6PcwmTS+Pt6rLvv3G8AvhcRJ7LhjN37AX9H+QNaFz+6Q9lx4k8FfpWZXx/R9msbYudQ7s/bV7xr7g+2rPc0t/m0cq/k9WraD6e9L7XF38hszvYPzQNBNMSmPSCyknNPczCmabClbcCky2DKt3rO3We8z0GmNTHFmcjpNht4l3jbTOQzzVOUl1lE3Bd4AhtfI3B2U6yt7DTrNvds5Y6I7YDrM/NXjNAl3mfdfeeuXnMnyh/B4Wu2rmmLdyk7TnxereRtPq3cK3m9ZlnM6Gz/EXEo8NeU022HBx6+SjkFu2lAo65s1/gs534q8JbMPIkhEfENyoBJXfxq4I8nLPsNymDLuymnr9YNmNTFj6KcjdQ0mHJUZn5zRO6PUU6T7St3U9mu8a65m7bLCcB2TGcm8jOBI3Py2cC7xBtnIs/MVw4vnyV2cKek+mebzLx6MbGu8T7rNre55yW3lleUewbe2jEYHlFuincpu5Jzz+t6TTv3KBGxdWYOf9vWGus7Phib1QGRlZx7mroMmHQdTOkzd5/xvgeZYoozkWvpeYryMoqI3Sg3fd+XMnITEXFHykjkuyj3kxsVO5xyikVd2bZ4n3Wbe3ZzP5pyz7yljC/k7qPuXnPn0KRcgyLijMx8wCTxLmXnPTfwTMo9CLehjGwHsEuU01VfQHnP6uLvBF4yYdmu8T5zr+T1WvgmYhsGJu5Zovg/An/RU90vyMzvUe9syrc9i431Hb81VnXoTmLDf5CvaYv1HZ/l3DC9wZjMXAd8myFRDWq0xVtiIw3Ec+BB9XPdwEub4l3Kdo13qjui+faJVYdzuFN66y0G+44Pa4r1HW8rOwvs4C6vj1P+gXj6wIjT5pRTJ75IubXLqNjCDHF1ZdvifdZtbnPPRe6IeAujBbBjRPxRQ3y3mvg4ZbvGZzY3ZWbr52XmdzYIRjycckpaNsRPAvaZsGzXeJ+553W9ppo7Io5mtAC2j4iX1cS2roktVbwxd7UOD2I2B0RWcu4uAyadBmM6DLa0xVvLRsRzqbnFYES8oHpeF6+9BeEYZbvGe82d9bdPPJHmbdpnfCXnXvE8RXkZRcSazNyzJnZjZm5ZVw6goWxbvM+6zW3uecm9O/BR1o/+DnoScLuG+LMoE/RMUrZrfJZzX97wnpwH5ITvd1vZrvE+c8/rek079y7APwA3j3jJ64C/r4m9lLIf15XtGm/MnZnbRsRpTDYw8H6aO/5d4+aerO6mwZbXUPaHuvjfUe4fPknZ11Bmn57H2x92qftU4CNsLFj5tzfsLXdm3rGm7Eywg7uMosxqezXlw21wdttDgccDn62JbU8Zjawr2xbvs25zm3tect+D8qF+JkOizCh4ZUP8RuAhE5btGp/l3J+ibPdj2PA9OQS4gPKe1cV3oXwzM0nZrvE+c8/rek0790OAF2fmqQyJiBuAR9TELq7qqyvbNd6YOzN37TA4PuuDEvOau2mwpdOASEvZlwJrgftk5gbxKPdsPpvSOa+L/wK4/YRlu8b7zH098GeMvoXg24DbUn+Lwa7xo1Zq7szcfsTymWEHdxlVB9JzgAMZmt2W8kf5mTWxD1IOzrqybfE+6za3uecl997AhZl5EUMiYjXlvnB18ecCJ05Ytmt8ZnNn5ikRsT8j3pPM/Hz1utp4l7Jd47Na96aaOyLuBVyVmT9lSEQ8Aji3JrYK2LahbNd4Y+7MvCIi3sWmOSgxr7mbBls6DYi0lL2YcpruUyiX7Qy27WDguOp5XfwKyu12JinbNd5n7jsCz8zRtxC8gPKevban+K+BR6/E3Jm5x/DyWWIHV5IkaQXbFAcl5jV3NdhydWauZcjAgEhd/BHAmgnLLgyY3KembQu3IKyNdynbNd5X3ZSJn2by9oZ95p51dnCXUURsQfkG6SA2PMA+TTlP/tCa2OC3T5PE+6zb3Oaet9xPpNwwfjHxhdyTlN1kc2fmTdSIiCMz87BJ4l3KruTc87pe5p4styRpNDu4yygi/o1ye5KjWX+z510o/yAeQLkQflRsO8opL3Vl2+J91m1uc5vb3JPW/XxGC+D7wG82xE8HHjBh2a7xPnPP63qZe4LcmblLRGwDvIry7dMqysDRlZSBovdQZu4dFTuiel5Xtmvc3N3qPgi4yyLjC7knKXtEZl5LjYg4ITP3nyTepexKzj2v69U19yywg7uMIuLczNyrJtY0acG5AA1l2+J91m1uc5vb3JPWfQ/gQso/8wuyer4zsHlDfHfK9UWTlO0a7zP3vK6XuSfInZlbRsQXKffrPjrLvTSJiB0ps5j/JWVSoVGxfau66sp2jZt7aes+lHKf9qb4X9XkHqfsoyn3hB8lKJM0/kFD/AvAYycs2zXeZ+55Xa9OuTNzp5qysyEzfSzTg3Jj7icDmw0s2wx4KnBdQ+w7LWXb4n3WbW5zm9vck9a9Btit5vPy4pb4TR3Kdo33mXte18vcE+Sufp4zKl7FbmyIndNStmvc3LOX+xZKB/ikEY9ft8SzQ9mu8T5zz+t6dcpdtx/NymPqDdiUHpSR2o9TThc5t3pcWS37Pw2xPVrKtsX7rNvc5ja3uSet+4XAA2s+L1/cEj+uQ9mu8T5zz+t6mXuC3NXPE4FXAKsGYquAVwJXNcS+3FK2a9zcs5f7TGDPmv3t4pb4TR3Kdo33mXte16tT7lHLZ+nhKcrLrGYWt09n5g+aYm1lp1m3uc1tbnN3qPveI+LHjxPvUnYl557X9TL3xLnvRDm19EDKdZVQbptyPPBeyrXso2JHVM/rynaNm3v2cj8aOCMzz2FIRBwEbNEQfzPwoQnLdo33mXte16tT7sz8z+Hls8QO7jKKiFdS7rl1LOUPGJQJVw4GLgN2rIkdSzmVoK5sW7zPus1tbnObe9K61wF/Uv1+ySLjP6HMzDxJ2ZWce17Xy9wT5M7MI2gQEc/OzKMWG+s7bm5zz0PueV2vrrlnQq6Ar5E3lQfl9LzbjFi+JXBjQ2xNS9m2eJ91m9vc5ja3uWejbnPPWO7h5SNed9Eksb7j5jb3POSe1/XqmnsWHlug5bSOMlJ74dDynSjfdNTF1nWM91m3uc1tbnObezbqNvfs5SYiTme0AHapiQewqqVs17i5zT0Pued1vTrlrik3M+zgLq+XAF+JiDWUi78BdgPuCfxtQ+xF1fNJ433WbW5zm9vc5p6Nus09m7lXUW7ncQ0bCuBHwCE1sW+1lO0aN7e55yH3vK5X19wzzQ7uMsrML0TEXsDebDiZxMmZeUtEvKkuBtBUti3eZ93mNre5zW3u2ajb3LOXm3I/y60z8zSGRMSPG2Jfo9wqpK+4uc09D7nndb265p5pTjIlSZIkSZoLm027AZIkSZIkLQU7uJIkSZKkuWAHV5KkRYiI10VERkTtPBYRsU/1mn0Glr0kIv5ognwPqnJut4gyG+WXJGlTYAdXkqSl9z3gt6ufC14CLLqDCzwI+Btg7A5uTX5JkuaesyhLkrTEMvPnwLeXO29EbE6ZQHIq+SVJmja/wZUkaTL3iYiTIuJXEXFZRPxtRGwGG58iXN2S4W7A06vlGREfrmJ7RcSnIuLKiLg+Ii6KiH+PiC0i4lnAUVW+NQNld6/KZkS8ISIOj4gLgBuBB9ScIv21iPhmRDwmIr5XtfvMiHji8IpFxNMi4odVe86IiCdU5b828JqtI+KfqvbeULX/yxFx7yXdypIkLYLf4EqSNJn/BD4EvAl4LPD/gHXA60a89onA54HvD8TXVj8/B1wDPB/4KeV+qAdQBqE/B/w98FrgycAlVZnLBup+FnA+8JfAL4GfANvUtPkewD9Wbf4p8HLg3yPi3pl5HkBE7Ad8FDgeeBmwA/BO4HbAuQN1vQN4AvBqYA1wZ+B3gG1rckuS1Ds7uJIkTeZfMvOI6vcTI+KOwMsj4p3DL8zM/42IG4CfZuatpw5HxPbAPYEDM/P4gSIfq36ujYgfVb+fttAJHRLA72fmrwfqvU9Nm7cHfjcz11Sv+x6ls/wU4I3Va14PnA08MTOzet2ZwCls2MH9beCjmfnBgWWfqskrSdKy8BRlSZImc9zQ82OBrYH7L6KOqyjfvh4REX8aEXtO0I4vDHZuW6xZ6NwCZOaVwJXAbnDrNbyrgU8sdG6r150KXDBU18nAsyLi1RGxuiorSdJU2cGVJGkyV9Q833ncCqpO5H6Ub0ffBJwbEedHxPMX0Y7L2l9yq6tHLLuBcvoxlG94b0Pp9A4bXt8XA+8H/i+ls3tlRLwjIm6/iPZIkrSk7OBKkjSZVTXPL11MJZl5fmYeQrnW9cHAV4H3RMT+41axmHwtfgrcBNxlRGyD9c3MX2TmqzLznsDulFOcX0S5pZEkSVNhB1eSpMk8Zej5wcAvgDNqXn8DsFVdZVmcRpnYCdaf6nxD9bO27FLJzFso3yb/cUTEwvKI+C1gj4ZyF2bm2yjrvphTtCVJWlJOMiVJ0mT+tLot0MmUWZSfC7wuM3820DccdDbwyIh4PHA55dvSO1JmNf44cB6wOWVW5Jsp3+QulAN4YUQcTfmG9fTMvLGPlaJ8A3si8KmIOJJy2vLrqjavW3hRRPwPZablMygd+98DHggc3VO7JElq5Te4kiRN5kDK9bPHA8+g3M7n7xpe/yrgHMrkVCezvtN4EeVb2+OBfwPuCjy+mtiJzFy4tdAfAt+syt51qVdmQWZ+CXg6cB/KrMivpNxO6HLgZwMv/QblW+yPUm5n9CTgpZn5j321TZKkNjEwSaIkSdJGImIXyjfMb8jMpk68JElTZQdXkiTdKiK2At4OfJlyGvXdgVdQJpm6X2YuZtZmSZKWldfgSpKkQbcAOwLvBu4M/BL4L+DJdm4lSSud3+BKkiRJkuaCk0xJkiRJkuaCHVxJkiRJ0lywgytJkiRJmgt2cCVJkiRJc8EOriRJkiRpLtjBlSRJkiTNhf8PQxnvcikiwGsAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dw_preserved =['1110000000', '0111000000', '0011100000', '0001110000', \n", + " '0000111000', '0000011100', '0000001110', '0000000111', '1000000011', '1100000001']\n", + "\n", + "for n_cycle in [2*k for k in range(int(N_cycles/2))]: # Runtime close to 2 min !\n", + " color_dict = {key: 'red' if key in dw_preserved else 'black' for key in samples_evol[n_cycle]}\n", + " plt.figure(figsize=(16, 5))\n", + " plt.title(r'Cycle $= {}$'.format(n_cycle), fontsize=18)\n", + " plt.bar(samples_evol[n_cycle].keys(), samples_evol[n_cycle].values(), color=color_dict.values())\n", + " plt.xlabel(\"bitstrings\", fontsize=16)\n", + " plt.ylabel(\"counts\", fontsize=16)\n", + " plt.xticks(rotation=90)\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pulser-dev", + "language": "python", + "name": "pulser-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/applications/Preparing state with antiferromagnetic order in the Ising model.ipynb b/tutorials/quantum_simulation/Preparing state with antiferromagnetic order in the Ising model.ipynb similarity index 99% rename from tutorials/applications/Preparing state with antiferromagnetic order in the Ising model.ipynb rename to tutorials/quantum_simulation/Preparing state with antiferromagnetic order in the Ising model.ipynb index 1f70e96fe..6700ab7b0 100644 --- a/tutorials/applications/Preparing state with antiferromagnetic order in the Ising model.ipynb +++ b/tutorials/quantum_simulation/Preparing state with antiferromagnetic order in the Ising model.ipynb @@ -451,7 +451,7 @@ " seq.add(sweep, 'ising')\n", " seq.add(fall, 'ising')\n", "\n", - " simul = Simulation(seq, sampling_rate=0.02)\n", + " simul = Simulation(seq, sampling_rate=0.2)\n", " results = simul.run()\n", " \n", " final = results.states[-1]\n", diff --git a/tutorials/applications/Shadow estimation for VQS.ipynb b/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb similarity index 98% rename from tutorials/applications/Shadow estimation for VQS.ipynb rename to tutorials/quantum_simulation/Shadow estimation for VQS.ipynb index b8536511c..e0baf2c07 100644 --- a/tutorials/applications/Shadow estimation for VQS.ipynb +++ b/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb @@ -2,9 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, - "id": "d69d787b", - "metadata": {}, + "execution_count": 1, + "metadata": { + "tags": [ + "remove_cell" + ] + }, "outputs": [], "source": [ "import warnings\n", @@ -14,15 +17,13 @@ }, { "cell_type": "markdown", - "id": "9f85fda0", "metadata": {}, "source": [ - "# Efficient estimation techniques for VQS" + "# Efficient estimation techniques for Variational Quantum Simulation" ] }, { "cell_type": "markdown", - "id": "a780a7b7", "metadata": {}, "source": [ "## Introduction" @@ -30,7 +31,6 @@ }, { "cell_type": "markdown", - "id": "4b2c1220", "metadata": {}, "source": [ "$\\newcommand{\\ket}[1]{\\left|#1\\right>} \\newcommand{\\bra}[1]{\\left<#1\\right|}$\n", @@ -41,7 +41,6 @@ }, { "cell_type": "markdown", - "id": "05595e94", "metadata": {}, "source": [ "## Random classical shadows" @@ -49,7 +48,6 @@ }, { "cell_type": "markdown", - "id": "f19f70f2", "metadata": {}, "source": [ "### Main ideas and implementation" @@ -57,7 +55,6 @@ }, { "cell_type": "markdown", - "id": "1b5acf38", "metadata": {}, "source": [ "Classical shadow estimation relies on the fact that for a particular\n", @@ -101,7 +98,6 @@ }, { "cell_type": "markdown", - "id": "284ded31", "metadata": {}, "source": [ "We start by defining several useful quantities, such as the unitary matrices associated with Pauli measurements : the Hadamard matrix, change of basis from $\\{\\ket{0}, \\ket{1}\\}$ to the eigenbasis of $\\sigma_X$, $\\{\\ket{+}, \\ket{-}\\}$, and its $\\sigma_Y, \\sigma_Z$ counterparts. We will then draw randomly from this tomographically complete set of $3$ unitaries.\n", @@ -112,7 +108,6 @@ { "cell_type": "code", "execution_count": 3, - "id": "7388f3c5", "metadata": {}, "outputs": [], "source": [ @@ -137,7 +132,6 @@ }, { "cell_type": "markdown", - "id": "e34e7ce6", "metadata": {}, "source": [ "We first define a function that spits out a random bitstring sampled from a given density matrix." @@ -146,7 +140,6 @@ { "cell_type": "code", "execution_count": 4, - "id": "66b045bc", "metadata": {}, "outputs": [], "source": [ @@ -161,7 +154,6 @@ }, { "cell_type": "markdown", - "id": "0434868f", "metadata": {}, "source": [ "We will need to compute the number of shadows needed given :\n", @@ -176,7 +168,6 @@ { "cell_type": "code", "execution_count": 5, - "id": "f17762c3", "metadata": {}, "outputs": [], "source": [ @@ -208,7 +199,6 @@ }, { "cell_type": "markdown", - "id": "99057a11", "metadata": {}, "source": [ "Next, we design a function that returns snapshots (bitstrings) of the rotated state as well as the sampled unitaries used to rotate the state $\\rho$." @@ -217,7 +207,6 @@ { "cell_type": "code", "execution_count": 6, - "id": "44ee0fd1", "metadata": {}, "outputs": [], "source": [ @@ -243,7 +232,6 @@ }, { "cell_type": "markdown", - "id": "ae6b66e2", "metadata": {}, "source": [ "We then reconstruct an estimate of the quantum state from the sampled bitstrings, using the inverse quantum channel $\\mathcal{M}^{-1}$ defined above. In the particular case of Pauli measurements, we can actually compute the inverse channel : $$\\mathcal{M}^{-1} = \\otimes_{i=1}^n (3 U_i \\ket{b_i}\\bra{b_i} U^\\dagger_i - \\mathbb{1}_2)$$\n", @@ -253,7 +241,6 @@ { "cell_type": "code", "execution_count": 7, - "id": "7218f3ee", "metadata": {}, "outputs": [], "source": [ @@ -282,7 +269,6 @@ }, { "cell_type": "markdown", - "id": "54db9c45", "metadata": {}, "source": [ "We finally write a median of means procedure. We feed it an observable, the list of snapshots computed above and the number of blocks needed. It returns the median of the means of the observable acting on the snapshots in each block." @@ -291,7 +277,6 @@ { "cell_type": "code", "execution_count": 8, - "id": "cc3a5349", "metadata": {}, "outputs": [], "source": [ @@ -312,7 +297,6 @@ }, { "cell_type": "markdown", - "id": "f069f2a3", "metadata": {}, "source": [ "### Reconstructing a given quantum state" @@ -320,7 +304,6 @@ }, { "cell_type": "markdown", - "id": "f384de9c", "metadata": {}, "source": [ "Let us try out the efficiency of this method. We will reconstruct a given density matrix from classical shadows estimation, and observe the evolution of the trace distance between the original state and its reconstruction according to the number of shadows used." @@ -329,7 +312,6 @@ { "cell_type": "code", "execution_count": 9, - "id": "62f9bc7e", "metadata": {}, "outputs": [], "source": [ @@ -340,7 +322,6 @@ { "cell_type": "code", "execution_count": 10, - "id": "16811f00", "metadata": {}, "outputs": [ { @@ -383,7 +364,6 @@ { "cell_type": "code", "execution_count": 11, - "id": "85f97f9f", "metadata": {}, "outputs": [ { @@ -408,7 +388,6 @@ }, { "cell_type": "markdown", - "id": "de522cd5", "metadata": {}, "source": [ "As we can expect, the estimation gets better and better as shadow size gets larger, with about $2$% accuracy at $10000$ shadows. This mostly serves as a reality check, as we will be using classical shadows to estimate observables acting on quantum states, not to reconstruct those states." @@ -416,7 +395,6 @@ }, { "cell_type": "markdown", - "id": "ce56aafe", "metadata": {}, "source": [ "## Derandomized Paulis" @@ -424,7 +402,6 @@ }, { "cell_type": "markdown", - "id": "cd468f15", "metadata": {}, "source": [ "### Derandomization Algorithm" @@ -432,7 +409,6 @@ }, { "cell_type": "markdown", - "id": "2a0f0d8c", "metadata": {}, "source": [ "Randomized classical shadows are useful when dealing with low-weight, general observables. However, suppose, as is the case when estimating the Hamiltonian of the $H_2$ molecule written as a sum of Pauli strings, that we're dealing with Pauli observables of varying weights. In this setting, choosing wisely each Pauli measurement instead of randomly drawing a basis is particularly useful : indeed, say one wants to measure observable $\\sigma_x^1 \\otimes \\sigma_x^2 \\otimes \\dots \\otimes \\sigma_x^n$. Using random rotations in each Pauli $X,Y$ or $Z$ basis and projection in the $Z$ (computational) basis, there is a probability $\\frac{1}{3^n}$ to get each measurement basis right (i.e. rotate the system using the Hadamard matrix). This is extremely unlikely and unefficient as the number of qubits goes up. [Huang et al](https://arxiv.org/abs/2103.07510) outline an interesting greedy algorithm used for choosing suitable measurement bases for the efficient estimation of $L$ $n-$qubit Pauli strings, $\\{O_i\\}$. \n", @@ -442,7 +418,6 @@ }, { "cell_type": "markdown", - "id": "23edee89", "metadata": {}, "source": [ "In order to implement this cost function, we first need to design two auxiliary functions. The first one decides if a given Pauli measurement $p$ is compatible with (\"hits\") a Pauli observable $o$. This means that each time $o$ acts non-trivially on a qubit $q_i$ with Pauli matrix $\\sigma \\in \\{\\sigma_X, \\sigma_Y, \\sigma_Z\\}, \\sigma \\neq \\mathbb{1}$, $p$ acts on $q_i$ with $\\sigma$. We denote it by $o \\triangleright p$." @@ -451,7 +426,6 @@ { "cell_type": "code", "execution_count": 12, - "id": "343399b5", "metadata": {}, "outputs": [], "source": [ @@ -473,7 +447,6 @@ }, { "cell_type": "markdown", - "id": "585f7865", "metadata": {}, "source": [ "The second function simply computes the number of qubits observable $o$ acts non-trivially upon." @@ -482,7 +455,6 @@ { "cell_type": "code", "execution_count": 13, - "id": "facd57b1", "metadata": {}, "outputs": [], "source": [ @@ -493,7 +465,6 @@ }, { "cell_type": "markdown", - "id": "b1250774", "metadata": {}, "source": [ "We now implement the conditioned cost function using these auxiliary functions. We call it \"conditioned\", since we feed it only the first $m \\times n + k$ single-qubit Pauli measurements, and average over the others, not yet determined ones." @@ -502,7 +473,6 @@ { "cell_type": "code", "execution_count": 14, - "id": "d99ccfcb", "metadata": {}, "outputs": [], "source": [ @@ -532,7 +502,6 @@ }, { "cell_type": "markdown", - "id": "5dae4535", "metadata": {}, "source": [ "Finally, we design a simple greedy algorithm which purpose is to minimize this conditioned cost function, choosing one single-qubit Pauli at a time." @@ -541,7 +510,6 @@ { "cell_type": "code", "execution_count": 15, - "id": "38712dc5", "metadata": {}, "outputs": [], "source": [ @@ -576,7 +544,6 @@ }, { "cell_type": "markdown", - "id": "ff3e6a1b", "metadata": {}, "source": [ "### Estimating expectation values from Pauli measurements" @@ -584,7 +551,6 @@ }, { "cell_type": "markdown", - "id": "6b3c897f", "metadata": {}, "source": [ "Now that we have our Pauli measurements, we proceed differently from randomized classical shadows, where we gave an estimate of the actual quantum channels. Here, we're only interested in the Pauli averages $\\tilde{\\omega}_l$, that we can infer from Pauli measurements $p$ that **hit** observable $o_l$. Indeed, we have the following formula :\n", @@ -595,7 +561,6 @@ { "cell_type": "code", "execution_count": 16, - "id": "1fc48ac3", "metadata": {}, "outputs": [], "source": [ @@ -611,7 +576,6 @@ { "cell_type": "code", "execution_count": 17, - "id": "5746d37e", "metadata": {}, "outputs": [], "source": [ @@ -637,7 +601,6 @@ { "cell_type": "code", "execution_count": 18, - "id": "27196d9c", "metadata": {}, "outputs": [], "source": [ @@ -667,7 +630,6 @@ { "cell_type": "code", "execution_count": 19, - "id": "76ce6b86", "metadata": {}, "outputs": [], "source": [ @@ -698,7 +660,6 @@ }, { "cell_type": "markdown", - "id": "8b1aa92c", "metadata": {}, "source": [ "## Variational Quantum Simulation for the $H_2$ molecule" @@ -706,7 +667,6 @@ }, { "cell_type": "markdown", - "id": "3509cba4", "metadata": {}, "source": [ "The main problem with usual variational classical algorithms, the classical counterparts of VQS, is computing the value of the $2^n \\times 2^n$ matrix on the output state vector $\\bra{\\psi}H\\ket{\\psi}$ after each loop of the algorithm, which grows exponentially in the size of the system. The purpose of VQS algorithms is to offer a solution which time complexity only grows polynomially, thanks to reading all the important properties on the quantum state. Therefore, we need accurate and efficient methods to estimate these properties, which we'll present afterwards.\n", @@ -716,7 +676,6 @@ }, { "cell_type": "markdown", - "id": "38b77112", "metadata": {}, "source": [ "### Jordan-Wigner Hamiltonian (cost function)" @@ -724,7 +683,6 @@ }, { "cell_type": "markdown", - "id": "7192fa80", "metadata": {}, "source": [ "We need to write the Hamiltonian in a way that's compatible with the formalism of quantum computing. We first second-quantize the Hamiltonian, obtaining an expression in terms of fermionic operators $a, a^\\dagger$. Then, we use the Jordan-Wigner transformation, which maps the fermionic operators to Pauli matrices. We obtain the Hamiltonian below, acting on $4$ qubits, decomposed in terms of the coefficients in front of the Pauli matrices.\n", @@ -735,7 +693,6 @@ }, { "cell_type": "markdown", - "id": "1e17315a", "metadata": {}, "source": [ "$$H_{J W}=-0.81261 \\mathbb{1}+0.171201 \\sigma_{0}^{z}+0.171201 \\sigma_{1}^{z}-0.2227965 \\sigma_{2}^{z}-0.2227965 \\sigma_{3}^{z}\n", @@ -747,7 +704,6 @@ { "cell_type": "code", "execution_count": 20, - "id": "ba1a755e", "metadata": {}, "outputs": [], "source": [ @@ -759,7 +715,6 @@ { "cell_type": "code", "execution_count": 21, - "id": "72ebe271", "metadata": {}, "outputs": [], "source": [ @@ -786,7 +741,6 @@ { "cell_type": "code", "execution_count": 22, - "id": "43d3cee0", "metadata": { "scrolled": true }, @@ -813,7 +767,6 @@ }, { "cell_type": "markdown", - "id": "692a4141", "metadata": {}, "source": [ "Let us keep the exact ground-state energy of the molecule for future reference, by diagonalizing it exactly - this is possible for such a small system, however, this quickly becomes an intractable problem for large molecules." @@ -822,7 +775,6 @@ { "cell_type": "code", "execution_count": 23, - "id": "51f155cb", "metadata": {}, "outputs": [ { @@ -854,7 +806,6 @@ }, { "cell_type": "markdown", - "id": "e42dc730", "metadata": {}, "source": [ "### Quantum Loop (VQS)" @@ -862,7 +813,6 @@ }, { "cell_type": "markdown", - "id": "99aeffa4", "metadata": {}, "source": [ "Much like in the *Using QAOA to solve a MIS problem* notebook, we will use a mixed classical-quantum approach for minimizing the energy. The quantum part will do the exploration in Hilbert space, according to a certain set of parameters $\\theta_i, \\tau_j$, and the classical part will find the optimal parameters given the value of the energy after each loop. For now, we will ignore sampling problems and simply compute the exact expectation value of $H_{JW}$. See [this article by Xiao Yuan et al.](https://arxiv.org/abs/1812.08767) for details about VQS algorithms." @@ -870,7 +820,6 @@ }, { "cell_type": "markdown", - "id": "150c5806", "metadata": {}, "source": [ "Two mixing Hamiltonians are used for the exploration of the solution space :\n", @@ -881,7 +830,6 @@ { "cell_type": "code", "execution_count": 24, - "id": "65ebfe35", "metadata": {}, "outputs": [], "source": [ @@ -915,7 +863,6 @@ }, { "cell_type": "markdown", - "id": "dba4c28c", "metadata": {}, "source": [ "We choose to act on the quantum states with $5$ layers of noncommuting mixing Hamiltonians, and an initial set of parameters such that pulses with Hamiltonian $H_1$ last $2\\mu s$, and those with $H_2$ last $4\\mu s$." @@ -924,7 +871,6 @@ { "cell_type": "code", "execution_count": 25, - "id": "eb02e547", "metadata": {}, "outputs": [], "source": [ @@ -935,7 +881,6 @@ }, { "cell_type": "markdown", - "id": "92cb57fc", "metadata": {}, "source": [ "We now obtain the ground-state energy :" @@ -944,7 +889,6 @@ { "cell_type": "code", "execution_count": 26, - "id": "4914f70a", "metadata": {}, "outputs": [ { @@ -962,7 +906,6 @@ }, { "cell_type": "markdown", - "id": "05fdb26a", "metadata": {}, "source": [ "As we can see, it's not so far off, since we're about $2$% off from the exact value. Adding more layers, tweaking the mixing Hamiltonians or the initial parameters can help with the accuracy. \n", @@ -973,7 +916,6 @@ { "cell_type": "code", "execution_count": 27, - "id": "8935c585", "metadata": {}, "outputs": [ { @@ -1006,7 +948,6 @@ }, { "cell_type": "markdown", - "id": "7b63f7f0", "metadata": {}, "source": [ "Seems like we can cut on calculation time by only allowing $100$ iterations, since we don't get much more accurate afterwards." @@ -1014,7 +955,6 @@ }, { "cell_type": "markdown", - "id": "c4732f2b", "metadata": {}, "source": [ "## Estimating Jordan-Wigner $H_2$ Hamiltonian with classical shadows" @@ -1022,7 +962,6 @@ }, { "cell_type": "markdown", - "id": "f6ee64af", "metadata": {}, "source": [ "### Randomized measurements" @@ -1030,7 +969,6 @@ }, { "cell_type": "markdown", - "id": "178451db", "metadata": {}, "source": [ "We now consider the real-life problem where we don't have access to the exact value $\\bra{\\Psi(\\theta_i, \\tau_j)} H_{JW} \\ket{\\Psi(\\theta_i, \\tau_j)}$. It can be estimated with classical shadows.\n", @@ -1040,7 +978,6 @@ { "cell_type": "code", "execution_count": 28, - "id": "754df65c", "metadata": {}, "outputs": [], "source": [ @@ -1059,7 +996,6 @@ { "cell_type": "code", "execution_count": 29, - "id": "ed7176b7", "metadata": {}, "outputs": [], "source": [ @@ -1103,7 +1039,6 @@ { "cell_type": "code", "execution_count": 32, - "id": "fe0ebe3d", "metadata": {}, "outputs": [ { @@ -1125,7 +1060,6 @@ { "cell_type": "code", "execution_count": 33, - "id": "57e6d360", "metadata": {}, "outputs": [], "source": [ @@ -1138,7 +1072,6 @@ { "cell_type": "code", "execution_count": 34, - "id": "9860658b", "metadata": {}, "outputs": [ { @@ -1173,7 +1106,6 @@ }, { "cell_type": "markdown", - "id": "beb39526", "metadata": {}, "source": [ "As could be expected, the estimation can be worse than what we got before : we added both randomness and sampling issues to the problem. Raising shadow size will allow more and more precise results. However, it can also be closer to the exact value for the same reasons." @@ -1181,7 +1113,6 @@ }, { "cell_type": "markdown", - "id": "a07ae036", "metadata": {}, "source": [ "### Derandomized measurements" @@ -1189,7 +1120,6 @@ }, { "cell_type": "markdown", - "id": "ec0b65a1", "metadata": {}, "source": [ "Finally, we try out the derandomized measurements method. To implement this one, we need to decompose the Hamiltonian into individual Pauli strings, rather than group them when they share the same leading coefficient as we did before, as it reduced the number of estimations." @@ -1198,7 +1128,6 @@ { "cell_type": "code", "execution_count": 35, - "id": "b62cc268", "metadata": {}, "outputs": [], "source": [ @@ -1225,7 +1154,6 @@ { "cell_type": "code", "execution_count": 36, - "id": "ba92cf7e", "metadata": {}, "outputs": [], "source": [ @@ -1235,7 +1163,6 @@ }, { "cell_type": "markdown", - "id": "80458815", "metadata": {}, "source": [ "Then, we ask the derandomization algorithm to return $60$ suitable Pauli measurements regarding our input Pauli observables. $60$ is arbitrary, but is small enough that the algorithm runs quickly and large enough that it gives good results." @@ -1244,7 +1171,6 @@ { "cell_type": "code", "execution_count": 37, - "id": "7a9c7be6", "metadata": {}, "outputs": [ { @@ -1264,7 +1190,6 @@ }, { "cell_type": "markdown", - "id": "4b6bcc43", "metadata": {}, "source": [ "As we can see, since all Pauli observables appearing in the Jordan-Wigner Hamiltonian involving the $Z$-basis never involve another basis, we find that it is always worth it to measure Pauli string $ZZZZ$ rather than $ZZZX$, or $ZYZZ$, etc. This is a sign that our cost function is doing its job !" @@ -1273,7 +1198,6 @@ { "cell_type": "code", "execution_count": 38, - "id": "795d8fe7", "metadata": {}, "outputs": [], "source": [ @@ -1311,7 +1235,6 @@ { "cell_type": "code", "execution_count": 39, - "id": "84675919", "metadata": { "scrolled": true }, @@ -1335,7 +1258,6 @@ { "cell_type": "code", "execution_count": 40, - "id": "777fdab7", "metadata": {}, "outputs": [], "source": [ @@ -1349,7 +1271,6 @@ { "cell_type": "code", "execution_count": 41, - "id": "c5979d01", "metadata": {}, "outputs": [ { @@ -1384,7 +1305,6 @@ }, { "cell_type": "markdown", - "id": "d2fc69a1", "metadata": {}, "source": [ "We consistently obtain accurate results using this derandomized technique, and we obtain them far quicker than when dealing with randomized classical shadows. For roughly the same number of samples ($\\sim 60$ for each method, be it for shadow size or number of measurements), we experience much less computing time using the derandomized method. This was to be expected : by restricting the observables to Pauli strings, we allow for efficient estimation that can be easily computed in $O(M\\times n)$, as well as remove randomness problematic with higher-weight observables (such as $YYXX$ or $YXXY$).\n", @@ -1394,6 +1314,7 @@ } ], "metadata": { + "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", @@ -1409,7 +1330,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/tutorials/applications/Spin chain of 3 atoms in XY mode.ipynb b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb similarity index 99% rename from tutorials/applications/Spin chain of 3 atoms in XY mode.ipynb rename to tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb index efe4ddf6b..936d86c1f 100644 --- a/tutorials/applications/Spin chain of 3 atoms in XY mode.ipynb +++ b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Simulation in XY mode" + "# XY Spin Chain" ] }, { From 922ccc5e2905f47591b709da42209c1de80758d3 Mon Sep 17 00:00:00 2001 From: Constantin Dalyac <58850838+cdalyac@users.noreply.github.com> Date: Mon, 29 Nov 2021 09:54:25 +0100 Subject: [PATCH 18/51] Slm mask ising (#286) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * added rydberg and raman_global SLM mask + changed make_interaction * Update pulser/sequence.py Co-authored-by: Henrique Silvério * Update pulser/tests/test_simulation.py Co-authored-by: Henrique Silvério * modified according to remarks. Haven't written the local test yet. * Adding local pulse to masked simulation test Co-authored-by: Constantin Co-authored-by: Henrique Silvério --- pulser/sequence.py | 11 +- pulser/simulation/simulation.py | 79 ++++++------ pulser/tests/test_sequence.py | 16 --- pulser/tests/test_simulation.py | 220 ++++++++++++++++++-------------- 4 files changed, 160 insertions(+), 166 deletions(-) diff --git a/pulser/sequence.py b/pulser/sequence.py index 0fed7efb6..f31afdd2f 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -423,12 +423,6 @@ def declare_channel( else: raise ValueError(f"Channel {channel_id} is not available.") - # Remove this check once SLM is available in Ising mode - if self._slm_mask_targets and ch.basis != "XY": - raise NotImplementedError( - "SLM mask is not yet available in Ising mode" - ) - if ch.basis == "XY" and not self._in_xy: self._in_xy = True self.set_magnetic_field() @@ -1169,9 +1163,6 @@ def config_slm_mask(self, qubits: Set[QubitId]) -> None: if not targets.issubset(self._qids): raise ValueError("SLM mask targets must exist in the register") - if not self._in_xy and self._channels: - raise NotImplementedError("SLM mask can only be added in XY mode") - if self.is_parametrized(): return @@ -1183,6 +1174,8 @@ def config_slm_mask(self, qubits: Set[QubitId]) -> None: # Find tentative initial and final time of SLM mask if possible for channel in self._channels: + if not self._channels[channel].addressing == "Global": + continue # Cycle on slots in schedule until the first pulse is found for slot in self._schedule[channel]: if not isinstance(slot.type, Pulse): diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index 2c96ac7f1..368322e5e 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -29,6 +29,7 @@ import matplotlib.pyplot as plt from pulser import Pulse, Sequence +from pulser.register import QubitId from pulser.simulation.simresults import ( SimulationResults, CoherentResults, @@ -635,7 +636,7 @@ def _construct_hamiltonian(self, update_and_extract: bool = True) -> None: if not hasattr(self, "basis_name"): self._build_basis_and_op_matrices() - def make_vdw_term() -> qutip.Qobj: + def make_vdw_term(q1: QubitId, q2: QubitId) -> qutip.Qobj: """Construct the Van der Waals interaction Term. For each pair of qubits, calculate the distance between them, @@ -643,18 +644,11 @@ def make_vdw_term() -> qutip.Qobj: The units are given so that the coefficient includes a 1/hbar factor. """ - vdw = cast(qutip.Qobj, 0) - # Get every pair without duplicates - for q1, q2 in itertools.combinations(self._qdict.keys(), r=2): - # no VdW interaction with other qubits for a badly prep. qubit - if self._bad_atoms[q1] or self._bad_atoms[q2]: - continue - dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) - U = 0.5 * self._seq._device.interaction_coeff / dist ** 6 - vdw += U * self.build_operator([("sigma_rr", [q1, q2])]) - return vdw + dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) + U = 0.5 * self._seq._device.interaction_coeff / dist ** 6 + return U * self.build_operator([("sigma_rr", [q1, q2])]) - def make_xy_term(masked: bool = False) -> qutip.Qobj: + def make_xy_term(q1: QubitId, q2: QubitId) -> qutip.Qobj: """Construct the XY interaction Term. For each pair of qubits, calculate the distance between them, @@ -662,8 +656,31 @@ def make_xy_term(masked: bool = False) -> qutip.Qobj: The units are given so that the coefficient includes a 1/hbar factor. """ - # Calculate the total number of good, unmasked qubits + dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) + mag_norm = np.linalg.norm(self._seq.magnetic_field[0:2]) + if mag_norm < 1e-8: + cosine = 0.0 + else: + cosine = ( + np.dot( + (self._qdict[q1] - self._qdict[q2]), + self._seq.magnetic_field[0:2], + ) + / (dist * mag_norm) + ) + U = ( + 0.5 + * self._seq._device.interaction_coeff_xy + * (1 - 3 * cosine ** 2) + / dist ** 3 + ) + return U * self.build_operator( + [("sigma_du", [q1]), ("sigma_ud", [q2])] + ) + + def make_interaction_term(masked: bool = False) -> qutip.Qobj: if masked: + # Calculate the total number of good, unmasked qubits effective_size = self._size - sum(self._bad_atoms.values()) for q in self._seq._slm_mask_targets: if not self._bad_atoms[q]: @@ -671,8 +688,8 @@ def make_xy_term(masked: bool = False) -> qutip.Qobj: if effective_size < 2: return 0 * self.build_operator([("I", "global")]) - xy = cast(qutip.Qobj, 0) - # Get every pair without duplicates + # make interaction term + dipole_interaction = cast(qutip.Qobj, 0) for q1, q2 in itertools.combinations(self._qdict.keys(), r=2): if ( self._bad_atoms[q1] @@ -686,34 +703,12 @@ def make_xy_term(masked: bool = False) -> qutip.Qobj: ) ): continue - dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) - mag_norm = np.linalg.norm(self._seq.magnetic_field[0:2]) - if mag_norm < 1e-8: - cosine = 0.0 - else: - cosine = ( - np.dot( - (self._qdict[q1] - self._qdict[q2]), - self._seq.magnetic_field[0:2], - ) - / (dist * mag_norm) - ) - U = ( - 0.5 - * self._seq._device.interaction_coeff_xy - * (1 - 3 * cosine ** 2) - / dist ** 3 - ) - xy += U * self.build_operator( - [("sigma_du", [q1]), ("sigma_ud", [q2])] - ) - return xy - def make_interaction_term(masked: bool = False) -> qutip.Qobj: - if self._interaction == "XY": - return make_xy_term(masked) - else: - return make_vdw_term() + if self._interaction == "XY": + dipole_interaction += make_xy_term(q1, q2) + else: + dipole_interaction += make_vdw_term(q1, q2) + return dipole_interaction def build_coeffs_ops(basis: str, addr: str) -> list[list]: """Build coefficients and operators for the hamiltonian QobjEvo.""" diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 9934d2ca3..86a69aed8 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -478,22 +478,6 @@ def test_slm_mask(): pulse1 = Pulse.ConstantPulse(100, 10, 0, 0) pulse2 = Pulse.ConstantPulse(200, 10, 0, 0) - # Try to set mask when Ising was already declared - seq_ising1 = Sequence(reg, MockDevice) - seq_ising1.declare_channel("ch_rg", "rydberg_global") - with pytest.raises( - NotImplementedError, match="SLM mask can only be added in XY mode" - ): - seq_ising1.config_slm_mask(targets) - - # Try to set mask and then declare Ising - seq_ising2 = Sequence(reg, MockDevice) - seq_ising2.config_slm_mask(targets) - with pytest.raises( - NotImplementedError, match="SLM mask is not yet available in Ising" - ): - seq_ising2.declare_channel("ch_rg", "rydberg_global") - # Set mask when an XY pulse is already in the schedule seq_xy1 = Sequence(reg, MockDevice) seq_xy1.declare_channel("ch_xy", "mw_global") diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index 0844d6c2f..69458a30a 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -707,19 +707,21 @@ def test_noisy_xy(): def test_mask_nopulses(): """Check interaction between SLM mask and a simulation with no pulses.""" reg = Register({"q0": (0, 0), "q1": (10, 10), "q2": (-10, -10)}) - seq_empty = Sequence(reg, MockDevice) - seq_empty.set_magnetic_field(0, 1.0, 0.0) - seq_empty.declare_channel("ch", "mw_global") - seq_empty.delay(duration=100, channel="ch") - masked_qubits = ["q2"] - seq_empty.config_slm_mask(masked_qubits) - sim_empty = Simulation(seq_empty) + for channel_type in ["mw_global", "rydberg_global"]: + seq_empty = Sequence(reg, MockDevice) + if channel_type == "mw_global": + seq_empty.set_magnetic_field(0, 1.0, 0.0) + seq_empty.declare_channel("ch", channel_type) + seq_empty.delay(duration=100, channel="ch") + masked_qubits = ["q2"] + seq_empty.config_slm_mask(masked_qubits) + sim_empty = Simulation(seq_empty) - assert seq_empty._slm_mask_time == [] - assert sim_empty._seq._slm_mask_time == [] + assert seq_empty._slm_mask_time == [] + assert sim_empty._seq._slm_mask_time == [] -def test_xy_mask_equals_remove(): +def test_mask_equals_remove(): """Check that masking is equivalent to removing the masked qubits. A global pulse acting on three qubits of which one is masked, should be @@ -728,32 +730,46 @@ def test_xy_mask_equals_remove(): reg_three = Register({"q0": (0, 0), "q1": (10, 10), "q2": (-10, -10)}) reg_two = Register({"q0": (0, 0), "q1": (10, 10)}) pulse = Pulse.ConstantPulse(100, 10, 0, 0) + local_pulse = Pulse.ConstantPulse(200, 10, 0, 0) - # Masked simulation - seq_masked = Sequence(reg_three, MockDevice) - seq_masked.set_magnetic_field(0, 1.0, 0.0) - seq_masked.declare_channel("ch_masked", "mw_global") - masked_qubits = ["q2"] - seq_masked.config_slm_mask(masked_qubits) - seq_masked.add(pulse, "ch_masked") - sim_masked = Simulation(seq_masked) - - # Simulation on reduced register - seq_two = Sequence(reg_two, MockDevice) - seq_two.set_magnetic_field(0, 1.0, 0.0) - seq_two.declare_channel("ch_two", "mw_global") - seq_two.add(pulse, "ch_two") - sim_two = Simulation(seq_two) - - # Check equality - for t in sim_two.evaluation_times: - ham_masked = sim_masked.get_hamiltonian(t) - ham_two = sim_two.get_hamiltonian(t) - assert ham_masked == qutip.tensor(ham_two, qutip.qeye(2)) + for channel_type in ["mw_global", "rydberg_global", "raman_global"]: + # Masked simulation + seq_masked = Sequence(reg_three, MockDevice) + if channel_type == "mw_global": + seq_masked.set_magnetic_field(0, 1.0, 0.0) + else: + # Add a local channel acting on a masked qubit (has no effect) + seq_masked.declare_channel( + "local", + channel_type[: -len("global")] + "local", + initial_target="q2", + ) + seq_masked.add(local_pulse, "local") + seq_masked.declare_channel("ch_masked", channel_type) + masked_qubits = ["q2"] + seq_masked.config_slm_mask(masked_qubits) + seq_masked.add(pulse, "ch_masked") + sim_masked = Simulation(seq_masked) + + # Simulation on reduced register + seq_two = Sequence(reg_two, MockDevice) + if channel_type == "mw_global": + seq_two.set_magnetic_field(0, 1.0, 0.0) + seq_two.declare_channel("ch_two", channel_type) + if channel_type != "mw_global": + seq_two.delay(local_pulse.duration, "ch_two") + seq_two.add(pulse, "ch_two") + sim_two = Simulation(seq_two) + + # Check equality + for t in sim_two.sampling_times: + ham_masked = sim_masked.get_hamiltonian(t) + ham_two = sim_two.get_hamiltonian(t) + assert ham_masked == qutip.tensor(ham_two, qutip.qeye(2)) -def test_xy_mask_two_pulses(): - """Similar to test_xy_mask_equals_remove, but with more pulses afterwards. +def test_mask_two_pulses(): + """Similar to test_mask_equals_remove, but with more pulses afterwards. Three global pulses act on a three qubit register, with one qubit masked during the first pulse. @@ -763,79 +779,85 @@ def test_xy_mask_two_pulses(): pulse = Pulse.ConstantPulse(100, 10, 0, 0) no_pulse = Pulse.ConstantPulse(100, 0, 0, 0) - # Masked simulation - seq_masked = Sequence(reg_three, MockDevice) - seq_masked.declare_channel("ch_masked", "mw_global") - masked_qubits = ["q2"] - seq_masked.config_slm_mask(masked_qubits) - seq_masked.add(pulse, "ch_masked") # First pulse: masked - seq_masked.add(pulse, "ch_masked") # Second pulse: unmasked - seq_masked.add(pulse, "ch_masked") # Third pulse: unmasked - sim_masked = Simulation(seq_masked) - - # Unmasked simulation on full register - seq_three = Sequence(reg_three, MockDevice) - seq_three.declare_channel("ch_three", "mw_global") - seq_three.add(no_pulse, "ch_three") - seq_three.add(pulse, "ch_three") - seq_three.add(pulse, "ch_three") - sim_three = Simulation(seq_three) - - # Unmasked simulation on reduced register - seq_two = Sequence(reg_two, MockDevice) - seq_two.declare_channel("ch_two", "mw_global") - seq_two.add(pulse, "ch_two") - seq_two.add(no_pulse, "ch_two") - seq_two.add(no_pulse, "ch_two") - sim_two = Simulation(seq_two) - - ti = seq_masked._slm_mask_time[0] - tf = seq_masked._slm_mask_time[1] - for t in sim_masked.evaluation_times: - ham_masked = sim_masked.get_hamiltonian(t) - ham_three = sim_three.get_hamiltonian(t) - ham_two = sim_two.get_hamiltonian(t) - if ti <= t <= tf: - assert ham_masked == qutip.tensor(ham_two, qutip.qeye(2)) - else: - assert ham_masked == ham_three + for channel_type in ["mw_global", "rydberg_global", "raman_global"]: + # Masked simulation + seq_masked = Sequence(reg_three, MockDevice) + seq_masked.declare_channel("ch_masked", channel_type) + masked_qubits = ["q2"] + seq_masked.config_slm_mask(masked_qubits) + seq_masked.add(pulse, "ch_masked") # First pulse: masked + seq_masked.add(pulse, "ch_masked") # Second pulse: unmasked + seq_masked.add(pulse, "ch_masked") # Third pulse: unmasked + sim_masked = Simulation(seq_masked) + + # Unmasked simulation on full register + seq_three = Sequence(reg_three, MockDevice) + seq_three.declare_channel("ch_three", channel_type) + seq_three.add(no_pulse, "ch_three") + seq_three.add(pulse, "ch_three") + seq_three.add(pulse, "ch_three") + sim_three = Simulation(seq_three) + + # Unmasked simulation on reduced register + seq_two = Sequence(reg_two, MockDevice) + seq_two.declare_channel("ch_two", channel_type) + seq_two.add(pulse, "ch_two") + seq_two.add(no_pulse, "ch_two") + seq_two.add(no_pulse, "ch_two") + sim_two = Simulation(seq_two) + + ti = seq_masked._slm_mask_time[0] + tf = seq_masked._slm_mask_time[1] + for t in sim_masked.sampling_times: + ham_masked = sim_masked.get_hamiltonian(t) + ham_three = sim_three.get_hamiltonian(t) + ham_two = sim_two.get_hamiltonian(t) + if ti <= t <= tf: + assert ham_masked == qutip.tensor(ham_two, qutip.qeye(2)) + else: + assert ham_masked == ham_three def test_effective_size_intersection(): - np.random.seed(15092021) simple_reg = Register.square(2, prefix="atom") rise = Pulse.ConstantPulse(1500, 0, 0, 0) - seq = Sequence(simple_reg, MockDevice) - seq.declare_channel("ch0", "mw_global") - seq.add(rise, "ch0") - seq.config_slm_mask(["atom0"]) - - sim = Simulation(seq, sampling_rate=0.01) - sim.set_config(SimConfig("SPAM", eta=0.4)) - assert sim._bad_atoms == { - "atom0": True, - "atom1": False, - "atom2": True, - "atom3": False, - } - assert sim.get_hamiltonian(0) != 0 * sim.build_operator([("I", "global")]) + for channel_type in ["mw_global", "rydberg_global"]: + np.random.seed(15092021) + seq = Sequence(simple_reg, MockDevice) + seq.declare_channel("ch0", channel_type) + seq.add(rise, "ch0") + seq.config_slm_mask(["atom0"]) + + sim = Simulation(seq, sampling_rate=0.01) + sim.set_config(SimConfig("SPAM", eta=0.4)) + assert sim._bad_atoms == { + "atom0": True, + "atom1": False, + "atom2": True, + "atom3": False, + } + assert sim.get_hamiltonian(0) != 0 * sim.build_operator( + [("I", "global")] + ) def test_effective_size_disjoint(): - np.random.seed(15092021) simple_reg = Register.square(2, prefix="atom") rise = Pulse.ConstantPulse(1500, 0, 0, 0) - seq = Sequence(simple_reg, MockDevice) - seq.declare_channel("ch0", "mw_global") - seq.add(rise, "ch0") - seq.config_slm_mask(["atom1"]) - - sim = Simulation(seq, sampling_rate=0.01) - sim.set_config(SimConfig("SPAM", eta=0.4)) - assert sim._bad_atoms == { - "atom0": True, - "atom1": False, - "atom2": True, - "atom3": False, - } - assert sim.get_hamiltonian(0) == 0 * sim.build_operator([("I", "global")]) + for channel_type in ["mw_global", "rydberg_global", "raman_global"]: + np.random.seed(15092021) + seq = Sequence(simple_reg, MockDevice) + seq.declare_channel("ch0", channel_type) + seq.add(rise, "ch0") + seq.config_slm_mask(["atom1"]) + sim = Simulation(seq, sampling_rate=0.01) + sim.set_config(SimConfig("SPAM", eta=0.4)) + assert sim._bad_atoms == { + "atom0": True, + "atom1": False, + "atom2": True, + "atom3": False, + } + assert sim.get_hamiltonian(0) == 0 * sim.build_operator( + [("I", "global")] + ) From 4b03702ed3cf6143646266e054096f329072c2d9 Mon Sep 17 00:00:00 2001 From: Mauro D'Arcangelo <32898410+darcangelomauro@users.noreply.github.com> Date: Mon, 29 Nov 2021 10:08:44 +0100 Subject: [PATCH 19/51] Changed the way attributes of frozen classes are modified (#283) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * changed the way frozen attributes are modified * changed __dict__ to __setattr__ * rewritten avoiding getter method * defined private attribute setter method for simconfig Co-authored-by: Mauro D'Arcangelo Co-authored-by: Henrique Silvério --- pulser/devices/_device_datacls.py | 2 +- pulser/parametrized/variable.py | 13 ++++++++----- pulser/pulse.py | 6 ++++-- pulser/simulation/simconfig.py | 26 ++++++++++++++++---------- 4 files changed, 29 insertions(+), 18 deletions(-) diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index e6e811403..13955925b 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -58,7 +58,7 @@ class Device: def __post_init__(self) -> None: # Hack to override the docstring of an instance - self.__dict__["__doc__"] = self._specs(for_docs=True) + object.__setattr__(self, "__doc__", self._specs(for_docs=True)) @property def channels(self) -> dict[str, Channel]: diff --git a/pulser/parametrized/variable.py b/pulser/parametrized/variable.py index 4510d2963..43bcd2914 100644 --- a/pulser/parametrized/variable.py +++ b/pulser/parametrized/variable.py @@ -54,7 +54,7 @@ def __post_init__(self) -> None: raise ValueError("Variables must be of size 1 or larger.") self._count: int - self.__dict__["_count"] = -1 # Counts the updates + object.__setattr__(self, "_count", -1) # Counts the updates self._clear() @property @@ -63,8 +63,8 @@ def variables(self) -> dict[str, Variable]: return {self.name: self} def _clear(self) -> None: - self.__dict__["value"] = None - self.__dict__["_count"] += 1 + object.__setattr__(self, "value", None) + object.__setattr__(self, "_count", self._count + 1) def _assign(self, value: Union[ArrayLike, str, float, int]) -> None: if self.dtype == str: @@ -85,8 +85,11 @@ def _assign(self, value: Union[ArrayLike, str, float, int]) -> None: + f"variable of size {self.size}." ) - self.__dict__["value"] = self.dtype(val) if self.size == 1 else val - self.__dict__["_count"] += 1 + if self.size == 1: + object.__setattr__(self, "value", self.dtype(val)) + else: + object.__setattr__(self, "value", val) + object.__setattr__(self, "_count", self._count + 1) def build(self) -> Union[ArrayLike, str, float, int]: """Returns the variable's current value.""" diff --git a/pulser/pulse.py b/pulser/pulse.py index 189d8288c..81d57e15c 100644 --- a/pulser/pulse.py +++ b/pulser/pulse.py @@ -87,8 +87,10 @@ def __post_init__(self) -> None: "greater than or equal to zero." ) - self.__dict__["phase"] %= 2 * np.pi - self.__dict__["post_phase_shift"] %= 2 * np.pi + object.__setattr__(self, "phase", self.phase % (2 * np.pi)) + object.__setattr__( + self, "post_phase_shift", self.post_phase_shift % (2 * np.pi) + ) @property def duration(self) -> int: diff --git a/pulser/simulation/simconfig.py b/pulser/simulation/simconfig.py index 188fdbe84..0222fd9f1 100644 --- a/pulser/simulation/simconfig.py +++ b/pulser/simulation/simconfig.py @@ -17,7 +17,7 @@ from sys import version_info from dataclasses import dataclass, field -from typing import Union +from typing import Union, Any import numpy as np import qutip @@ -93,11 +93,14 @@ class SimConfig: def __post_init__(self) -> None: self._process_temperature() - self.__dict__["spam_dict"] = { - "eta": self.eta, - "epsilon": self.epsilon, - "epsilon_prime": self.epsilon_prime, - } + self._change_attribute( + "spam_dict", + { + "eta": self.eta, + "epsilon": self.epsilon, + "epsilon_prime": self.epsilon_prime, + }, + ) self._check_noise_types() self._check_spam_dict() self._calc_sigma_doppler() @@ -141,12 +144,12 @@ def _process_temperature(self) -> None: + f" (`temperature` = {self.temperature}) must be" + " greater than 0." ) - self.__dict__["temperature"] *= 1.0e-6 + self._change_attribute("temperature", self.temperature * 1.0e-6) def _check_noise_types(self) -> None: # only one noise was given as argument : convert it to a tuple if isinstance(self.noise, str): - self.__dict__["noise"] = (self.noise,) + self._change_attribute("noise", (self.noise,)) for noise_type in self.noise: if noise_type not in get_args(NOISE_TYPES): raise ValueError( @@ -157,6 +160,9 @@ def _check_noise_types(self) -> None: def _calc_sigma_doppler(self) -> None: # sigma = keff Deltav, keff = 8.7mum^-1, Deltav = sqrt(kB T / m) - self.__dict__["doppler_sigma"] = KEFF * np.sqrt( - KB * self.temperature / MASS + self._change_attribute( + "doppler_sigma", KEFF * np.sqrt(KB * self.temperature / MASS) ) + + def _change_attribute(self, attr_name: str, new_value: Any) -> None: + object.__setattr__(self, attr_name, new_value) From 3f2d6020b4a31add1c7551a5d1530ae94ca77a76 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Mon, 29 Nov 2021 16:08:48 +0100 Subject: [PATCH 20/51] Generalizing XY mode simulation for 3D (#291) --- pulser/simulation/simulation.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index 368322e5e..9d289f115 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -657,14 +657,15 @@ def make_xy_term(q1: QubitId, q2: QubitId) -> qutip.Qobj: includes a 1/hbar factor. """ dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) - mag_norm = np.linalg.norm(self._seq.magnetic_field[0:2]) + coords_dim = len(self._qdict[q1]) + mag_norm = np.linalg.norm(self._seq.magnetic_field[:coords_dim]) if mag_norm < 1e-8: cosine = 0.0 else: cosine = ( np.dot( (self._qdict[q1] - self._qdict[q2]), - self._seq.magnetic_field[0:2], + self._seq.magnetic_field[:coords_dim], ) / (dist * mag_norm) ) From 9810cef4048e42308d92fa6e55732034e3e93209 Mon Sep 17 00:00:00 2001 From: Mauro D'Arcangelo <32898410+darcangelomauro@users.noreply.github.com> Date: Tue, 30 Nov 2021 12:00:29 +0100 Subject: [PATCH 21/51] Added tutorial on the SLM mask (#290) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * added slm tutorial * changed magnetization over time to final vector state * Adding the SLM mask tutorial to the docs * better final state printing * Clearing notebook output Co-authored-by: Mauro D'Arcangelo Co-authored-by: Henrique Silvério --- docs/source/index.rst | 1 + docs/source/tutorials/slm_mask.nblink | 3 + .../State Preparation with the SLM Mask.ipynb | 257 ++++++++++++++++++ 3 files changed, 261 insertions(+) create mode 100644 docs/source/tutorials/slm_mask.nblink create mode 100644 tutorials/advanced_features/State Preparation with the SLM Mask.ipynb diff --git a/docs/source/index.rst b/docs/source/index.rst index c4fb217b2..e4f11b9b2 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -69,6 +69,7 @@ computers and simulators, check the pages in :doc:`review`. tutorials/paramseqs tutorials/interpolated_wfs tutorials/serialization + tutorials/slm_mask .. toctree:: :maxdepth: 1 diff --git a/docs/source/tutorials/slm_mask.nblink b/docs/source/tutorials/slm_mask.nblink new file mode 100644 index 000000000..98d88849a --- /dev/null +++ b/docs/source/tutorials/slm_mask.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../tutorials/advanced_features/State Preparation with the SLM Mask.ipynb" +} diff --git a/tutorials/advanced_features/State Preparation with the SLM Mask.ipynb b/tutorials/advanced_features/State Preparation with the SLM Mask.ipynb new file mode 100644 index 000000000..cbe7cb6e8 --- /dev/null +++ b/tutorials/advanced_features/State Preparation with the SLM Mask.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# State preparation with the SLM mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When performing quantum computations with global pulses, it might be hard to prepare the system in an arbitrary initial state. This is especially true in the XY mode, where only a global $\\sigma^x$ pulse can produce excitations whose number is otherwise conserved during free evolution. A partial solution to this problem is to utilize an SLM mask.
\n", + "Assume a system of three qubits in XY mode is initially in state $\\left| \\downarrow \\downarrow \\downarrow \\right\\rangle$, and that we are interested in preparing the state $\\left| \\uparrow \\downarrow \\downarrow \\right\\rangle$. Acting naively with a global $\\sigma^x$ pulse of area $\\pi$ would result in state $\\left| \\uparrow \\uparrow \\uparrow \\right\\rangle$. Using an SLM pattern, however, it is possible to detune the last two qubits away from resonance, and the same global $\\sigma^x$ pulse will produced instead the desired state $\\left| \\uparrow \\downarrow \\downarrow \\right\\rangle$.
\n", + "Let's see how it works in practice. First create the register:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pulser import Pulse, Sequence, Register\n", + "from pulser.devices import MockDevice\n", + "from pulser.waveforms import BlackmanWaveform\n", + "from pulser.simulation import Simulation\n", + "\n", + "# Qubit register\n", + "qubits = {\"q0\": (-5,0), \"q1\": (0,0), \"q2\": (5,0)}\n", + "reg = Register(qubits)\n", + "reg.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the sequence and add a global $\\sigma^x$ pulse of area $\\pi$ in XY mode:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the sequence\n", + "seq = Sequence(reg, MockDevice)\n", + "\n", + "# Declare a global XY channel and add the pi pulse\n", + "seq.declare_channel('ch', 'mw_global')\n", + "pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi), 0, 0)\n", + "seq.add(pulse, 'ch')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Drawing the sequence will show the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To set up the SLM mask all we need to do is to create a list that contains the name of the qubits that we want to mask, and pass it to the $\\verb:Sequence.config_slm_mask:$ method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Mask the last two qubits\n", + "masked_qubits = [\"q1\", \"q2\"]\n", + "seq.config_slm_mask(masked_qubits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point it is possible to visualize the mask by drawing the sequence. The masked pulse will appear with a shaded background, and the names of the masked qubits will be shown in the bottom left corner." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sequence drawing method also allows to visualize the register. If an SLM mask is defined, the masked qubits will appear with a shaded square halo around them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.draw(draw_register=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see how the system evolves under this masked pulse. Since the pulse only acts on the first qubit, we expect the final state to be $\\left| \\uparrow \\downarrow \\downarrow \\right\\rangle$, or, according to Pulser's conventions for XY basis states, $(1,0)^T \\otimes (0,1)^T \\otimes (0,1)^T$ in the Hilbert space $C^8$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import qutip\n", + "\n", + "qutip.tensor(qutip.basis(2, 0), qutip.basis(2, 1), qutip.basis(2, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now run the simulation and print the final state as given by Pulser:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sim = Simulation(seq)\n", + "results = sim.run()\n", + "\n", + "results.get_final_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the two states agree up to numerical errors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the SLM mask is mostly useful for state preparation, its use in Pulser is restricted to the first pulse in the sequence. This can be seen by adding an extra pulse in the previous example and drawing the sequence:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.add(pulse, 'ch')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example also illustrates the fact that the SLM mask can be configured at any moment during the creation of a sequence (either before or after adding pulses) and it will automatically latch to the first pulse.
\n", + "However, in order to reflect real hardware constraints, the mask can be configured only once. Trying to configure the mask a second time will raise an error:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " seq.config_slm_mask(masked_qubits)\n", + "except ValueError as err:\n", + " print(err)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although the example shown here makes use of the XY mode, everything translates directly to the Ising mode as well with the same syntax and restrictions." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9ffcc42f8e71e38e0a0709385bec528880cc9a69 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Henriet?= Date: Tue, 30 Nov 2021 14:34:52 +0100 Subject: [PATCH 22/51] Applying SLM mask for the state preparation on the XXZ quantum simulation tutorial (#292) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Applying SLM mask * Typo plot Co-authored-by: Henrique Silvério --- ...ltonians in arrays of Rydberg atoms .ipynb | 141 ++++++++---------- 1 file changed, 64 insertions(+), 77 deletions(-) diff --git a/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb index 6496185e2..ad6ba6fb6 100644 --- a/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb +++ b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -129,12 +129,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -161,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -173,12 +173,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -200,10 +200,17 @@ "\n", "plt.plot(sim.evaluation_times, Sigma_y_res[0], 'o')\n", "plt.xlabel(r\"Time [µs]\", fontsize=16)\n", - "plt.ylabel(fr'$ \\langle \\sigma_1^y + \\sigma_2^y \\rangle$', fontsize=16)\n", + "plt.ylabel(fr'$ (\\langle \\sigma_1^y + \\sigma_2^y \\rangle)/2$', fontsize=16)\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that one cannot directly measure off diagonal elements of the density matrix experimentally. To be able to measure $\\langle \\sigma^y_1 + \\sigma^y_2 \\rangle$, one would need to first apply a rotation on the atoms (equivalent to changing the basis) and then measure the population. " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -215,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -247,46 +254,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's also create the initial state of the system using QuTiP." + "In the experiment, all the atoms start in the same initial state. In order to create a domain-wall state, one must apply a $\\pi$-pulse on only half of the atoms. On the hardware, this can be done by using a Spatial Light Modulator which imprints a specific phase pattern on a laser beam. This results in a set of focused laser beams in the atomic plane, whose geometry corresponds to the subset of sites to address, preventing the addressed atoms from interacting with the global microwave pulse due to a shift in energy.\n", + "\n", + "This feature is implemented in Pulser. For implementing this, we need to define a $\\pi$-pulse, and the list of indices of the atoms we want to mask. " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "# Creation of the initial DW state\n", - "initial_DW_state=[]\n", - "for m in range(N_at):\n", - " if m" ] @@ -354,7 +358,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -393,22 +397,22 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -435,43 +439,26 @@ "To investigate even more this delocalization effect, let's consider a smaller region of only 3 spins prepared in $|\\uparrow \\rangle$. The delocalization timescale will then be shorter, and we will see it more clearly happening in the system" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Creation of the initial DW state with only 3 spins up.\n", - "initial_DW_state=[]\n", - "for m in range(N_at):\n", - " if m < 3:\n", - " initial_DW_state.append(qutip.basis(2, 0))\n", - " else:\n", - " initial_DW_state.append(qutip.basis(2, 1))\n", - " \n", - "initial_DW_state = qutip.tensor(initial_DW_state)" - ] - }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "N_cycles=26 # Runtime close to 4 min !\n", - "\n", + "N_cycles = 26 \n", "magnetizations_pbc = np.zeros((N_at, N_cycles), dtype=float)\n", "samples_evol = []\n", - "for m in range(N_cycles):\n", + "masked_indices = [0, 1, 2]\n", + "for m in range(N_cycles): # Runtime close to 4 min!\n", " seq = Sequence(reg, MockDevice)\n", " seq.set_magnetic_field(0., 0., 1.)\n", " seq.declare_channel('MW', 'mw_global')\n", - " seq.set_magnetic_field(0., 0., 1.)\n", + " seq.config_slm_mask(masked_indices)\n", + " seq.add(initial_pi_pulse, 'MW')\n", " seq.add(X_pulse, 'MW')\n", " Floquet_XX2Z_cycles(m, t_pulse)\n", " seq.add(mX_pulse, 'MW')\n", " sim = Simulation(seq)\n", - " sim.initial_state = initial_DW_state\n", " res = sim.run()\n", " samples = res.sample_final_state(N_samples)\n", " samples_evol.append(samples)\n", @@ -488,7 +475,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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K8iIiToyIDRGxYePGjSt5qSRJkiRpJFYywL0ROLj8+Sjg3zNz6dPVXSi+X1srIu5CMbh9d2b+A0Bm3piZd2TmJuCv2XwZ8vXA/stevl+5bQuZeWpmrs/M9XvvvfcKuiRJkiRJGouVDHDPAv4kIv6M4ru3f78s9nPAFXUVREQA7wAuycw/X7Z9n2XFngxctKzNYyNip4g4CDgE+NIKcpYkSZIkbSMar6IMnAzcFXgsxcDzNctiTwQ+Me1FE34BeAZwYUScX257KfAbEXEYkMBVwLMBMvNrEfE+4GKKT4if4wrKkiRJkqRpIjPXOodOrV+/Pjds2LDWaUiSJEnqW8Ts2MjGPduyiDgvM9dPizW+RDkiroiIB86IPSAiai9RliRJkiRpXlbyHdwDgZ1mxO4K3Kt1NpIkSZIkrdJKBrhQfEd2mvXAre1SkSRJkiRp9SoXmYqI3wd+v3yawD9FxH9OFNsZ2AM4s/v0JEmSJElqpm4V5SuAT5U/Hw9sADZOlLmdYpXjv+k2NUmSJEmSmqsc4Gbmh4APARS3sOUPM/PKHvKSJEmSJGlFGt8HNzOfOc9EJEmSJElqo/EAFyAifgY4BjiAYuXk5TIzn9VVYpIkSZIkrUTjAW5EPAl4H8XKyzdRfPd2Oe+cLEmSJElaMyv5BPePgM8CT8/MyYWmJEmSJElaUysZ4P4M8CIHt5IkSZKkIdpuBWW/Duw5r0QkSZIkSWpjJQPcFwMvLReakiRJkiRpUFZyifKrKD7BvSQiLgNunohnZv5iV4lJkiRJkrQSKxng3gFcOq9EJEmSJElqo/EANzMfOcc8JEmSJElqZSXfwZUkSZIkabAaf4IbEY+oK5OZ/9wuHUmSJEmSVmcl38H9LJA1ZbZffSqSJEmSJK3eSga4j5qybU/gV4BfBJ7bSUaSJEmSJK3CShaZ+tyM0D9ExBuBXwXO7iQrSZIkSZJWqKtFpj4CHNNRXZIkSZIkrVhXA9z7AJs6qkuSJEmSpBVbySrKx03ZvCPwAOBZwD80qGN/4AxgHcWCVadm5psjYg/gvcCBwFXAMZl5S0QE8GbgCcAPgRMy8ytNc5YkSZIkbTtWssjUu2Zsv51icPr8BnX8BHhRZn4lIu4GnBcRnwBOAD6VmadExMnAycBJwOOBQ8rHw4C3lf9KkiRJkrSFlQxwD5qy7bbMvLFpBZn5LeBb5c/fj4hLgH2Bo4FHlsVOp7gl0Unl9jMyM4FzI2K3iNinrEeSJEmSpDutZBXlq7tsOCIOBB4EfBFYt2zQegPFJcxQDH6vXfay68ptWwxwI+JE4ESAAw44oMs0JUmSJEkLYiWf4AIQEUv3vd0DuBn4bGZ+ZIV17AJ8AHhBZn6v+KptITMzInIl9WXmqcCpAOvXr1/RayVJkiRJ47CSRabuBnwYeDjFd2m/A+wJvDAi/gX4lcz8QYN67kIxuH13Zi4tTHXj0qXHEbEPcFO5/Xpg/2Uv36/cJkmSJEnSFlZym6DXAg8GngHsnJn7ADsDx5XbX1tXQbkq8juASzLzz5eFzgKOL38+HvjQsu3HReEI4Lt+/1aSJEmSNM1KLlH+deDlmfnupQ2ZeQfw7ojYC3gx8L9q6vgFigHyhRFxfrntpcApwPsi4lnA1cAxZeyjFLcIupziNkHPXEG+kiRJkqRtyEoGuHsCF8+IXVzGK2Xm54GYEX7MlPIJPKdpgpIkSZKkbddKLlG+EviVGbEnlHFJkiRJktbESj7B/SvgDeUKyO+muFXPPYFjgd8GXth9epIkSZIkNbOS++C+MSL2phjInlBuDuA/gVMy883dpydJkiRJUjMrug9uZr40Iv4UOILN98E9NzNvmUdykiRJkiQ1tZL74J4E7JeZzwPOnoi9Bbg2M/+04/wkSZIkSWpkJYtMPRO4YEbsq3gLH0mSJEnSGlrJAPcA4LIZsX8H7tU+HUmSJEmSVmclA9wfAvvOiO0H3N4+HUmSJEmSVmclA9x/Af4gInZavrF8/qIyLkmSJEnSmljJKsqvAr4AfCMi/ha4nuIT3d8C9mTzrYMkSZIkSerdSu6D+9WIeBTwZ8BJFJ/+bgI+D/x6Zn51PilKkiRJklRvpffB/RLwiIjYGdgduCUzfzSXzCRJkiRJWoEVDXCXlINaB7aSJEmSpMFYySJTkiRJkiQNlgNcSZIkSdIoOMCVJEmSJI2CA1xJkiRJ0ig4wJUkSZIkjYIDXEmSJEnSKDjAlSRJkiSNggNcSZIkSdIoOMCVJEmSJI1CrwPciHhnRNwUERct2/aqiLg+Is4vH09YFntJRFweEZdGxGP7zFWSJEmStFj6/gT3XcDjpmx/Y2YeVj4+ChARPwscC9y/fM1bI2L73jKVJEmSJC2UXge4mfnPwM0Nix8NnJmZt2fmlcDlwOFzS06SJEmStNCG8h3c50bEBeUlzLuX2/YFrl1W5rpy21Yi4sSI2BARGzZu3DjvXCVJkiRJAzSEAe7bgHsDhwHfAt6w0goy89TMXJ+Z6/fee++O05MkSZIkLYI1H+Bm5o2ZeUdmbgL+ms2XIV8P7L+s6H7lNkmSJEmStrLmA9yI2GfZ0ycDSyssnwUcGxE7RcRBwCHAl/rOT5IkSZK0GHbos7GI+DvgkcBeEXEd8ErgkRFxGJDAVcCzATLzaxHxPuBi4CfAczLzjj7zlSRJkiQtjsjMtc6hU+vXr88NGzasdRqSJEmS+hYxOzaycc+2LCLOy8z102JrfomyJEmSJEldcIArSZIkSRoFB7iSJEmSpFFwgCtJkiRJGgUHuJIkSZKkUXCAK0mSJEkaBQe4kiRJkqRRcIArSZIkSRoFB7iSJEmSpFFwgCtJkiRJGgUHuJIkSZKkUXCAK0mSJEkaBQe4kiRJkqRRcIArSZIkSRoFB7iSJEmSpFFwgCtJkiRJGgUHuJIkSZKkUXCAK0mSJEkaBQe4kiRJkqRRcIArSZIkSRoFB7iSJEmSpFFwgCtJkiRJGoVeB7gR8c6IuCkiLlq2bY+I+EREXFb+u3u5PSLiLRFxeURcEBEP7jNXSZIkSdJi6fsT3HcBj5vYdjLwqcw8BPhU+Rzg8cAh5eNE4G095ShJkiRJWkC9DnAz85+Bmyc2Hw2cXv58OvCkZdvPyMK5wG4RsU8viUqSJEmSFs4QvoO7LjO/Vf58A7Cu/Hlf4Npl5a4rt0mSJEmStJUhDHDvlJkJ5EpfFxEnRsSGiNiwcePGOWQmSZIkSRq6IQxwb1y69Lj896Zy+/XA/svK7Vdu20pmnpqZ6zNz/d577z3XZCVJkiRJwzSEAe5ZwPHlz8cDH1q2/bhyNeUjgO8uu5RZkiRJkqQt7NBnYxHxd8Ajgb0i4jrglcApwPsi4lnA1cAxZfGPAk8ALgd+CDyzz1wlSZIkSYul1wFuZv7GjNBjppRN4DnzzUiSJEmSNBZDuERZkiRJkqTWHOBKkiRJkkbBAa4kSZIkaRQc4EqSJEmSRsEBriRJkiRpFBzgSpIkSZJGwQGuJEmSJGkUHOBKkiRJkkbBAa4kSZIkaRQc4EqSJEmSRsEBriRJkiRpFBzgSpIkSZJGwQGuJEmSJGkUHOBKkiRJkkbBAa4kSZIkaRQc4EqSJEmSRsEBriRJkiRpFBzgSpIkSZJGwQGuJEmSJGkUHOBKkiRJkkbBAa4kSZIkaRQc4EqSJEmSRsEBriRJkiRpFHZY6wSWRMRVwPeBO4CfZOb6iNgDeC9wIHAVcExm3rJWOUqSJEmShmton+A+KjMPy8z15fOTgU9l5iHAp8rnkiRJkiRtZWgD3ElHA6eXP58OPGntUpEkaY1EzH5IkqQ7DWmAm8A5EXFeRJxYbluXmd8qf74BWDfthRFxYkRsiIgNGzdu7CNXSZIkSdLADOY7uMB/z8zrI+IewCci4uvLg5mZEZHTXpiZpwKnAqxfv35qGUmSJGlNzLraIv2zVeraYD7Bzczry39vAj4IHA7cGBH7AJT/3rR2GUqSJEmShmwQA9yI+KmIuNvSz8BRwEXAWcDxZbHjgQ+tTYaSJEmSpKEbyiXK64APRnH5xg7AezLzYxHxZeB9EfEs4GrgmDXMUZIkSZI0YIMY4GbmFcADp2z/DvCY/jOSJEmSJC2aQQxwJUmah6i4jU66uIskSaMziO/gSpIkSZLUlgNcSZIkSdIoOMCVJEmSJI2C38GVJEmStOZcN0Fd8BNcSZIkSdIoOMCVJEmSJI2ClyhLkiRJas1LjDUEfoIrSZIkSRoFP8GVtDCcGZYkSVIVP8GVJEmSJI2CA1xJkiRJ0ig4wJUkSZIkjYLfwZUkSRqYWWsOuN6AJFVzgCtpVPyjUCvlObNtcbE6SRo3B7iSJElaE04wSeqa38GVJEmSJI2Cn+BKkrTG/BRLGi4va5cWiwNcSZIq+MetJEmLwwGuJGDb+QRpW+mnNEROFkiLzf9DtQgc4EqSJGmb5cSLNC4OcCVJmjM/9ZAkqR8LMcCNiMcBbwa2B/4mM09Z45TUA2dUNVaLcm47KGtmUY5nrYp+sEj92EYM4bzrI4e6NoawH7owhH25rRjLfhhLP+Zh8APciNge+EvgSOA64MsRcVZmXry2mS023xTN9fFHvgMJSZLmw/9jpW3L4Ae4wOHA5Zl5BUBEnAkcDWyzA1wHp8MyhONhDt3p5NOCWWUWaD8MhX+YDsdY3uNdWITz0uPVr3mfE2M5nmPpR51F6eci/C5bjUUY4O4LXLvs+XXAw5YXiIgTgRPLpz+IiEt7yq0LewHfbhHfosyME3VFbXRRxxziW5SZU44rqqNJDm3zHMLxXOXru6ij0zbaxjupo4dzZpXxFdWxKO+/tnX0cU50kUOT86p1vIs8a+JD+L9nCL8vu2ijk/OqZbyPfbkIv/f7+H9hCO+/QezLmhzLJObfRk2ZRWijaoA+IPeaGcnMQT+Ap1B873bp+TOAv1jrvDrs34Y28S7qGEIbQ8hhLG0MIYextDGEHMbSxhByGEsbQ8hhLG0MIYextDGEHMbSxhByGEsbQ8hhTG0symM7hu96YP9lz/crt0mSJEmSdKdFGOB+GTgkIg6KiB2BY4Gz1jgnSZIkSdLADP47uJn5k4h4LvBxitsEvTMzv7bGaXXp1JbxLuoYQhtDyGEsbQwhh7G0MYQcxtLGEHIYSxtDyGEsbQwhh7G0MYQcxtLGEHIYSxtDyGFMbSyEKK+5liRJkiRpoS3CJcqSJEmSJNVygCtJkiRJGgUHuJIkSZKkUXCAK0mSJEkahcGvoqzFFRHrgH3Lp9dn5o0riXfRxrxf31UdbQ1hXzdpY9559pFDF210YRHO/Xnvy4jYFXjc8jjw8cy8dUj9WKv3RkTcNzO/Xv7cyb6SJGnoXEW5R+UfGC8BngTcA0jgJuBDwCnl85nxqj9EIuLCzPy5ujaa1LHa+FIZ4BnA24FdKf6IAtgPuBX4PWBTVTwzv9Kgn4e1qaMux6rXr6Cf/84qj8XyvrbMYdX7uss26o5XmzybHs+2OXTUz1WfE0M495vmQD/78gHAK4FzJuJHAq/OzDPWuh8d9bPNe+OazDwgIo5b7b5aqbWaNJwYzC/85E0XOQ65H8uPV4Oy20Q/u8qzKofVnnMrqaOPNupe31UdbeJ1k4qrnXRsck41yPOZmXnamCc+/QS3X+8DPg08MjNvAIiIewLHl7GsikfE22fUG8A9m7RRV0dE/FpdGw3KvAt4dmZ+cYtgxBHAaWU/Z8Yj4tUN+lnZRoM66nJ8YAf9vIHq431UXRvz3tcd9bO2jbZ1dHE8O+hHF/2sPCcavscr22i7r7p4/9HPvrwr8JDJ/4gjYnfgixHxgwH0o4/3xucq+rlb+fPLqNhXQO1kwKz4UhlmTJxExK20nDhpUgdwTkQ8scXrG/WDGRMOTeNVkxpN91Vdjk3aWOt+UEy2HGA/m537TfKsyqHte6NJHS1z7CLPc4AD5nm8msZj+qTio4DXLvs/dma8ZtKxLocmZV4dEXe0yGHw/AS3RxFxaWbeZ1YMoCb+M8C7Kf7gmfSUzLxbgzYq66D4o7GujR/X1HFDZh4yI4fLgayJ36tBDpe1qaMux8w8uIN+3lF1LDLzPg3aqDweDXKo3Ncd9bNJG3XHq9U5UZdjRzl00c/Kc4Jm7/G5nvt1r2+YQx/7chPw0Mz87kRsV2ADcOBa96Ojfta1sQ54EXD7lCJvyMy9IuIbVO+rk6bVTzFIfntm7h3VE2Fvp/gDadZA/K+AqomTpTbOr6mjajB/PHBF1eszs25Cr0k/qiYcauNlDnX9rNxXdTk2bKOPftQdrxPsZ+Nzv64fXbw33tKmjrocO2qjsp+ZefcOjte72sTLHC4FHlYxqZg18Y81aKNuX15VET+0jM/MITMPnfH6heAAt0cRcQ7wSeD0LC+ViOISihMoLhXbVBPfneLEvmhK3ddm5v4N2qisg+Jyybo2zqup44PAvSk+Fbi2DO0PHAdcWfazKv7zDXJ4S5s66nLMzOd20M9DqTgWmflLDdqoPB4Ncqjc1x31s0kbdcer1TlRl2NHOXTRz8pzgmbv8bme+3Wvb5hDH/vyy8ArKGafl+IHlPvxj4DnrXU/OupnXRs/C7w8M78wpZ9XZuZBEXF8zb76a+Y/sdnFxEnlYB64pcGEwrwnaLuY1BjLhF7d8doV+9nVZFsX743vt6mjLseO2mgyodf2eO3UJp7NJhWzJn7PBm3U7cs7gMcCt0zEAvgC8IOqHGbtw0XhALdH5azIycDRFN+/A7gROAt4HcUJvxRfV8ZvWBa/P3B1Zl4zpe71mblhoo0V1wHs3KCNhzco8/gyh+XX9Z+VmR8ty82MN6m/izoa5NiqjrpjkZk317VBs+Ox6n3dRT+btNEwj7kez7Y5dNHPtu/PPs79Lt5/fezLMr47xX/gk98fumUo/ejhvbEHcFtm/nCynxN9rtpXlZNc2c3EZhcTJ5WDeeCfql6fPUzo1cWz2aTGWCb06o7Xzfazs8m2Lt4bn25TR8N92baNJhN6bY/XlW3i2WxSMWvixzVoo25ffho4LTM/PyX+HuDjVTlk5rsmX7dIHOBKkhZWDGAV86EoB7tk5s0z4lP3VcNJkTWfOGkymB/ChF4HE2FjmdCrPF72s7vJto7eG13UMdc2VjCh1+Z4tYovKzdzUrEu3nA/NcqjTY4LLTN9DOABPLP897HA2yg+0Tmr/PlxDV7/imU/r7oOioXHnk1x/f8F5eNs4H8CdynL1ZapaONUist1TgEuoZjZ/E758ynAbk37WdNGqxy76GebY7GS49F2X/fURmWZBvEujkXbHFr3s6v3+DzP/Tavb3pOdHTOHAacW277BMWl318vtz14CP3oqJ918QOAM4GNwGXA5RRfbTgTOLBso3JfNfl95MOHj/oHsAewx1rn4aOf49kgvq78/+jBwLqVxpu00bTMRPldVpLDIj78BHcgIuIa4B8ovqN3BnBdGdqP4lKFyzLz+VWvz+J2EG9qUwfwrxS3nzh94vXHU7x5nhYRf1dVBvjdWU0AXwW+RnHpxOm55UqyJwCPzsyjGvRzj5o2/qVNjpm5Xwf9fD+rPBZLfaXmeDTIoXZf93E8I+LjVWXYvIL4rPh32uRYHs+2OVTGG/bzYtq/x+d67te9vvwdUJdD5TlBN/vyHlQvJHLxWvejo37WtXE34E3A+zPzjjK+PfBU4AWZeUTULLqSmQ+csR+IiFdk5h9GxA7As4AnAz9dhq+nuMXVOzLzxxV1nEqxyuqqXr9UR2aeWBE/GziW4jZcR1P80ZZM3Iarg378QVUbbL7dX1UOu9bU8YM2OWbmiQ3aqMyzi35k9W3PzgZ+dQz9BO4OvB54DMXvnCi3fZriKynXrbafS32l5rxrsK9r3xs1OdTWQYN92baNun5m5uM7OC+fTfXx3FQVz8yrYsuVnK8ry8y6/du0+LcbtHFAXZmKfl4DPLEqh6xf9XrQHOD2KCIumBWi+KP36pyyallEBPANNn9nb9rrd87MHSLiG23qAK6Y9vqyjm9k5qGz2lgqQ/Hdh6vLOpdk+Xxfiu9i3GfG6y8F9qnKseznHTVtXNUmx8zcsYN+Ts1h6Vhk5iER8b2qvlJzPBrkULmvs1jJea7Hs2yj7Qri0cHxbJtDZbxhP6f2Y4Xv8bme+3WvL38H1OVQ9x7vYl9ulzWrLK91PzrqZ10bVfvhsvL3TOWiK5l58LRYGV+aWGk7EdbFxMkTK+IfpviUfk0n9OhmUmMsE3onV+T5YeCzI+ln5SQTxXc2Z/azo8m2un3d5L3x4DZ1NNyXbduo7Gdm7tPBeXkV1cczq+JNJhWpXxX7hw3a+H81eb6vop8vA66pyqFq4nMROMDtUUTcSPWKZt8GnpWZX5543eHAOyhmWR6a029AvrRQwQVt6qCYVXwD8IHM3FRu347iDfPCzHxYRJxbVYbiP6bH5PTvm1xLcZlc1Uqyhzbo52U1bVT2oy7Hso22/byFimORxb3vrqnqa9t+ULOvs1jJea7Hs2yj7Qriu7TJMZutMF6XQ2W8YT/vQfv3+FzP/brXl78D6nKoe493sS8vpnohkfVr3Y+O+lnXxrcpLl0+fWI/HA/slZnHRP2iK8dN9n9pN1AzeVrm09fEyfYUt/iIrWvhCOCaBhMK856gndtEWNMcczgTegdTfbyuHUk/KyeZiibmPtlWt6+bvDfuaFMHNNqXbduo7Gdm7tzBeXldzfGkKp4NJhVpt7p3kzYuo/gd/6fAT6YU+X1gY1UOWTHxuRByANdJbysPij9g//uM2Hsorn//IsUfbueUj0sovif1EOCPgcNnvP515b+t6qC4d+R7Kb7P9Y3ycVO57aCy3GSZy8qf3wscBDwHeOCMNp5HcSuU11F8B+yW8nFJuW2Phv2sa6NVjh31s/JYlOVWejza7Ouby8ed+3oOx3NWG3XHvC7exfGszLNtjg372cV7fK7nft3rG+Yw931Zlnk8xSVW/1Q+3g48ocl5vYp+rNU5UxffkeKTro8BF5aPj1Fc5rbTsv5U7atrmP39r2vLf8+lmBzYbllsO+BpFOf0ZcABs+qoe335vK6Oi4BDKuLnAC9e3heKqyJOAj7ZUT8q22iYQ10drXJs2EYf/ag7XmPp55nAW4GHUVyC/NPlz2+l+BSti3O/7b5u0s9WdfTURuXrOzov645nZbys5y3AR8pj/N/Kx9PKbX/RIN6kjbo8v0D5t+aMflbmMO11i/RY8wR8TDkoxf2vHlI+7rmGdewJ7Nm2zFo/usixTR1dHIu+9vW2cjzX+tHVOTHvfTWGfT2mfsxx/zSZWDmQtZ84eQpwnxnxJ9FsQqFtP1pNepT1bCsTenXHq+t+rmaCqIt+Vk4y1fWz4blfdzy7eG+0qqOnNipf39F5Oe14nr3seLaeVKyLN2mjrgxwH4qreKb1c12THBf54SXKPYuIAA5nyyW5v5TlgYjiy/GPY+slu29tEu+qjhm5H5mZnyh/vi9bL8H+ocz8ekX8rMy8pIw/luIXyeTrP9Ymx+V5ts2xo352cbxa92PGfnpmZp7WYxt1x7wuPpccl+fZNseG/ejiPV533rU9bytf37COue7LaLCQSBf9mKXnc6ZqPywtmrRVnHIBmyb7qqqvU/q+J0Bmfmclr+vq9V0ZSh5VFiHHLthPSV1ygNujiDiK4tKByyj+AIFioYGDKWZc7gm8kuLyiuXxI4FXl89nxjPzjIg4rm0dFfkvLTZyEvAbFJdHLF8w4dhyW9bE70nFSrLAeavNcSlP4C/b5JiZp3TQz2/W9aPB8dqnbT+q9lMXx7NhG2+i+phnTfyb88pxKU9qVjGvyzEzn9+gn5Xndvm87pxpe162ijd8b9S9x7vYl/eleiGRT7XtR925TT/nTN174x7UL2Dz8ap9lcXiL6udWOl14mSWphMOXfSjoxy2lQm9ufezgxzaTrY1mWSa+7lfF+/peI6ijVki4hXAa6k53jV11K6KzeaV1Ge20eS8a5PDSic+h8YBbo8i4hLg8TmxdHdEHAR8lOI7GQ+bPKmiuBHzFylOvpnxLBYquLRNHRSXdExNn+IPoZ+KYvGH+0++eSJiR4qV/rIuntUryW5q0M+zqvKkeJOvOscsvsDftp9N+lF3vGiZw4+m7qViPx2amTt10M9GbdQc87pzovacqoqXx7PtKuaVOS6dM23ObZq9x1u//9rEm743+tiXWbNQTwf9qDy36eecWdW+LMssLWBTt+jKa1jjiZU+JhzKCYW5Tug1zOFNVWUYz4Te3CcuO8ihi35WTjIBX2nQj1bnfmYeUBWnm/Oyj3255m1kN7fT3GNWFTRbFbtyJfVc41t2LoQcwHXS28qD4o21w5TtOwKXU/yxsuuU+K7layvj5c+t6qD4rsIvA7848XgkcGNZ9uvAvabUcS/g0gbxCyhWip2MH07xHYIm/azMs22OHfWzi+PVNocbgcPK58sfBwLf7KifTdqoO+Z18S6OZ2WebXNs2M8u3uNtj1cf740+9mXdQiJd9GMI50xd/FzqF7Cp21eXArtNaWN3ioE45bl5lylldlx27q463rCNC2Y8LgRuX8p1yutjBW30kkNVmY72Zas8u+pHD/1sncO8+rnsfOvj3O/lvBzCOdNDG9+b8fg+xYrElce7/PcO4AqKVeqXHkvP/5Py/6AZdVzasI26865VDrNii/LYAfXpncCXI+JMtrxVw7EUKyx/E/hKFLeNWIofQDGL/kcUs05VcShm4tvU8TTgh5n5ucnky5l+KO6v9akoliFfXsfBwHPL51Xxm4C3RcTd2DzrtD/wXYqZowc06Oe5NXm+tmWOXfRzXYN+1B2vG1rm8FRgl8w8f8p++mxH/WzSxglUH/Osie/dMkco7m9XleeftcyxST/rzu0m7/EXdLAv5v3eqHuPd7Evr6C4n+HnIuIeZfxG4CzgGIoBYNt+1J3bfZwzdXV8h2LxlLdGxC0Uf6TtRjEzf2xZ/mk1++rcsp1Jm8r6ln7+aYpbmSy3TxnLlvEmbayj+lZ7t0XEQ3PiNlzAQ4HbOupHFznUlblLyxzpIM/soB91dXTRz7Y5dNHP/4iIpzL9lmS3UExQzvvc7+O87GNfDqGNW6m+fePNNccbiv+fqm5Bd3VEvJjpt3+7FtilQRt1ebTNYaF5iXLPIuJnKW5WP/kdi4vL+O4Uv6SWxz+embc0iXdVR4N+bMfWi2V9OTffbLoyXpa55/J4lpdIDCzHVnV0dLxa96OPfdWwnZnHvC6+CDk27EcX7/G25+Xc3xtN9lXbfVmnq340aGfu/WxYx6oWsImI44FXUHzSu9XESma+KyIeR3H7iqoJg1XHs/guY10bTwVOy8zPT+nDeygnHIBpkwHPyczzOuhHFzk8uKoMxYRe2335jjZ5Uk6stOxHZR0d9bNtDl30c2mS6dEUA4vlk0wnU6xmO+9z/0c18S7Oyz725RDaeDLF3+VfmrIvX1e+dvnxhuJ4fwY4OTOvjIjnAJ/PzK9OqeN5wN9SnBtHU1ziDpsnHV8H3J2Kc6ps48CqMsATVpDDujJ0w1IOmXnz5OsWiQPcNRLl9fnTTqByBmX5HzI3riTeRR0N4kH1atCV8Vki4r65eXGJ1v2c0cYumfmDJjl20c+2/WibQx/9XO3xLl9738z8etSvLjz3frbNsSzbpMyavv/GtC9ntLG0qEov7/F597NB/L7ULGBTkefSvlrziZWmZRr0qW6yoI9Jw7YTYaOY0Ourn2370dVk26xJpr7O/Tp9TLaNpY0mZh3vLjVpo488Fo0D3B5FxAHA6ylmW75LMdtydzbPtuxGcQ+qXSlmlYLiC+O3UqyyvKkqnplfiYjD2tTB5pmtXdlysZHlbRxF9WrQVMUz85yKfXQNxSfcdf18UF2eNW38dl2ObftJcZlm2+O1V8scKuMd9bO2DSqUx+PlVK8ufMO8+9ngvKzMMZutin0B7d/jcz1ei7Ivs2bFd3p4j/d0zlATr1xpPRssOJTlAjWrmTAsX7dLZv6gLj6ECYcu2ugoh21iQq+PfrbtR0f9rJxk6uPc7+K8rCvT075ciDZm7Kc7b6e52jLLJh1rJy4bnHfT4o1v57fIHOD2KCL+H/Am4P3LZie3p7j05AXAXYFnZ+YXJ153BPBXFIPPmfHMfGBEnN+mjoZt1K0GTU181hs7KFZ/u6KDfp5e0cbLKC4FmZljZt6vg37e3kE/dmyZQ2W8o342aeMtTLd0zL9F9erCd/TQz7rzsjLHbLYq9g9Z+/dfq/iA9uVtFW0cSvF7ZN7nfh/9zJo4tF9B/GHUTKzMeP1SW01WcV3zCYcsblm2ZhN6y3Kom9QYy4Qe8+7nrD407Uddjg37WTnJRLGK8mAnmuvev0tl6GdfVtYxlDaq9lOTfdng9+VfMv9bWd6TVa4mvQh2WOsEtjF7ZeZ7l28oB7pnRsQflc+/OPmizDw3In6q+LEyDvBTLeto0sYObH4zLHc9xaIRWRN/JvAiigHgpN9o0Icm/Xwt8KcUK95N2q5BH2hQpq6fO3RxvFrmUBeH9v1s0kbdMQ+qF7lpmyMN6mibIw3KtH1/NunHEM6ZPvZl3aIqfbzH++gnNfE7aL/g0LuYPbFyGvDAiHjhlByW6tilLg68GfilismC+zUoQ1U8IqomHHYrf27VRkc5vAx4SM2EXpsca/dlgzzrcjyjQZmcdz9rJk+b9KMuxyb9hOmTTH9OcSuW4+v6wQDOy4Hsy7o61ryNiHgK0wWwZ1n2rKoyNZOO6yjub1t1Tp3SoEzWxXP6LejeS7EKswNcNXZeRLyV4tPF5asoHw/8G3BDRHyE4g26PH4c8DFgU00c4OyWdTRpo241aGriRwIXZeYXJndQRLyqQR+a9PPBwD9m5nlT2vjthn1o2899OujH9S1zaBJv289GcaqP+WuoXj14nx76WXde1uVIgzIPHsD7r4v4EPblw6le4fjCDvpRty/66GfWxG+g/QriD20wsVI3aTiEScW6CYcu2ugih21lQo+aeBf97KIfbftZN8nUx7nfxXk5hH3ZpI61buPhwG8Bk1/LCIpLxGlQpm7SsW5VbRqUyZr47VG/IvXC8hLlHkVxydizmHI9PPCOzLw9Ih4/LZ6ZHy3rqIw3KdNRG/ebUebiungUC2zdlpk/rNhXrfoZEfcBbs7MjVPqXpeZN9b1oSxbt+p1XbyL47Xqfd0k3lE/6+JNjvnuVK8u3CqHujJd5NiwH328/+Z6PIeyL+u07cdQ+tkg3mpxmvKTm3szfWLlysx8bkR8AXjejEnDa8vXVcXfSnFLommTBe/LzD+JiJdUlSmfV8WPBF4+Y8Lhysw8qIM2usjheCpWrab4A3TVOTTcl5V5Aq+qyjGLlbXr+pE99PPTbfpRl2PDft5A9QrID2rQjyGcl0PYl3XnzJq3QXHbtddn5mem7Kd/zsxHRMTZVWUozpXTcvaq12fQfuVtauI3UbMi9WRui8QBrtRAVKx63SS+KNr2s8l+aLuv+sihgxxXtVhP14ZwPNdiX8bEokd99GPe/ayKR3SygE3dxEvlpCHFJYxdTCrOdfK0rGNuE0AryGFbmdCbaz87mjztop91q0HPdbKtLt5wPw1lXy5EG/PWZOKywXnX+nZ+CyszffT0oLgE5dnA2RSrqV5Q/vw/gbvUvPbUJnGKRUJOAS4Bbqa4P9sl5bbd2sYb9PHstvEmOaygH19faT+WcqSY6TqTYpbrMuDy8uczgQMbxFv3Y977uqN+VsYn2tg4q0xFnhd2nENdHavKsfz3MODc8hh+AvgkxTl4LsVl83N///V8PNdsX9bUcU3H/VjLc6YuflTZ7tnA35SPj5XbjirbqC2zLT6APYA91ireR4595ElxueWDy8e6lcb76mebPtSVobi09GHAr5WPh1F+iNT38Vr0fdnhOTXXNrrIYcZrdml6TtWVaRDfleIT6ReWj6fR4G+NRXj4Hdx+/V+KVSlfzZYrlh0P/G1E/O6M1wXwhKVPCGbFy5/fR3HboUdlOQtTzs6cUMayZfyoKG6iPSuPw9rGG/ThqBX085ET8eOB90XEyTU5ALyXYtXrp+fWq16fWbZRFf9u237U5dnBvu6in3XxI+raiIjXV+R5zw5yrM2hro4GOUL9Yj03MOf3Xwf7aiH2ZUScXlHHLg1ybN2PPvpJ9crap1GstN52waEjgJdQfDK6rmzzJuBDwCmZeWsUt814CcUtJe4xWaZ8PjOeFbfXiIizM/Pxs+JNyjSNx5Tb9UXE3dl8u75NHcUfQ/H//RbxyWMwJc8LgV9uk0NmXrWCfq44z4i4MDN/Lmbc5i4ibqXitmfL4t/usJ+r6gfwjKocs+J2fsv6MfN2fhHxexQTUl0dr7mdlwPZl3XnTGW8jzaYcTvNiRweVFdm1r4ELo5irZiZ51TWrLxdnnfUxO/J1qtFPwp4bURUrha9CLxEuUcR8Y2csmLZUoziO1BXw51fcofijRQUlw9sXxXPzB0j4tLMvM+MNi4FaBPPzPtExB3A5ybyWHIExR9cbeLXNMihbT8PrsohM3eOiMsy85AZdVxWtlEV39RBPyrzpOW+7qiflfEsblNS18aBwLuZvrDDU4Abesihrp+VOWbm3WrauBy4o4f333UDOJ597Mv9mL2o0e8DG3voRx/9zLo4cL/M/MlEbEfg4sw8uMxlZhng3yn+iD19ysTKozPzqIj4+Iwyx1P8QZw18arJug9n5j5RPXnzYYqB36rjZRt1t+vLecYz84iI+LWKPN9O8cl62zba9rNqcubtmbl3tL8t4Q976GdlPyj+sJ/37fxu7aAffZyXQ9iXdedMZbyPNjrKoWqC9mX0cyvL7ai5jd2MHBeCA9weRcS5wBuAD2TmpnLbdhS/PF5IcUnIYzLzmimvvZZiVbOZ8czcP4qVNj9J8UfGjWVsHcUfKkdSzEytOp6ZvxQRFwFPzszLZuT53ZbxSxrk0Laf96zKodyXZ1JcJno6W696vVfZRlV8tw76UZln233dUT8r45l5TIM27g0cn5kXzejHv/aQQ10/K3Ms92XlYj0U95yb9/uv1b5aoH35YKoXNarcDx31o49+bqqJX0/7BWxOaDCx0sek4lwnT7OHCb26eDmx8mO2jQm92smZHvpZ2Q+q9/XlSxNEdf2gegKpsp8dHa+hT0Q33pdt4n200VEOtRO0tJ+4rDsvN1GsoP/difiuwIZZ+S8KB7g9iogDgddRXEJyC9x5b7GlS0ieAHw+M7865bXPozgZZ8Yz8/+UMy8nU1xqdo8yfCPFSs2vozjhVx3PzJujuAfYhZl56ZQ8nkTxXeM28c8ty2FdGbphIofdq8pM9GNa/NFVOWTmP8b0Va+vA/6J4hYhWRP/Lx30ozLPBvuyMt5RPyvjWawOXtfG4cDVMyZv1lN8X33y9deX+2lWDnfGK3JoXEddjpm5ofy5anXvVudtw/ffR6f0o+3xHOK+vA/wncz89pQ61lH8jp13P+bez4bxVos31U20NZyM62JSca6Tp31M6NXFy4mV89g2JvTqJmf26qGfdZOnH6zKMYsVxOv6WTnJBDywg370cV4OYV/WnTOV8T7a6CiHugnaPlad/yY1K1JP5rZIHOCukYjYEyAzv7PWuUiStl11Ey89TirOdfK0jwm9ung5sfJwtoEJvbp42xwb9rO2H3V9aNjPqgmkyhx7mmxbpH3Zxa305tpG2xxiOLey3J01Xi16Xhzg9iwi7svWJ+yHMvPrFfGzMvOSJvGatp+ZmafNK95lGxHxWIrFSib308eWla0s0zZe089XZOYf1sWH0I8++rnaeNM6gNdS/Mf7JCb6wZb/eU+NZ+aPI2KHNnUsiz+Z4ubpW7VR089TM/PELo7Xao9pF/t6SPuyKk6xGEhf/ZhbP4E/oGYBqIrXt168SZI0bLGN3MpyJRzg9igiTgJ+g+JygeWrKB/L5hU5Vx3PzFNq2r8mMw+YV7yrNoB/oPiu4hls2c/jgMsy8/kR8aaqMhT7atXxzHx+234OoR999LOPc4bikr1bKS6dmlyBfA+KS4ZmxjPzaRHxd23qaNjGHrO6AXwVeD8tj1fdOVN1TLvY1wPal/+1Jv4va92Pjvr5NSoWgKKDxZvKMms+GddXG7PMe0Kv6YQfI5rQq4pTMwnVcT9X3I9lk5K70mKSCfjVDvsxl3hf+3JWfKkMNRN6bF6tfeaxqDtedXW0ja8whyexylXnY8rK28DdqVhZe3k8G6yUPiu+CBzg9iiKlZLvP/lLoLws5GsUJ/iq41ksEnDBrOYp/ije6tKtlcQzc6d5t0FxCcxWq7dFRADfKPs5dUXqpTIUiwCsOl628b2KPHemWAGyKn7FWvejp35WxjNzh3ntS7jzfUVVPDMPnbWvm9bRsI07qF4J/aoOjlfdObNuMrZUhA729YD25fY18an7us9+dNTPK3P+izf9FQOYVFyEics+JvwYz4Req0monvr5u1U5ZuZ+MXsF8RNoMMkEfLaHfnRxPPvYl20n9LIqntUrvjeqo228ZQ7H03zV+bmvlD4jvhhyADfj3VYeFPdCu9eU7feiGPS1ipc/30hxL8x7TTwOpPhCeat4H21QfP/ooVP6eTjF96uoK9M2Xv58DbNv3n1tg/ia96OnflbGO2rjXIpfytst274dxU3Jv1gXL5+3qqNhG5cBB6z2nOjonJnrvh7QvqyLr3k/OurnOcCLlx8zikmMkygWdboIOKTm/VdZhmLyZFosKAaG1JVpG++xje/NeHyfYkXTucYb5jC1H0v7qC5etS+b1tGwjTuAKygWzVl6LD3/zwbxIfSzMsey3KUVdVxa1vFp4DNTHj8awvEa0L6sOyfq9nVlvOHxmmu8oxwqz6my3GUVdVzWIP5jivuwnzbl8f1Zr12Uxw6oTy8APhXF0t7LVyw7GHhu+bxt/MPALpl5/mTjEfFZil+2beJ9tPFnwNsi4m5sniXcn+ISixPK5yfUlMmWcSg+BbgXxYB80nsofjlUxd87gH700c+6eBdtvI1ioZq3RsQt5fbdKH7ZH0vRz6o45b9t6mjSxpuA3SkGkZNeT/HJTNvjdUJNmScz330Nw9iXm2ri/zTRRrB51fpZ/ZgsM5ln1/Em/fxbitn8z0XEPcrtSyvbH0PxacB2U14L8Lzy31fVlPnDiHhoZn55IvZQitvTAdxWUyZbxvtq41aKCaKtzv8oVi/NOceb5HBzRDyV6bcUvKXsZ1WcDupo0sYVtLi14UD6WdcHgKsj4sVMXyH8Worfqc/O2at71+XYRT+6OJ597Mu6MnX7elNNvMnxqqujbbyLHOrOKYDzIuKtTF/1+t/KNqri3wP+LKeviv1Lk9sWjZco96z8ZXA4W36/4cu5+fKBVvExKS/XuLOfWV7GsZIybeNdGEI/+uhnX6JmBfK6eBd1NGmjShfHq6dzd/D7sokh9KOPfq5WFN/RfRswbdLkOZl5Xl0ZysmZ1cZ7bOOPKRZm/NKU/fA6ij8s5xbPzJMa5LA0yfRoNg8+dqOYADqZzRMnU+OZeWVsfVvCFdXRsI3n0OLWhmyehFpqY/kE0LQctojP6OeK6qDm9ozZ7PaLj6Z6de/ze+hHq3iP+7LunFma0Ju1r7Mqnt2s+N4q3lEOledUdrMifO2q2JPbF4kD3J5FRLD1APVLWR6ItvExtTFj/903yxWnV1umbXwldUSx0MDj2HoJ9luXla0sM+/4orQxS0QcmZmfWG28izpW0sYQ9uW89nWTMn0er6hZtb4sW1lm3vGKMrWr40fHK98PYTKurzYWxRgm9JoYQj/7MITjNZZ9KVVxgNujiDiK4ubNl1H8AQPFF/QPplhNkDbxzDxnLG1M7rsl0dNKzl20AbwceCXFd+iW9/NI4NWZeUZEHFdVpnw+t3gfOXTRRmaewQx9Hc8u2qDmnCifr/nxatvPgbz//pKaVedjzivb18Wb5JAVq+N3uS+HMrEyhja6yGGWRZvQq4vPcYKo7SRUp7dfXPDJtk73ZR8TejGA2y/20UbFvpj7iu9D5wC3RxFxCfD4nFiaOyIOAj5aPl11PDPvN4Y2gFn/KQZwfGbePSLeUlWG4ovzq4531Ma3gIdN/sESxaUrX8xiBdVLq8pQ/IE8t3gfOXTUxqxPy4PiUp5PVcUz86ci4qw2dXTUxnWs/b5sta8HtC/r2rie+lXn57qyfV28YQ4/quhnVyvf/w4DmFgZQxtd5DCWCb22k1B0MEFUV0eTNubdz0WZbOujjS4m9GIAt1+cdw45gFs8Dp2LTPVrBzafqMtdD9yF4oRuEx9LG88EXgTcPqXMb5T/1pVpG++ijaDo66RNZYyGZeYd7yOHtm08HPgt4AcT8aVL3evidFBHF210sa/nfbwWZV/WxTdR3Kvx6on4PmWMBmVyzvEmOawDHsvm70Eu7+cXGsRpUOZlwEMqJj3OaFAmW8bH0kbrHCLiKUwXwJ41kz97lnVVlmkb76INiu8HTpvc+XOqJ4iW4qd0UEdtGzUTROvq4g1y7KMfg9iXdW1ExG8yXdN9DfCEnH4rvfdSfTu+ruLP7yGH50fN7RfbxmfEFoYD3H69E/hyRJzJliuaHUvxhW86iI+hjSOBizJz6Y+zO0XEq5ZeX1PmgpbxLtp4DfCViDiHLVe9PhL4o/J5XZmcc7yPHLpo42nADzPzcxO7mvITzf+oiUNxy5Y2dXTRxjta7geY//Gq29dN+tnHvqyr47XUrzr/ggZl5h2vy+GpzH/l+/Ws/cTKmNpom8MQJoiGMAnVxQRRF5NQbSeZxjLZtigTevNejX1bWRF+oXmJcs8i4meBJ7L1dw8u7iI+hjaiuBH4bZn5w4r9WFmmbbzDOnan+GU8+T2rW5qWmXd8UdoYiyHsy21oX9euOl9XZt7xpmXmKSKOB15BccnsVpMemfmuujIUfyytOj6WNjrK4WnA6zPzM0yIiH+mmNyZGc/MR0TE2W3q6KmN1wJ/QXFJ5qwJoJnxzPxYRDyuTR0N23gHcFpmfn5KP95DMclUFT9jrfvRsJ997Mu6Np5asy8r93Vm/mbMeTX2uniOZEX4zDxpcvsicYC7RsrBEZl58zziY2ljCDl0VYe0JIr73d05mJmcQa2Ld1HHIuTQVR1T6twlMyc/VVpRmXnH+25jKBMrY2ijixy2FX1MEHXRxrz72Uc/hpBD0zJdiAHcfrGPNjSdlyj3KCIOAF5PsQDKd4tNcXc230NsU5t4Zl41hjaWxR9DcQlGVQ5Ty9TV0UcbObGI1nIRcWFm/tyseJMy846PpY0h5LBUBngG8HZgV4pZ2QD2i4hbKVYY31QVz8yvRMRhbepoEF+aOd6VZQvgTOTwoKoydXX00UZmfmX20eBiik8NqtSVmXe81zYy85aI+Axb/jG1xWCrrkzb+Fja6CIHGMYE0bzbyMxNFF9HmKxzl8z8QV28izqatDHLCiaZctmD8t9NE8Xrysw7Pog2Irq5zWQ5GJwcMN55i8d5x/tqY5q6Mm3ji8ABbr/eC7wJePqy2a7tKS7JWFqBrk38iJG0MYQcWrcREa9nugDuWZb/taoy8473kUMfbQwhhyZtUKy8/ezM/OIWwYgjgNMozqmq+AM7qKNt/IHlv4NuIyJOZ7oAdinLvrCqzLzjfeTQsI3DWOOJlbG00VEOaz5B1EcbLSehBjVBVBWPiN9mxq0RI6L29osR8Xvl87nF+8ihrzaodg7Vx2ve8TG1MWheotyjiLgsMw+ZFQNoE8/ilhML38YQcuiojQOBd7N5lnK5p2Tm3SLix1VlgLvOM95HDvZzizpuqDhnLgeyKp6ZB9ecl7V1tI33kUNHbewH/CnwkylFfj8zd4uI26rKUBzzucX7yKFhG1cxe7LgrzLzgRFxflUZqiccauNjaWMIOSxQG1WTUC8D/rgqnpl71Eze1NbRUxs3sva3X6yM95FDj228hemCDm4jWRfPxbmVZW0bM+ILwQFuj6JYNfhmil/qy1cPPh7Yi2Lmd9XxzDxmDG0MIYeO2rg3xS+Ji5gQEddm5v4RcV5VGeCmecb7yMF+blHHBynOizPY8pw5DriS4pyaGc/M55b/qa26jrbxPnLoqI0HA8/LzPOYsOx4faGqTFnv3OJ95NCwjdtGMqmx5m0MIYcFaqNyEorFmSCqa2MjcL/M3CIexb2uL17al1VlKCYL5hbvI4ce2/g+s2/h+AZgp3nGM3OveefQVxtTti8MB7g9Kt+AzwKOZmL1YIrbh2SbeGbePoY2hpBDR20cDlydmdcwISLWZ+aGiHh4VRmKe5HNLd5HDvZzqzKPZ8o5k5kfLctVxpuUmXd8EdqIiPsA38nMbzMhItZl5o11ZYDd5hnvI4eGbbyMcUxqrHkbQ8hhgdqonIRicSaI6tp4K3AMxVeblu+HY4H3ZeafRMRLqsqUz+cW7yOHHtv4NPDynH4Lxyspzr25xTPzoHnn0Fcbk9sXiQNcSZK2cWOY1BhKG0PIYRHaKCdebs7MjUxYNnkzM75s8mbVdfTRRlnH/Wbsh4uXla0sM+/4WNqIOd9Gsi4+pjYWmQPcHkXEDhSf+D2JLd+YH2LLTwRXFc/MH4+hjSHk0HEbT6a4sXlVG1PL1NXRNt5HDvZzyzLMEBGnZuaJq413Ucci5DCWNoaQQ9MykiQtEge4PYqIv6O4nczpbL5x834U39ncg+JynlXHM/NpY2hjCDmMpY0h5GA/t6jjd5kugK8C/7Uqnpn7lbOuq66jbbyPHMbSxhByaNjG/YGXUHwqso5iMucmiomZUzLz1ojYtapM+XzV8bG0MYQcFrCNJwH3WGl8IodV1dFHG5l5KzNExNmZ+fhZ8SZl5h23jcXKoa82hs4Bbo8i4huZeeisGECbeGYeOoY2hpDDWNoYQg72c4s67g1cTTGwWJLl832B7avimbljRNzRpo628T5yGEsbQ8ihYRufobh/9+lZ3HeRiLgncALw6Mw8KiI+XlWmrG/V8bG0MYQcRtDG8RT3l6+M1+TQqI6e2jiZ6QL4cGbuExEPrioD/PI8433kMJY2hpBDX23MiC8EB7g9iohzKVYu+0AWNxYnIrajuG/qCyl+Sa46npkPG0MbQ8hhLG0MIQf7uUUde1D80XQNE6JczbYqnsWCJ5e1qaNtvI8cxtLGEHJo2MYPM/M+k7Eyfmlm3mfp31llANrEx9LGEHIYSxtDyKGjNg4GPseWE0xLjsjMnaOYhJpZBthxnvE+chhLG0PIoa82pmxfHJnpo6cHxX1R30tx6co3ysdN5baD2sbH0sYQchhLG0PIwX5uUcdzgAfO+P3wvLp4+W+rOhYhh7G0MYQcGrZxDvBiYN2y7euAk4BPls8ry7SNj6WNIeQwljaGkENHbVwEHDLj/Xdt+W9lmXnH+8hhLG0MIYe+2ljkh5/g9iymr/72ocy8pIv4WNoYQg5jaWMIOdjPLeq475T4WU3jXdSxCDmMpY0h5FBXJiJ2p7iM8miK7xAC3Ehxy7NTMvOWujLl81XHx9LGEHIYSxtDyKGjNh4DXJiZlzIhIp6Umf8YEU+pKgPsMM94HzmMpY0h5NBXG5PbF4kD3B5FxEkU9+o6k+KPCygWoFnalm3imXnKGNoYQg5jaWMIOdjPLerYBPxm+fN1K42Xbby4TR2LkMNY2hhCDk3ayMxTmCEinpmZp82KNynTNj6WNoaQw1jaGEIOY2ljCDmMpY0h5NBXG4OXA/gYeVt5UFyueJcp23cELmsbH0sbQ8hhLG0MIQf7uVhtDCGHsbQxhByatDG5faLMNVXxJmXaxsfSxhByGEsbQ8hhLG0MIYextDGEHPpqY+iPHVCfNlHcG/Pqie37lLFsGR9LG0PIYSxtDCEH+7lYbQwhh7G0MYQcatuIiAuYLii+S0hdmbbxsbQxhBzG0sYQchhLG0PIYSxtDCGHvtpYZA5w+/UC4FNRrGh5bbntAIoV9p5bPm8bH0sbQ8hhLG0MIQf7uVhtDCGHsbQxhBzq2jgdeCxwC1sK4Avlz+tqyrSNj6WNIeQwljaGkMNY2hhCDmNpYwg59NXGwnKA26PM/FhEHAoczpYLfXw5M+8AaBsfSxtDyGEsbQwhB/u5WG0MIYextDGEHOraiIgPA7tk5vlMiIjPlj/WlflRy/hY2hhCDmNpYwg5jKWNIeQwljaGkENfbSwsF5mSJEmSJI3CdmudgCRJkiRJXXCAK0mSJEkaBQe4kiStQES8KiIyImauYxERjyzLPHLZthdExK+tor3Dyjb3WMFrtmpfkqRtgQNcSZK69xXg58t/l7wAWPEAFzgMeCXQeIA7o31JkkbPVZQlSepYZn4POLfvdiNie4oFJNekfUmS1pqf4EqStDr3i4jPRMQPI+JbEfGHEbEdbH2JcERcBdwLeHq5PSPiXWXs0Ij4YETcFBG3RcQ1EfH3EbFDRJwAnFa2d9my1x5YvjYj4jURcXJEXAn8J/BzMy6R/mxEfD4ifikivlLmfVFEPHmyYxHxGxHx9TKfCyPiieXrP7uszC4R8X/KfG8v8/9kRNy3070sSdIK+AmuJEmr84/AO4E/AR4L/H/AJuBVU8o+Gfgo8NVl8Y3lvx8BbgF+F/g2xb1qn0AxCf0R4I+BlwNPBa4rX/OtZXWfAFwB/G/gP4BvArvOyPnewJvLnL8NvAj4+4i4b2ZeDhARRwLvBs4CXgjsDbwJuCvwjWV1vRF4IvBS4DJgT+AXgN1mtC1J0tw5wJUkaXX+OjNPKX8+JyLuDrwoIt40WTAz/y0ibge+nZl3XjocEXsBBwNHZ+ZZy17ynvLfjRHx7+XP5y8NQicEcFRm/mhZvfebkfNewCMy87Ky3FcoBsvHAK8ty7wauBh4cmZmWe4iYANbDnB/Hnh3Zr5j2bYPzmhXkqReeImyJEmr876J52cCuwAPWEEd36H49PWUiPidiDhkFXl8bPngtsZlS4NbgMy8CbgJOADu/A7veuADS4Pbstx5wJUTdX0ZOCEiXhoR68vXSpK0phzgSpK0OjfOeL5v0wrKQeSRFJ+O/gnwjYi4IiJ+dwV5fKu+yJ1unrLtdorLj6H4hPcuFIPeSZP9fR7wV8D/oBjs3hQRb4yI/7KCfCRJ6pQDXEmSVmfdjOfXr6SSzLwiM4+j+K7rg4BPA2+NiMc3rWIl7dX4NvBj4B5TYlv0NzN/kJkvycyDgQMpLnF+LsUtjSRJWhMOcCVJWp1jJp4fC/wAuHBG+duBnWdVloXzKRZ2gs2XOt9e/jvztV3JzDsoPk3+9YiIpe0R8RDgoIrXXZ2Zb6Do+0ou0ZYkqVMuMiVJ0ur8TnlboC9TrKL828CrMvO7y8aGy10MPDwifgW4geLT0rtTrGr8XuByYHuKVZF/QvFJ7tLrAJ4TEadTfMJ6QWb+5zw6RfEJ7DnAByPiVIrLll9V5rxpqVBE/D+KlZYvpBjY/yLwQOD0OeUlSVItP8GVJGl1jqb4/uxZwG9R3M7njyrKvwS4lGJxqi+zedB4DcWntmcBfwf8NPAr5cJOZObSrYV+Ffh8+dqf7rozSzLzE8DTgftRrIp8EsXthG4Avrus6D9TfIr9borbGT0F+P3MfPO8cpMkqU4sWyRRkiRpKxGxH8UnzK/JzKpBvCRJa8oBriRJulNE7Az8OfBJisuofwZ4McUiU/fPzJWs2ixJUq/8Dq4kSVruDuCewF8AewL/AfwL8FQHt5KkofMTXEmSJEnSKLjIlCRJkiRpFBzgSpIkSZJGwQGuJEmSJGkUHOBKkiRJkkbBAa4kSZIkaRQc4EqSJEmSRuH/B8GKycpTk6VPAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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B6+fgAkTEg4A9gU3IlZPLUkrp5XPox6OB16aUfh4RHyJfjjwteUTUFrlKKR0NHA2w7bbbNhXCkiRJkiRNsNYD3IjYA/gS+azvDeR7b8vmMrC8Grg6pfTz4vuvkAe410fE+imlayNi/WK5kiRJkiT16fKYoHeS74FdP6W0QUpps8q/B822Eyml64CrImLLIvQkcuGqE8nFrSj+P2G2y5AkSZIkTbYulyg/CDgopTRfN7m+Fvh8UUH5cmA/8gD8SxHxcuAK8uXRkiRJkiT16TLA/S2w1nx1JKX0a2Dbmh89ab6WKUmSJEmaHF0uUX4jcFhRaEqSJEmSpJHS5Qzu28hncC+KiEuBmyo/TymlnYbVMUmSJEmSuugywL0HuHi+OiJJkiRJ0ly0HuCmlHaex35IkiRJkjQnXc7gSpIkSZJqRMS071NKi9STpVvrAW5E7DhTm5TSD+fWHUmSJEmSZqfLGdzTgZmmIZadfVckSZIkSZq9LgPcJ9TE1gKeAewEvGYoPZIkSZIkaRa6FJk6o+FHX4uIDwLPBE4aSq8kSZIkSepomSHl+Taw55BySZIkSZLU2bAGuFsC9w4plyRJkiRJnXWporxPTXg54GHAy4GvDatTkiRJkiR11aXI1HEN8TuBLwKvm3NvJEmSJEmapS4D3M1qYneklK4fVmckSZIkSZqtLlWUr5jPjkiSJEmSNBddzuACEBG9596uCdwEnJ5S+vawOyZJkiRJUhddikytAnwLeDxwN/BnYC3gDRHxI+AZKaVb56WXkiRJkiTNoMtjgg4HHg3sDayQUlofWAHYp4gfPvzuSZIkSZLUTpcB7nOBN6eUPp9SugcgpXRPSunzwFuKn0uSJEmStCi6DHDXAi5s+NmFxc8lSZIkSVoUXQa4vwee0fCzpxU/lyRJkiRpUXSpovxx4IiIWBn4PHAtsB6wF/AK4A3D754kSZIkSe10eQ7uByNiHfJA9qVFOIC7gPemlD40/O5JkiRJktROp+fgppQOi4j/AnZg6jm4Z6aUbp6PzkmSJEmS1FaX5+AeDGyUUnotcFLlZ0cBV6WU/mvI/ZMkSZIkqZUuRab2A85r+Nm5xc8lSZIkSVoUXQa4mwCXNvzsd8AD594dSZIkSZJmp8sA9zZgw4afbQTcOffuSJIkSZI0O10GuD8C/r+IuH85WHx/UPFzSZIkSZIWRZcqym8DfgpcEhGfA64hn9F9CbAWU48OkiRJkiRpwXV5Du65EfEE4APAweSzv/cCPwaem1I6d366KEmSJEnSzLo+B/cXwI4RsQKwBnBzSun2eemZJEmSJEkddBrg9hSDWge2kiRJkqSR0aXIlCRJkiRJI8sBriRJkiRpIjjAlSRJkiRNBAe4kiRJkqSJ4ABXkiRJkjQRHOBKkiRJkibCSA1wI2LZiPhVRHyr+H6ziPh5RFwWEV+MiOUWu4+SJEmSpNE0UgNc4HXARaXv3wd8MKW0OXAz8PJF6ZUkSZIkaeSNzAA3IjYCng58ovg+gCcCXymafBrYY1E6J0mSJEkaeSMzwAWOBN4I3Ft8vxZwS0rp7uL7q4ENF6FfkiRJkqQxMBID3Ih4BnBDSunsWf7+ARFxVkScdeONNw65d5IkSZKkcTASA1zgccCzIuIPwPHkS5M/BKweEUuKNhsB19T9ckrp6JTStimlbddZZ52F6K8kSZIkacSMxAA3pXRoSmmjlNKmwF7AqSmlFwOnAc8rmu0LnLBIXZQkSZIkjbiRGOAOcDDwhoi4jHxP7rGL3B9JkiRJ0ohaMnOThZVSOh04vfj6cmC7xeyPJEmSJGk8jPoZXEmSJEmSWnGAK0mSJEmaCA5wJUmSJEkTwQGuJEmSJGkiOMCVJEmSJE0EB7iSJEmSpIngAFeSJEmSNBEc4EqSJEmSJoIDXEmSJEnSRHCAK0mSJEmaCA5wJUmSJEkTwQGuJEmSJGkiOMCVJEmSJE0EB7iSJEmSpIngAFeSJEmSNBEc4EqSJEmSJoIDXEmSJEnSRHCAK0mSJEmaCA5wJUmSJEkTwQGuJEmSJGkiOMCVJEmSJE0EB7iSJEmSpIngAFeSJEmSNBEc4EqSJEmSJoIDXEmSJEnSRHCAK0mSJEmaCA5wJUmSJEkTwQGuJEmSJGkiOMCVJEmSJE0EB7iSJEmSpIngAFeSJEmSNBEc4EqSJEmSJoIDXEmSJEnSRHCAK0mSJEmaCA5wJUmSJEkTwQGuJEmSJGkiLFnsDkiSFl9ETPs+pbRIPZEkSZo9z+BKkiRJkiaCA1xJkiRJ0kRwgCtJkiRJmggjMcCNiI0j4rSIuDAifhMRryvia0bE9yLi0uL/NRa7r5IkSZKk0TQSA1zgbuCglNJDgR2AV0fEQ4FDgB+klB4C/KD4XpIkSZKkPiMxwE0pXZtSOqf4+m/ARcCGwO7Ap4tmnwb2WJQOSpIkSZJG3kgMcMsiYlPgUcDPgXVTStcWP7oOWLfhdw6IiLMi4qwbb7xxYToqaTgipv+TJEmSZmmkBrgRsTLwVeD1KaW/ln+W8kMZax/MmFI6OqW0bUpp23XWWWcBeipJkiRJGjUjM8CNiPuRB7efTyl9rQhfHxHrFz9fH7hhsfonSZIkSRptIzHAjYgAjgUuSin9d+lHJwL7Fl/vC5yw0H2TJEmSJI2HJYvdgcLjgL2B8yPi10XsMOC9wJci4uXAFcCei9M9SZIkSdKoG4kBbkrpx0BTdZknLWRfJEmSJEnjaSQuUZYkSZIkaa4c4EqSJEmSJoIDXEmSJEnSRHCAK0mSJEmaCCNRZEqzEDU1uVJa+H4MS3V9xnldJEmStDAm7TOx5swzuJIkSZKkieAAV5IkSZI0ERzgSpIkSZImggNcSZIkSdJEsMiU+nmzviRpxEXlb1Uak79T49pvSRoXnsGVJEmSJE0Ez+BKkiQNWfVMLXi2VpIWgmdwJUmSJEkTwTO4SzFnlyVJkiRNEs/gSpIkSZImggNcSZIkSdJE8BJlqcxHJEmSJEljyzO4kiRJkqSJ4BlcSdKCs8idJEmaD57BlSRJkiRNBAe4kiRJkqSJ4CXK0hx4maUkaaGN0t+eal/8GyhpsXkGV5IkSZI0ETyDK2moms4sOMsvLS5fg5I0HL6fjjbP4EqSJEmSJoJncDUxRumepFHnthptzgyPBveDFpvv1ZLUnWdwJUmSJEkTwQGuJEmSJGkieInyhPGSOi2UpeXSuXFdz3Htt/otxr70+OnnNtG48ZjV0sozuJIkSZKkieAZ3EWw0GdZhzWD59nh0eB+kMaHZ1BGm++nKpvv1+skHW++t82d23D+eAZXkiRJkjQRHOBKkiRJkiaClyiPCC9TkGbW9DoZ19eP/Z4cbhNNukm6vFaqms/3cP8+LDzP4EqSJEmSJoJncDXShjFjvBizzpM20z1p66OF5Wtw8rm9x49nlbSQRuFxZ0vT8b20v749gytJkiRJmggOcCVJkiRJE8FLlEfcpF1iMIz1GYXLXBZimV3VXYozDv2uMw79HpVLn0ZpW41SX7oY135PmqV5P4zS8+rHdT+Ma7+ltkblc8c4GPkzuBHx1Ii4OCIui4hDFrs/kiRJkqTRNNJncCNiWeB/gV2Aq4FfRsSJKaULF7dnKhvXM4cLPRPW9RE3o3CmetT2WVddjs35PIMyymdnBuUYhWNwpmUuxvrM534Yxvp0Wc+5bu+u7WfzeliMs5JLw3vhQi6vt8y28UGvh2Eds/P5WluM3POxzMV6rS3G3+m2bWe7zC7ms3/zue9Hyaifwd0OuCyldHlK6S7geGD3Re6TJEmSJGkEjfQZXGBD4KrS91cD21cbRcQBwAHFt7dGxMUL0LdhWBv4U81sSl18beBP0Df7cl+cmnjL3DMucz5z18bnMXfXftete2O/h7TMpTl3y33vfhiD3PO9zIV+XxrX3JO2PvP5Hj6fx+Z8L9Pc7XJP2vr4d9q/D3PJ3WGZo+iBjT9JKY3sP+B5wCdK3+8NfHix+zXE9TurbbxL22HFze1+WBpzT9r6jGvuSVufcc09aeszrrknbX3GNfekrc+45p609RmH3OP2b9QvUb4G2Lj0/UZFTJIkSZKkaUZ9gPtL4CERsVlELAfsBZy4yH2SJEmSJI2gJYvdgUFSSndHxGuAk4FlgU+mlH6zyN0apqM7xLu0HVbc3AubezGWae7RWKa5R2OZ5h6NZZp7NJZp7tFYprlHY5lLe+6xEsX11pIkSZIkjbVRv0RZkiRJkqRWHOBKkiRJkiaCA1xJkiRJ0kRwgCtJkiRJmggjXUVZCy8i1gU2LL69JqV0/TDaDmrfcZlbpZR+O/OadF/mfPZjGP0bFK/rH3DzXHL08tSt57D6XY0DdwBPrcROTind0tTHLuZrexdtF3RbDTpmu/abyrHCLPbDXPsdEas1LbPDcb8a8DIgyjmA7YE9KrlPSCl9t2l9upjn4+pxwO3lthERwHaV9flFaqga2fR+0HGf1e4fINXFm46VYSyz6ZjouF23BR7cod/7pZQ+1ZRPkjQ6rKK8iIo/3ocC/w+4l/xB4Qag96FrN+ABpfgJwHurf4CLPJcCN7Vsf35K6eGV2DbAT4EryH/oATYCbgFelVI6p9L2Y8A2wI8HtZ0h913F1/drsczetjoI+FuLdeyyzAcDawN/An7XYn1WA64ErqXF9i5+Z9o2n8U26bKttgF+Dlw+2xylXFemlDap5K7b9137XRd/KLAm8E3g3FLbXYHfAJszt+09jH7fQrdtNYx93OWY7dTvhmOlaT/sArw9pfSZmhxzfa0tAe4PfKuS4+nAncA/ZlqfiNgH+E/yMfK+Utu9ir69B7i6FH8ZsAawCiN4XJVyPwY4o9Q2ASsDF1RybF7kOKXS523o38ddj5+m/fPs4uuvVeJ9x8osXg9djomu7+H7AMcCn5ip36Xfmfb6nq1hTAwPazJ2GLkZzkRq3yTOgP41Tno05Z+Pfg+YTOs04TOoj9X9NmCiqutEUOttOGhiZ4Qm4muPn6ZlNkxE961nROwKvBRYqZebAROjA3K3nkwr9s17yZ/pB7Zt6vcM63MwcGt5fcjH7B6V/g1tAnixOMBdIBGxZk34y+QPRfunlDYo2q0H/LD4+Y4ppetK8fcDjwDeUcnzVvKHm81L7V8G7FzTfgfg34D9KjmOAFZPKa1R6fch5AH4GyptPw4clFJaZ4a2g3JfQj4GH1KJfwl4AvCFUnh38ofTR6SUVinaDdomrZcZEb8GjgJel1J65Az96PVlXWDTFvtne/IfwOo277pN6vp9FLBeTR9fCKyYUlppphwzrOeDi/hLKv2u2/et+z1gfS4GXg0cUdkPPyBv14e3fD3UHePD6nfdtnpw8X/dtprTPm6KDzhmu+7jvmNlwH7YmzxwfE0lxzBea1cAf62ZeDsfWK06sGhYn72BLwEvTCmtWmp7GXBvSmmLSo6TyYPHh43ocfVC4DTgqZX1+T1we0rpoZUcxwHPAj5X6XfdPv413Y6fpv1zadF+81LsOeQPhNVjpevrofUxMYv38L2BZcvbtWjfm0i7uNJ+WWBL4DKGP6l5C+0nhjsN5Is8fQPzYeQexkTqgEmcrpMegyYm6vbDXPvdNJnWesKnqX+leN2EaV2/u04OdtqGHY6fBZ+IH3D89LUdtD518Yg4EtiCfKXMHqXc+wCXppRe1yZ3l8m0AcdV54m3DuvznuLrQ5k+Ady4nuPCS5QXzo3kN4MoxTYEHkT+owJASum6iEi9ryvxFwK3Ac+s5N4sN5lqT/7Q9fniZ+X2+wL31ORYk/p7st9Jnhl7ZqXtlsDyLdoOyh1M3x49uxV5zi7Fnk9++PQRvcAM26TLMldKKX0yIg5r0Y9eX25tuX/2JX9YWJH+bdhlm9TF9yOf0d6p0sc9mZrNbJO7aT1fBdxd0++6fd+l303xAH7J1GxpzybALR1eD3XH+LD6XbetXgX8jLw/h72Pm+JNx2zXfVx3rDTth08y99d3U7/vrFke5NfNnTXxuvXZE/gV8NxK2zuAVem3FXDtCB9XewLfBp5YaXs3sFxNjufW5Ojlqe7jrsdP0/6pa/9F8kTDSszt9dDlmOj6Hr4n+QN01TrAdfTv+88AGwM710yGnB4RdZOamxSD/Z4jgDtTSluXExcTw1+LiLqJ4Wnti4H8O8kD+SeX4l8CTo6IuknKtSv9GFbuFwJ316zPoEmcap7eJM5DK8scNOlxS0rplZX43sD7IuLWUvjlvW1QTAYPs9/lybR3ldo+qcjRpn+9Plb7tyP5+Fm3Zb+btklvImjzmdpHxHnk98ljIuI/Ss1XA9Zvc/wUeQZNxNft+/k6fpqO2RcBaxTrW7YBsHple+9Nniz815RS7+ocIuJfgH0jovqetRP9+6yX5/aa/fMb+rf3FuTPi8tVjqu6tgAPAe5fsz518S2BS4BVKuvzDvL2Pr7Svy8W7R3gakaXA09KKV3ZC0TEKcD3KR1AkS/LiPxlrJumX65xPXBVSmna2deI2BDYrtweuJB8yfIvy+0j4hHABjU5/gbsHxEvAK4qwhuTP+h9u5Ljb+Q/nHcWL/bGtjPkXlKsZzV+J/DDlNKnSzleTD5r+vfKtmraJl2WeVlE3ACcU1mfvn6U+rJtm/1TbO99gZNqtmGXbVIXv6LI/eXKtnoM8Iq5bO8iz4HAui33fZd+N8W/B/wRuLz0QXUT8kTQNzu8HvqO8SH2u+7YPBB4JfDNedjHXY/Zrvu47lhp2g/3Au9LKb27kmMYr7XbgM0i4qOltpsAawFXtNwPkK9mWa7S79WAuyLiQqZmqDcu4t8Y4ePqMeSB2L2V3PcAa0XEwZUcAF9tuY+7Hj9N+2eVon05fgv5Q/rrUkrHVbZVl9dDl2Oi63s4wCdqci8PHJNSuqLSfj3gW/M0qdllYrjrQL5uknJYuYcxkdo0idN10qNu8u0pwC+KPMOeAG6aTGvK0TQ5+BTy67mce3/gePLJjzb97jo5WNd+XfK6fLbSx8vIZ4DnMqk5nxPxTcdP0zG7L/BX+tfnN+TLdsvtnw38makzyj0vJ19WXc29f9F2LpNpp5FvqTm5RVuAs8i37LWJfxd4E/mMd1nT9n4sedJjbHmJ8gKJiFcDP04pnVuKrQEcQr7Gv/cCuJ58cAf53sN1i/h1wDnky6HOr+ReAziSPHPca/8X4DvAm1NKN5XaPh5YNaX07Zo+Hki+/658Hf7vgC+WB+ZF292AVzB1JqGx7YDcJ5Iv9dq9Ej+V/GHitppt1bs8eOA2mcUyrym+H9iPrn0ptvcVwANSSmfNoX918T8BJ6aUvl6z7ru1zDFoPR8PXNFy33fp90x9WaYSO5M8gGy175uO8SH1u+7YrN1Oxc/muo+7HrOd9nFpu7TZD38BfjOE9Wzq98/I73nVIkY7tF2f4rXZlyOldHMxSCnH76TDe8pCH1el3HU5fl8TPwM4t8M+7nL8DNo/VOKrkAfac31P7npMtH4PL/rSeKzUtO1NRn+6MhlyNnky5J9Lbc9malJz41L8KPKH4JcyfSB/NHli+EWVZda1fwv5csxzmLqEfmPgf4HTU0rPq+Q4mzxJudE85D6KfNzvV1mf95I/v1QnYPryFDkeTP4wvUeLHIeSr0r7P6ZPTOwHvKM8+RYRpwJvJn8mqe6HufZ7X/Jk2jrF7/X68ewix1dn6l+pj/+UUlq3Ensz8PmU0mYt+t20TXq1Bw5v0X5v8lUub6pMSp0NXJdSenql303Hct02PJr8t+NXKaXXVHLM1/HTdMweC+yWilsCS/FTyWeTn1KKPRr4KPAo4PRS7g2A16f++1tPBbaqyb0v+fLkTzB9/7wI+J+U0psqbd9Knkz+1KC2pfXZJKW0y0zx0vpsRb40vLc+/yBv72WZPgH8F+DVKaXqgH1sOMCVWojiHuryZMEkGoX1jI5FWNRN2328GPthNsusrk+XHDGkauijZr5ex03bdhxes3XbpK7fEbUVqi9h/iY1u04Md5mM7Tr51nWSYBgTqV1yNE169E2+Ffv7DvLlq9X9MIx+106QFF/P2L9SH7dOKf2k2u+O27vLRFBj++rkzpAmb+d7Ir5pgrb2mK0zwzavTozeVdd2hhxdJtNat52N6vqk6bdc9MXHmQPcBRS5YlzfizmldFFN2yPIl5i0qmoWEZ8hX747sH1ELCEXQFiOPBN1X1vg2JTSP2pyH51SOqAm/taU0jtK3y8hX2Lx9za5I1eLO4Nc8GBdZq6MvCtwDPDrlttkWv8GLPNP5Mu51yTPyPb6cVrRZkfyZXdBnuU8lXx2/J9pt71fDvwHpSp/Tduk+J2+7d1lWxVtzyG/EZfb9vr21Eq8aT1PI1fR3ZEZ9ueA/jUtsy7+l+L3byVXqQ6mCkZ8FngYLV4PXY/xIWzv3j5+F7my9kzL67qtmvbxoeSiR6lFjqZ9/KPi5zvTbj98H9i2sl1PLr5/Zov1aXqt/Qx4OLk68NWlZf6NXC14hxbrsyZ55vsW8hnOcr+bCo7cQD7bMtTjasj7+DXk1/JM74+bkK92uB8z7+Om/dDUv6b9Uy4IU44vRx4ArjxoO82wrZrWczVyhdE/t1ifpuP+nCK+fKXfiQ4VqrU4FmtCpe1k2rD612Wiqusyq+3Jr5fWjx5bTPMxgRcRK6eUbm2Tuzcx2tR2rv2LiM3IFf5h5knalVNK1Xu7iYiVgX9i+v78LUN41Nu4cIC7QCLfL/VC8r0V5UplewHHp5TeW2p7JFPVOmesatalfeSb7p9BrspWbnsA+QXVK8xw36+QL3vbqJRjzeLL88jVRnuOIc88Pbll7q+QL4nasjKLdBj5kpNXl9oeBDyQ/ILt3VcwaJusWdO/pmWeRp7pXSGltHOpHz8nfyh6dErpniK+LPnxKf9Mvmy2zfa+hXzp0rYttska5CraD2/R712KbVHdVv9bbKctKtv1h8XPq9W5m9bzh+QPdruX1vOh5GO22vemfdm0zLr4BeQPug+pXCp0PPC0Ypu1eT3UHeNN/e6yvZuOzcPJA7EnkB8X0+tf1+O+aVu9Eng8UL7c6st0q8DetI/PKfq4XYv9cArwaPK2Le+HL5HvSdq1xfo0vdbOIV/quX15Q0XEmeRLtB7dYn1+XWzbZ6SUdijl+BLUVtLdkfw62ZvhH1dd93HdcfVh8r1UL0jFJYwD2gIcR760bO0W+7hpPzT1r2n/1BWV+QJ5gPsI8nbvbdeur4f/Ir8u31Fp/1b6nxrQ9T38d+RCgQ+rrM/vqa9QvRn5NfFthjyp2WVieMDEVtNkwBLyhPDV85C7aSK1tn3xO9MmEwdM4nSdaLmF+sm3pvWc6wRw02Ra04RPl/5tQi5e9hymCpM2TVTNZnKwrv0W5Mq9ZwO9Ey69iZ1vkS9rHepJiyHth9XIBVyXI++TNsfgSSml3SqxTYr1vo3+kxmHpJT+0KLtmcXXO8yUo5Sr6bFzjyFfFt1mkraucvNTyO9V32dqom47YGvy9u1dojyryt/jwgHuAik+CPxT5Y3sPKbeXC4uNd8SIKV0/0qOv5KLA/y9kn5l8mMwllTaUtO+r23R/h5ysYNrSuEEbFr08W+l+CrFzwL4Q6ntxrnbaVqFzwG5NyrWs679HeQCET3bk1+UO6SUVqisZ902WaX4/w+lWO0yI+LilNKWvf9L8UvJ22rLUo7aD3UD+rIS+Y1whcr+adomvXtu2vT7HvIfkx2YesOCoopneTv11rPIUV2f1utZLPMK8r7ufcgftC+bltkXj4hLU0oPqdkPlwDLpP5qkF1eD039br29S3naHptdj/umbVWXZ8Pi+w2rx3JDjqZ9fDF525b3cdf9cDH5OKk+hqduH3d9rV1MfpxLdZl97Uv9vrSyPn8jDzT+P6Z7H7AkpbR2OTik42o2+7jLcVVt22vf9Lqv7uOm/dD1+Lm0WObmpdglKaUtIuKySrzr6+Ef5PfOr1XW8znF+qxSatv1uLq0WGb1PbxvfYr4UeRB68sZ/qRml4nhpomtpkmPw8mTY0+ah9xNE6l1E3IAjyRP8JcHF02TOF0nWuom355UfL8qeRA9U7+7TA7+mvrJtKbPBk2TgwfV9O848v2xr0sprV/8ftNE1WwmB/vaR8RF5P38tjT9MVsnkvflbsz+pMV8TsSfTL6SY8uU0mNKbZuO2ZeQ7/t9WyV+IPlpJquVcv8PeYC/Dfm10fN88sB6lcqk2UXkz8RbVeLvJ+/38j6G/Ei355En7HoOKrbh3iml3okkIuJDRdsjKjl2Ig9E31yJHwzcP6W0einHxUXfv5imV09ves9bA/h59e/6OLGK8sK5lzwDdkUpti75Jv0P01/tbNqHrsJt5MsVti4Hi4HyupW2t5DfhP6rMkN0JrB5RCyTUrq3iC1DnvW6LqX0qEruK8kfMDcsxS4l//H4SZpeBKFr7lOoVH+OfNnMjcDvUkpPKLU9D3gj/R92mrbJpeSqkJtV4n3LBK6NiO+QC3z12q1L/oC7VkRsz/RiB2uSKyhW9fWl2CZHAP9dig3aJl36fQn5w8G9aXqJ/KbtGvnLvnjTeq5KrqR73/4kVwP/ILBv5Q9k12X2xcmP27iIXPH2X0r9WI+pWd2B27u0zTdv2e/W23vAsXkm8BHyH8NebDbHfdO2uhm4KKX0+EqOLhXYm/bxisBNLffDOsCZNa/vO8iPXmizj5teazcAW0Z/Jd01gUtars85EfGHYhnlft9JfSXdg8gVeauGcVx13cd1x1VvH29RitW2LX52PPD0lvu4aT809a9p/9RVQI7IxWm+W8o9m9fD9bR/akDX9/DbyY/Pqa7PPdRXqD6AfDbj+EpfjgZ2jYhyH8uTmr1BwdUR8RP6K+aWJ4bPLMWb2m9I/aMFX01+HVY/APcmPeYj9yOL3NdV2r+5YT03K/4v59mG/PpctZIj9b6uxP8OrE6/TcmPvrlvPSPik0xNAJeX2dTvpmXeBSzfG7QUVkopvStyUaCyKP7N2D/yfnhOsf7l/m1OnvC4bz1TSvdExPLkR/O03SZrALe1bL+EfGXaf1XiWwF/bnn8lCerrivFv0veD49k+Pth05TSrsXf8XLbpmN2R/Jn8VUq8VXIRabKuV9KHnA+iv7KzbeV2xb7J3pfV+IHUl9B+yXkbVjuy/LkWy2WqbT9N/J7SrXfT6vJAfVjuyA/HeF+NfG6Y/behvjYcIC7cF4P/KB4Ifb+cP6DPHv7ilR6LEHkx9CcEv2PtbibqYcyl70U+Fal/YrkN6uXVtruBZwEXB8RNxex1Ys+VWeBID//b/lK7Ejym+f/1eT+Wil3FLmvbMj9AvLljWdExAOK2PXkSz0Or1nHj8J9H4hg8DY5kjyT2GaZN5IHEWuWtsl15EtzrgPeztRlaVeT99mTWu6fvchnitaKPLsLg7f3kRR/JFr0+zbyftizpu2x9G/X75D3yRnFh7+Z1vPD5D9C5f25Hnnmca8W/Ru0zKb4+eQPpb3ZzmvIpe1f0uH1UHeMN/X7SNpv76Zjs7ePVy328WyP+6Ztcg75USLVHIcASyLiphY5mvbxkcD6Ne3r9sO/k2fyq6/v3qVzbdbnBupfaycWfXkS0y8BfTX5ddxmfa4p4stV+v2qIl71UuCj83RctdnHMx1XvX2cKtuqri3ks4lH0W4fN+2HpuNn0P5JTK8t8QvydnxB5EvlZvt6OIOpCrXV9ke2XJ+m4+RjxbZ5WiX+BvLVHM8i34bSi19BnmyoGsakZuuJ4Wie2Gqa9JjP3E0TE30TcsXPLgDWajmJ03WipW/yjXxG7QTyYLS6zLlOADdNptVN+AyaHLwGuKnSv+PJg5yHRsQGpRx9E1UzbJOmycG69r8knwn9TUS8qJRjQ/Kj1GZ90oL5nYi/JSJ+Sn4sJqW2TcfsT4EHpZTeXolvTf/k4MXky6Srj2/bDXha9E+aFT/ui98MnFMzUbdltS8RsRb5UuK7K8fV7cD3avq9a8P63AW8PaZP1P2KfLz9KqY/Rm+Vot8fZXqV513o/9wxVrxEeQEVbwzVm/h/WZk1Krdfj+lFA66razeH9msBpJT+3G4N2puv3F3XcT6N0vaeb6PQ99ns+4Xu9yhsp4UwSes5DsfVuBrn7RSlQjEx9YiNVZg+GbIKcFgq3acWEZuSJ7yeWWq7OsWkZkrppMpyNiVPDPc+DE+bECi3j+ZHC14MHJ5SmnbZepH7c+Tbnoadew3yROpDmV5Z+irgnSmlH1XaPw94ZErpLTXLfDFTz2a9juZHJZ5IHixVJ1rOJE++PbG0ng8AfkJ+1MnvW/S7aZm9SZJdmD5x9DvyZNr6pX40VQCu69/q5MHfW1JKp5b6txz50t/9mdoPV5MnAtYnX+7aZpucSp4c3L1l+1+RJ8zLsbOKflT73Xf8FH1fgzxZtTF5+0OeBDoJODhNr2A+jP1wPflM4wNLy7uO5mN2S/Il3p+txJcjn4R6Yin3DcW2+lhK6c5K25fTv4+/XXz99Er8N8BHU0qX1fRlo5TSDyrx3cgTpmuWcpwNfCOldGNNjmVTShdSEbkC9uMqfTmdfDVBtcgUzGPl5sXiAHcBRdQ+fuAXdbHUsGOi4bEWEbEt+Xlg5Tw/o7/Sb2NltIjYJaX0vZr47uSzuDPmiYiXkWcrpxXiKL6uviHUVpAu8uyX+p8zthp5Rr93OcrACnDksyutllnMhB1Mrhx7X79Tc4Xmd5OLWFWX2boa3YDtXbcNu26rvlgRP4Ju1bmPYurREb32veJdbftXu0zyttqjTV8i4lPkS51mvb2LPH3bfEjbeytyMZze4Gi2x33TtvpD2zyz2MetKrAXbb9IPqNcbdv3njSgL1eTz5q37d93yGfQyu2/Sf4QtUcpfj35cq0H0r7ozffIzyecj+Oq6fXd9NqsO65q35fo8Nop8tTt49r90CV3NBeV+VHxe2uVcxRftz2Ol5D3zXr0F7i5kjyAnGl96o6T3j7egP7K371CNo8nVxMPSoViyJc9ztuk5jhOOo+aUV/PMehfU2Xgke636jXtz5p26zLij3rrygHuAol8qdZHyIOzXpGNRxb/fk0eNMAMjySI+opp+5Bnwj5Ryv1E8h/pH5L/aPdy70JDZbQOuWvzFJdDvJN803y5IMGBxddHVeJ7Uakg3dSXoh//SZ6le18px7OLr6sV4J5P/lD40ZmWGbkK9RbkiYY9Sm2bqqk2bZOmvtRu84bt3bQNW2+rAbEj6Vadu64vLyZXcTwd+PxM/RuwzN4loIeW4lsWeX5HLhLRsxf52DmGOWzvoj/V42rO2zumKqRvSq4G2svR6bgfsK3eRh4oHDFTnlns47r2TfvhdeT91WpbNeQ+jFx452zg3aUcL69ZHuRLcfetWZ//Jj8aplxZ+ysUA5WU0u5FH9ajueDIM4p1Gfpx1RTrEh/wvlT32um6j5v2Q1Pupv1TV1TmnUX7W5mqgDyb18MXqK/4fzT5PtE3tFifuuNkI5orf9cVslmW/PfkIOADLNykZtcJgaaJk0PJZ7nmI/cwJuSGNYnTevJtGJOuRZ7qoxKbJnxOID+OrHyGcKbJwboJr7qJqu+R3yN6Z2rLy/wF0894NrW/iXzL3Dr0VwA+hvzamo+TFl32Q9Nk1aXk7b1+ixyDHgF3GvkYrPalWuV6Cfkz+1U1/Uvk2xvq4rsz+0rmvT7vRv4MPFN84ERdml4Vehvgp+RJ5Grl79rKzePCAe4CiVy0ZbfKgXUR+fKPY9L0e3iOI79IPldJsxP5pv+PV+J7ky9TuK9QQ+SKaU8hX7dfvr/lJPJBf2olx3bkDw3fqcSfSD5OepcPEbmy3v1q8vS1Ldo3VRY8n/4K0gAPIb9hXVCKbUH+gLV5KlWXjuYKcJeR7/moVnatW+aW5EuFtqjkbl1NtakvA7ZV6+09oN+97VndVnXbr7eec63OfQm5MMi5aXpV1qZ92bTMvmMiulekbdr3dcf4dsX/1W3eZXtD/bZtOn66HvddttV8VmBv2g+9YjjV/dB6fYp12RK4pGbf31ta3kzLrNsmvUq6l1Te8+6hW9XhYRxXOzP1PNWyptfmMI6rrq/juv3QlLtp/9RVd7+EXB31gtRfhXtYrwcq+7jr+lxcxKt/Hy6lvuryPuQJzWNZuEnNrhMCC537SIYzIddlEqdpoqX15FvHSdemStm9y0erjyKsm/BZD/gU+XLRw2fqX2kZdRNedf3+H/Jr53mVZX6F/Jp4bYv2Z5Hfa9ZOxaPhiomdz5IHZu9kDsdPx/Vp2g91k1UfIF+leA25COlMOZqOn2Por2TeVIW7qTL5Z8l/k1/SMt6lknmXxy02TdQ9r9hWz6iszxHA6imlNcobJCJ2AD6eSlW1x40D3AVS/OHcOqV0dyX2COD8yqDob9Q/1uJj5AIor6nE3wfcL6V0X0XQ4g/7k4BTK3/wbybfE/GiSo5vFbn3qMQ/Ry433ru/oZfjAHL10BeX2n6WXDlyncq6X0Y+1h5cid9I/jC2U2WZZ5E/PD22FDuN/EZ7cu+FP0PuS8jbZLMWy/wueQb5Y5Xc1wE3p/5qqpcAK6bSs4Gb+jJgWzVt76ZtWNfvs8hntz7F1AftXry6/XrruXZN7qb1/C2wamWb/BZ4GfCpyofXpn3ZtMy6yYBLyWdlDk/TK3+33t5FvO4Y/xb5zM4bmfpQCt22N9Rv2x+Q98OXWx6bXbfVH4A7UkpblWLXM1WB/YktcjTt4/OAdVPxiI4i1rQf+o6HrutTLO8I4D8qua8kV6bcqpygrn9F/HzyB4/N0lTxk1PIs9dbp6lHRqwLnEsuOPK4So75PK6+Tj5OHl3ZJk2vzbp40/tS0wC8yz5u2g9NuZv2zynk954t01RBmEvJH64fm6YXlen6evgG+XLztdP0Aje/B/6WSs+wHbA+dcfJMkzdb/jYNL2QzcnkwdnzmF4o5rvA6Sml8j6e70nNLhNbXSc1FzR3Ee8yudx1oqVvImwWk4BdJo5Wof5RiU2TsZcU/atOnNRtk38q/l+G6Y9nbJpYv5jKY8BmWGZf+xj8aLi6bdJlHzdNxHfdD3XHbO+RZNVJzWEcP/fQ7TGMfRNvM8TvYe6P/2uK903URX7s2ufJf6fKTyN5DpXHrpV+Z9qj3sbNkpmbaEg+CfwycoW83h/Oy8iV686I6ZXrAL6a+h9rsS/5GVvVOMAnYnoVtKvJj7D4UUyvmHZ/8qzMGZUcPwY2qIm/GfhYJfffyFUs31RuHxEHAF+PfKajXI1txeLn1fj9yffGXVFZ5onAJml6Zem3kz/w/D3aVYBbGVi2zTIjV63+KLBStKvQ/G7gyJpl1vWlaVs1be+mbVjX7xOBvwI/qIlvUrNdX0y36tyvB75Q6cut5MuTz438iIzG/s2wzNuLbVWOr0Leti+t9OPdwAdabu/aY7zY3j8nn1Vqc8x2OTZfA3y6+Lq8Tboe94O21cqVPP9g/iqwN+2H1wOfneP6rEIelN9Yea3dQ/+zCXv9+1rN+txB/hBVrui8Brl65nIxvbL0mdRXHX438N55Oq6+DjymzfHTFB/wvlT32pnNPq7bD025m/bPC+ivgHwr8P+As+f4eng2+QNmteL/r4B1W65P3XGyOtMrf/cG/uVCNm9nekGhu8gTlVVR/Ct7fNH24Ux/TMm3yH8Pjqi03wZYgX7L1ORel/pHC55V5H5mpf2p5L+F85H7u9Q/znA58navWg+4lv5HIr6JPHlfVrddIX/4r5to+S2lRw0Vmtanqd9Ny6yrlH0p9Y9KbKoMvDp5IF9Vt01+Sp7MP7EysXUe/Y+DhHyp/QNrlrkKU6+zge2BCyLiZ8BfYnrl5nXIhbqq6o6fpvUpT8TPZT/cSX5SQblS9h2RL8G/udK26/FzJv2PgLuI+ircfW0jT5pF7+uW8b5K1NH98X9N8b/TX3H6MnItnZNTqaJz5BNq+0d/Fe59qH8849jwDO4CioiH0n99/m/IM3bl2BnkS0Bvq/z+muQzOdPixc/WoL8K2pnk2afqvUHVN4OZ+l2XuzZPNFSKJs+Sta4g3aUfxdd18b90WWZ0qKbatS9dtnnTNuyyrWbI32U96/pyFv335AzsX9My2/Zl1Lf3MI/7um3StY9d9nGX9rPZVg3rM7T+xRyKn8zncTUsc33tDMozrNw1yxrq34G6fdz1uJrjcbIv+fLXU5g+OH82+UPmV0vx/wcsS57UPK6U4yTypOa0S/4i4qnkM/6nV3L3Ln09txR/WvH/K1LpXsKIOJY8QbJLTe7eZY/Dzv3oYnvcwPTJEJi6PL+8zH8hX7Z9RCXHR8m3Xv28lOMf5O26bCX38sChqf95xE8lX41zVov1aep30zJXob9S9qvJ2/RFKaWDS/E16K8ifD35sWvbkidbZtom7yJX7319SulFpfijyZMkt1T6dyv5c+QOlWX+HHgYeWKpqX15cudK8kmvXo6ryZfzPpV8n+tMx0/T+hxLHty+qmZ9uuyHO8gTmI9iqqLz2uQzsjeRr+yYKUfT8bMp/ZXMm6pwb0p9ZfIzi6+3bxnvUsm8XFn6AQ3xuom6XZh6P7yNfFx9IJWqQhfLPZBczbp6P3X1apOx4gB3EURNVbO62Gzi8yVqKqzVxQb8/soppVuHEN+MfKZm2jLnqy/RXLV6K/KbVd8yu2yreez3yvRPnMymOvdWwGrVPNRXA6cuPmCZ29Jf+buxOAuVqtUzbcOFPGYjIsiXWJZL+zduk6b4oP1Dy/0wi33cdT/8dS7rE7mQxsuYmmFvXF7RfndqqrhTXyW9qahMY+Vi8iWjC3pcUfPaLL5uuw0bX69D2Me1belWlT7IBaZ+1WYdu6x7kb/umPgZNU8NaOj3CeTLn/eoxlNzQaHDqa+cD/M0ycZwJobnLXeRf94m5IYwGTuvy+xiWBPX8zV5OeD3F/z4manf1cmqYe7LLhNhTW27xjU/HOAukIjYBHg/+X6bXlWz1cmXEqzE1MzOqkzN+OzA9Ip2TfG+ymil5Z6fSvckNcUGtN2G/gprDybPnP2JPMMHM1Rdi7lXFN2GfBnTY8gz3VEs866iyf1K/RtaXxpi25BnRy+vLLOuL03bqtz2mlJ8zv2OXLH72+RLXcq5N6dbde66PI+kvvJ377g5r80yo1t17m1ov72btu28HbMxVSF9Q6ZXlm7aJl23VZf90HUfd9kPTcdV6/WJ5mrofcuboX/Pp2WV9AHrvg0LfFwNYxs2rc+A9eyyj+e8vRfg9VDXx6anBjT1+yDy7UFvq8T7CgqVlnsl+R7pkZ3UbGpLhwmVhlht21lMyLXO02VSpmjfNPk250lXhlMpez/gwpr+UdPH39Ytr0s/imW+inycV/NsT/sKw28tfqft8dMUr4ut2mU9qZ+s+gFz3GfFetZWMm+YMOxSmbxrfBiVzOsqTv+RfCysTX/F6WnVnEu5j04p1d2aMRYc4C6QyPc3HAl8JU1VNfsZ+UW+fUpphyK2LPna/yDfb3tPi/j76a+Mtn3R9t/IVep6dqiJDYofQaXCWkT8mlw973WpdLlVRHyIXJyjeo/RTuQPU2+eQ/wgcuGSvVNKvbNkRHMxgS592YO8rR5LfgRFue1W9FetfiG5OM1KlWXWFUH4NfXbahj97hVlqcYPJhcGW72S+zi6Ved+CblQ133FB6K58vdlAKm/OE3TMvemv/J3U3GWnYDlUn1Rh7bFKH7N/B2zB5Pvh31V5dhs2iZdt1WX/dCUo2kfd9kPTyjarjiH9dmbfAnfCyvLPKlmedBcgOcyKlXSo3uRnIcU61NXEGa+jqum12bfNoz8DOpV6d+GTfuyyz4+irxN9iRf2tnYttS/tlXptyAX3tksTS8c1PX18H3y37HTmK6usn/TUwOa+t2loBDkSx1XJA88Rm5Sc0Csy4TKsCYvhzUR1mpSZsAyO00CDljms4uvv9ayL3PdD9sBW5Pvf/x5qW3XfjStz17kExbvYeZq0V33ZZfj6lHke/tPoN32rpuseib589uvyJflzpSjaVsdTMvHBQ5oe2DxdbWydNd4l0naI6mvRP3f9Fec/iT5MUXXMVVfYyPqqzlDfi87N1WKMI6TJYvdgaXI2imlL9bEXhe5YAEAKaV7InLVqFS61GOG+IHk4iDlG/j3Jf9xXbEmfg/9BSOa4muSCwqUrZRS+mRMFXvq+Tfydf6rVOJPK3LPJb48+T6Lal+aigl06cv25Bm/e8jPUezZn/yhpByD/KHw9ppl1vWlaVsNo99PI59dj0q86XX93KLf1fUZtJ73VmJLinb3q8R7FSXbLnPPmhyPp744y6Nr2kLzNuyyH4ZxzC4pYtVjs2mbzGZbtd0Ps9nHc90PXdZnT/KHkedW2u5AvrKlOiB8FHkgVnVvTV+6Fsk5vSH3fB5XTa/Num24H7k68260e1/qso/3I0/S3En//qnbx3XbG+qLypwOPJ1cyKms6+vhMdQXZao7JqLoYzVPU7/voSh6VdFXUAjum8hYvybeNOnxTvKkx5MHtS3iHwK+WZyJKdsJWDsi3lCJ0RCvxiBPqPw9pbRbZZm9SYXdSrGLyJMHx6SUXjGobRE/Djg+Iuom5O5oucyjyB+4q3n2Bm5PKb2ykuMk4CORH3lS9gTgHy3Xp6nfTct8Enm/vbIUO5FcNKral53JBQHPY7otyBMtbbbJxeRj/4sppWeW4n39KOK/AY6JiP+oLHPLYpnV9k8mPzng+FLsr8WXuxZnmntWyt2bud8D1qdpP1xO3mczbu9Sv++tDDbfQh6k/TSl9K4WOZqOnycWfSnn7k2Yvj2misBC3q6pZgD6smKZs44PWOZDgPvXHFdbkjtTvaf4HeR9fGYptmmaqjjdG/ReHRE/of+zb+89+QGMMQe4C+fsiPgIudpq76b830fEBcAVMb1yHUDE9Apog+I3A+ek6ZXRHkEetJ5UE9+gHJshXldh7bKIuAE4JyL+pdSP28kz6G+v5NgVeNBc4pHvXdgOuLuyzCXFNqlWgGvdl4jYCXgv8KhUqlAdzVWrHwO8omaZdX1p2lbD6Peu5Me5fKMSv4v8Bnkw/cdPl+rcGwBvq+S5jPrK3ysVv9N2mdBf+bup4vTXgBe33N5d98Ocj9lie+9ZfN1mm3TdVl32Q1OOLhXYm/bD/wJvmsv6FMt7K7nScW9QWFuhuGj/ZvqruG9CfZX0f1BfWbqpcvE3gecu8HHV9Nqs24bXA/8OHNnyfanLPv4r+TF0h9Tsn2rbpu1dWwE5cuXkr5ArF8/l9bAs8O2Wx8TV1D81oKnfK5Mr8retRL0ScFxNfFQmNZsm5LpMqAxz8rI6IdeUp2kSp2mipWkirG7yreskYNMy6/Zb0yTgv5I/j7WdTKvbJkG+nLTal6bjZx3yJEF1madRX537DvorTt9CXp//Sv2PDVu+Zb+b4k374e6aGDSvZ91kVZAnNqvHW1OOpuNnG/q3VdOEaZfK5F3jXSdpv0t9Jeq6itM3RcQHKVWcjoZqzqWf11XhHhteorxAImI58iUA5evt/0g+K7km+dIByJdTfLv4+ulMv66+Kf4b4KMppctKy3s8+TKUB6SUzqrEV00p9XINjBc/q6uwdk3xfTl2NvD1lNKfKr+/Jfk5WxfNMb4b+fKNciGfE8lvqtX7GM4mD/xurMm9bErpwlJsTfKb/iqpdD9UDK5avVvNMpv6UrethtHvLSnOaqfKfVwR8a/kP7bl3GfQvTp328rfJxZfV9vWLrPIvQbtq3N32d5d9kPr7T1DfGvyc45Xm0P/Bm2rtvthNvu4y36o60enfd9leYPa07FKekPuBT2uimXWvTbrct9Evgeseh9v7b7suI9vIU9+/rFF287bu+Nx0hivvs5m6OOZ1Dw1YFC/o3318KPIlx5/humD8PeSP6SWB+dvIZ+BO4dcaGtQ243JtSVOSSm9oLLMn5In09arxF5LPrY2HtS2iB9KfuTRWyrL7F2G+qFSfG/ymeAzmLpsvantxuQrAD5Xc5bsUPK9zW9tscx3kAcnn0opvaeUY1/ypbWfYPrExD7AUSmlQ2qW+SbymfOZ1qep303LfDa0rpR9LPnxYNvU9O//I9cdmGmb7M7UZbe910ZTPzYhP5f7f1JKb6pZnw+Tb28ot386+UqPO5ma3Hk0+UqMl6aUzi7lOJT8eLj/btHvrsfVq8gDxS8x8/behPz+sCz5ddWL70C+nPtk8sTPTDmajp+6SuZPK/6vVuHuUpm8a7xpmcfSrZJ5teI05M/Nt5En6/5cxFYvlj2tmnMp/2tTSv9TjY8LB7iSZhRjWPl7Eo3KfljoZcYcC/PUxQbFx8UwtnfDMTWn7d2Lk5+JOi/HSV0fG2LBcKqN708euI7cpGbTxFvxs7YTKteQ71N/WMu2XSfk6iYyaidxihxdJ8LmNAk4aJnF16370qF/TZM7pwObzrUfg7Zhh8mdYUxWNU2KX1vXv6b1pH6y6lKmPw5ntttqmZrcTRN4tW1pqCzdNd5lkrboT+uK002xSeQAd4FExBLyGdw96K9qthbTz+B+k3zgV98kZorvXsrTVDGtru2g+Al0qLBWFxtGPPKjRQ4lX86Vin83MPUg6qeSL++oxncj30cwU/wE8qV2t1T6cVLqv89kNfIs4l0Nyyz35U9MnaVfZ8j97uVei7yfB65L0/oMWM9NyGdFltC+8vf2pbbl+Kwrfxfb+1Ly8TzT9p7tfmh7nMz5+Okan+V+GHoF9qIf55GP+7p9PKd9X11eEduG/irunQvzNMRWIz+L8I8s3HE159dm6Xi4H7PcxzFV1f955OdH9tqeU6zb8sxye5dyP5982XDb46QabzpOtmFwZf/LSv1OTD2T9ZoiRWOhobr1mSmuZl0mMoY1ATrXScCmyZ22EyoD+rVySunWuU7udF0m+W9E2/Z9kzu92DAmNec6gTWg7WZ0eHzkMCZM6VaZvGu8S9tV6VYtuq/Kd11sUHxceA/uwvks+UPC25le1eyR5PsnepcmbFS0DXKxhqtnGR9W7gPIhQfKFdZWL9o+vfeGVVijJjaseO+SkNtTShvAfbNWPyx+vmOa/uyzLvFdyFWoT4r8APeerYFti8tAyv6X/GF2ixa5TyNfLnJnSmnLIff7NPK9RHemokpo0fbQmnUZtD5N8ePI96WsndpX/l4/1Vf+Xi/1V/4+OfIlUD3bF203iYjnlOJvJReEecQct9V87Yeux89C7Ifq9h7GfngP+b6hpn3cZt8/j/ys0uoyd6hZHuR7pe5M/cV9PkR/YZ6div/bFuA5gDxIfMICHlddXptbkbdfNX4c/cdD1338HnLV0ZtTUfSoaPs74NaW2xvqt+2Bxfr/pZJ7puOkGq/rN9QcEzG9sNMupfjvgb/VTBodR32hoZ2AdSNfklx2/yJ+EaM5qTlvEyqzaNs3IRcRtRMZEbE6eaJu5WK9em1bT7yVljlt8q2U+75JwEo/7psIKuK1kzuR75uHSqXsiOirlB0Rt1D/eLBNgIsi4u8ttknTpNQ21Ez2DVjmNtQ8Bq2pfeEU8uWz5X6f3aHfg/ZxdT90mcCq7Xc0PD6yYZ9V4/dNeM2wTS6sbJNhVJYeViXzR9FfifoJwOER0VctunBseX0GxAbFx4ID3IXzmNT/qIKmqmaJXKXtzFLzTvEh5v4J/RXWNiUXCFhSiidgs+LrctthxTcEHkTphvqUHyifel/PNk7+cHAG+Y2l/AFuZ/L9QdUPdY8EomXuDVJKW0aujDjsfjflfg35Q3e1303r0xTfvFjP8uUyi1H5e7P8a/OyrRbj+OkaH5X9sF61H12XCXyB/GzU9WqWeQ/tqrhDfWGergV41gbuWuDjqstrc2fyB/BVK/G+42EW+3g98rZdvtL2H9QXw+lSCGkV8vFz334b4rEJ3Sr7301xmXRFU6Gh/amvRP0fRa42kyGLMak5nxMqw5iQa5rIaJqoa5rcaJoIq5t86zoJ2DS506VSdtNE0IHkzzZtJneaJgG7TPZBfrTiHTXtv1Tk/kIpvGPRj+rkzvNr+td1sqrrPu6yngeRC9ptkaZPbDVVLO9SybxpwvRgWlYm7xqP7pXML6e+EvVJ9FeL3q60PidW4tUY5P24FmPMAe7CuSkink+uKnpvKfZB+quaRe/rXtuu8SHm7quwVnyofhLwk5TSZpX4SuXYsOIRcQp5xux1pdi6Rb8jItZN0y8/aR0nl/s/i1yCvvzH6gJgrZTSEyr9OwXYrmXuayPiO+SKqMPud1PuG4Hf1fS7aX2a4seTz6Rvz+JW/t6w7fZepP3Q9fgZ1/2wQk0/ui7zMvKg6uSaZbat4r4xNVWKo7mq+K7UV8R+HAt/XLV+bRaxZwOnVuJ1x0PXfbxCsQ1vqxw/t5M/7My4vZu2beSCa+sCdw772Cwa1h0TTdWs7wHWirlXlD8MuGGeJj1GfUKlru2geOsJGJon6pomN5omwvom3wbkbppQaZrcieJfWdfHg60C3N1ymzRNAnaZ7IP8Plt3D+Ju9E/u7A8cTz6JUI7vCdzWYVJqGPu4y3ouT7fHRzbFu0yYdqlM3jXetZJ5UyXqHeivFv0t4N3kquXV+N/ofx0HU4PiseQAd+HsRa6g95GI6N0ztyb5fteVophZIl/+27vM4/pS267xYeW+klx1sOxI8mXE76+J1z0UehjxFwCHAEsionf/xvXAd4q+nhERD5hl/LZiffasLPNt1D8H7AXkSzfOKD60Qr4UvJy7F7+B/EFtzWK7DmrbNd6U+0zg8Jp+N61PU3wf8kPI3870+zvOJx9fvUIOVxftqLQdFP8s+YHtZa8nP77k2ZX4C8jHxHxsq2Hsh7/T7fjpGh+V/bBP8fvVftTlbor/tej3B2qWuWolRkrpwMiz10+o5H4juepl2X7k+7S3rYnXfWB6AbmK60IeV11em28r+v3aSrzueOi6j/ch14TYhqnj5xry5X5Xkz/czbS9oX7b9nJvVcnd5Ti5pqHfTcfEFeQPoxuSz1r2cryBfI/x7uSzYr34U8jVS6ueRx4QVl0BfH+eJj1GfUJlGBNyTRMZTRN1TZMbTRNhdZNvXScBmyZ3hvF4sK07bJOmScDWk31F+7Ua2t8J/DD1P3rsGGDnSnw34Gkt+z2sfdxlUnMtuj0+svXjGaP7YxhXKn4+l3jXxzDen/zc5Y+W4ptQ89i9iPgx+ZL1C2riG6TK49iKn11cjY0Ti0wtguhQ1WwY8WHlliRpaRO5Gu0h5IFyecLiZPLgctdS/L5JD/I9uIPado13zX0xcHhKqVekprc+zyM/QvAjldj5wNYppW8MajtDfDnypMWmTJ9o+U7x9dOZPpHRW58NSm0vpPLowyL346l/zOFy5AmSR5Vy/5Fuj2E8kfrJnROh0+PBvp76H5W4HHkS74mV9nV9ua1Y5gdSSndW8hxI/yMba5dZtH8h+VLbcvtTgW+lUhXpaH702HL0P95y0Dasizfth99Qs49nWM+6auO70f7xkU3xvtzR/TGMvUt8nzXHeFPF6aYc1zLHCt+TygHuAoqIreh/YZ1Hfh5WOXZC8XW1bdf4fOY+n3zje5s3j6HFU+XZuAARsV9K6VPzER/Q9gjyPYrVbZWYXin7GvIfzY1atu0an8/cJ6TSs9gq6//WlNI7ZooNKx4RnyGfKR3VbTXx+yFyJfivkS+JKvevS9X3rtXdT6BDFfem+IC23yGfnRvVfV+bu8vxUBePqar+BzF1xnIo27uU+03kS157uYfxdIDaPkZzlf0TGEKRpUFxSSqLeaw43RRXPwe4C6S4tOCF5PscekWfXky+j+V08j0XkD/MHFh8fRTTKxp3iY9r7kHxvYDjU0rvpSQ6PtahS7whdiT5g9R+lf69p/j60FL8MPIlk2eT738Y1LZrfD5zb0S+1PDSlNJ99z2XtkGrbTWMeMft7X6Yv/3wBXK16F0q/eta3X0D8tmll7bIcQD58u9yFXeK2A+Zqi4JU9Xdq/G6tpAvFd6X8TuuOh0PdfFiX95CviS1d0l31+3dFD+GfP/XM4BeAaJhPR2gqY9fJlfZ3z9Nr7J/GPBYoK7I0pHkMx9t4kGuMPslRnTSY5Ryz3UCZhZt6ybfuj6Gcd4m04r+nQbcyuwnATv1r5j0OYOiAjizmPQp+v1r8uWvc53UbNoP1Qmsk4vvn8nM1cZnW7G8Kd6mYnnt9ov6x/nVVpYeEF+dbo9hrK1EXfSnr9p4U7xL23HiAHeBRL4P9p8qs86XkO+BOjdNr9DXtQJcXZW/scw9YJnnkV/UW5Avu+p5CPkN/AKm6xLvLacuXpdjS4CU0rSCFAO21ZbAJfO0H+YldxH/K/mN9u+lcLnYwt9q4uVY13hT7pXJBZym1QwYlW3lfohLAFJ/lfi+eOSq7r3q7lu0yHEPuajMNaVwYqrS+h9K8U2Zqu7+hxnaJvI9TCmlNK3K7qjs+wG5644H6LaPVyJfCrlCeX923N5N8Q2L39+wvG27HCczxOv62LTMe8hnqKddostUgaQftoxvTv7Q/WJGc9JjlHLPeQJmFm3rJt9GaTLtGPKkyZOZ/SRg18mnr5Afn7Nlml5Zu27Sp1dB+0imT+4cDjyeXFR0oSY1v0Qe3O2a2lcVXzGltPMMbWdbsbya+1Dyfb/VSbPjyH9TmqqHb9Ui/jMGV/6u5ng/+bivqzbeOynQs32Roxqva0vR9mMppXUYU0tmbqIhuZf8Ir+iEtum+L/svqrGc4iPa+6m+LrkN8cPM73q3lnkD27VSnxd4meRz+R8qmXu71J6XFFJ1PT7DvIf/Grhkrq2XePzmRvyB+Br0vTnTV5J/uN4Vkpp40p82ZTShuUEXeIDcp/H1H1ls10f98Pc98OZwOaxsNXd+6q4Fz+7lP5K65fSrbr7uB5XfccDdN7HZ5KrZv53KdZpezfFB+RufZx0PSaiucr+jQynovwlwB9TSsdX4u8AohyPiM3Jx9UlKaUfD2rbNT4muY8Gdo2I6gfmVYqf/7Uaa4pXYoPivcm3M0v5uj4q8Sf0PxKxPIlTjm9Kt0cl9ibTqsvs68uAfnfpX6Io1Jn6K2u/mv7K2jtTX0F7e/I+nrHfs1ifphy3kyfeRrVieVNl8s2LbXVPqf2wKk435ehSbXxf6h//V9e2Z/ma2NhwgLtwXg/8oDhwe9XObiVf5ntu8UcBcgW0FQEiP8vqqlnGxzV3U/wf5FnQV6SU7pskiPzsrk3Ksa7xIvZX4Actc78YOCUiLmRq5rFXiS8q8VXIg/Ibiw9hg9p2jc9n7o3Jf8B7s/Q9nwEeCPxfTbxvtr1jvCn3S4FvjfC2Wlr2w17ASUxVWod8JuPnMK0C+6D4mkyv7j5Tjqvor+IO9ZXWj6RbdfeXAl8b4X3flLvueIBu+7hX1X+tyn7osr2b4r3cq8fwnw6wOvWV/V9AfZX9YVWUvwM4uiY+KpMeo5S79QRMDG+irm/yjdGaTGs9OTig310nn06h/jFofZM+0VxBezEmNe8A1qjpd+QvF71iedOkWZfq4U3xrpW/b6ZltfFofvxfX9vSz55cjY0TL1FeQMWLdzum37NwFvmyoHLsl+QZuGrbrvFxzd0Yr8x2LarIl6vc1780/ZKXafEubUcp92y3zXwY9W21tOwHgFiE6u7zZdT3/XwfD/O5vYdxnMx3H9uIiEeTH1m0CtMnJv5B/nC8bCn+EPIVPjeSH/sxqG3X+DjkXgU4LKX0mco2fBd5wnifSuxE4LkppYMHtZ0hvil58m1tpgZSa5IH2yuR7wGF6ZNp25fark4xuZNSOqmS+9XARimlQyuxH5Mvaf2fQW1L/fsaU4OS6iTO9qX4mkzdh/nnUtsr2/aviK9Bvtx3Y6Ymba6nprJ2NFfQ3hT4HPlWiZn63XV9mnL8iDyY37nS73Kl8F78Rha+YnlTZfIu1cOb4tdQX1W8KUfrauNF7Apy5fOzBrUt/Wzbcttx4wB3AUVE0D9o+0VDjCHFxzX3SK9PanjhRMRWKaXftol3aTtKuRdpfbYFHsz0/XAyeSLkqS3jPyM/C3MuORYj98isT6opTAIQEbuklL43l/gwcswi9+7ky7DGaj8MKXft/hzS9t6KXKH59tLyTii+3r3Sj2HF573KPnkQNbKTHqOUe7GM8mRa174s9sRO2SRNamrp4AB3gUTEU4CPAJcyVRzjkcW/X5Mf6QP5cq9esYDzSm27xsc19zisz+bAq1JKvcsG7xPzVERjlHIv9DIjYh/gWOATTN8Pzy6+/lqL+BPJxTJ+SK5mOZsci5F7lNZnF+Dt1TMzMNrHz4DY0n5c1e7PIbxeDyY/MWBT4DWl5R1YfH0U41ll/yrgP1j8iYlxzT2vy+wy+bZIk2kvI58JbDNZcx7tH/F4IlPViGec8Cn60uVRiYeS663MZfKpy/qcUDfxPaB/uwIHk2+dK+dOLHBV8TSE6uFN8WHkGFbuceEAd4FExEXAbqlUzruI7Q8ck6YXkLkMIKW0eSVH6/i45h6T9TmOXAb/c0y3E7ka4cdLsR0hV7+rxOvado3PZ+5RWp+9yfddrVpOEPneo6jZP33xyIUingJ8L02v3ts6x2LkHrH1OYk84DqV6bYjXxr4nRbx7Yr/6+Jtcwwr9xPJ675SOTgG+2EYuU8kP1Kluj+HsR+eSK7R8ITyto3xrbIP+d7ujYCPMbmTHuM8Uddp8m0RJtMOBt4JvJWFewxj7YRPl/Xp2O9hrU/rfkd+hOAW5PehPUo5FqOq+D4MoXp4U3yUTk6MiyWL3YGlyBKmXgzl2NnkDxplCWqLN3SJj2vuxVhm19zPJV96d3Ylvj9wVyW+P/nZxw+qiVfbdo3PZ+5RWp896d83QKfiJ0Geha6Lt82xGLkXY5lNuXcgP4PviEr8W+Rq423i3yJ/WHhjTbxtjmHlfhR5MFM16vthGLkfT37syMMZ/n54JPkRII+ttL2vsMw8xOezyj7kSYDrUkqvLAcj4knkwfYrS7G9yVf5fC+l9K5BbbvGxzX3AizzJOAjke8l7blvwquY0CnHq7Gu8a65nwj8o+bqgpcV6/PeUuyt5GevnptS+tygtjPEXwy8PSJeVOnLQ4D7F5NC5Rg18S3JlY7bLnMY69Ol31sClwCrpMWvKt6lenhTfJVSvr54yxzDyh3ACowxB7gL55PALyNXWutVQbuMfNP9GaUX88bkm/F7s2dXzTI+rrnHYX0AvppS+jQlEbEv+Tlln67EjgF2rolv1SbHYuQesfUB+EREfJTp1bZXyT9uFb8auBz4UUQcNssci5F7lNbn/sDHU0pnUBIRPyZXYZwxXsR+DlxQE2+VY4i53wx8bAz3wzBy/41cAflN87Af/o080LxfTECV/SJ+D7moUNUkTXqM80Rd3eTbKE2mbUP9YKFusuZehvMYxvWAa5nboxJPJT+Cqe0yh7E+Xfr9XeBN5CsryoZxbN5Bt6riw3h825Us/OP/anMUP7uqGhsnXqK8gCLioeRLW8vX7f8G+Cf676mgpm3X+LjmHvX1OYM8G3kbJRGxJnBHOV4XG1Z8PnOP0voUP1uDXOGwej8WHeJnkj8IzSXHYuQemfVJKd3MBFnaj6v52p9R/8SAXzKmVfaLybe3AqcwfbD9bPKH3a+W4k8mnyH/EfnZvIPado2Pa+75XuY+wFEppUOKWG8C5P3kS5d3rMQ3SCk9kpIu8VnkfirwdfJluuV+P6L4+txS/NFF/FzgVzO0HRT/l6J/0wbbEXEseRJnl0rsU+TaIi8qxZ8KfJk8udNmmcNYny79fjS5uvlW5MlNWLyq4qvQsnp4UzyGV1V8zrmLn72v3HbcOMBdBMUHeVJKNw2KDSs+rrknbX3GNfcorY80bJGfbXjfQClNfz5iq/gwckxa7v4tDRGxckrp1oWMDzH3RsC/svgTE+Oae16XOeqTbx0nfYbyGMZhTPgMabKq0/p07XeMUFVxjQ4HuAskIjYhz/g9kXwpTZCf+fV38qWwNxexVZn+fLC/zDI+rrnHaX12AG6ZId7LvTJw0yxzLEbuUVqfU4FDUqlAW09EnJ9Sevhc4sPIsRi5J219Fjp3RGwD/JT8bMCrycfbRuR7wyHf9z1T/MHkWf4/kWf5Z5NjsXP/rvh5te01c4jfQj4bdA4lMebV3cmX+C365MG45p609ZnrhE/RdjEmazotk/6r20bqEY+pZiATY/pIxMVYZtcc48J7cBfOF8n3PL24NzsVET8jv2i3TyntUMSWBS4ifwhZv9S2a3xcc4/T+qzXIj5T7jY5FiP3KK3P+4GTIz+uoGwHYJOIeE6L+PbF8qrxLjkWI/ekrc8o5T4CuDP13zPVpXrvr8lVRl+Xpl86N4zKwAuV+8lDzv0G4IHANyOifJnhTuQCPG9gumHEdyr+r4sPY5kbAhuQLzG9b/IgImonDyKib2KioW3X+LjmXshl9iZayrnvm8RpaNs13jX3LdRM+BQuJF+a2ybepe2c45Efb/lt8uXj5Qms3mTheS3i8/1Yyc0jou6xjac0rOMw4vOZezGW2TXHWHCAu3DWTil9sSb2usiPcQAgpXRPRK6qk0qXaXSNj2vuSVufcc09YutzILlqdbXoxL7APS3j+5I/iKxYE2+bYzFyT9r6jFLuNcmFTqqi+NcmvlJK6ZMxVQRqNjkmLffhwH8By1Oq3Ak8jbwfVqm0H0b8aeSz8TFPy3wRcHubyZAJmfQYau4RWJ9hT+J0zf0h+id8YHEma7os82Dg7yml3Srr03uE4ozxyI9hfCL5MYyvmE2OAbmPAq4Djo+Iz5Wa7wSsW/y8uu5t4zuS30+q8WHkXoxlds3du6pubDnAXThnR8RHgE8zdUP97yPiAuCKiNigiG1c/B8RsX2pbdf4uOaetPUZ19yjtD43A+eklPajJCIeQS7oMWO8iO0LnFQTb5VjMXJP2vqMWO6/AftHxAuYfmwuyT9uFb8sIm4AzomIf5lljknLfQWwM/CFlNLbS9t7V+BB5diw4kXsDcA35mmZLwHupt8kTUzMZ+7FWOYo5f43cpXdUZis6bLMpjHCqDzicT9yJevd8DGMw84N8MKa2NjwHtwFEhHLAS8Hdmfq/oE/ku81XJN8+RPkSy++XXz9dKbfa9AlPq65J219xjX3KK3Pb4CPppR6lQwBiIjHA6umlL49U7yIXQE8IKV01mxyLEbuSVufUcpd/OxA4KFMPzZPJH+Y2r1l/Jri+7nkmKTctwInpP7nRG4JLJNSumjY8SL2Z/LjLq6fTY4Z4kcBWwOfYPoA/73kD90Hl+JvAR4DnAO8Y4a2XePjmnvS1qdr7o8Bp6SUXkBJRPyUPKGyXiX2WvJkzcaD2g4rPmCZhwJvL9a3vD6vK77+UIv43uSzhGeQn489mxxN8XeQHz/0qZTSe0r9PpX8GMLe54jO8SL2ZuDzKaXNhpl7MZbZNXfxs9+X244bB7iSJEkDRMRuLP7kwTjnnrT16ZL7bPLg8UZKigmVZVNKF1ZiN5EnWqqTNdPaDivetMziZ/9Kf/XwrtvqAuBhc8xRF7+JPJlWLWa3JiP8SMTFWGbXHJPAAe4CiYgl5DO4ezD9jNWfgbWYfsbqm+QX8rOY/gLvEh/X3JO2PuOaexTXZ/c5xHu51wbWH6Pck7Y+o5T7BODYlNI/qIiIo1NKB8wlPowc5h79ZUqSRo/34C6cz5IfnfB2ph4O/UlydbnryPdAQK4K91nyZS4vKbXtGh/X3JO2PuOae9LWZ1xzT9r6jFLuA4BdI+LlTLcG8PRiZnum+OpF7mq8Sw5zj/4yVwX2Ki53X5c8YXID8N3i508txf/E1K0W68zQtmt8XHNP2vrMNvduwANmiPdyr0WerJtNjmEt8wTgvSmlW6iIiJNSpRBU1/gwckxa7nFYn3HhAHfhPCaltEU5EBGbppS2iIhLUkq9D15XR0QCUkrpzFLzTvFxzT1p6zOuuSdtfcY196Stz4jl/gm52Eq5uEYCevcctYlvSi4+tKQU75rD3KO/zPWA5YAnpJSuA4iI9YAfFm12LMVPA+4gP4Jqyxnado2Pa+5JW5/5zn17kXuLBdwPdcs8FDgpIl7NdFsD20bEo1vEtyJPPlXjXXJMWu5xWJ8AtmGMOcBdODdFxPOBr6aU7i3FPkiuEgtARCxDPrCIiGV6bbvGxzX3pK3PuOaetPUZ19yTtj4jlvsG4LqU0qMoifyYqpVSpbhGXbyIPQn4SU28VQ5zj8UyLwZW7A0IAFJK10WePKEcJ1fs3rL4nYFtu8bHNfekrc+45p7FMl9DHsgfwXQ7k4s7tYnvTL56cdWaeNsck5Z7MZbZNTf4mCC1tBfwPuB/Iz/0G6aqxa4Y+flpkA+on5M/lF1Xats1Pq65J219xjX3pK3PuOaetPUZpdxXkatKVh1JvoS5TfxI8mWt759DDnOP/jKvAG6LiHVTUYQnItYlH1dRjgPXRsR3gHKBoNq2XePjmnvS1mdcc89imTcCv0spPYGSyI/zW6tNvIg9Gzi1Jt4qx6TlHof1KX52VTU2ThzgLpCU0h8i4m3Ar5hebOd84OGV2AnF17vPMT6uuSdtfcY196Stz7jmnrT1GaXcKSIOrsRP7BjfkDyoPmoOOcw92st8C/Ac4IyIeEARvx74TvF1OX4j+SqCNSPiphnado2Pa+5JW59xzd11mWcC76bf28j377aJvw1YhvwYotnmmLTci7HMrrmpaTtWllnsDiwtij+Y/0e+n+fnxb+dgC8X//diAD8Avl9p2zU+rrknbX3GNfekrc+45p609Rm13KeSz2j8ovgXHeM7AV8lX+Y12xzmHv1lfhy4OaW0VUppzeLf1iml1wPnVuJbppR2AN7Tom3X+LjmnrT1GdfcXZe5B/nxPtOklL5Cvmd3xnhK6SsppYvJV1jMKsek5R6H9Sms0RAfDykl/y3AP+AS4H41sRWBS2vilzbkaBUf19yTtj7jmnvS1mdcc0/a+oxr7klbn3HNvYjLXK4uXvzsyrbxLm2XltyTtj7jmnvS1mdcc4/D+ozLPy9RXjj3ku8Pu6IS26b4v+y+wihziI9r7sVYprlHY5nmHo1lmns0lmnu0VnmucDGEXFeJf4Q4P6V+EOK/+vi1VjX+LjmnrT1Gdfck7Y+45p7HNYnyI+6GltRjNI1zyLiqcCHgUvJxU0AHg08gvzH81dFbJMiRhG/apbxcc09aeszrrknbX3GNfekrc+45p609RnX3Iu1zCcDBwLfZrqzyJe+P7YS2xf4FLDdDG27xsc196Stz7jmnrT1Gdfc47A+Afw0pbQBY8ozuAskpfTdiNiCfGD1ild8knxwbcv0gha/JB9w280hPq65J219xjX3pK3PuOaetPUZ19yTtj7jmnuxlvlH4LyU0hWURMSJwCbleBH7K/CDmvgmbXJMWu5JW59xzT1p6zOuucdhfYqfnV6NjRPP4EqSJEmSJsIyi90BSZIkSZKGwQGuJEmSJGkiOMCVJKmDiHhbRKSIaKxjERE7F212LsVeHxHPmcXytimWuWaH3+lbviRJSwMHuJIkDd85wD8X//e8Hug8wCU/Auc/gdYD3IblS5I08ayiLEnSkKWU/gqcudDLjYhlyQUkF2X5kiQtNs/gSpI0O1tHxGkRcVtEXBsR74iIZaD/EuGI+APwQODFRTxFxHHFz7aIiK9HxA0RcUdEXBkRX46IJRHxUvKzCwEuLf3upsXvpoh4d0QcEhG/B+4CHt5wifTpEfHjiHhyRJxT9PuCiHh2dcUi4oUR8duiP+dHxLOK3z+91GbliPifor93Fv3/fkRsNdStLElSB57BlSRpdr5Bfgbre4BdgbcA9wJvq2n7bOA7wLmln99Y/P9t4GbglcCfyM9gfRp5EvrbwLuANwPPB64ufufaUu6XApcD/wH8nfzc1tUa+vxg4ENFn/8EHAR8OSK2SildBhARuwCfB04E3gCsAxwJLA9cUsr1QeBZwGHApcBawOOA1RuWLUnSvHOAK0nS7ByTUnpv8fUpEbEqcFBEHFltmFL6VUTcCfwppXTfpcMRsTawObB7SunE0q/8X/H/jRHxu+LrX/cGoRUBPCWldHsp79YNfV4b2DGldGnR7hzyYHlP4PCizduBC4Fnp5RS0e4C4CymD3D/Gfh8SunYUuzrDcuVJGlBeImyJEmz86XK98cDKwMP65Djz+Szr++NiP0j4iGz6Md3y4PbGVzaG9wCpJRuAG4ANoH77uHdFvhqb3BbtDsb+H0l1y+Bl0bEYRGxbfG7kiQtKge4kiTNzvUN32/YNkExiNyFfHb0PcAlEXF5RLyyQz+unbnJfW6qid1JvvwY8hne+5EHvVXV9X0t8HHgZeTB7g0R8cGIWLFDfyRJGioHuJIkzc66Dd9f0yVJSunylNI+5HtdHwWcCnwkInZrm6LL8mbwJ+AfwANqfjZtfVNKt6aUDk0pbQ5sSr7E+TXkRxpJkrQoHOBKkjQ7e1a+3wu4FTi/of2dwApNyVL2a3JhJ5i61PnO4v/G3x2WlNI95LPJz42I6MUj4jHAZgN+74qU0hHkde9yibYkSUNlkSlJkmZn/+KxQL8kV1F+BfC2lNJfSmPDsguBx0fEM4DryGdLVyVXNf4icBmwLLkq8t3kM7m93wN4dUR8mnyG9byU0l3zsVLkM7CnAF+PiKPJly2/rejzvb1GEfEzcqXl88kD+52ARwKfnqd+SZI0I8/gSpI0O7uT7589EXgJ+XE+7xzQ/lDgYnJxql8yNWi8knzW9kTgC8AGwDOKwk6klHqPFnom8OPidzcY9sr0pJS+B7wY2JpcFflg8uOErgP+Umr6Q/JZ7M+TH2f0PODfU0ofmq++SZI0kygVSZQkSeoTERuRzzC/O6U0aBAvSdKicoArSZLuExErAP8NfJ98GfWDgDeSi0z9U0qpS9VmSZIWlPfgSpKksnuA9YAPA2sBfwd+BDzfwa0kadR5BleSJEmSNBEsMiVJkiRJmggOcCVJkiRJE8EBriRJkiRpIjjAlSRJkiRNBAe4kiRJkqSJ4ABXkiRJkjQR/n95IHw8m60wOQAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -630,7 +617,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -678,8 +665,8 @@ } ], "source": [ - "dw_preserved =['1110000000', '0111000000', '0011100000', '0001110000', \n", - " '0000111000', '0000011100', '0000001110', '0000000111', '1000000011', '1100000001']\n", + "dw_preserved = ['0001111111', '1000111111', '1100011111', '1110001111', \n", + " '1111000111', '1111100011', '1111110001', '1111111000', '0111111100', '0011111110']\n", "\n", "for n_cycle in [2*k for k in range(int(N_cycles/2))]: # Runtime close to 2 min !\n", " color_dict = {key: 'red' if key in dw_preserved else 'black' for key in samples_evol[n_cycle]}\n", @@ -695,9 +682,9 @@ ], "metadata": { "kernelspec": { - "display_name": "pulser-dev", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "pulser-dev" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -709,7 +696,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, From 702baf6e1824cc5f360aae90020b72e40dcdf3ea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Wed, 1 Dec 2021 02:31:12 +0100 Subject: [PATCH 23/51] Docs review (Part 2) (#293) * Bugfix for XY spin chain tutorial * Rerun Intro tutorial * Bugfixes on MW Engineering tutorial * Renaming the MW Engineering tutorial * Reviewed all tutorials in "Quantum Simulation" - only QEK missing * Reviewing API docs * Bugfix attempt on XY Spin Chain docs build * Getting rid of ODEError * Getting rid of bugs in the Shadow estimation tutorial * Fixing version of docs requirements * Adding titles to XY tutorial * Increasing sampling rate * Reverting version fixing for docs/requirements.txt * Final change on XY Spin Chain --- docs/requirements.txt | 2 +- docs/source/apidoc/creation.rst | 1 + docs/source/intro_rydberg_blockade.ipynb | 6 +- docs/source/tutorials/mw_engineering.nblink | 2 +- pulser/devices/_device_datacls.py | 11 +- pulser/register.py | 40 ++-- pulser/sequence.py | 97 +++++---- pulser/simulation/simulation.py | 28 +-- ... antiferromagnetic state preparation.ipynb | 28 +-- ...ltonians in arrays of Rydberg atoms.ipynb} | 4 +- .../Shadow estimation for VQS.ipynb | 203 ++++++++---------- .../Spin chain of 3 atoms in XY mode.ipynb | 154 ++----------- tutorials/simulating_sequences.ipynb | 2 +- 13 files changed, 218 insertions(+), 360 deletions(-) rename tutorials/quantum_simulation/{Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb => Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb} (99%) diff --git a/docs/requirements.txt b/docs/requirements.txt index 5ca76d1ea..b3b4ae2f8 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,5 +1,5 @@ # For generating documentation. -Sphinx~=3.3.0 +Sphinx sphinx_rtd_theme nbsphinx nbsphinx-link diff --git a/docs/source/apidoc/creation.rst b/docs/source/apidoc/creation.rst index f3be7aef5..1732f168f 100644 --- a/docs/source/apidoc/creation.rst +++ b/docs/source/apidoc/creation.rst @@ -13,6 +13,7 @@ Register .. automodule:: pulser.register :members: + :show-inheritance: Pulse ------------------- diff --git a/docs/source/intro_rydberg_blockade.ipynb b/docs/source/intro_rydberg_blockade.ipynb index c4ea6a4f6..2e91174e5 100644 --- a/docs/source/intro_rydberg_blockade.ipynb +++ b/docs/source/intro_rydberg_blockade.ipynb @@ -37,7 +37,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -107,7 +107,7 @@ "outputs": [ { "data": { - "image/png": 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IhC3IMXI4i+6I0QjsBmYD+4ENuB+KCYObfeiILfn1tpktDVuLIxycxXc4HK5F4HA4XIvA4XDgDIHD4cAZAofDgTMEDocDZwgcDgfOEDgcDuD/AWozJ8bBw6T7AAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -235,7 +235,7 @@ "outputs": [ { "data": { - "image/png": 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jS5cu5Re/+AXV1dXMnDkTeyMFzZRSFmdmGVR4yKray6wpl/Pd2mxK86s448oJnbboXmcX0EQhIucBjwN24DljzMMN5g8EXgRivMssNMa825Z9tvSbv7+1pcz41KlT67pD/eCDD9i1a9cxyyYmJh5VDyojI4MzzzyzPd6KUp1axbY8PMaDY0gkIWERbPxgK32SIxk4Oi7QoXVdxpiADFgf/HuBIUAw8C0wusEyzwI3esdHA/uPt92TTjrJNLR9+/ZjpnW0iIiIuvENGzaYgQMHGqfT2aJ1Dx8+bIwxpqqqypx11lnm448/PmaZ/Px8M3jwYFNQUGAKCgrM4MGDTX5+vl9i7wzHT6mWSn/4S7N+wctmy8oPTNqmHPPEDR+b79ZmBTqsTg9Yb5r4XA3kzeyTgT3GmDRjTA2wFJjdYBkD1Pb7GQ0c6sD42k39MuMtsXjxYkaNGsX48eO56KKLOOusswCrzHhtj3dxcXHcfffdTJ48mcmTJ3PPPfcQF6ffoFTP4sqvhEIXWZV7GXLSyWz84CBRcaEMm9Q30KF1aWIlkgDsWOQy4DxjzDzv66uBKcaYm+st0x/4AIgFIoBzjDHfNLKt+cB8gIEDB5504MCBo+bv2LGDUaO0E/XW0uOnuorSzzMp/l8a34Ss4tTrf8FrD67je/83nAlnJx9/5R5ORL4xxqQ2Nq/VZxQikiQiwa0Pq0WuAF4wxiQBs4CXReSYmI0xzxpjUo0xqfHx8e0cklKqsyr99hDFNbkkn3Ii2784hD3IxshTEgIdVpfnU6IQkRNF5D4R+RY4AOSJyOsi8iMRifFx35lA/TSf5J1W3/XAawDGmK+AUKCPj/tRSvUAnho37sxKsir3MXjCZL5bm82wk/oSGhF0/JVVs46bKERklIgsEZEDwEpgOPAg1uWg72HdhL4FOCwiH4vIz1u473XAcBFJ8Z6ZXA4sb7DMQeDs2jiwEkVuC7evlOpBqvcVI0aoiq4ie58HZ5WbMd8bEOiwuoWWNI89GRCsb/efGGPqFxra7B3uF5FErJvRFwN/Od5GjTEuEbkZeB+rBdTzxphtIrII6+77cuBXwN9E5BdYN7avNYG6qaKU6tTKt+Xg8jiJO3EQ2784RGz/CBKG+r9b0J7ouInCGPMi8KKI2IGrReRtY8wxRYSMMZnAU96hRYz1TMS7DabdU298OzCtpdtTSvVcFdtzya1Kp8+Q8Wz8tJCplwxFRB+w84cW36MwxrixkkBMu0WjlFKt4CqqxlYGee5DFOdGAWjPdX7ka6unr4CR7RFId9eWMuNPPPEEw4YNQ0TIy8urm26MYcGCBQwbNozx48ezYcOGY9atqKjgggsu4IQTTmDMmDEsXLiwbt4LL7xAfHw8EydOZOLEiTz33HNteo9KBUrVrgIAHIPD2b0+j/7DoomKCw1wVN2Hr4niWeABEUlpj2C6s9aWGQeYNm0aH330EYMGDTpq+ooVK9i9eze7d+/m2Wef5cYbb2x0/V//+tfs3LmTjRs3snr1alasWFE374c//CGbNm1i06ZNdQ/vKdXVlHybSYWrlMiUFAqzKxgxWc8m/MnXRPEqcCKw1dss9mYROVVEwtshtm7LlzLjYD3JPXjw4GOmv/3221xzzTWICKeccgpFRUVkZWUdtUx4eDjTp08HIDg4mEmTJpGRkdGm+JXqTIzH4DpQzuHK/VRXD8BmE4aepE9i+5OvRQGTgQnARO/PnwNDASMiu40xo/0bnv9lP/gg1Tv8W2Y8ZNQJJPz2ty1a1tcy46NHN31Imyot3r9//0aXLyoq4p133uGWW26pm/bmm2/y2WefMWLECP70pz8dtT2lugJndjk2l42KiHJydtSQNCqWsMj2fha4Z2lxovC2epoBvG3qVXD1nk1MAMb7P7zuoy1lxv3B5XJxxRVXsGDBAoYMGQLARRddxBVXXEFISAh//etfmTt3LitXrmz3WJTyp8rd+QA4kuMo2VbFpHMHHWcN5asWJwpjjFtEngI+BQrrTa/Ausn9lf/D87+WfvP3t7aUGW9KYmLiUZ0YZWRkkJiY2Oiy8+fPZ/jw4dx6661103r37l03Pm/ePG677TYf35VSgVe8JZMKZxGukAQQGDxeizf4m6+XnmpbPe1rh1h6hPDwcJYsWcKcOXO46aab2nRGcfHFF/PEE09w+eWXs2bNGqKjoxu97HTXXXdRXFx8TKumrKysuuWXL1+uhf9Ul2M8BpNVQ251BoWHx9NvcAgR0SGBDqvb0VZPAeBrmfElS5aQlJRERkYG48ePr2udNGvWLIYMGcKwYcP4yU9+wlNPHXnWceLEiYB1lvHAAw+wfft2Jk2adFQz2CVLljBmzBgmTJjAkiVLeOGFF/z6PpVqb66cCuxuO1WR1eRnVpMyQc8m2oNPZcZFxOMdrcR6ovpTYAOwyXsJKuBSU1PN+vXrj5qmZbLbRo+f6qyKPt1P2Yp0NvXazoGDw7ny3inEJkQEOqwuqbky4z2u1ZNSqvso2ZJJpauYMmdvYvqFa5JoJz4lCm89p0zq1WfSVk9KqUAwxnt/oiqDgoohTDhbLzu1F1/PKI7R1Vo9KaW6B+v+hIOKsGpMqYOBY7Tr3/YSyD6zlVKq1cp3Wl3TlIeG4Ai2MWBoTGAD6saOe0YhIvuw+oLw1Z+NMUtasZ5SSh1X8dZMql2lFDpjSBwRiz1Iv/e2l5Zcerq2ldve38r1lFLquDzZ1eTXHKKieiDJo/WyU3s6bgo2xnzayuFAR7yBrqI9yozv3LmTqVOnEhISwmOPPdbk+tdeey0pKSl15cQ7omSIUu3JXVxNkDOIckclIg4GaqJoV3qu1kHao8x4XFwcS5Ys4de//vVxt7F48eK6cuK1D+Mp1VVVpln9TxTbbETFhRLTTwtYtydNFAHgrzLjffv2ZfLkyQQFBfkxOqU6v6KtGbg8Tg6XhZA8Jq5VXZ56Kipw5uTgqegUzwp3am1uHtvVfP7aLvLSy/y6zT7JkZz2gxEtWtafZcZ9ceedd7Jo0SLOPvtsHn74YUJCtB6O6rpqDpRQXHMYjyfBp8tOVd/tovCVVyj77DNc2dl104MSEwmfPJleF8wiYupUxNHjPhqb1ZJWT6cDXxpjXB0QT7cVyDLjDz30EAkJCdTU1DB//nweeeQR7rnnnnbdp1LtxTg9OMocFJkixJ5A4ojY467jLivn8IMPUvyf/yDh4USedhqhV16JvVcU7qJiqnbsoPTjjyletoygQQPp85OfED1njiYMr5YchVVAfyBHRNKAycaY/PYNq/209Ju/v7VHmfGWqq0QGxISwnXXXdfsjW+lOrvqjBJs2CjGTZ+kSEIjmr/0WpOezsF583CmZ9D7J/Poff312GNijlnOU1ND2cpV5P/tb2TddTcFL75Ewj13Ez55cju9k66jJYmiEEgBcoDB6H2NNvFnmfGWqi0nboxh2bJljB07tl33p1R7KtpideV7uMLBmOHNn01Up+3j4Ny5mJoaBr38EuEnndTksrbgYHqddy5R586k9MMPOfzwwxy4+hqiL7mEfr+9A3tkpF/fR1fSkg/9N4FP6z14t15E0hob2jfU7sNfZcazs7NJSkrij3/8I/fffz9JSUmUlJQAVgnyQ4cOAXDVVVcxbtw4xo0bR15eHnfddVf7vDGlOkDFnlxKnQXU0JfEkTFNLucqLCT9pz/FeDwM+ufLzSaJ+kSEXjNnMvR//6P3/PkUL1vGvotnU752rZ/eQddz3DLjYjUnmAUMB/4ILAJKG1vWGPMHfwfoKy0z7n96/FRnYYxh/52rOFS2l401Q5j3h9MbvfRkPB4OXn89ld9sYNBLLxLWhibhFRs3cmjhQpwH04m77jrib70FW3D365O7TWXGjZVJ/ufd0ATgD8aYRhOFUkq1J3dBFUGeIIqpJj45qsn7EwUvvUTFV1+T8PtFbUoSAOEnnsiQ//yHw48upuD55yn/+isSH3uMEG/f8z2BT/cbjDHXAeEiskhE3hCR10XkXhHp107xKaVUnZIdVpPWnEqabO1UnZZG7h//ROT06cRcdplf9muLiKD/ffeS9OQTuA5lse+SSylc+m986fitK/MpUYjINGAPcCVWL3dVwI+A3SIy1f/hKaXUESXbM3F6qik1MSSOPDZRGGM4/OBDSEgI/X+/qFUP4jUn6uyzSVn+NuEnnUT2vfeS8bObcRUU+HUfnZGvLZgeA14FRhhjrjbGXA2MAJYCAb8/oZTq3lxZlRRUZyOOvgwYFn3M/LJPP6X8iy/o87ObcPRpn46Mgvr2Jflvz9LvjoWUf/45abNnU/bF6nbZV2fha6KYiHWPorbvbLzjfwRO9GNcSil1FOPyEFwZTLGnlL4DYwkJP/r+hHG7yVn8GMGDBxN35ZXtGovYbMTNncvg11/DHh1N+rx5HH7oYTw1Ne2630DxNVEUYz1T0VAKUNTmaJRSqgnVmdaDdgU1Tvo3cjZR+sEH1OzdS/yttyAd1Cop9IQTSHnjDWKvuoqCF19k///9gKpduzpk3x3J10SxFPi7iFwlIine4UfAc1iXpHwiIueJyHciskdEFjaxzA9EZLuIbBORf/m6j86iPcqMv/LKK4wfP55x48Zx6qmn8u233za6/mmnnVZXYnzAgAHMmTMHgE8++YTo6Oi6eYsWLWrLW1SqXRVtTQeg0BVCwpCjE4XxeMh76mmChw0laubMDo3LFhpKwt13kfTM07jy8th3yaUcXrwYd1l5h8bRnnwtZHIbIMDz9dZ1Ak8DjX7QN0VE7MCTwAwgA1gnIsuNMdvrLTMcuAOYZowpFJG+PsbbadSW8ACYO3cuTz75JHfeeWeL1p02bRoXXnghZ5555lHTU1JS+PTTT4mNjWXFihXMnz+fNWvWHLP+559/Xjd+6aWXMnv27LrXp512Gv/97399f0NKdbDyPXl4XFVU2XofkyhKP/qI6t27GfDYY4gtMMUjos48k7B3lpPzxz9S8PfnKVn+DvG/+iXRF12E2O0BiclffG0eW2OMuQWIxbpfMRGIM8b8whjj68W5k4E9xpg077pLgdkNlvkJ8KQxptC7/xwf99Ep+avM+KmnnkpsrNXy45RTTiEjI6PZ7ZSUlLBy5cq6MwqlupQ8F4XOHCJj+xEZe3T144IXXyIoOZle558XoOAsjrg4Btx/P4P/vRRHv35kLbyDtAsvoviddzBud0Bja4tWlUY0xlQAW9q470Qgvd7rDGBKg2VGAIjIasAO3GuMea/hhkRkPjAfYODAgc3udNULz5JzwL/VRvoOGsL0a+e3aNn2KjP+97//nfPPP7/ZZZYtW8bZZ59Nr1696qZ99dVXTJgwgQEDBvDYY48xZsyYFu1PqY7kqXIRXBNCoauc/qNjjmr2WrVjB5XffEPf22/vNN/cwyZMYPBr/6b0w4/Ie+IJDv3mNnKX/IXYy39I9CWX4Ig9fsXbzqSz19B1YJUOORNIAj4TkXHGmKL6CxljngWeBauERwfH2CLtWWZ81apV/P3vf+eLL75odrlXX321rk4UwKRJkzhw4ACRkZG8++67zJkzh927d7cpFqXaQ0VaPiJCQY2HExpcdip45RUkLIyYS74foOgaJzYbvc6dSdSMcyj98CMKX36ZnMWPkfv4EiLPOJ2omTOJPPNM7FFRgQ71uAKZKDKB5Hqvk7zT6ssA1hhjnMA+EdmFlTjWtXanLf3m72/tVWZ88+bNzJs3jxUrVtC7d+8ml8vLy2Pt2rW89dZbddPqn1nMmjWLm266iby8PPq0U/tzpVqrcOtBBCgy4fQfeiRRuAoLKXnnv0TPno09+tiWUJ1BbcLode5Mqnbtouj1Nyh97z1KP/wIgoIIGzuW8MmTCT9pEiEjR+Lo18/vDwq2VSATxTpguIikYCWIy7Ge+K5vGXAF8A8R6YN1KapLV6n1Z5nxgwcPcskll/Dyyy8zYkTz/Wy88cYbXHjhhYSGhtZNy87Opp/3j3Lt2rV4PJ5mk41SgVK1vwiXsxIT3I/45CPfwEuWL8dUVxN7Vfs+N+EvoSNGkHDnb+l3x0IqN31L2aqVVKxdR/7zz5P/7LMA2KKiCBkyBEdCAo6+fXH0jcceE4MtLBxbuDVIcDBit4HNBmJDbAI2G45+/drlslbAEoUxxiUiNwPvY91/eN4Ys01EFgHrjTHLvfNmish2wA38pit3mlSrfpnxq6+++rjLL1myhEcffZTs7GzGjx/PrFmzeO6551i0aBH5+fncdNNNADgcDmor59YuM2DAAACWLl3KwoVHN0x74403ePrpp3E4HISFhbF06dJO901GKQB7EeQ68+k7aBz2oCNtcIqWvU3omDGEjhwZuOBaQWw2wiedSPgk6zllT0UFVdu2UbV7NzV79lCdto/q3bspX70aT1nLu25OuPd3xF5+uf/j7W5FrbTMuP/p8VOB5C6tIeuBNXxbvIXos2bwvf8bDkDVd9+xb/Yc+t15J3FX/yjAUbYfT3k57tJSPBUVeMor8FRUYGpqwHgwHg94jDXudhM6ahTBycnH32gj2lRmvJGN/RA4G+hLg+a1xpiLWxWhUko1oWSH1QFXocvGyHo3sovfWgZBQfS68IIARdYxbBER2CIiAhqDT4lCRBYDt2L1o30Iq8c7pZRqN0XbMrAbDyUmioQhVgMM43JR/N//EnnG6V2uqWlX5OsZxTXAFcaYN9ojGKWUasiZWU6Zs4zw3klExlqNMcq//BJ3Xh4x+vBoh/D1WXcbsKkd4lBKqWMYYwgqc1DkLCJh6JEWeSXvvY8tKoqI008PYHQ9h6+J4lmsjoqUUqrdOfPKCSKYAmfNkctOTielH39M1FnTu2Xf1Z2Rr5eeYoArRWQGsBmrIGAdY8wCP8WllFIUbj4AQJE7iCneG9nlX6/BU1xM1LnnBjK0HsXXM4rRWJeeaoATgHH1hrF+jaybaY8y47XWrVuHw+HgjTcav3X0zTffMG7cOIYNG8aCBQt6TD+/qusr/S4Hl8dJuT227kG70g/exxYRQcS0aQGOrufwtXrs9GaGs9oryO6gtoTH1q1biYuL48knn2zxutOmTeOjjz5i0KBBx8xzu93cfvvtzGymBv+NN97I3/72N3bv3s3u3bt5771j6ioq1Sl5DldT5Mylb0oy9iCbddnpw4+InD4dW0jI8Teg/MLnwu0i0k9EFonIGyLyuojc25X7iQgEf5UZB/jLX/7CpZdeSt++jf8KsrKyKCkp4ZRTTkFEuOaaa1i2bFkrolaqYxm3IaQqhIKaUhKGWk1gK9atw11URNS5Hds5UU/n63MU04D3gMPAV97JPwJ+KSLnGmO+anLlTqLonb3UHPJvz1PBAyKIuWhoi5b1Z5nxzMxM3nrrLVatWsW6dY3XSczMzCQpKanudVJSkk9JSqlAqcwswi4OCl0uJqRY9ydKPvgACQ8nson/M6p9+Hoz+zGsLk9/aozxAIiIDXgG+ANwqn/D6z7ao8z4rbfeyiOPPIItQD16KdWeCjbtB6DIY3V9aoyh7JNPiZx2KrZ6xS1V+/M1UUwErq1NEgDGGI+I/BHY6M/A2ktLv/n7W3uUGV+/fj2XewuA5eXl8e677+JwOI7qwS4xMfGonu8yMjJITEz0z5tSqh1VpuVhcxskpj+RsSFU7dyJKzubyJ/fHOjQehxfE0UxkAJ812B6ClDkj4C6O3+WGd+3b1/d+LXXXsuFF154TDen/fv3p1evXnz99ddMmTKFl156iZ///OdteAdKdZA8N4U1+SSOnQBA2SefABCpD9l1OF+vWSwF/i4iV4lIinf4EfAc1iUp1QL1y4y3xJIlS0hKSiIjI4Px48cf1UtdUyZOnFg3/tRTTzFv3jyGDRvG0KFDj9tlqlKB5qlxE+IMo8BZToL3+YmyVZ8QOm4cjvj4AEfX8/h6RnEbIMDz9dZ1Ak8DC5taSUFZg5ry77zzTovXXbBgAQsWNP8s4wsvvHDU6/pnKampqWzdurXF+1Mq0Mr35mITG0Uuw4Qh0bjy86ncvJk+N/8s0KH1SD4lCmNMDXCLiNwB1F7s32uMqfB7ZEqpHqtgywHsQKktgj7JkZS+sxyMIfLMMwMdWo/Uqh7uvIlhi59jUUopAKoPFCEuN70GDcbusFH2ySc4+vYl9Dh9yKv2cdxEISLLgR8ZY0q8403qzB0XGWO0m89W0HIfKhCkEApq8hkwYgSmpobyL76g16xZ+n84QFpyRpHPkQ6KumR/1aGhoeTn59O7d2/9Q/OBMYb8/HxCtc266kCeKhdhnnAKXZUMH9KLig0b8JSXEzn9zECH1mMdN1EYY65rbLwrqW0xlJubG+hQupzQ0NCjnuxWqr0V77AqBxS5hIQh0ZQ/txqCgoiYMiXAkfVcvpbwGAikm0auR4jIQGPMQb9F5kdBQUGkpKQEOgylVAsUbcsgCKiJjCEiOoSc1V8SPmFCwPuN7sl8fY5iH3BMI2YR6e2dp5RSbVKTXkqZs4i+I4fhKiykascOwk+dGuiwejRfE4Vw5H5FfZFAVdvDUUr1dPZiGwU1BfQf3oeKr7+2msWeqmXkAqlFl55EZIl31AAPiUj95ybswMloX9pKqTZylVYTShiFrmqGDomm/O9fYouKInSs9osWSC29RzHO+1OAUVg93NWqATZgVZZVSqlWK9xi3eYsNg56J0awf/WXhE85GXG06pEv5SctOvrGmOkAIvIP4BZjTEm7RqWU6pFKdhwi2AhB/fvhOZSB89Ah4uZdH+iwejxfS3hcJyIOETkVGAgEN5j/kj+DU0r1LM5D5VQ5qxkwejTlX1n9oEVM1RvZgeZr89gTgHewyooL4PZuwwlUA5oolFKtYowhuCyITGcOicPiKH/+SxwD+hPcRDfAquP42urpz8A3QDRQgXW/IhXrRval/gxMKdWzOPMrCJZQipxO+g2KpHzNGiKmTtVqCp2Ar3eIJgNnGGPKRcQDOIwxG0TkNuAvwHi/R6iU6hEKNu8HoCI0FFvGbjwlJURos9hOoTXPUdQ2jc0FavvUzACG+SsopVTPU/pdNm7jJmLIIMq//BKAiFNOCXBUCnxPFFuBCd7xtcDtInIGcB+wx9edi8h5IvKdiOwRkSY7PhKRS0XEiEiqr/tQSnUNrqwqimvySB6bQvmXXxEyahSO3r0DHZbC90TxANZZBcBdWC2fVgEzgea7YGtAROzAk8D5wGjgChE5pti8iEQBtwBrfIxVKdVFGI8htDqUQmcpCYkhVGzcSISW7eg0fG0e+3698TRglIjEAYWNFQo8jpOBPd7tICJLgdnA9gbL/R54BPiNj9tXSnURVVlFBEkwRW43Ydk7wekkYqren+gsfD2jqCMikSISaYwpaEWSAOv+Rnq91xkcuedRu49JQLIx5n/HiWW+iKwXkfVaSlyprid/k1VT1BMXTeVXXyHBwYSnnhTgqFQtnxOFiNwqIgeBYqBYRNJF5Bfi5zZsImID/gj86njLGmOeNcakGmNS4+OPKW6rlOrkynbl4vLU0HvMcMq//JKwSZOwaYdZnYZPiUJEHgXuBf4KzPAOzwD3YF0e8kUmkFzvdZJ3Wq0oYCzwiYjsB04BlusNbaW6H5PjpKAmjwHJUVTv2qXNYjsZX5+jmAfMM8a8UW/aShH5Dit53ObDttYBw0UkBStBXA5cWTvTGFMM9Kl9LSKfAL82xqz3MWalVCfmqXET4Ym0nsjO2U4BWrajs2nNPYrNTUzzaVvGGBdwM/A+sAN4zRizTUQWicjFrYhLKdUFFe3MxCZ2yoMduNd/hT06mtDRowIdlqrH1zOKl4CfYTVXre9G4GVfd26MeRd4t8G0e5pY9kxft6+U6vwKNx8gBAhO7kf5288TPnUqYrcHOixVj6+JIgS4UkTOBb72TpsCDABeqdfBEcYYn56rUEr1TFVpxbickDCwP67Dh/WyUyfka6I4AauTIoBB3p/Z3qH+uWJrmssqpXqg4HIHuc5c+ueXUQVETNMb2Z2Nrw/cTW+vQJRSPU91QTlhEkGR+xBJG78iKDmZ4KSkQIelGmj1A3dKKdVW+Rv2AuCOjaRy3VptFttJ+dpx0fLm5htjtLWSUqrFindkE26CieoXjae8XBNFJ+XrPYr8Bq+DsKrJJgP/8UtESqkew5NVTVFNCX3KS0GEiCknBzok1Qif+8xubLqI/AEo8UtESqkewbgNEe4I9jnzSNr8KY6xY7HHxAQ6LNUIf92j+CvW8xVKKdUiJXuycEgQ5UE2XN+u18tOnZi/EsVIP21HKdVD5G+0KsbaokLA7dZE0Yn5ejN7ScNJQH+szoee91dQSqnur3xXAW5XEHEmHwkLI+zEiYEOSTXB15vZ4xq89mD1nf0LNFEopXwQUuYgryaPqLTPCJ+cii04ONAhqSboA3dKqQ5XlVtCuC2SNJNNzJ7NRFx2e6BDUs3QB+6UUh0uZ80uANwh1mu9P9G5+dpx0QMi8tNGpv9URH7vv7CUUt1ZydbDOD01RFXl4oiPJ2T48ECHpJrh6xnF1cDGRqZ/A1zT9nCUUj2BvdCQX51Lry2fEXHqVPzck7LyM18TRV+sm9cN5QP92h6OUqq7c5VXE0kvCt2VBB/eq5edugBfE8VB4LRGpp8OZLQ9HKVUd5ezbjciQo3dhQDh2v9Ep+dr89i/An8SkWBgpXfa2cBDwCP+DEwp1T0VbkwnyoQRUpJFyPDhBPXtG+iQ1HH42jz2DyLSB1gC1DZ6rgEeBxb7OTalVDdkDjsprCknZvtqIuZoi/uuwNczCowxd4jI/cBo76Qdxpgy/4allOqOPE43USaKNOcB+hWna292XYSvJTweANKNMc8A6+pN/ymQaIy528/xKaW6kbyNe7GLgwpTjS04iPDJkwMdkmoBfzWP3YA2j1VKHUfO12l4jAdH3n7Cp0zBFhYW6JBUC/h66amp5rF5aPNYpdTxHKqmqKac3ru/IvIX81q1iYMlB9mYs5Hs8myC7EGk9EphcsJkIoMj/RysquVroqhtHpvWYLo2j1VKNctVVUMvE80e5z4SKg4TeXpjLe2b9nXW1zy58Uk25W46Zl6IPYRzB5/LTRNvIjEy0U8Rq1raPFYp1SFyvt6FTexU1pQSMngwwYMGtWi9CmcFD619iGV7lpEQkcCvU3/NaUmnkRyZTI2nhh35O3hv/3ss27OMFftWcMukW7h69NXYREvZ+YvfmscaYx71d3BKqe4jb90BYkwEwfu/JXL66S1aJ7cilxs+uoE9hXv4ybifcMOEGwixh9TND7IHkZqQSmpCKvPGzePBNQ/y2PrHWJe9jkdPf5TwoPD2ejs9is8p1xhzB9AHOMU7xBtjFvo7MKVU92LPdZNfnUt89jYiTj9+osgqy+KaFdeQUZrBM+c8w4JJC45KEg0lRCTw+PTHWXjyQj7P/Jzr37+ewqpCf76FHsvnRCEi0cAowAC79RkKpdTxOMsqiZJo8p0lhNprjtsstri6mBs/upGi6iKem/kcpya27HkLEeGqUVfx5zP/zO6i3cz/cD4lNSX+eAs9WosThYgMFJF3sAoArvEOeSKyXERadrFRKdUjZX2+A5vYqC7LJeKUU5rtzc7pdnLrqls5UHqAx6c/zvj48T7vb/rA6Tw+/XH2FO3hpo9uosJZ0Zbwe7wWJQoRSQS+Bk4E7gEu9Q6/A04CvhSRAe0VpFKqaytYn47TU0NE2kYizzij2WX/tOFPrD+8nt9P+z0n9z+51fucljiNxacvZkveFu5afRce42n1tnq6lp5R/A7YBww3xjxojFnmHR4Ahnvn/a69glRKdV3GGMJLgsmtzqFPwU4iz2q6vtPKgyt5efvLXHnClVw45MI27/ucQefwy5N+yYcHPuSpTU+1eXs9VUsTxSzgt8aYyoYzjDEVwF3ABb7uXETOE5HvRGSPiBxzQ1xEfiki20Vks4h8rJe4lOp6SvZmE26PpLCqgMhxo5usFptdns1dq+9idO/R/Cr1V37b/zWjr+H7w77PXzf/lff2v+e37fYkLU0U8cDeZubv8S7TYiJiB54EzscqMHiFiIxusNhGINUYMx54A9AmuEp1MRkfbgXAZO4kasY5jS5jjGHRV4twup0sPn0xwfam72H4SkS465S7mBA/gd+t/h0HSg74bds9RUsTRQ4wrJn5w73L+OJkYI8xJs0YUwMsBWbXX8AYs8p7xgLWPZIkH/ehlAow94FyimsKiM/YSNQ5jSeK/6b9l88zP2fBpAUM7DXQ7zEE24NZfPpiguxB/PrTX1Ptrvb7PrqzliaKFcD9InJMI2YRCQV+D7zr474TgfR6rzO805pyvTeOY4jIfBFZLyLrc3MbK0WllAoEV2UN0Saa3Op8YpPjGn0aO68yj4fXPsyE+AlcecKV7RZL/8j+PDDtAXYW7GTxOu0+xxctfTL7XmA9sEdEngB2eqePBm7ybueHfo/OS0R+BKQCjTaXMMY8CzwLkJqaatorDqWUbzJWbcYhDsoLMog85+xGl3l07aNUuapYNG0Rdpu9XeM5I/kMrhtzHf/Y9g9SE1I5b/B57bq/7qJFZxTGmEPAqcAW4EHgLe9wv3faNGNMpo/7zgSS671O8k47ioicA9wJXGyM0fNFpbqQwrVWs9ioAxsavey0LnsdK/av4MfjfsyQ6CEdEtPPJ/2c8fHjue/L+8go1VqmLdHiB+6MMfuNMbM4tnzHLGNMw2qyLbEOGC4iKd4ig5cDy+svICInYhUivNgY4+s9EKVUAHncHqLKw8muyiYhvILQ0Ue3VXF5XDy45kEGRAzgx2N/3GFxBdmCePT0RxGE2z67DafH2WH77qpaU+up0Biz1jsUtHbHxhgXcDPwPrADeM0Ys01EFonIxd7FFgORwOsisklEljexOaVUJ3N4zXeE2sMpKj5EzAWzEJGj5v/7u3+zp2gPv5n8G8IcHduBUWJkIveeei9b8rbwl41/6dB9d0U+95ntT8aYd2lwE9wYc0+98cabSCilOr3slbuIM70I3/8Nve5cdNS8/Mp8ntz4JFP7T+XsgY3fu2hvMwfP5AdZP+AfW//BlIQpTEucFpA4ugIt2K6U8jtjDOHFQeRUZZPYq4rQESOOmv/4hsepdFWycMrCY840OtJvJv+GYTHD+O0XvyW3QltMNkUThVLK7/I37yfCHkVBSRYxF5x/1LwtuVt4a89b/Gj0jzrsBnZTQh2hPHbGY1Q4K/jtF7/VelBN0EShlPK79BVb8RgPQQc20WvWkUThMR4eXPMg8WHx3DD+hgBGeMTQmKEsPHkhX2d9zfNbnw90OJ2SJgqllF95PB7C8+3kVGUxqK+b4IFHnrR+e8/bbM3fyi9O+gWRwZEBjPJolwy/hPMGn8cTG59gU86mQIfT6WiiUEr51aHVO4iwR5FflE7vy75fN72spozHN1j9S1wwxOcaou1KRLhn6j0kRCRw+2e3U1xdHOiQOhVNFEopv8r5aBcuTw2RB9fT6/wjl53+uvmv5Fflc8fJd2CTzvfRExUcxeLTF5NTkcN9X92HMVrkoVbn+20ppbosd42TmMpIDlVmMvjU4dgiIgDYX7yff+74J3OGzWFsn7EBjrJp4+LHccukW/jwwIe8vuv1QIfTaWiiUEr5Tdrb6wi2hVKSl0bc/11WN33x+sWE2EO4ZdItAYyuZa4Zcw3TBkzj0XWPsqtwV6DD6RQ0USil/KZ8XTYVrjISK3cTOmECAJ9nfM5nGZ/x0/E/pU9YnwBHeHw2sfHA9x6gV3AvFqxcQEFVqwtQdBuaKJRSflG4O5M+tngySvaTdOVsRASn28mj6x5lcK/BXDXqqkCH2GK9w3qz5Kwl5Ffmc8vKW3p8/xWaKJRSfpH273V4jAf7oY1EX2yVa3tx+4vsL9nPbyb/hiB7UIAj9M3YPmN54HsPsCl3E3evvrtH39zWRKGUajN3tZPo4nCyKtM5YfoobKGhpJek88y3zzBj0AxOTzo90CG2yszBM7ll0i2s2LeCxesX99hkEdCigEqp7mH7i58Saw+j+PBO4n57M8YYHljzAA6bg9sn3x7o8Nrk+rHXk1eZx8vbXybUHsqCSQsCHVKH00ShlGoTj9uDY1clhZ5SRg6xE5SQwIp9K1h9aDV3nHwH/SL6BTrENhERbp98O1WuKv625W8E2YL46YSfBrSYYUfTRKGUapNdb35FlCOG7RmfM/KheRRXF/PI2kcY23ssPxzZbj0kdygR4e5T7sbpcfLUt09RUFXAwpMXtnvXrZ2FJgqlVKt5PB6ca3MpM0GkDKgmODmZuz69jeLqYp6Z8Uy3+iC12+z8ftrviQuN44VtL5Bflc/90+4nPCg80KG1O72ZrZRqtV2vf0Wsozfp+TsY9LMfs2LfClbsX8GNE2/khLgTAh2e39nExq9Sf8VvUn/Dxwc/5sr/XUlaUWt6gu5aNFEopVrF43LjWZdPibOQYSluiuLDuP/r+xnfZ3yH9oEdCNeMuYZnZzxLYXUhl//vcpbuXNqt+7LQRKGUapUtf/+YXo5YMnO2MmDBPO74/A5q3DU88D2rtVN3N6X/FF678DUmxE/ggTUPcO1717Ijf0egw2oXmiiUUj4ryykiYo8hv+owY6Yn88y+f7I2ey13nXIXg6MHBzq8DtMvoh/PzniW+6fdT1pxGj/47w/45Se/7HY1ojRRKKV8tuPPHxJsC6Wo8Fu+mz6A57Y8x2UjLmP2sNmBDq3DiQizh83m3Uve5YbxN7A6czWXLr+UuSvmsnzv8m7Rt4V0tycNU1NTzfr16wMdhlLd1q631xL2ZRVpxTvpNy+Fa3fdzcCogbw862VC7CGBDi/giqqKeHvv27z23WscLD2IXeyc1O8kUhNSGd9nPGN6jyEmNCbQYR5DRL4xxqQ2Ok8ThVKqpcqyCzn02Ne4jQtXQhq3DloOAq/MeoWEiIRAh9epeIyHrXlb+ST9Ez7N+JTdhbsxWJ+3UcFRJEclkxiZSHRINNHB0USHRBPuCMdhcxBkD8IhDhw2B3axQwuf7RsZO5KkqKRWxauJQinVZh6Xm423v0kfR18OFHzGn87/huyqHF48/0VGxI4IdHidXllNGdvyt7GzYCfppelklGaQVZ5FcXUxxdXFuIyrzfu4+5S7+cHIH7Rq3eYSRfdvmqCU8ov1977JgKD+7MrfwHNnriWj4jBPnfOUJokWigyOZEr/KUzpP+WYecYYKlwVVLoqcXlcON1OnMaJ0+2sOwtpiX7h7VMuRROFUuq41j+6jAGu/uwv2cU/J61gDwU8dc5TTE6YHOjQugURISIogoigiECH0ihNFEqpZq176D/0L44nqyKdFxJfYlesm2emP8OkfpMCHZrqIJoolFKNcjtdrLv73yQxkEMVB/lz/F8oHR3Pv85+koG9BvpvRzUVUJ4DVcXgqj4yGA84gsEeYv0MjoKIPhAaAzZt2d+RNFEopY6Rs3kfGf9YR1LIQPaX7GLxwGc44dSzuOuUu4gKjvJtY8ZAWQ7k7oDc7yB3p/WzJBPKcsFZ7tv2xA7hcRDRF2IGQuwgiBlk/YxNgd5DwaHNdP1JE4VSqk51WQUbHl1Gv6q+xAX1YWP+5zxx6goWnHEvFwy5oPmVjYHSLMhpkBByd0JV0ZHlQqMh/gRImgwR8UeGsBhwhFkf8o5QELHOLNzV4KqB6lKoyIPyPOtn6WEoOgj7Pjs62Ygd4oZA/EhrP/EnWON9hkNQWHsctm5PE4VSiuJ92Wz72wf0rulDsiOZnOpM3nEsI2J+Km9MePfoswiPB4rTIW+XNxnUJoTvoLrkyHJhcdB3FIy95MiHdfwoiOxrJQF/MQYq8qHwABSkQV69JPXdCjBu74JinXX0GQnxI7w/R0KfEVaSUk0K6HMUInIe8DhgB54zxjzcYH4I8BJwEpAP/NAYs7+5bepzFEq1TObX20l7Zw3BZUH0CxmAwxZEfvVhNtasoeSKvlw58gL6VFdA4X7I220lhrzdkL8HXJVHNhTRt963d+/PvqOs+wmB5qqBgr1W4sjZ6U0iu6z34K4+slxkPyth1L6HmEEQkwzRyRASGbj4O1CnfOBOROzALmAGkAGsA64wxmyvt8xNwHhjzE9F5HLg+8aYZrvM0kShejpnVQ1FGdkUZmZRlp1LRU4B1YfLMGWGIHcQEbYoooNjCbNbTTErXWUcqkxnf+gGkkd/x2ll+QQVHwKPs26bRmx4ogfhjhuGK3Yo1THDqI4eSmXsMGqCYnG6Pbg9BpfHg8ttcHmswe3x4PZw5KcxeLzzPB6D2xjcniODx/va5TGIgE0EW91P77hNEO+43TtdBOw2qRscNsFus+GwCba613LkNR7CyjMIK95DaPEeQgr3EFy4h6DC3dhqSo86niY0BhOdjEQnQWRfJLy3lQTDe1tDWCwEhUNwOARFWJe3gsK73A33zvrA3cnAHmNMGoCILAVmA9vrLTMbuNc7/gbwhIiIaYfs9r+fP0pK0MhG5jRxiuzb5BbNbenyvp+0N7GdVp39+xqTbztp1Xaa/F345/KG79vx1++NJn9JzR0nhwQhIoQD4UQD0dasUHB5nJS5ismpPESpK4fK4A2M6L2KiRF2hhLF4YNxvE8SmZ6JpHviyDR9yDR9OGD6UV0ZDNn191UJbGnNu+pEQoGx3gHAEE8RyZJLouSRKHkMcOWTWJ7HgKyt9JZSYiklSNzNbNNSTTBubLix48FmjYs1Xn9ojGnk924a/a0fPS3vpFs56YJ5x43NV4FMFIlAer3XGUDDRxbrljHGuESkGOgN5NVfSETmA/MBBg5sXbM9E2woqfGtyqMvT0zWX6vlU1s1owVzW7fk8dbxfUv+OhbN7cHXlXx9b+1/Ru7bezC4jAs3Ttw4cYkTl91FdVg1rlhB4mOwB/ei2h5BpWMMFY5pbLPficcWVLcFuw3sNhuxdiHeZiPVfuQbeZDdhsP72mGrHa83zW4jqPbbu936Vm8Xqfdt3zo7cNhs2Gzes4B682u//du8H5TGYJ1lGIPxWOPW0GC89qykwRlK/bMal8dTN81TN6/h6yNnQLXzKz2GnR7Dttpl3R7szlJCagoIriki1FmM3V2Jw12J3VVJkKcSh7uKYFONzVipwmY8iHFbqaHupwehsc6OGvl9N/LdWBpZLjgyzoe/lZbrFjezjTHPAs+CdempNdu48A+3+zUmpZTqLgJ5ES0TSK73Osk7rdFlRMSBdQ6d3yHRKaWUAgKbKNYBw0UkRUSCgcuB5Q2WWQ7M9Y5fBqxsj/sTSimlmhawS0/eew43A+9jNY993hizTUQWAeuNMcuBvwMvi8geoAArmSillOpAAb1HYYx5F3i3wbR76o1XAf/X0XEppZQ6oms19FVKKdXhNFEopZRqliYKpZRSzdJEoZRSqlkBLQrYHkQkFzjQytX70OCp705C4/JNZ40LOm9sGpdvumNcg4wx8Y3N6HaJoi1EZH1TRbECSePyTWeNCzpvbBqXb3paXHrpSSmlVLM0USillGqWJoqjPRvoAJqgcfmms8YFnTc2jcs3PSouvUehlFKqWXpGoZRSqlmaKJRSSjVLE4WXiJwnIt+JyB4RWdjB+04WkVUisl1EtonILd7p94pIpohs8g6z6q1zhzfW70Tk3HaMbb+IbPHuf713WpyIfCgiu70/Y73TRUSWeOPaLCKT2immkfWOySYRKRGRWwNxvETkeRHJEZGt9ab5fHxEZK53+d0iMrexffkhrsUistO777dEJMY7fbCIVNY7bs/UW+ck7+9/jzf2NvUv20RcPv/e/P3/tYm4/l0vpv0issk7vSOPV1OfDR37N2aM6fEDVpnzvcAQIBj4FhjdgfvvD0zyjkcBu4DRWP2F/7qR5Ud7YwwBUryx29sptv1AnwbTHgUWescXAo94x2cBK7A68j0FWNNBv7tsYFAgjhdwOjAJ2Nra4wPEAWnen7He8dh2iGsm4PCOP1IvrsH1l2uwnbXeWMUb+/ntEJdPv7f2+P/aWFwN5v8BuCcAx6upz4YO/RvTMwrLycAeY0yaMaYGWArM7qidG2OyjDEbvOOlwA6s/sKbMhtYaoypNsbsA/ZgvYeOMht40Tv+IjCn3vSXjOVrIEZE+rdzLGcDe40xzT2N327HyxjzGVZfKQ3358vxORf40BhTYIwpBD4EzvN3XMaYD4wxLu/Lr7F6lWySN7ZexpivjfVp81K99+K3uJrR1O/N7/9fm4vLe1bwA+DV5rbRTserqc+GDv0b00RhSQTS673OoPkP6nYjIoOBE4E13kk3e08hn689vaRj4zXAByLyjYjM907rZ4zJ8o5nA/0CEFetyzn6P3Cgjxf4fnwCcdx+jPXNs1aKiGwUkU9F5DTvtERvLB0Rly+/t44+XqcBh40xu+tN6/Dj1eCzoUP/xjRRdCIiEgm8CdxqjCkBngaGAhOBLKzT3472PWPMJOB84Gcicnr9md5vTgFpYy1WF7oXA697J3WG43WUQB6fpojInYALeMU7KQsYaIw5Efgl8C8R6dWBIXW631sDV3D0l5EOP16NfDbU6Yi/MU0Ulkwgud7rJO+0DiMiQVh/CK8YY/4DYIw5bIxxG2M8wN84crmkw+I1xmR6f+YAb3ljOFx7Scn7M6ej4/I6H9hgjDnsjTHgx8vL1+PTYfGJyLXAhcBV3g8YvJd28r3j32Bd/x/hjaH+5al2iasVv7eOPF4O4BLg3/Xi7dDj1dhnAx38N6aJwrIOGC4iKd5vqZcDyztq595roH8Hdhhj/lhvev3r+98HaltkLAcuF5EQEUkBhmPdRPN3XBEiElU7jnUzdKt3/7WtJuYCb9eL6xpvy4tTgOJ6p8ft4ahveoE+XvX4enzeB2aKSKz3sstM7zS/EpHzgNuAi40xFfWmx4uI3Ts+BOv4pHljKxGRU7x/o9fUey/+jMvX31tH/n89B9hpjKm7pNSRx6upzwY6+m+sLXfku9OA1VpgF9a3gzs7eN/fwzp13Axs8g6zgJeBLd7py4H+9da50xvrd7SxZUUzcQ3BalHyLbCt9rgAvYGPgd3AR0Ccd7oAT3rj2gKktuMxiwDygeh60zr8eGElqizAiXXd9/rWHB+sewZ7vMN17RTXHqzr1LV/Y894l73U+/vdBGwALqq3nVSsD+69wBN4qzn4OS6ff2/+/v/aWFze6S8AP22wbEcer6Y+Gzr0b0xLeCillGqWXnpSSinVLE0USimlmqWJQimlVLM0USillGqWJgqllFLN0kShlJeIXCsiZQHcf6yIHBaRoX7aXoiIHBSRVH9sT/VcmihUjyAi5jjDC1hP3w4JYJi/Bd41xuz1x8aMMdXAYqxKsUq1mj5HoXoEEUmo9/JCrFIR9Z8IrjTGFHdsVEeISDhWSYWLjDFf+HG7cVgPkk0yxmzz13ZVz6JnFKpHMMZk1w5AUcNpxpjihpeexOpQZ6tYHb7sF5FyEfmHiASLyE0iki4i+SLyRxGx1VsvWEQeEZEMEakQkXVy/M6SZmE9gbu63nbO9J7tnC0ia7zbWi9Hd0YTLSIvi9XpTpWIpInIrfXed4F3m1e07QiqnswR6ACU6uQGY9X4vxCrLPObWGciWVj1ck4AXsP6MH7Tu84/sKqhXolVDmIW8I6ITDbGfNvEfk4DvjGNn+I/BNzu3efjwCsiMtq77P3AOG98h7E6+IlvsP5a4Ayf3rVS9WiiUKp5dqy6OMXAVhF5D+tDN9FYnebsEJHVwHTgTe+N6CuAwcaYg95tPCEi5wA3ADc1sZ9BwKEm5t1tjFkFICKLgC840vfBIKwKurVFDhvrwOkQVsJTqlU0USjVvIMN7l0cBnZ5k0T9aX2945OwCrNtl6O7Sw4BVjaznzDvdhqzud54bTLpi5UongbeEJGTsHote8cY82mD9Su921eqVTRRKNU8Z4PXpolpdu+4zft6ciPLVTaznzysvoyPF0PtpSkbgDFmhYgMwuqb42zgfyLyujHmunrrxAG5zexbqWbpzWyl/Gsj1hlFgjFmT4OhuY5iNgKjW7NDY0yeMeZlY8y1WGW754pISL1FxmKVw1aqVTRRKOVHxphdWF2MviAil4nIEBFJFZFfi8glzaz6PjBKRHr7sj8RWSQic0RkuIiMwuqNLc37DEWt04D3fH0vStXSRKGU/12H1fLpUWAn8F/gdBq/0QyAMWYLVuuky33cVzXwAFbnUquBKOCi2pkiMhWIBt7wcbtK1dEH7pTqJLxdlT4OjDbGuP20zdeBjcaYB/2xPdUz6RmFUp2EMeY9rG4sk/yxPe99is3An/yxPdVz6RmFUkqpZukZhVJKqWZpolBKKdUsTRRKKaWapYlCKaVUszRRKKWUapYmCqWUUs36f/ZK7JhdRjqhAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] diff --git a/docs/source/tutorials/mw_engineering.nblink b/docs/source/tutorials/mw_engineering.nblink index 7ef1dadd1..9b5caebdd 100644 --- a/docs/source/tutorials/mw_engineering.nblink +++ b/docs/source/tutorials/mw_engineering.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb" + "path": "../../../tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb" } diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index 13955925b..bc22a7cc6 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -34,9 +34,9 @@ class Device: Attributes: name: The name of the device. dimensions: Whether it supports 2D or 3D arrays. - rybderg_level : The value of the principal quantum number ``n`` + rybderg_level : The value of the principal quantum number :math:`n` when the Rydberg level used is of the form - ``|nS_1/2, m_j = +1/2>``. + :math:`|nS_{1/2}, m_j = +1/2\rangle`. max_atom_num: Maximum number of atoms supported in an array. max_radial_distance: The furthest away an atom can be from the center of the array (in μm). @@ -72,7 +72,7 @@ def supported_bases(self) -> set[str]: @property def interaction_coeff(self) -> float: - """C_6/hbar coefficient of chosen Rydberg level.""" + r""":math:`C_6/\hbar` coefficient of chosen Rydberg level.""" return float(c6_dict[self.rydberg_level]) def print_specs(self) -> None: @@ -202,16 +202,13 @@ def _specs(self, for_docs: bool = False) -> str: lines = [ "\nRegister requirements:", f" - Dimensions: {self.dimensions}D", + fr" - Rydberg level: {self.rydberg_level}", f" - Maximum number of atoms: {self.max_atom_num}", f" - Maximum distance from origin: {self.max_radial_distance} μm", ( " - Minimum distance between neighbouring atoms: " f"{self.min_atom_distance} μm" ), - ( - r" - Interaction coefficient (:math:`C_6/\hbar`): " - fr"{self.interaction_coeff} :math:`\mu m^6 / \mu s`" - ), "\nChannels:", ] diff --git a/pulser/register.py b/pulser/register.py index 9e62cca37..0ce466bda 100644 --- a/pulser/register.py +++ b/pulser/register.py @@ -248,18 +248,15 @@ def _register_dims( return np.array(diffs) - def draw( + def _draw_checks( self, - with_labels: bool = True, blockade_radius: Optional[float] = None, draw_graph: bool = True, draw_half_radius: bool = False, ) -> None: - """Draws the entire register. + """Checks common in all register drawings. Keyword Args: - with_labels(bool, default=True): If True, writes the qubit ID's - next to each qubit. blockade_radius(float, default=None): The distance (in μm) between atoms below the Rydberg blockade effect occurs. draw_half_radius(bool, default=False): Whether or not to draw the @@ -268,12 +265,6 @@ def draw( draw_graph(bool, default=True): Whether or not to draw the interaction between atoms as edges in a graph. Will only draw if the `blockade_radius` is defined. - - Note: - When drawing half the blockade radius, we say there is a blockade - effect between atoms whenever their respective circles overlap. - This representation is preferred over drawing the full Rydberg - radius because it helps in seeing the interactions between atoms. """ # Check spacing if blockade_radius is not None and blockade_radius <= 0.0: @@ -711,8 +702,8 @@ def draw( fig_name(str, default=None): The name on which to save the figure. If None the figure will not be saved. kwargs_savefig(dict, default={}): Keywords arguments for - `matplotlib.pyplot.savefig`. Not applicable if - `fig_name`is `None`. + ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` + is ``None``. Note: When drawing half the blockade radius, we say there is a blockade @@ -720,8 +711,7 @@ def draw( This representation is preferred over drawing the full Rydberg radius because it helps in seeing the interactions between atoms. """ - super().draw( - with_labels=with_labels, + super()._draw_checks( blockade_radius=blockade_radius, draw_graph=draw_graph, draw_half_radius=draw_half_radius, @@ -786,8 +776,7 @@ def cubic( (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register3D : A 3D register with qubits placed in - a cubic array. + Register3D : A 3D register with qubits placed in a cubic array. """ # Check side if side < 1: @@ -821,8 +810,7 @@ def cuboid( (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) Returns: - Register3D : A 3D register with qubits placed in - an cuboid array. + Register3D : A 3D register with qubits placed in a cuboid array. """ # Check rows if rows < 1: @@ -875,11 +863,11 @@ def to_2D(self, tol_width: float = 0.0) -> Register: the register to be projected. Returns: - Register : Returns a 2D register with the coordinates - of the atoms in a plane, if they are coplanar. + Register: Returns a 2D register with the coordinates of the atoms + in a plane, if they are coplanar. Raises: - If the atoms are not coplanar, raises an error. + ValueError: If the atoms are not coplanar. """ coords = np.array(self._coords) @@ -969,6 +957,11 @@ def draw( if the `blockade_radius` is defined. projection(bool, default=False): Whether to draw a 2D projection instead of a perspective view. + fig_name(str, default=None): The name on which to save the figure. + If None the figure will not be saved. + kwargs_savefig(dict, default={}): Keywords arguments for + ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` + is ``None``. Note: When drawing half the blockade radius, we say there is a blockade @@ -976,8 +969,7 @@ def draw( This representation is preferred over drawing the full Rydberg radius because it helps in seeing the interactions between atoms. """ - super().draw( - with_labels=with_labels, + super()._draw_checks( blockade_radius=blockade_radius, draw_graph=draw_graph, draw_half_radius=draw_half_radius, diff --git a/pulser/sequence.py b/pulser/sequence.py index f31afdd2f..23a9dd53a 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -16,7 +16,7 @@ from __future__ import annotations from collections import namedtuple -from collections.abc import Callable, Generator, Iterable, Set +from collections.abc import Callable, Generator, Iterable import copy from functools import wraps from itertools import chain @@ -64,7 +64,7 @@ class _TimeSlot(NamedTuple): type: Union[Pulse, str] ti: int tf: int - targets: Set[QubitId] + targets: set[QubitId] # Encodes a sequence building calls @@ -367,6 +367,48 @@ def set_magnetic_field( # No parametrization -> Always stored as a regular call self._calls.append(_Call("set_magnetic_field", mag_vector, {})) + @_store + def config_slm_mask(self, qubits: Iterable[QubitId]) -> None: + """Setup an SLM mask by specifying the qubits it targets. + + Args: + qubits (Iterable[QubitId]): Iterable of qubit ID's to mask during + the first global pulse of the sequence. + """ + try: + targets = set(qubits) + except TypeError: + raise TypeError("The SLM targets must be castable to set") + + if not targets.issubset(self._qids): + raise ValueError("SLM mask targets must exist in the register") + + if self.is_parametrized(): + return + + if self._slm_mask_targets: + raise ValueError("SLM mask can be configured only once.") + + # If checks have passed, set the SLM mask targets + self._slm_mask_targets = targets + + # Find tentative initial and final time of SLM mask if possible + for channel in self._channels: + if not self._channels[channel].addressing == "Global": + continue + # Cycle on slots in schedule until the first pulse is found + for slot in self._schedule[channel]: + if not isinstance(slot.type, Pulse): + continue + ti = slot.ti + tf = slot.tf + if self._slm_mask_time: + if ti < self._slm_mask_time[0]: + self._slm_mask_time = [ti, tf] + else: + self._slm_mask_time = [ti, tf] + break + def declare_channel( self, name: str, @@ -926,14 +968,14 @@ def draw( sequence, with a visual indication (square halo) around the qubits masked by the SLM, defaults to False. fig_name(str, default=None): The name on which to save the - figure. If draw_register is True, both pulses and register - will be saved as figures, with a suffix "_pulses" and - "_register" in the file name. If draw_register is False, only - the pulses are saved, with no suffix. If fig_name is None, - no figure is saved. + figure. If `draw_register` is True, both pulses and register + will be saved as figures, with a suffix ``_pulses`` and + ``_register`` in the file name. If `draw_register` is False, + only the pulses are saved, with no suffix. If `fig_name` is + None, no figure is saved. kwargs_savefig(dict, default={}): Keywords arguments for - `matplotlib.figure.Figure.savefig`. - Not applicable if `fig_name`is `None`. + ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` + is ``None``. See Also: Simulation.draw(): Draws the provided sequence and the one used by @@ -1152,43 +1194,6 @@ def _reset_parametrized(self) -> None: self._variables = {} self._to_build_calls = [] - @_store - def config_slm_mask(self, qubits: Set[QubitId]) -> None: - """Setup an SLM mask by specifying the qubits it targets.""" - try: - targets = set(qubits) - except TypeError: - raise TypeError("The SLM targets must be castable to set") - - if not targets.issubset(self._qids): - raise ValueError("SLM mask targets must exist in the register") - - if self.is_parametrized(): - return - - if self._slm_mask_targets: - raise ValueError("SLM mask can be configured only once.") - - # If checks have passed, set the SLM mask targets - self._slm_mask_targets = targets - - # Find tentative initial and final time of SLM mask if possible - for channel in self._channels: - if not self._channels[channel].addressing == "Global": - continue - # Cycle on slots in schedule until the first pulse is found - for slot in self._schedule[channel]: - if not isinstance(slot.type, Pulse): - continue - ti = slot.ti - tf = slot.tf - if self._slm_mask_time: - if ti < self._slm_mask_time[0]: - self._slm_mask_time = [ti, tf] - else: - self._slm_mask_time = [ti, tf] - break - class _PhaseTracker: """Tracks a phase reference over time.""" diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index 9d289f115..75f167b06 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -375,8 +375,8 @@ def draw( fig_name(str, default=None): The name on which to save the figure. If None the figure will not be saved. kwargs_savefig(dict, default={}): Keywords arguments for - `matplotlib.pyplot.savefig`. Not applicable if - `fig_name`is `None`. + ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` + is ``None``. See Also: Sequence.draw(): Draws the sequence in its current state. @@ -502,26 +502,26 @@ def write_samples( self.samples["Local"][basis][qubit][x][0:tf] = 0 def build_operator(self, operations: Union[list, tuple]) -> qutip.Qobj: - """Creates an operator with non trivial actions on some qubits. + """Creates an operator with non-trivial actions on some qubits. - Takes as argument a list of tuples [(operator_1, qubits_1), - (operator_2, qubits_2)...]. Returns the operator given by the tensor - product of {operator_i applied on qubits_i} and Id on the rest. - (operator, 'global') returns the sum for all $j$ of operator - applied at qubit $j$ and identity elsewhere. + Takes as argument a list of tuples ``[(operator_1, qubits_1), + (operator_2, qubits_2)...]``. Returns the operator given by the tensor + product of {``operator_i`` applied on ``qubits_i``} and Id on the rest. + ``(operator, 'global')`` returns the sum for all ``j`` of operator + applied at ``qubit_j`` and identity elsewhere. - Example for 4 qubits: [(Z, [1, 2]), (Y, [3])] returns ZZYI - and [(X, 'global')] returns XIII + IXII + IIXI + IIIX + Example for 4 qubits: ``[(Z, [1, 2]), (Y, [3])]`` returns `ZZYI` + and ``[(X, 'global')]`` returns `XIII + IXII + IIXI + IIIX` Args: - operations (list): List of tuples (operator, qubits) - operator can be a qutip.Quobj or a string key for - self.op_matrix qubits is the list on which operator + operations (list): List of tuples `(operator, qubits)`. + `operator` can be a ``qutip.Quobj`` or a string key for + ``self.op_matrix``. `qubits` is the list on which operator will be applied. The qubits can be passed as their index or their label in the register. Returns: - qutip.Qobj: the final operator. + qutip.Qobj: The final operator. """ op_list = [self.op_matrix["I"] for j in range(self._size)] diff --git a/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb b/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb index d187eae1d..85a792908 100644 --- a/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb +++ b/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb @@ -68,14 +68,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Interatomic Radius is: 6.8878941690266595µm.\n" + "Interatomic Radius is: 6.979121718087879µm.\n" ] }, { "data": { - "image/png": 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+ "image/png": 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r2Xl9eoaSkhJveUxxPk+88QRP/39PM3bw2KZltYdruftDd7P353sZNGhQpENcJD0ymQyga/RJp9QJy7dQW8p5lNWUZf/na6agTwHlNeWMYlSgVJIEM2bMCB1BEiKuOqGinMfcwrm8tfMthjCkaVnD6QbmFM7hvj+7L1wwCa6goKD9laRHiKtOaEdfHotLFjN84HBqD9dy6MQhag/XMnzgcBaXLA4dTQI7ePBgh441lfSLq06op9yK+qP1rCpdRXlNOXMK57C4ZDEjLh0Rc0JJOvWUpbmLrBN5e8oqyiIRnDx5EoD+/fsHTiIpoB19IhdLxVjipp6ySAQHDhzgwIEDoWNIimlLWSSCd955B4Bhw4YFTiJppaIsEsGsWbNCR5CUS1z7wswmmNkbZrbZzDaZ2efyrHOtmR02s0zu9j9DZJWep2/fvvTt2zd0DEmxJG4pNwJ/4+6/NbNLgQ1m9hN339xivfXu3v6USyKdqL6+HoARI3R4pMQjcUXZ3WuB2tz9o2a2BSgEWhZlkS63e/duQEVZ4pO4otycmU0C/g/gP/M8fbWZbQTeBZa6+6auzCY90+zZs0NHkJRLbFE2s0HAD4G/cveW13T/LXC5ux8zs5uBl4C8V7Q0s3uAewAmTpwYX2DpEfr06RM6gqRc4nb0AZhZH7IF+Vl3f6Hl8+5+xN2P5e6/CvQxs7x/T7r7U+5e4u4lI0eOjDW3pF9dXR11dXWhY0iKJa4om5kBTwNb3P1/tbLOmNx6mNlVZH+O/V2XUnqqmpoaXQhXYpXE9sWHgP8OlJtZJrfs/wYmArj7k8CfAveZWSPQANzhPWkSDwmmI5fzEbkYiSvK7v5LWpmoo9k6jwKPdk0ikfddckni/slIyiSufSGSZPv27WPfvn2hY0iK6b99kQjeffddAEaN0mXBJB4qyiIRzJkzJ3QESTkVZZEIevfuHTqCpJx6yiIR7N27l71794aOISmmLWWRCGprawEYPXp04CSSVirKIhHMnTs3dARJORVlkQh69VLHT+KlT5hIBHv27GHPnj2hY0iKqSiLRKCiLHFT+0IkguLi4tARJOW0pSwikiBtbimb2dXAnwH/FRhLdka2CuAV4N/c/XDsCUUS5Nxp1uPGjQucRNKq1S1lM/sP4NPAWuAmskV5FvAVoD+w2swWdkVIkaTQJPcSN2ttGmIzG+Hu9W2+uAPrJElJSYmXlpaGjiEiAq1MUdxq+6JlsTWzy5qv7+4HulNBFhHpDto9+sLM7gX+HjgJnNusdmBKjLlEEuncpaAKCwsDJ5G06sghcUuBIm0Vi8D+/dlLQaooS1w6UpTfBk7EHUSkO9DcFxK3jhTlLwO/MrP/BN47t9Dd/0dsqUREeqiOFOVvA68D5cDZeOOIJNvu3bsBGD9+fOAkklYdKcp93P2vY08i0g0cPHgQUFGW+HSkKP+Hmd0DvMz57YsDsaUSSShdo0/i1pG5Lz5Brq8MbMjdYj8Dw8xuMrNKM6s2sy/leb6fmf177vn/NLNJcWcSEYlbu1vK7j655TIzGxBPnKb37w08BiwAdgNvmdkad9/cbLW7gYPuPs3M7gC+Dnw8zlwiu3btAmDChAmBk0hadeTkkT9vsag32S3nK2JJlHUVUO3u23MZVgK3As2L8q3Astz9HwCPmpl5a+eNi3SCw4ezc3CpKEtcOtK+uLLZ7b8AfwWsiTETQCGwq9nj3blleddx90bgMDC8rTc9ceJE0wTlZ8+eJZPJNF2Z+MyZM2QyGfbt2wdAY2MjmUymafKZ06dPk8lkqK/PnkNz6tQpMpkMBw5kW+snT54kk8k07QhqaGggk8lw6NChpu+dyWSa/lEfP36cTCbDkSNHADh27BiZTIZjx44BcOTIETKZDMePHweyxSCTyXDiRPaQ8UOHDpHJZGhoaACyO6AymQwnT54E4MCBA2QyGU6dOgVAfX09mUyG06dPA9mJdTKZDI2NjQDs27ePTCbDmTNngOxVmzOZDGfPZg+42bNnD5lMpmks3333XTZu3Nj0uKamhrKysvd/Ybt3U15e3vR4165dVFRUND1+55132Lz5/f9jd+7cyZYtW5oe79ixg61btzY93r59O5WVlU2P3377baqqqpoeV1dXU11d3fS4qqqKt99+u+lxZWUl27dvb3q8detWduzY0fR4y5Yt7Ny5s+nx5s2beeedd5oeV1RUsGvXLoqKiigqKqK8vLzpSAyAsrKyprP9ADZu3Ng0oxxAJpPRZ0+fPeD3P3stdaR9cX/zx2bWD/hle69LitxOyntAe8xFJPlanSWu1Rdki/Jad782lkQ0zeO8zN1vzD3+MoC7f63ZOmtz6/zazC4B9gAj22pfaJY4uVjntp4nTpwYOImkQLRZ4ppeZfYy709EZEARcMjM1gC4exxzKr8FTDezyUANcAfw31qsswa4E/g18KfA6+onS9zO/YkvEpeOHKf8jdhTtODujWb2WbIT7PcGnnH3TWb2VaDU3dcATwP/r5lVAwfIFm6RWM2aNSt0BEm5tia5b/dIhu52tIPaFyKSIHnbF20dffGGmd1vZuc1z8ysr5n9kZmtINs+EOkxdu7ced5RGiKdra32xU3AXcBzud7uIaCAbCFfB/yzu//v2BOKJMi5w8BE4tKhoy/MrA8wAmhw90Nxh4qL2hcikiAXdvQFgLufBmo7NY6IiPyejpzRJyI5O3bsaPNsLJGL1aEtZRHJeu+999pfSeQidOTkkfuBf3P3g12QRyTRZs6cGTqCpFxH2hejyU6d+XxujuO8zWkREbl47RZld/8KMJ3sGXSfBLaZ2T+a2dSYs4kkzvbt28+b8Uuks3VoR1/urL09uVsjMBT4gZk9HGM2kcQ5ffp00xSUInHoSE/5c8CfA/XAd4EvuPtpM+sFbAO+GG9EkeSYMWNG6AiSch05+mIYcJu7/675Qnc/a2a3xBNLRKRn6sgk93/XxnNbWntOJI3OXVFi6lTtUpF46DhlkQjOXbJIJC4qyiIRXHFFnNcLFtFp1iIiiaKiLBJByysXi3Q2FWURkQRRT1kkgmnTpoWOICmnLWURkQRRURaJoKqqiqqqqtAxJMXUvhCJoHfv3qEjSMqpKItEoDP5JG6JKspm9k/AnwCngLeBT+W7UKuZ7QSOAmeARncv6cKYIiKxSVpP+SdAkbvPBaqAL7ex7nXuXqyCLF2psrKSysrK0DEkxRJVlN19nbs35h7+BhgfMo9IS3369KFPnz6hY0iKJap90cJdwL+38pwD68zMgW+7+1OtvYmZ3QPcAzBx4sRODyk9y5QpU0JHkJTr8qJsZq8BY/I89YC7r86t8wDZK5w828rbfNjda8xsFPATM9vq7r/It2KuYD8FUFJS4hf9A4iIxKjLi7K7f6St583sk8AtwPW5y1Dle4+a3Nd9ZvYicBWQtyiLdKatW7cCuqq1xCdRPWUzu4ns5aUWuvuJVtYZaGaXnrsP3ABUdF3KrFWrVjF79mx69epFaWlp0/L9+/dz3XXXMWjQID772c92dSyJWb9+/ejXr1/oGNLNfPOb38TMqK+vb3fdpPWUHwX6kW1JAPzG3T9jZuOA77r7zcBo4MXc85cA33f3H3d10KKiIl544QXuvffe85b379+fBx98kIqKCioquvz/ConZ5MmTQ0eQbmbXrl2sW7euw/u0ElWU3T3vbC/u/i5wc+7+duADXZlr+fLlrFixglGjRjFhwgTmz5/P0qVL8647cOBAPvzhD2t6R5EeprU68fnPf56HH36YW2+9tUPvk6iinEQbNmxg5cqVZDIZGhsbmTdvHvPnzw8dSwLZsiV7Wco/+IM/CJxEkqS1OrF69WoKCwv5wAc6vh2potyO9evXs2jRIgYMGADAwoULAyeSkAoKCkJHkATKVydOnDjBP/7jP7Ju3bpI76WiLBLBpEmTQkeQbsLM2LFjR9NW8u7du5k3bx5vvvkmY8bkOyo4K1FHXyTRNddcw0svvURDQwNHjx7l5ZdfDh1JRBImX50oKChg37597Ny5k507dzJ+/Hh++9vftlmQQVvKrao/Ws+q0lWU1ZQx6cpJFM0pYuyYsVx55ZUAvPjii9x///3U1dXx0Y9+lOLiYtauXQtkt6aOHDnCqVOneOmll1i3bh2zZs0K+eNIJ9m8eTOAfp8CtF8nLoSKch71R+tZ8t0l7D++n4I+BTRMbmBa0TSe/fSzPPrNRwFYtGgRixYtyvv6nTt3dmFa6UqDBg0KHUESoiN1ormO1gUV5TxWla5i//H9jB08FoAhDKH2cC2rSlcFTiahaf4UOSeuOqGinEdZTRkFfc7fy17Qp4DymnIeX/Z4oFQikiRx1Qnt6MtjbuFcGk43nLes4XQDcwrnBEokSaEzNeWcuOqEinIei0sWM3zgcGoP13LoxCFqD9cyfOBwFpcsDh1NAhs8eDCDBw8OHUMSIK46Ya1MxJZKJSUl3nzyoLac26taXlPOnMI5LC5ZzIhLR8ScUES6k4usE5Z3oYqyiEgQeYuy2hciEZSXl1NeXh46hqSYjr4QiWDo0KGhI0jKqSiLRDB+vK7lK/FS+0JEJEFUlEUiKCsro6ysLHQMSTG1L0QiGD58eOgIknIqyiIRFBYWho4gKaf2hYhIgqgoi0SwceNGNm7cGDqGpJjaFyIRjBw5MnQESbnEbSmb2TIzqzGzTO52cyvr3WRmlWZWbWZf6uqc0jONGzeOcePGhY4hKZbULeVH3P0brT1pZr2Bx4AFwG7gLTNb4+6buyqgiEgcErel3EFXAdXuvt3dTwErgVsDZ5IeIJPJkMlkQseQFEtqUf6smZWZ2TNmlm+ygUJgV7PHu3PLRGI1ZsyYdq9GLHIxghRlM3vNzCry3G4FngCmAsVALfDNi/xe95hZqZmV1tXVXXx46dFUlCVuQXrK7v6RjqxnZt8BfpTnqRpgQrPH43PL8n2vp4CnIDufcrSkIuc7e/YsAL16JfWPTOnuEvfJMrOxzR4uAvJdEO0tYLqZTTazvsAdwJquyCc9m+a+kLgl8eiLh82sGHBgJ3AvgJmNA77r7je7e6OZfRZYC/QGnnH3TYHySg8yduzY9lcSuQi6HJSISBi6HJTIxTpz5gxnzpwJHUNSTEVZJAJdo0/ilsSeskhi6RRriZuKskgEo0aNCh1BUk7tC5EIGhsbaWxsDB1DUkxFWSSCiooKKiryHTov0jnUvhCJQJeDkripKItEoEnuJW5qX4hEcPr0aU6fPh06hqSYirJIBJs2bWLTJp3RL/FR+0IkgvHjx4eOICmnoiwSwYgRI0JHkJRT+0IkglOnTnHq1KnQMSTFVJRFIti8eTObN+v6vBIftS9EIpg4cWLoCJJyKsoiEQwbNix0BEk5tS9EIjh58iQnT54MHUNSTEVZJIKtW7eydevW0DEkxdS+EIng8ssvDx1BUk5FWSSCoUOHho4gKaf2hUgEDQ0NNDQ0hI4hKaaiLBJBZWUllZWVoWNIiql9IRLBpEmTQkeQlEtUUTazfwdm5B4OAQ65e3Ge9XYCR4EzQKO7l3RRROnhhgwZEjqCpFyiirK7f/zcfTP7JnC4jdWvc/f6+FOJvO/EiRMADBgwIHASSatE9pTNzIDbgedCZxFprqqqiqqqqtAxJMUStaXczH8F9rr7tlaed2CdmTnwbXd/quuiSU82efLk0BEk5bq8KJvZa8CYPE894O6rc/c/QdtbyR929xozGwX8xMy2uvsvWvl+9wD3gCaTkYs3ePDg0BEk5czdQ2c4j5ldAtQA8919dwfWXwYcc/dvtLduSUmJl5aWXnxI6bGOHz8OwMCBAwMnkRSwfAuT2FP+CLC1tYJsZgPN7NJz94EbgIouzCc92LZt29i2rbWumsjFS2JP+Q5atC7MbBzwXXe/GRgNvJjdF8glwPfd/cddnlJ6pClTpoSOICmXuPZFnNS+EJEE6TbtC5HEOnbsGMeOHQsdQ1JMRVkkgurqaqqrq0PHkBRLYk9ZJLGmTZsWOoKknIqySASDBg0KHUFSTu0LkQiOHDnCkSNHQseQFFNRFolg+/btbN++PXQMSTG1L0QimD59eugIknIqyiIR6PRqiZvaFyIRHD58mMOH25rmW+TiqCiLRLBjxw527NgROoakmNoXIhFcccUVoSNIyqkoi0Sgy0BJ3NS+EIng0KFDHDp0KHQMSTEVZZEIdu7cyc6dO0PHkBRT+0IkghkzZoSOICmnoiwSQUFBQegIknJqX4hEcPDgQQ4ePBg6hqSYtpRFIvjd734HwNChQwMnkbRSURaJYObMmaEjSMqpKItE0L9//9ARJOXUUxaJ4MCBAxw4cCB0DEkxbSmLRPDOO+8AMGzYsMBJJK1UlEUimDVrVugIknJB2hdmttjMNpnZWTMrafHcl82s2swqzezGVl4/2cz+M7fev5tZ365J/r4DBw6wYMECpk+fzoIFC3SYVAxWrVrF7Nmz6dWrF6WlpaHjANC3b1/69u3yj1ssvvCFLzBz5kzmzp3LokWLdPp4DP72b/+WuXPnUlxczA033MC7777b7mtC9ZQrgNuAXzRfaGazgDuA2cBNwONm1jvP678OPOLu04CDwN3xxv19Dz30ENdffz3btm3j+uuv56GHHurqCKlXVFTECy+8wDXXXBM6SpP6+nrq6+tDx+gUCxYsoKKigrKyMq644gq+9rWvhY6UOl/4whcoKysjk8lwyy238NWvfrXd1wRpX7j7FgAza/nUrcBKd38P2GFm1cBVwK/PrWDZF/0R8N9yi1YAy4An4sq7fPlyVqxYwahRo5gwYQLz589n9erV/OxnPwPgzjvv5Nprr+XrX/96XBFSL98YL126NHSs37N7924ARowYEThJNO2N7wc/+EF+8IMfBEzY/bU3xsePH89X835P0nrKhcBvmj3enVvW3HDgkLs3trFOEzO7B7gHYOLEiZEDbdiwgZUrV5LJZGhsbGTevHnMnz+fvXv3MnbsWADGjBnD3r17I7+3ZLU2xkk0e/bs0BEi68j4PvPMM3z84x8PlLD7a2uMH3jgAb73ve8xePBg3njjjXbfK7b2hZm9ZmYVeW63xvU983H3p9y9xN1LRo4cGfn169evZ9GiRQwYMIDLLruMhQsX/t46Ztah/wElv46McVL06dOHPn36hI4RSXvju3z5ci655BKWLFkSKGH319YYL1++nF27drFkyRIeffTRdt8rtqLs7h9x96I8t9VtvKwGmNDs8fjcsub2A0PM7JI21ond6NGjqa2tBaC2tpZRo0Z1dQQJoK6ujrq6utAxOs2//uu/8qMf/Yhnn31WGxYxW7JkCT/84Q/bXS9pJ4+sAe4ws35mNhmYDrzZfAV3d+AN4E9zi+4E2ir0F6T+aD1PvPEEvzz2S55+9ml27dvF0aNHefnllwFYuHAhK1asAGDFihXcemuX/gHQ7Z0b3/v+7T72Fuzlhy/8kIaGhvPGOIlqamqoqenybYAL0t5n+Mc//jEPP/wwa9as0RVVLlB7Y7xt27amdVevXt2h0/QtW+O6lpktAr4FjAQOARl3vzH33APAXUAj8Ffu/h+55a8Cn3b3d81sCrASGAb8b+DPcjsH21RSUuIdObSq/mg9S767hP3H91PQp4DtP9nOobJDFE0tYurkqcybN49PfepT3H777bzzzjtcfvnlPP/88zqhoINajm/D6QYO/+owZ6vOMnbMWCZOnMi8efOYOnUq999/P3V1dQwZMoTi4mLWrl0bNHtjY3ZXxiWXJG13zPk68hl+8sknee+99xg+fDiQ3dn35JNPBk7efXRkjH/9619TWVlJr169uPzyy3nyyScpLGzaBZb3T5NQR1+8CLzYynPLgeV5lt/c7P52skdlxGJV6Sr2H9/P2MHZHXnzbp1H7bW1fPJDn2Tvz7M79IYPH85Pf/rTuCKkWsvxHcIQ+C9w9xfu5r7r7mPZsmUALFq0iEWLFoULmkfSi/E5HfkMV1dXh4zY7XVkjDvSrmgpae2LRCirKaOgz/mTmRf0KaC8pjxQonTpzuO7b98+9u3bFzpGu7rzGHcXcY1x9/hvv4vNLZzLWzvfym7B5TScbmBO4Rzu+7P7wgVLibbGF2jaUk6ic2dkJX3Hrj7D8YtrjLWlnMfiksUMHzic2sO1HDpxiNrDtQwfOJzFJYtDR0uF7jy+c+bMYc6cOaFjtKs7j3F3EdcYB9nRF0pHd/RBtom/qnQV5TXlzCmcw+KSxYy4tHudxZVkGt/4aYzjd5FjnHdHn4qySATnztwcPXp04CSSAsk5+kKkuzp3wpCKssRFRVkkgrlz54aOICmnoiwSQa9e2jcu8dInTCSCPXv2sGfPntAxJMVUlEUiUFGWuPWooy/MrA74XSe93QggHZegiK4n/+zQs3/+nvyzQ+f+/PXuflPLhT2qKHcmMyt195L210yfnvyzQ8/++Xvyzw5d8/OrfSEikiAqyiIiCaKifOGeCh0goJ78s0PP/vl78s8OXfDzq6csIpIg2lIWEUkQFeWIzGyxmW0ys7NmVtLiuS+bWbWZVZrZjaEydgUzW2ZmNWaWyd1ubv9V3ZuZ3ZT73Vab2ZdC5+lqZrbTzMpzv+9Uz+xlZs+Y2T4zq2i2bJiZ/cTMtuW+Do3je6soR1cB3Ab8ovlCM5sF3AHMBm4CHjez3l0fr0s94u7FudurocPEKfe7fAz4Y2AW8Inc77ynuS73+077YXH/SvbfcXNfAn7q7tOBn+YedzoV5YjcfYu7V+Z56lZgpbu/5+47gGpivI6gdLmrgGp33+7up8heuFeXME8pd/8FcKDF4luBFbn7K4CPxfG9VZQ7TyGwq9nj3bllafZZMyvL/akXy59yCdITf78tObDOzDaY2T2hwwQw2t1rc/f3ALHM36pZ4vIws9eAMXmeesDdV3d1nlDaGgfgCeBBsv9QHwS+CdzVdekkgA+7e42ZjQJ+YmZbc1uUPY67u5nFcuiainIe7v6RC3hZDTCh2ePxuWXdVkfHwcy+A/wo5jihpe73G5W71+S+7jOzF8m2dHpSUd5rZmPdvdbMxgKxXNZc7YvOswa4w8z6mdlkYDrwZuBMscl9KM9ZRHYHaJq9BUw3s8lm1pfsTt01gTN1GTMbaGaXnrsP3ED6f+ctrQHuzN2/E4jlr2ZtKUdkZouAbwEjgVfMLOPuN7r7JjN7HtgMNAJ/6e5nQmaN2cNmVky2fbETuDdompi5e6OZfRZYC/QGnnH3TYFjdaXRwItmBtm68X13/3HYSPExs+eAa4ERZrYb+DvgIeB5M7ub7GyTt8fyvXVGn4hIcqh9ISKSICrKIiIJoqIsIpIgKsoiIgmioiwikiAqyiJ5mFmBmf28MyaVMrORZpbaw8ekc6koi+R3F/BCZxxr7u51QK2ZfejiY0naqShLj2JmV+YmUeqfO0ttk5kV5Vl1CbkztszsWjP7UbP3eNTMPpm7v9PMvnZujmEzm2dma83sbTP7TLP3eyn3niJt0hl90qO4+1tmtgb4B6AA+Dd3P+904dxp1FPcfWcH3/Yddy82s0fIzsP7IaA/2dOQn8ytU5r7niJtUlGWnuirZOeyOAn8jzzPjwAORXi/c3NglAOD3P0ocNTM3jOzIe5+iOzkNeMuOLH0GGpfSE80HBgEXEp2i7alhjzLrdn9Pi2eey/39Wyz++cen9vw6Z97X5E2qShLT/Rt4G+BZ4Gvt3zS3Q8Cvc2seWEuyh2R0Q+4muykRFFcQc+bVU0ugIqy9Chm9ufAaXf/PtlZv640sz/Ks+o64MPNHh8kO0Pcr4DXgC/nprDsqOuAVy4stfQkmiVOJA8zmwd83t3/u5ldCyx191su4v1+Adya2woXaZW2lEXycPffAm901skjwP9SQZaO0JayiEiCaEtZRCRBVJRFRBJERVlEJEFUlEVEEkRFWUQkQVSURUQS5P8HlEU8Pcs+QocAAAAASUVORK5CYII=\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "S_Neel(AF state) = 13.0\n", - "computed in 0.33928399999999925 sec\n" + "computed in 0.27140900000000023 sec\n" ] } ], @@ -390,7 +390,7 @@ " seq.declare_channel('ising', 'rydberg_global')\n", " seq.add(create_interp_pulse(params[:m], params[m:]),'ising')\n", " \n", - " simul = Simulation(seq, sampling_rate=0.2)\n", + " simul = Simulation(seq, sampling_rate=0.5)\n", " results = simul.run()\n", "\n", " sampling = results.sample_final_state(N_samples=N_samples)\n", @@ -409,7 +409,7 @@ { "data": { "text/plain": [ - "0.8336491282051282" + "0.8377870256410256" ] }, "execution_count": 12, @@ -515,7 +515,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -568,7 +568,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -599,7 +599,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -640,8 +640,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "final = {'010101010101': '45.7%', '101010101010': '51.1%'}\n", - "S_Neel (final_sampled) = 12.645\n" + "final = {'010101010101': '46.7%', '101010101010': '48.2%'}\n", + "S_Neel (final_sampled) = 12.774\n" ] } ], @@ -683,7 +683,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -718,7 +718,7 @@ "outputs": [ { "data": { - "image/png": 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3N2e8qu8QgOH83MBwfna8J3f3F3cufkI8TS0Z0/NJJEnSoFiTIUnSUAQLPyVJUhdCTVEng0mGJEkDMW1PYTXJkCRpQKap8NMkQ5KkAbnHDmGVJEldclpxSZLUEWsyJEnSxBV2l0iSpC7E7hJJktSRaWrJmJ50SZKkKVBZMvayIUlWJPl+ksuTHDnL+/dO8h/t+99IsvMkPotJhiRJA1Ltk1jHWdYnyVLgWOBZwDLgBUmWzdjt5cAtVfUI4J3A2ybxWUwyJEkaiGqHsE64JWNv4PKquqKqbgdOAg6asc9BwInt65OBpycLH+ZikiFJ0oDMsyVj2yQrR5ZXjZxyB+DqkfVr2m3Mtk9VrQZuBbZZ6Gex8FOSpAGZ5zwZN1bV8knHslAmGZIkDUjVxEeXXAvsNLK+Y7tttn2uSbIZsCVw00IvbHeJJEnT7Xxg1yS7JLkXcChw2ox9TgMOb18fAny+qmqhF7YlQ5KkwQg14b//q2p1kiOAs4ClwAlVdUmStwArq+o04H3Ah5JcDtxMk4gsmEmGJEkD0dW04lV1OnD6jG1Hjbz+DfC8SV/XJEOSpAGZphk/TTIkSRoQkwxJktSBDc/guSkxyZAkaUA6GMLaG5MMSZIGoqvCz76YZEiSNCAmGZIkqRMmGZIkqQOxJkOSJE1eAWtsyZAkSV2wu0SSJE1eOYRVkiR1ZJpaMjLOk1yT3AD81wKvuS1w4wLPMQlDiQOGE4txrGsosRjHuoYSi3GsayixTCKOh1fVgycRzMZY9pg96oOf+PzYxz1+t60vqKrlHYS0IGO1ZEziRidZOYQbMZQ4YDixGMe6hhKLcaxrKLEYx7qGEstQ4hjHtE3GNdmH1kuSJLWsyZAkaUAs/FyY43q45myGEgcMJxbjWNdQYjGOdQ0lFuNY11BiGUocY1nTdwATNFbhpyRJ6s6jHrNHnfDx88Y+7knLttz0Cz8lSVJ3ilj4OV9JnprkwiSXJBk/VZtsHLe2sVyY5Ki+YmnjeXyS1UkO6en6ByW5qL0XK5M8uY842lhe1MZycZKvJnlcT3HsluRrSX6b5K/7iKGNY0WS7ye5PMmRPcZxQpJVSb7bVwxtHDsl+UKSS9vfI6/rMZb7JPlmku+0sfzvvmJp41ma5NtJPtNjDFe2P7sXJlnZYxwPSnJyku8luSzJE/uKZT6qMvYyVIvWkpHkQcC7gRVVdVWShyzWtefwpap6ds8xkGQp8DbgP3sM4xzgtKqqJI8FPgbs1lMsPwb2q6pbkjyLpk/1CT3EcTPwP4CDe7g2cNf/G8cCBwDXAOcnOa2qLu0hnA8A/wZ8sIdrj1oN/FVVfSvJA4ALkpzd0z35LfC0qvpFks2BLyc5o6q+3kMsAK8DLgMe2NP11/qDqup7jox3AWdW1SFJ7gXcr+d4xmJLxvy8EPhkVV0FUFWrFvHaQ/Za4BNAb/ejqn5R/784Zwuaodp9xfLVqrqlXf06sGNPcayqqvOBO/q4fmtv4PKquqKqbgdOAg7qI5Cq+iJN4tWrqrq+qr7Vvr6N5kt1h55iqar6Rbu6ebv08rOTZEfgj4Dj+7j+kCTZEtgXeB9AVd1eVT/rNahxFKyZxzJUi5lkPBLYKsm5SS5I8pJFvPZsntg2c56R5NF9BJBkB+C5wHv6uP6MWJ6b5HvAZ4E/7Tue1suBM/oOokc7AFePrF9DT1+oQ5RkZ2AP4Bs9xrA0yYU0fyScXVV9xXIM8Ab6H5hQwH+2v+Nf1VMMuwA3AO9vu4+OT7JFT7GMbe1kXOMuQ7WYScZmwF402fYzgTcneeQiXn/Ut2imin0c8K/AqT3FcQzwxqrq+xcDVXVKVe1G0z3w1p7DIckf0CQZb+w7Fg1PkvvTtAC+vqp+3lccVXVnVe1O0+K2d5LHLHYMSZ4NrKqqCxb72rN4clXtCTwLeE2SfXuIYTNgT+A9VbUH8Eugt3qm+ZimmoxOk4wkr1lbXAlcB5xVVb9s++u+CCxaUd+MWO6/tpmzqk4HNk+ybQ9xLAdOSnIlcAjw7iQHL3YcSX5n7fa2Wfx3F+t+zBZLWxdyPHBQVd3UVxyLdd31uBbYaWR9x3bbPVpb//AJ4CNV9cm+4wFom+O/AKzo4fL7AAe2v0dOAp6W5MM9xEFVXdv+uwo4habLb7FdA1wz0qp0Mk3SscmoGn8Zqk6TjKo6tqp2bzP9U4AnJ9ksyf1oivku6/L664llTZIAJNmb5j4sypfZaBxVtUtV7VxVO9P8IPx5VZ262HEA9xu5H3sC92aR7scssWwGfBJ4cVX9YLFimBlHVV23mNeew/nArkl2aYvXDgVO6zmmXrX/n74PuKyq/rnnWB7cFrST5L40BbrfW+w4qupvqmrH9vfIocDnq+qwxY4jyRZtMS5t98QzgEUfjVRVPwGuTvL77aanA30UBs9TWDOPZagWbXRJVV2W5EzgIpp+w+Orqq/hcIcAf5ZkNfBr4NCRwsd7oj8GXpLkDpr78fwe78dRwDY0rToAq/uYYCbJQ4GVNJX6a5K8Hli2mE3zVbU6yRHAWcBS4ISqumSxrj8qyb8DTwW2TXIN8L+q6n09hLIP8GLg4rY1EOBv2xbJxbY9cGI7CmgJ8LGq6m346ABsB5zS/txuBny0qs7sKZbXAh9pk/MrgJf1FMfYiumaVtwZPyVJGohHPnqvOvajXx37uGfsfh9n/JQkSes35NEi4zLJkCRpKAY+78W4TDIkSRqIaavJMMmQJGlApqlUclEfkCZJku45TDKkAWufarl/33FIWjzOkyFJkjoxTd0lJhmSJA1EMexnkYzL7hJpE5HkUUl+nOQFfcciqSNT9qh3WzKkTUD7TJlTaZ5vc0+eulqaenaXSFpMT6F57P1hVXVuz7FI6pgzfkpaTK8GzjPBkKZfMezuj3FZkyEN36uBhyV5Z9+BSOpe1fjLUJlkSMN3G7AC2DfJ0X0HI6lb05Rk2F0ibQKq6mdJDgC+kOSOqnpz3zFJmrwqWDNFQ1hNMqQBq6qdR17fDDyuv2gkLYYht0yMyyRDkqQBMcmQJEmdcHSJJEmauAKqMvayEEm2TnJ2kh+2/241x35nJvlZko2eENAkQ5KkoZjHyJIJdK8cCZxTVbsC57Trs3k78OJxTmySIUnSgPTw7JKDgBPb1ycCB8+2U1WdQzOkfqOZZEiSdM+2XVVd377+CbDdpE5s4ackSQPR1GTM69Btk6wcWT+uqo5bu5Lkc8BDZznuTXe7flUlmVjpqUmGJEkDMs8k48aqWj73OWv/ud5L8tMk21fV9Um2B1bNK4JZ2F0iSdKA9FCTcRpwePv6cOBTCz5jyyRDkqSh6Gd0ydHAAUl+COzfrpNkeZLj1+6U5EvAx4GnJ7kmyTM3dGK7SyRJGogC1qxZ5GtW3QQ8fZbtK4FXjKw/Zdxzm2RIkjQgTisuSZI6YZIhSZImriZTyDkYJhmSJA1ITVFThkmGJEkDMkU5hkmGJElDstijS7pkkiFJ0kBMaN6LwTDJkCRpQCz8lCRJnbAlQ5IkdaKmqCnDJEOSpIGYtnkyfECaJEnqhC0ZkiQNiDUZkiSpE2umqL/EJEOSpIEobMmQJEldcDIuSZLUjWLNFGUZJhmSJA1I+ewSSZI0aU1Nhi0ZkiRp0sqnsEqSpI7YkiFJkiaumK5pxU0yJEkaivIBaZIkqSNT1FtikiFJ0pA4rbgkSZq4qrLwU5IkdcPJuCRJUiemaVrxJX0HIEmSppMtGZIkDYg1GZIkaeKqHF0iSZI6MkUNGSYZkiQNiTN+SpKkiauqqRpdYpIhSdKA2JIhSZI6YZIhSZImr3zUuyRJ6kBhS4YkSeqED0iTJEldcDIuSZLUFVsyJEnSxFmTIUmSulEmGZIkqRPO+ClJkjoyTS0ZS/oOQJIkTSeTDEmSBqJoRpeMuyxEkq2TnJ3kh+2/W82yz+5JvpbkkiQXJXn+xpzbJEOSpKFo58kYd1mgI4FzqmpX4Jx2faZfAS+pqkcDK4BjkjxoQyc2yZAkaUBqTY29LNBBwInt6xOBg9eJqeoHVfXD9vV1wCrgwRs6sYWfkiQNRi/Tim9XVde3r38CbLe+nZPsDdwL+NGGTmySIUnSQFRBrVkzn0O3TbJyZP24qjpu7UqSzwEPneW4N939+lVJ5sxykmwPfAg4vKo2GKhJhiRJAzLPGosbq2r5XG9W1f5zvZfkp0m2r6rr2yRi1Rz7PRD4LPCmqvr6xgRlTYYkSQOy2KNLgNOAw9vXhwOfmrlDknsBpwAfrKqTN/bEJhmSJA1FjV/0OYHCz6OBA5L8ENi/XSfJ8iTHt/v8CbAv8NIkF7bL7hs6sd0lkiQNRB8PSKuqm4Cnz7J9JfCK9vWHgQ+Pe26TDEmSBmTNhuspNxkmGZIkDYVPYZUkSV0oJlJjMRgmGZIkDUgPk3F1xiRDkqShKFgzv8m4BskkQ5KkAbG7RJIkTVxRbMRs3ZsMkwxJkoZiykaXOOOnJEnqhC0ZkiQNyDS1ZJhkSJI0GOWMn5IkafJqymoyTDIkSRqQcp4MSZI0cbZkSJKkbjhPhiRJ6kABa2zJkCRJE1fWZEiSpE74qHdJktQRazIkSdLkObpEkiR1oaipqslI1fRkTJIkbcqSnAlsO49Db6yqFZOOZ6FMMiRJUid81LskSeqESYYkSeqESYYkSeqESYYkSeqESYYkSerE/wOhC/E681koowAAAABJRU5ErkJggg==\n", 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\n", 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" ] diff --git a/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb similarity index 99% rename from tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb rename to tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb index ad6ba6fb6..0fec7863c 100644 --- a/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms .ipynb +++ b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb @@ -38,7 +38,7 @@ "source": [ "### Floquet Engineering on two atoms\n", "\n", - "We start by considering the dynamics of two interacting atoms under $H_{XXZ}$. To demonstrate the dynamically tunable aspect of the microwave engineering, we change the Hamiltonian during the evolution of the system. More specifically, we start from $|\\rightarrow \\rightarrow \\rangle_y $, let the atoms evolve under $H_{XX}$ and apply a microwave pulse sequence between $0.9\\mu s$ and $1.2\\mu s$ only.\n", + "We start by considering the dynamics of two interacting atoms under $H_{XXZ}$. To demonstrate the dynamically tunable aspect of the microwave engineering, we change the Hamiltonian during the evolution of the system. More specifically, we start from $|\\rightarrow \\rightarrow \\rangle_y$, let the atoms evolve under $H_{XX}$ and apply a microwave pulse sequence between $0.9\\mu s$ and $1.2\\mu s$ only.\n", "\n", "Let us first define our $\\pm X$ and $\\pm Y$ pulses. " ] @@ -217,7 +217,7 @@ "source": [ "### Domain-wall dynamics\n", "\n", - "Now, we will look at the dynamics of the system under $H_{XX2Z}$ when starting in a Domain-Wall (DW) state $|\\psi_0\\rangle = |\\uparrow \\uparrow \\uparrow \\uparrow \\uparrow \\downarrow \\downarrow \\downarrow \\downarrow \\downarrow\\rangle $, for two distinct geometries : open boundary conditions (OBC) and periodic boundary conditions (PBC). In the case of $H_{XX2Z}$, only 2 pulses per Floquet cycle are required, as the $X$ and $-X$ pulses cancel out." + "Now, we will look at the dynamics of the system under $H_{XX2Z}$ when starting in a Domain-Wall (DW) state $|\\psi_0\\rangle = |\\uparrow \\uparrow \\uparrow \\uparrow \\uparrow \\downarrow \\downarrow \\downarrow \\downarrow \\downarrow\\rangle$, for two distinct geometries : open boundary conditions (OBC) and periodic boundary conditions (PBC). In the case of $H_{XX2Z}$, only 2 pulses per Floquet cycle are required, as the $X$ and $-X$ pulses cancel out." ] }, { diff --git a/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb b/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb index e0baf2c07..d8ad3c4f2 100644 --- a/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb +++ b/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb @@ -1,20 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -107,7 +92,23 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import qutip\n", + "import matplotlib.pyplot as plt\n", + "from scipy.optimize import minimize\n", + "\n", + "from pulser import Register, Sequence, Pulse\n", + "from pulser.devices import Chadoq2\n", + "from pulser.simulation import Simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -139,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -157,6 +158,7 @@ "metadata": {}, "source": [ "We will need to compute the number of shadows needed given :\n", + "\n", "* A list of observables $o_i$\n", "* Desired precision on expectation values $\\epsilon$ : if $\\tilde{o}_i$ is the estimated expectation value for observable $o_i$, we wish for $|Tr(o_i \\rho) - \\tilde{o}_i| \\leq \\epsilon$\n", "* Failure probability $\\delta$ : we wish for the above equation to be satisfied with probability $1-\\delta$\n", @@ -167,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -234,13 +236,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We then reconstruct an estimate of the quantum state from the sampled bitstrings, using the inverse quantum channel $\\mathcal{M}^{-1}$ defined above. In the particular case of Pauli measurements, we can actually compute the inverse channel : $$\\mathcal{M}^{-1} = \\otimes_{i=1}^n (3 U_i \\ket{b_i}\\bra{b_i} U^\\dagger_i - \\mathbb{1}_2)$$\n", + "We then reconstruct an estimate of the quantum state from the sampled bitstrings, using the inverse quantum channel $\\mathcal{M}^{-1}$ defined above. In the particular case of Pauli measurements, we can actually compute the inverse channel : \n", + "\n", + "$$\\mathcal{M}^{-1} = \\otimes_{i=1}^n (3 U_i \\ket{b_i}\\bra{b_i} U^\\dagger_i - \\mathbb{1}_2)$$\n", + "\n", "where $i$ runs over all qubits : $\\ket{b_i}$, $b_i \\in \\{0,1\\}$, is the single-bit snapshot of qubit $i$ and $U_i$ is the sampled unitary corresponding to the snapshot, acting on qubit $i$." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -276,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -311,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -321,8 +326,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 9, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -334,10 +341,10 @@ " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]\n", " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]]\n", "Shadow reconstruction :\n", - "[[ 0.51+0.j 0.5 -0.01j 0.01+0.01j -0. +0.02j]\n", - " [ 0.5 +0.01j 0.49+0.j -0.01-0.01j -0.01-0.01j]\n", - " [ 0.01-0.01j -0.01+0.01j -0. +0.j -0. +0.j ]\n", - " [-0. -0.02j -0.01+0.01j -0. -0.j -0.01+0.j ]]\n" + "[[ 0.5 +0.j 0.52-0.01j 0. +0.02j 0.01-0.01j]\n", + " [ 0.52+0.01j 0.51+0.j 0.03+0.j 0. -0.01j]\n", + " [ 0. -0.02j 0.03-0.j -0. +0.j -0.01+0.01j]\n", + " [ 0.01+0.01j 0. +0.01j -0.01-0.01j -0. +0.j ]]\n" ] } ], @@ -357,18 +364,18 @@ "for i, shadow_size in enumerate(shadow_sizes):\n", " outcomes, unitary_ids = calculate_classical_shadow(rho_1, shadow_size)\n", " snapshots = [snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)]\n", - " dist[i] = tracedist(state_reconstruction(snapshots), rho_1)\n", + " dist[i] = qutip.tracedist(state_reconstruction(snapshots), rho_1)\n", "num_qubits = 4" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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lcu981Nqc4OTgCMf7NeJKRIrfTLahfYjZbUO7B2g1szVmVg3cBexIv8DMVgGPAL/i7vtzuXe+amuZGHGlpisRKX4z2Yb2a7PZhjbcgfAe4DFgH/BVd3/BzO42s7vDy/4AWAp82syeMbO9F7s31zpEoTVc40rhISKlIKsht+GTQCangMfDH8xsobufvtTnuftOYOeUY9vSXn8EyDiSK9O9xaApXsOSuiqFh4iUhGzna2SzR7kDfwd8YcalKWFmpmVKRKRkZBUe7n7LXBekHKSSCf7xx0dwd8y0n5aIFK/ZNltNdSqbZqtylWpJcGZolNdeP8dlixdEXRwRkRlTs1UBpZonO80VHiJSzNRsVUCp5ORw3ZvbmiMujYjIzGlJ9gJaUl9NU6JGuwqKSNFTeBRYW1K7CopI8VN4FFgqmeBAdz/j41rjSkSKl8KjwFLJOGdHxug6eTbqooiIzJjCo8BSLdoYSkSKn8KjwFqbtcaViBQ/hUeBJWqrWL54gcJDRIqawiMCKe0qKCJFTuERgVQywcHeAUbHxqMuiojIjCg8IpBKJhgeG+dQX66bMIqIzA8KjwikL1MiIlKMFB4RWN8cx0zhISLFK5LwMLM7zKzDzDrN7N4M5zeY2ffMbMjMPjrl3CEzey59e9pis6A6xpsa6hQeIlK0sl2SPW/MLAbcD9wGdAF7zGyHu7+YdtkJ4LeBd03zMbe4+/E5Legca00m2N+tBRJFpDhF8eRxLdDp7gfdfRh4GNiSfoG797j7HmAkgvIVRFsywcvHBxgaHYu6KCIiOYsiPJYDh9Ped4XHsuXAt83saTPbOt1FZrbVzPaa2d7e3t4ZFnXutCbjjI07B3sHoi6KiEjOogiPTJt357LE7A3ufjVwJ/CbZnZjpovcfbu7b3L3TU1NTTMp55xqa9GIKxEpXlGERxewMu39CuBotje7+9Hwdw/wKEEzWNFZ2xinssIUHiJSlKIIjz1Aq5mtMbNq4C5gRzY3mlm9mSUmXgO3A8/PWUnnUHVlBasb69VpLiJFqeCjrdx91MzuAR4DYsCD7v6Cmd0dnt9mZi3AXmAhMG5mvwtsBBqBR81souwPufu3Cl2HfGlLJnj+6OtRF0NEJGcFDw8Ad98J7JxybFva62MEzVlTnQaunNvSFU5rMs7O51/j7PAYC6pjURdHRCRrmmEeobZkAnfo7FHTlYgUF4VHhFqT2lVQRIqTwiNCq5fWUR2r4IDCQ0SKjMIjQpWxCtY1x/XkISJFR+ERsVQyzgEN1xWRIqPwiFgqmeDIqbOcOVeyy3iJSAlSeERscmMoPX2ISPFQeESsLQwPdZqLSDFReERsxZIFLKiKqdNcRIqKwiNiFRVGqzrNRaTIKDzmgVQyoScPESkqCo95IJWM03tmiJMDw1EXRUQkKwqPeWByxJWePkSkOCg85oH5sKvg6Nh4ZN8tIsUnkiXZ5UItC2tJ1FTO+VwPd+d4/zCdPf109pzhQE8/B7r7OdDTz4mBId6yagmbNzSzeUMzG1oShPumiIi8gcJjHjAzUi356zR3d46dPseB7n46e4JwmAiLU4OTM9kTNZWsT8bZvKGJJfXVfO+lPj75WAeffKyDyxbVsvnyZm7dkOT6dUuprdJ+IyIyKZLwMLM7gL8m2EnwAXf/synnNwCfA64GPu7uf5HtvcUqlYzzzeeP4e5Z/x//+Lhz5NRZDvScCUKieyIo+ukfGj1/3eK6KlLNCd7xU8tY3xSnNRmntTlBcmHNG76r5/Q5nujoZVd7N4/86Ahf/P6r1FZVcMO6RjZfHjyVLFu0IK91F5HiU/DwMLMYcD9wG9AF7DGzHe7+YtplJ4DfBt41g3uLUiqZ4Ms/PExv/xDNidoLzo2OjfPqicHzwRA8TQSBcW5ksq+iKVFDa3Oc91y9nPXNcdY3J2hNxllaX511IDUvrOW916zkvdesZGh0jB8cPMHu9h52tXezq70HgI3LFnJrGCRXrlhMRYWat0TKTRRPHtcCne5+EMDMHga2AOcDwN17gB4z+9lc7y1WE8uU7NrXw8LaqgsC4mDvAMNpHdqXLaplfTLBB65byvrmOK3NcdY3x1lcV53XMtVUxrgx1cSNqSb+8Oc38lJvP7v29bCrvYdPP/ESf7O7k6X11dzc1sytlzfz9tZGErVVeS2DiMxPUYTHcuBw2vsu4Lp832tmW4GtAKtWrcq9lAWWCkdcfeyR5wAwg5VL6mhtjnNTW1PY3JRgXVN9JH9BmxnrmxOsb07wn25ax+uDI/zLgV527+vmn/d187UfdVFZYVy3toFb2pq59fIkaxrrC15OESmMKMIjUxuH5/ted98ObAfYtGlTtp8fmcZ4Dfe//2pGxsZZ3xxnXVOcBdXzt5N6UV0V77zyMt555WWMjo3z48On2LWvh93t3fzJN/bxJ9/Yx9rG+mD01uXNXLO6gaqYRoaLlIoowqMLWJn2fgVwtAD3zns/e8WyqIswI5WxCq5Z3cA1qxu4984NHD4xyOMdPeza18MXvvcKD/zryyRqKrmxrYnNbc3c3NbE0nhN1MUWkVmIIjz2AK1mtgY4AtwFvL8A90qBrGyo44PXr+aD169mYGiUf+s8zu72Hna39/CNZ1/DDN6ycjG3Xp7UnBKRImXuhW/RMbN3AJ8iGG77oLv/qZndDeDu28ysBdgLLATGgX5go7ufznTvpb5v06ZNvnfv3jmpi2RvfNx58bXT55u3ftL1OgDLFtWyeUPQ6f7WdY2aUyIyT5jZ0+6+KeO5KMKj0BQe81PPmXM80d7L7vYevnugl4HhMc0pEZlHFB4Kj3lvaHSMH758Inwq6eHVE4MAXL5sIbeGne5XrlhMTHNKRApG4aHwKCruzku9A+xu72bXvh72vnKSsXFnaX01N7U1ceuGJG9PNbJQc0pE5pTCQ+FR1NLnlDyxv5dTgyNUVhjXrmkI+0o0p0RkLig8FB4lY2zc+fGrJ9nV3sPufT3nF5NcE84puXVDM5tWN1BdqTklIrOl8FB4lKyJOSW723t46qU+hkfHgzklqSY2b9CcEpHZUHgoPMrC4PAo/9bZx+72bna399B9eggzuGrl4qDTfUOSy5dpTolIthQeCo+y4+68cPR0uCJwDz85fAoI5pTcEjZvvXVd47xeAkYkagoPhUfZ6zkT7FOye9/knJKaygpuWN94fvfEyxZrTolIOoWHwkPSDI2Oseflk8EeJfsm55RsaEmE+5QkuWql5pSIKDwUHjKNiTklj4cbXu05FMwpaaiv5ua2oNP9xlST5pRIWVJ4KDwkS6+fHeHJ/cGSKY939JyfU3LN6obzuyeubYpHXUyRglB4KDxkBibmlEysCNx+7MI5JZs3BPuUaE6JlCqFh8JD8qDr5GDYvDU5pyReU8mNqUY2b0hyc1sTjZpTIiVE4aHwkDwbHB7lqc6+YKZ7e/cFc0o2twULOW5ctlBzSqSoKTwUHjKH0ueU7G7v4Sddp3CHloW1bL5cc0qkeCk8FB5SQL1nhngiXDLlyf2Tc0reum4pm8PdE5drTokUAYWHwkMiMjw6zp5DwT4lu9q7eaVvck7JuqY4DfXVLI1Xs7S+mqXxGhrqq2mMV9NQX8PiBVVUaK6JRGjehYeZ3QH8NcFWsg+4+59NOW/h+XcAg8CH3P1H4blDwBlgDBidrmLpFB4yH7g7B48PsHtfD08e6OXIqbOcGBjm1OBIxusrDBrqq4OAqa+hIV5NY30QLJkCZ2Gtwkby62LhURlBYWLA/cBtQBewx8x2uPuLaZfdCbSGP9cBnwl/T7jF3Y8XqMgieWFmrGuKs64pzm/cuPb88ZGxcU4ODnNiYJi+/mH6Bobp6x/ixMAwx/uHOTEwRF//MPuOnuZ4/xCnz41m/PxYhYVBEzzNNNTXBK/rq2mIBwF0PnTqa1i4oFId+jJjBQ8P4Fqg090PApjZw8AWID08tgBf8OCx6PtmttjMlrn7a4UvrsjcqopV0JyopTlRm9X1w6NB2ARB88aQ6RsIgui5rlP0DQxzZpqwqYoZS+qCp5fJwKmmMXyaWXq+SS146knUKGxkUhThsRw4nPa+iwufKqa7ZjnwGuDAt83Mgc+6+/ZMX2JmW4GtAKtWrcpPyUXmgerKCpILa0kuzC5shkbHODkwwvHwaaYvPWTCAOobGObVVwc5MTBM/1DmsKmOVUw2o03TT5N+vL46prApYVGER6Z/m6Z2vFzsmhvc/aiZNQPfMbN2d3/yDRcHobIdgj6P2RRYpJjVVMZoWRSjZVF2YXNuZCytCS0ImiB00prTBoY51DdAX/8wg8NjGT+nurIi6KOZaDI7HzyZn3TqFDZFJYrw6AJWpr1fARzN9hp3n/jdY2aPEjSDvSE8RGRmaqtiXLZ4QdZL1J8dHjvffDZtn83AMJ09/fQNDHFuZHya76043y8zMUhg4kkmPWQmXmveTLSiCI89QKuZrQGOAHcB759yzQ7gnrA/5DrgdXd/zczqgQp3PxO+vh344wKWXUSmWFAdY0V1HSuW1GV1/eDwaFrfzFAYMEHg9E0EUP8w+4+d4fjAMMOjmcNmQVXsDc1n6QMCgtFpNeGTTzW1VQqbfCp4eLj7qJndAzxGMFT3QXd/wczuDs9vA3YSDNPtJBiq+2vh7Ung0fDRthJ4yN2/VeAqiMgs1FVXUtdQycqGS4eNuzMwPMaJ/mGODwxd0EeT3pzWffoc+147TV//MMNjmcOmvjo2GTJpI9Ia45mb02oqFTYXo0mCIlIy3J3+odE3NJ/1DUw/Om10PPPfgYmayvNPLRcLmaX1QSCV4urK82qeh4jIXDEzErVVJGqrWN1Yf8nr3Z3T50bfEDIXNKcNDNF1cpCfdJ3ixMAwY9OFTW1l5mHOaa8nmtWW1FdTFSvusFF4iEjZMjMWLahi0YIq1jZd+vrxcef0uZHMIZPWZ/NK3yA/evUkJwaGmSZrWLSg6sInmHj6pM6aC0aqLamronKehY3CQ0QkSxUVxuK6ahbXVbMuy7B5/ezIBXNrLmhOC5vSDvYOsPfQSU4MDpOpJ8EMFi+oytBsNtmclj4ibUldNbE5XqpG4SEiMkcqKowlYTPV+uZLXz827pwanL6PZuL1gZ5+vn9wiFNnR6YNmyV1wVPMZ3/lp+dk62SFh4jIPBGrsODJIl4TjC29hNGxcU4OjkxpNpucyHmif5hEbdWclFXhISJSpCpjFTQlamhK1ACJgn73/OqBERGRoqDwEBGRnCk8REQkZwoPERHJmcJDRERypvAQEZGcKTxERCRnCg8REclZWSzJbma9wCs53NIIHJ+j4sxX5VhnKM96l2OdoTzrPds6v8ndM67iVRbhkSsz2zvdGvalqhzrDOVZ73KsM5Rnveeyzmq2EhGRnCk8REQkZwqPzLZHXYAIlGOdoTzrXY51hvKs95zVWX0eIiKSMz15iIhIzhQeIiKSM4VHGjO7w8w6zKzTzO6NujyzYWYrzexxM9tnZi+Y2e+ExxvM7DtmdiD8vSTtno+Fde8ws/+Qdvynzey58Nz/MbO53Rw5D8wsZmY/NrOvh+9Lut5mttjM/sHM2sN/5teXep0BzOy/hv9+P29mXzaz2lKst5k9aGY9ZvZ82rG81dPMaszsK+HxH5jZ6ksWyt31E/T7xICXgLVANfATYGPU5ZpFfZYBV4evE8B+YCPw58C94fF7gfvC1xvDOtcAa8I/i1h47ofA9YAB3wTujLp+WdT/94CHgK+H70u63sDngY+Er6uBxWVQ5+XAy8CC8P1XgQ+VYr2BG4GrgefTjuWtnsB/AbaFr+8CvnLJMkX9hzJffsI/0MfS3n8M+FjU5cpj/f4JuA3oAJaFx5YBHZnqCzwW/pksA9rTjr8P+GzU9blEXVcAu4DNTIZHydYbWBj+JWpTjpdsncPyLQcOAw0EW2p/Hbi9VOsNrJ4SHnmr58Q14etKglnpdrHyqNlq0sS/iBO6wmNFL3wEfQvwAyDp7q8BhL+bw8umq//y8PXU4/PZp4D/DoynHSvleq8FeoHPhU11D5hZPaVdZ9z9CPAXwKvAa8Dr7v5tSrzeafJZz/P3uPso8Dqw9GJfrvCYlKmNs+jHMZtZHPga8Lvufvpil2Y45hc5Pi+Z2c8BPe7+dLa3ZDhWbPWuJGjS+Iy7vwUYIGjGmE4p1JmwjX8LQdPMZUC9mf3yxW7JcKzo6p2FmdQz5z8DhcekLmBl2vsVwNGIypIXZlZFEBxfcvdHwsPdZrYsPL8M6AmPT1f/rvD11OPz1Q3AO83sEPAwsNnMvkhp17sL6HL3H4Tv/4EgTEq5zgD/HnjZ3XvdfQR4BHgrpV/vCfms5/l7zKwSWAScuNiXKzwm7QFazWyNmVUTdBrtiLhMMxaOovi/wD53/8u0UzuAXw1f/ypBX8jE8bvCURdrgFbgh+Hj8Bkz+5nwMz+Yds+84+4fc/cV7r6a4J/hbnf/ZUq43u5+DDhsZm3hoVuBFynhOodeBX7GzOrC8t4K7KP06z0hn/VM/6xfIPjv5uJPX1F3As2nH+AdBKOSXgI+HnV5ZlmXtxE8dj4LPBP+vIOgHXMXcCD83ZB2z8fDuneQNtoE2AQ8H577Wy7RkTZffoCbmewwL+l6A1cBe8N/3v8ILCn1Oofl/SOgPSzz/yMYYVRy9Qa+TNCvM0LwlPDhfNYTqAX+HugkGJG19lJl0vIkIiKSMzVbiYhIzhQeIiKSM4WHiIjkTOEhIiI5U3iIiEjOFB4igJl9PFyd9Vkze8bMrguPHzKzxll87hNmtil/Jc34He+0Il8FWopPZdQFEImamV0P/BzBKsRDYVhUR1ysrLn7Dop4QqsUJz15iASrjR539yEAdz/u7unLU/yWmf0o3AdhA4CZXWtmT4ULET41MbvbzBaY2cPhE8xXgAUTH2Jm7ws/43kzuy889l4z+8vw9e+Y2cHw9Toz+9epBTWz3zazF8PPfzg89iEz+9vw9TNpP2fN7CYzqw/3g9gTlnfLHPwZSplReIjAt4GVZrbfzD5tZjdNOX/c3a8GPgN8NDzWDtzowUKEfwD87/D4fwYG3f0K4E+BnwYws8uA+wiWib8KuMbM3gU8Cbw9vPftQJ+ZLSdYIeC7Gcp6L/CW8PPvnnrS3a9y96uA/0kw4/wpgtnGu939GuAW4JPhqrsiM6bwkLLn7v0Ef8lvJVja/Ctm9qG0SyYWlXyaYE8FCBaO+3sLdnb7K+DfhcdvBL4Yfu6zBMuFAFwDPOHBIn6jwJcIwucYEDezBMHCdA+Fn/F2MofHs8CXwtVjRzPVx8xagU8Cv+TBgoG3A/ea2TPAEwRLUay61J+LyMUoPEQAdx9z9yfc/Q+Be4D3pJ0eCn+PMdlP+AngcXd/M/DzBH8hn/+4DF9xsW1Nvwf8GsE6RN8lCI7rgX/LcO3PAvcThN3T4Qqok18SPFF8FfiNtKY3A94z8VTi7qvcfd9FyiNySQoPKXtm1hb+3/qEq4BXLnHbIuBI+PpDacefBD4Qfu6bgSvC4z8AbjKzRjOLEezi9i9p93w0/P1jgqalIXd/fUo5K4CV7v44wWZXi4H4lHJ9Dvicu6c/tTxG0G8zsV/1Wy5RN5FL0mgrkeAv4L8xs8UETUGdBE1YF/PnwOfN7PeA3WnHP0Owo9/EasY/hGCnNzP7GPA4wZPATnefWA77uwRNVk+6+5iZHSboU5kqBnzRzBaFn/FX7n4qzATM7E0Ey2mnzOzXw3s+QvCU9Cng2TBADhGMLhOZMa2qKyIiOVOzlYiI5EzhISIiOVN4iIhIzhQeIiKSM4WHiIjkTOEhIiI5U3iIiEjO/j8ihov+q2boogAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] @@ -425,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -454,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -509,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -554,13 +561,15 @@ "metadata": {}, "source": [ "Now that we have our Pauli measurements, we proceed differently from randomized classical shadows, where we gave an estimate of the actual quantum channels. Here, we're only interested in the Pauli averages $\\tilde{\\omega}_l$, that we can infer from Pauli measurements $p$ that **hit** observable $o_l$. Indeed, we have the following formula :\n", + "\n", "$$\\tilde{\\omega}_{l}=\\frac{1}{h\\left(\\mathbf{o}_{l} ;\\left[\\mathbf{p}_{1}, \\ldots, \\mathbf{p}_{M}\\right]\\right)} \\sum_{m: \\mathbf{o}_{l} \\triangleright \\mathbf{p}_{m}} \\prod_{j: \\mathbf{o}_{l}[j] \\neq I} \\mathbf{q}_{m}[j]$$\n", + "\n", "where $h\\left(\\mathbf{o}_{l} ;\\left[\\mathbf{p}_{1}, \\ldots, \\mathbf{p}_{M}\\right]\\right)$ is the number of times a Pauli measurement $p_i$ is such that $o \\triangleright p_i$, and $\\mathbf{q}_m$ is the output of the measurement of Pauli string $p_m$ ($\\mathbf{q}_m \\in \\{\\pm 1\\}^n$)." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -575,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -600,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -629,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -695,15 +704,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "$$H_{J W}=-0.81261 \\mathbb{1}+0.171201 \\sigma_{0}^{z}+0.171201 \\sigma_{1}^{z}-0.2227965 \\sigma_{2}^{z}-0.2227965 \\sigma_{3}^{z}\n", - "+0.16862325 \\sigma_{1}^{z} \\sigma_{0}^{z}+0.12054625 \\sigma_{2}^{z} \\sigma_{0}^{z}+0.165868 \\sigma_{2}^{z} \\sigma_{1}^{z}+0.165868 \\sigma_{3}^{z} \\sigma_{0}^{z}\n", - "+0.12054625 \\sigma_{3}^{z} \\sigma_{1}^{z}+0.17434925 \\sigma_{3}^{z} \\sigma_{2}^{z}-0.04532175 \\sigma_{3}^{x} \\sigma_{2}^{x} \\sigma_{1}^{y} \\sigma_{0}^{y}\n", - "+0.04532175 \\sigma_{3}^{x} \\sigma_{2}^{y} \\sigma_{1}^{y} \\sigma_{0}^{x}+0.04532175 \\sigma_{3}^{y} \\sigma_{2}^{x} \\sigma_{1}^{x} \\sigma_{0}^{y}-0.04532175 \\sigma_{3}^{y} \\sigma_{2}^{y} \\sigma_{1}^{x} \\sigma_{0}^{x}$$" + "$$H_{J W}=-0.81261 \\mathbb{1}+0.171201 \\sigma_{0}^{z}+0.171201 \\sigma_{1}^{z}-0.2227965 \\sigma_{2}^{z} \\\\\n", + "-0.2227965 \\sigma_{3}^{z} +0.16862325 \\sigma_{1}^{z} \\sigma_{0}^{z}+0.12054625 \\sigma_{2}^{z} \\sigma_{0}^{z} \\\\\n", + "+0.165868 \\sigma_{2}^{z} \\sigma_{1}^{z}+0.165868 \\sigma_{3}^{z} \\sigma_{0}^{z} +0.12054625 \\sigma_{3}^{z}\\sigma_{1}^{z} \\\\\n", + "+0.17434925 \\sigma_{3}^{z} \\sigma_{2}^{z}-0.04532175 \\sigma_{3}^{x} \\sigma_{2}^{x} \\sigma_{1}^{y} \\sigma_{0}^{y}\\\\\n", + "+0.04532175 \\sigma_{3}^{x} \\sigma_{2}^{y} \\sigma_{1}^{y} \\sigma_{0}^{x}+0.04532175 \\sigma_{3}^{y} \\sigma_{2}^{x}\n", + "\\sigma_{1}^{x} \\sigma_{0}^{y} -0.04532175 \\sigma_{3}^{y} \\sigma_{2}^{y} \\sigma_{1}^{x} \\sigma_{0}^{x}$$" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -714,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -740,14 +751,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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c8+4Bvhd/vidV9b+LyIMQmx8kfpr3ALCb2GneL6hq+UTPN2p8gRiTQsoCmexB+tixK7OBDwF/k26iUfFp1Y6Oe+yHY+4r8PA7/XdCN3pWyK7qnbpJFYiqfnvssoh8i1hLZDyQnZ3tOoK30r1YcQnQ8m4GMdNnw4YNriN4a7LXYlVy/QF6MdA++riq2kz1JiNNdg/yyWlNYabV6CfVNrrJ1E32GOTN6Q5ipk9ubq7rCN4K7gtTIVq/fr3rCN4K7iu3xkyFFUgAzp49e90VtGbyrMUKQF5enusI3rICCcDod0HM1FmLZUwKViABOHPmzHXf6jOTZy1WAObPn+86gresQAJw8803u47gLWuxjEnBCiQAp06dum6kETN51mIFYPxg3WbyrEACsHbt2htvZJKyFsuYFKxAAlBZWXnd6Idm8qzFCsCSJUtcR/CWFUgAxg7FaqbGWixjUnCyBxGR/wH8ATBAbFKeL6hqZ5Lt6oEeYBgYUtXSGYyZMUZHY9+508bWmCpXe5BXge3x0VCqgf+YYtuPqGqJFUf6CgoKKCgocB3DS072IKr6izGLvwU+7SJHKFavXu06grdmwzHIvwZ+NsE6BX4hIq/H5/+YkIg8ICLlIlI+OhmOMe/UtO1BROQ4sDLJqq+q6kvxbb4KDAETTflzm6o2ichy4FURqVLVXyXbUFWfAJ6A2ODV7/gFZJDRGaJ27drlOIl/pq1AVPX3U60Xkc8TG5DuYzrBEPOq2hT/eVlEXiA2bVvSAjETW7ZsmesI3nJ1Fms38B+AD6vqtQm2yQduUtWe+P07gT+bwZgZY9WqVa4jeMvVMcgBYAGxtikiIj8EEJFVIjI6HcIK4NcichL4B+AVVf25m7gmVK7OYiWdsDveUu2J368FrGl+F4zOWltSUuI0h4/sUpMArFyZ7FyJmQwrkABYgaRvNnwOYqbZyMgIIyPv1vT2YbECCUBFRUXieiwzNdZiBaCoqMh1BG9ZgQRgxYoVriN4y1qsAAwPDzM8POw6hpesQAJg30lPn7VYAbBLTdJnBRKA5cuXu47gLWuxAjA0NMTQ0JDrGF6yAgmAjc2bPmuxAmBfuU2fFUgA7AtT6bMWKwCDg4MMDg66juElK5AAnD59mtOnT7uO4SVrsQJgQ4+mzwokAIWFha4jeMtarAAMDAwwMDDgOoaXrEACYPOkp89arADYNNDpswIJwNKlS11H8JaTFktEHhORxviYWBER2TPBdrtF5JyI1IjIn850zkzR399Pf3+/6xhecrkH+a6q/sVEK0UkC/hL4ONAA3BCRF5WVWump6iqqgqwcbHSMZtbrFuBmvgAcojIs8BewApkitatW+c6grdcnsV6REQqRORJEUk2y+Rq4NKY5Yb4Y2aKlixZYhN5pmnaCkREjovIqSS3vcBB4D1ACdAMfDvZUyR5bMJpDWx+kIn19fXR19fnOoaXnE1/MEpE/gr4aZJVDcDaMctrgKYU/57NDzKBc+fOAXYMkg5X0x8UqWpzfPFuINm3eU4Am0RkPdAI3AP80QxFzCjFxcWuI3jL1UH6t0SkhFjLVA98CWLTHwCHVHWPqg6JyCPAMSALeFJV7ZLUNCxevNh1BG/JBJM7ea20tFTLy8tdx5g1rl2LzVE0b948x0lmpWTHugl2LVYAqqurqa6udh3DS7P5cxDzLlm/fr3rCN6yAgnAokWLXEfwlrVYAejt7aW3t9d1DC9ZgQTg/PnznD9/3nUML1mLFYANGza4juAtK5AALFy40HUEb1mLFYBoNEo0GnUdw0tWIAGoqamhpqbGdQwvWYsVgI0bN7qO4C0rkADMnz/fdQRvWYsVgO7ubrq7u13H8JIVSABqa2upra11HcNL1mIFYNOmTa4jeMsKJAD5+fmuI3jLWqwAdHV10dXV5TqGl6xAAlBXV0ddXZ3rGF6yFisAmzdvdh3BW1YgAbCv2qbPWqwAdHZ20tnZ6TqGl6xAAlBfX099fb3rGF6yFisAW7ZscR3BW1YgAcjLy3MdwVuuRlb8G2D0z9pioFNVS5JsVw/0AMPAkKqWzlDEjHL16lUAG8A6DU4KRFX/1eh9Efk2kOpTrI+oatv0p8pcb775JmAFkg6nLZaICPCHwEdd5sh0W7dudR3BW67PYv1zoFVVJxpyQ4FfiMjrIvJAqiey6Q8mNnfuXObOnes6hpembQ8iIseBlUlWfVVVX4rf/wzwTIqnuU1Vm0RkOfCqiFSp6q+SbWjTH0yso6MDsMk80+FsfhARmQPsA96X4jma4j8vi8gLxKZlS1ogZmIXL14ErEDS4fIY5PeBKlVtSLZSRPKBm1S1J37/TuDPZjJgprjllltcR/CWy2OQexjXXonIKhE5Gl9cAfxaRE4C/wC8oqo/n+GMGSEnJ4ecnBwAPvvZz1JUVMTChQvZvHkzhw4dcpxudrP5QQLQ1hY7S15YWMjp06fZuHEjubm5VFVVcccdd/DKK6/wvvdN2OlmOpsfJHQNDQ00NMQ62W3btpGbmwuAiCAiXLhwwWW8Wc0KJADbtm1j27ZtieUvf/nLzJs3j61bt1JUVMSePXscppvdrEACkJ2dTXZ2dmL5Bz/4AT09Pbz22mvs27cvsUcxb2cFEoArV64w/sPTrKwsbr/9dhoaGjh48KCjZLOfXc0bgMbGRgCWLVv2tnVDQ0N2DJKC7UECsH37drZv387ly5d59tlniUajDA8Pc+zYMZ555hk++lG7FG4itgfJcG09bRwpP0JFYwXr89fz4oEXefDBBxkZGWHdunV873vfY+/eva5jzlpWIBmsraeN/Yf2MxAdIHdOLieGT1DwLwuo+VkNhQsKXcfzgrVYGexI+RHae9tZl7eOldkrKVpURHtvO0fKj7iO5g3bg2SwisYK8rLz6MjpSDyWl51HZWOlw1R+sT1IBtu5eid9g32oKCqxS4r6BvvYsXqH42T+sALJYGWlZRTkFxDtjDIUHaK5q5mC/ALKSstcR/OGXayY4dp62njqp0/RcLWBTe/dRFlpmR2gXy/lxYpWIAEYGRkB4KabrGFIImWB2EF6AKww0mf/cwFoaWmhpaXFdQwvWYEEwAokfRl5DCIiV4A3k6wqBEIdhC7k1w4Tv/42Vd090S9lZIFMRETKQx2+NOTXDum/fmuxjEnBCsSYFEIrkCdcB3Ao5NcOab7+oI5BjJmq0PYgxkyJFYgxKQRXICLymIg0ikgkfsv4QaFEZLeInBORGhH5U9d5ZpKI1ItIZfy9nvIFesEdg4jIY0BUVf/CdZaZICJZQDXwcaABOAF8RlXPOA02Q+LT+JWmO0tZcHuQAN0K1KhqraoOAM8CNkrDJIVaII+ISIWIPCkimT5x32rg0pjlhvhjoZj0LGXJZGSBiMhxETmV5LYXOAi8BygBmoFvu8w6A5J93yGkvvo2Vf094C7gYRH50FR+OSO/D3Kj2a1GichfAT+d5jiuNQBrxyyvAZocZZlx73SWsozcg6QiIkVjFu8GTrnKMkNOAJtEZL2I5BCbuOhlx5lmhIjki8iC0fvEZimb0vudkXuQG/iWiJQQazPqgS85TTPNVHVIRB4BjgFZwJOqetpxrJmyAnghNts4c4CnpzpLWXCneY2ZiuBaLGOmwgrEmBSsQIxJwQrEmBSsQIxJwQrEmBSsQIxJwQokg4hIsYicGrP87+Lff/m/IvJdEfmViJwVkX8qIn8rIudF5L+5zDzbhfhJeqgGVPVDIvIV4CXgfUAHcEFEvquq7W7jzU62BwnH6PVXlcBpVW1W1beAWq6/mNGMYQWSecZe3p495v5b8Z8jY+6PLlsnMQErkMyzTkSWichNwIeIXaBo0mR/OTJPO/AUsBI4DnwO+0OYNruaN4OISDHwU1Xd7jpLprC/LMakYHsQY1KwPYgxKViBGJOCFYgxKViBGJOCFYgxKfx/287/NMNIW/EAAAAASUVORK5CYII=\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -774,14 +785,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-1.8510459284448646\n" + "-1.8510459284448648\n" ] } ], @@ -829,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -850,7 +861,7 @@ " seq.add(pulse_2, 'ch0')\n", " \n", " seq.measure('ground-rydberg')\n", - " simul = Simulation(seq, sampling_rate=.01)\n", + " simul = Simulation(seq, sampling_rate=.05)\n", " simul.initial_state = in_state\n", " results = simul.run()\n", " return results.expect([H])[-1][-1]\n", @@ -870,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -888,18 +899,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-1.81792129310567 -1.8510459284448646\n" + "-1.8404007292205296 -1.8510459284448648\n" ] } ], "source": [ + "import warnings\n", + "# Ignore the warnings\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", "loop_ising_results = loop_JW(param, gggg)\n", "print(loop_ising_results.fun, exact_energy)" ] @@ -915,22 +930,22 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -977,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -995,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1038,28 +1053,7 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-2.6202606749999986 -1.8510459284448646\n", - "CPU times: user 30.1 s, sys: 24 ms, total: 30.1 s\n", - "Wall time: 30.1 s\n" - ] - } - ], - "source": [ - "%%time\n", - "loop_results_shadows = loop_JW_shadows(param, gggg, 20)\n", - "print(loop_results_shadows.fun, exact_energy)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1071,22 +1065,22 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 34, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1127,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1153,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1170,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1218,7 +1212,7 @@ " seq.add(pulse_2, 'ch0')\n", " \n", " seq.measure('ground-rydberg')\n", - " simul = Simulation(seq, sampling_rate=.01)\n", + " simul = Simulation(seq, sampling_rate=.05)\n", " simul.initial_state = in_state\n", " \n", " # Classical shadow estimation\n", @@ -1232,29 +1226,6 @@ " return(res)" ] }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.8321093136363638 -1.8510459284448646\n", - "CPU times: user 35.1 s, sys: 12 ms, total: 35.1 s\n", - "Wall time: 35.1 s\n" - ] - } - ], - "source": [ - "%%time\n", - "loop_results_shadows = loop_JW_derand(param, gggg)\n", - "print(loop_results_shadows.fun, exact_energy)" - ] - }, { "cell_type": "code", "execution_count": 40, @@ -1276,7 +1247,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 41, @@ -1285,7 +1256,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb index 936d86c1f..e805f0b50 100644 --- a/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb +++ b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Rabi oscillations of 1 atom" + "## Rabi oscillations of 1 atom" ] }, { @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -86,32 +86,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 0.02s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.03s. Est. time left: 00:00:00:00\n", - "30.0%. Run time: 0.04s. Est. time left: 00:00:00:00\n", - "40.0%. Run time: 0.04s. Est. time left: 00:00:00:00\n", - "50.0%. Run time: 0.05s. Est. time left: 00:00:00:00\n", - "60.0%. Run time: 0.06s. Est. time left: 00:00:00:00\n", - "70.0%. Run time: 0.06s. Est. time left: 00:00:00:00\n", - "80.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", - "90.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", - "Total run time: 0.08s\n" - ] - } - ], + "outputs": [], "source": [ "simple_pulse = Pulse.ConstantPulse(4000, 2*np.pi*4.6, 0, 0)\n", "seq.add(simple_pulse, 'MW')\n", "seq.measure(basis='XY')\n", "\n", - "sim = Simulation(seq, sampling_rate=0.2)\n", + "sim = Simulation(seq)\n", "\n", "results = sim.run(progress_bar=True, nsteps=5000)" ] @@ -125,22 +108,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def magnetization(j, total_sites):\n", " prod = [qutip.qeye(2) for _ in range(total_sites)]\n", @@ -159,7 +129,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Spin chain of 3 atoms" + "## Spin chain of 3 atoms" ] }, { @@ -171,22 +141,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "coords = np.array([[-8., 0], [0, 0], [8., 0]])\n", "qubits = dict(enumerate(coords))\n", @@ -213,22 +170,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "magn_list = [magnetization(j, 3) for j in range(3)]\n", "\n", @@ -270,8 +214,9 @@ "An external magnetic field can be added to the experiment, and will modify the hamiltonian. The XY Hamiltonian is then\n", "\n", "$$\n", - "H_{XY} = \\frac{1}{2}\\sum_{i\n", @@ -285,22 +230,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "coords = np.array([[-1., 0], [0, 0], [np.sqrt(2/3), np.sqrt(1/3)]]) * 8.\n", "qubits = dict(enumerate(coords))\n", @@ -321,26 +253,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 0.04s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.08s. Est. time left: 00:00:00:00\n", - "30.0%. Run time: 0.12s. Est. time left: 00:00:00:00\n", - "40.0%. Run time: 0.16s. Est. time left: 00:00:00:00\n", - "50.0%. Run time: 0.20s. Est. time left: 00:00:00:00\n", - "60.0%. Run time: 0.24s. Est. time left: 00:00:00:00\n", - "70.0%. Run time: 0.28s. Est. time left: 00:00:00:00\n", - "80.0%. Run time: 0.31s. Est. time left: 00:00:00:00\n", - "90.0%. Run time: 0.35s. Est. time left: 00:00:00:00\n", - "Total run time: 0.39s\n" - ] - } - ], + "outputs": [], "source": [ "# State preparation using SLM mask\n", "masked_qubits = [1, 2]\n", @@ -359,32 +274,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 1.0)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "magn_list = [magnetization(j, 3) for j in range(3)]\n", "\n", @@ -418,7 +310,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -432,7 +324,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/tutorials/simulating_sequences.ipynb b/tutorials/simulating_sequences.ipynb index 3ec413195..367c62590 100644 --- a/tutorials/simulating_sequences.ipynb +++ b/tutorials/simulating_sequences.ipynb @@ -112,7 +112,7 @@ "metadata": {}, "outputs": [], "source": [ - "sim = Simulation(seq, sampling_rate=0.01)" + "sim = Simulation(seq, sampling_rate=0.1)" ] }, { From b798963b2134bf365f5c8531781e51cec4e8b5b2 Mon Sep 17 00:00:00 2001 From: Louis-PaulHenry <79902647+Louis-PaulHenry@users.noreply.github.com> Date: Thu, 2 Dec 2021 09:08:39 +0100 Subject: [PATCH 24/51] Qek tuto (#294) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update Quantum Evolution Kernel.ipynb * QEK tuto Co-authored-by: Henrique Silvério --- .../Fingerprint/Fingerprint_A.txt | 24756 ++++++++++++++++ .../Fingerprint_edge_attributes.txt | 24756 ++++++++++++++++ .../Fingerprint_graph_indicator.txt | 15167 ++++++++++ .../Fingerprint/Fingerprint_graph_labels.txt | 2800 ++ .../Fingerprint/Fingerprint_label_readme.txt | 23 + .../Fingerprint_node_attributes.txt | 15167 ++++++++++ .../Quantum Evolution Kernel.ipynb | 989 +- 7 files changed, 83245 insertions(+), 413 deletions(-) create mode 100644 tutorials/applications/Fingerprint/Fingerprint_A.txt create mode 100644 tutorials/applications/Fingerprint/Fingerprint_edge_attributes.txt create mode 100644 tutorials/applications/Fingerprint/Fingerprint_graph_indicator.txt create mode 100644 tutorials/applications/Fingerprint/Fingerprint_graph_labels.txt create mode 100644 tutorials/applications/Fingerprint/Fingerprint_label_readme.txt create mode 100644 tutorials/applications/Fingerprint/Fingerprint_node_attributes.txt diff --git a/tutorials/applications/Fingerprint/Fingerprint_A.txt b/tutorials/applications/Fingerprint/Fingerprint_A.txt new file mode 100644 index 000000000..4be33c09c --- /dev/null +++ b/tutorials/applications/Fingerprint/Fingerprint_A.txt @@ -0,0 +1,24756 @@ +1, 2 +2, 1 +3, 7 +7, 3 +7, 8 +8, 7 +8, 9 +9, 8 +9, 10 +10, 9 +10, 11 +11, 10 +11, 12 +12, 11 +12, 13 +13, 12 +13, 6 +6, 13 +4, 14 +14, 4 +14, 5 +5, 14 +15, 16 +16, 15 +17, 19 +19, 17 +19, 20 +20, 19 +20, 18 +18, 20 +21, 23 +23, 21 +23, 24 +24, 23 +24, 25 +25, 24 +25, 26 +26, 25 +26, 22 +22, 26 +27, 31 +31, 27 +31, 32 +32, 31 +32, 33 +33, 32 +33, 34 +34, 33 +34, 35 +35, 34 +35, 36 +36, 35 +36, 30 +30, 36 +28, 37 +37, 28 +37, 38 +38, 37 +38, 39 +39, 38 +39, 29 +29, 39 +40, 41 +41, 40 +42, 46 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a/tutorials/applications/Quantum Evolution Kernel.ipynb +++ b/tutorials/applications/Quantum Evolution Kernel.ipynb @@ -67,26 +67,30 @@ "outputs": [], "source": [ "import numpy as np\n", - "def JSdiv(p1,p2):\n", + "\n", + "\n", + "def JSdiv(p1, p2):\n", + " \"\"\"Compute the Jensen-Shannon divergence between two distributions.\"\"\"\n", " q1 = np.array(p1)/np.sum(p1)\n", " q2 = np.array(p2)/np.sum(p2)\n", " # Alowing for distributions to have different sizes\n", " delta = len(q1) - len(q2)\n", " if delta < 0:\n", - " q1 = np.concatenate((q1,np.array([0 for i in range(-delta)])))\n", + " q1 = np.concatenate((q1, np.array([0 for i in range(-delta)])))\n", " elif delta > 0:\n", - " q2 = np.concatenate((q2,np.array([0 for i in range(delta)])))\n", + " q2 = np.concatenate((q2, np.array([0 for i in range(delta)])))\n", " pq = (q1 + q2)/2\n", + "\n", " def entropy(pl_unscaled):\n", " # Making sure the probability distributions are similarly normalized\n", " pl = np.array(pl_unscaled)/np.sum(pl_unscaled)\n", " res = 0\n", " for p in pl:\n", - " if p>0:\n", + " if p > 0:\n", " res += p*np.log(p)\n", " return -res\n", " out = entropy(pq)-(entropy(q1)+entropy(q2))/2\n", - " return out" + " return out\n" ] }, { @@ -157,27 +161,29 @@ "# Load graph package\n", "import networkx as nx\n", "\n", + "\n", "def pk(G, theta=np.pi/4):\n", " cnt = nx.degree_histogram(G)\n", " kappamax = len(cnt)\n", - " \n", + "\n", " c = np.cos(theta)**2\n", " s = 1-c\n", " t = np.tan(theta)**2\n", " sp = 2 * c * s\n", - " \n", + "\n", " res0 = 0\n", " for kappa, m in enumerate(cnt):\n", " res0 += m * (1-c**kappa)\n", " res = [(sp * res0)]\n", - " for k in range(1,kappamax):\n", + " for k in range(1, kappamax):\n", " res0 = 0\n", - " for kappa in range(k,kappamax):\n", + " for kappa in range(k, kappamax):\n", " m_kappa = cnt[kappa]\n", - " binom = scipy.special.comb(kappa, k, exact=True) \n", + " binom = scipy.special.comb(kappa, k, exact=True)\n", " res0 += m_kappa * binom * (c**(kappa+1-k))\n", " res.append(((s**(1+k)) * res0))\n", - " return res" + " return res\n", + " " ] }, { @@ -198,10 +204,11 @@ "# Size of the dataset\n", "n_graphs = 100\n", "\n", - "def create_random_graphs(N_max = 100, \n", - " n_graphs = 100, \n", - " rho_low = 0.35, \n", - " rho_high = 0.65):\n", + "\n", + "def create_random_graphs(N_max=100,\n", + " n_graphs=100,\n", + " rho_low=0.35,\n", + " rho_high=0.65):\n", " # Dataset with graphs of two different Erdős–Rényi classes\n", " graphs = []\n", " # Classes of these graphs\n", @@ -210,7 +217,7 @@ " probability_distributions = []\n", " for _ in range(n_graphs):\n", " # Number of nodes in the graph in [N_max/2,N_max]\n", - " N = np.random.randint(N_max//2,N_max+1)\n", + " N = np.random.randint(N_max//2, N_max+1)\n", " if np.random.rand() < .5:\n", " rho = rho_low\n", " classes.append(0)\n", @@ -220,7 +227,7 @@ " G = nx.erdos_renyi_graph(N, rho)\n", " pdist = pk(G)\n", " probability_distributions.append(pdist/np.sum(pdist))\n", - " \n", + "\n", " return graphs, classes, probability_distributions\n" ] }, @@ -243,13 +250,15 @@ "metadata": {}, "outputs": [], "source": [ - "def kernel_matrix(pdist1,pdist2,mu=1):\n", - " Kmat = np.array([[np.exp(-mu * JSdiv(p1,p2)) for p1 in pdist1] for p2 in pdist2])\n", + "def kernel_matrix(pdist1, pdist2, mu=1):\n", + " Kmat = np.array([[np.exp(-mu * JSdiv(p1, p2)) for p1 in pdist1]\n", + " for p2 in pdist2])\n", " return Kmat\n", "\n", + "\n", "graphs, classes, proba_dists = create_random_graphs()\n", "\n", - "Kmat = kernel_matrix(proba_dists,proba_dists)" + "Kmat = kernel_matrix(proba_dists, proba_dists)\n" ] }, { @@ -259,7 +268,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -272,14 +281,14 @@ ], "source": [ "def plot_kernel_matrix(Kmat):\n", - " fig, ax = plt.subplots(figsize=(8,8))\n", - " im = ax.imshow(Kmat,cmap='OrRd')\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " im = ax.imshow(Kmat, cmap='OrRd')\n", " ax.set_xlabel('Graph #', fontsize=18)\n", " ax.set_ylabel('Graph #', fontsize=18)\n", - " cbar = plt.colorbar(im,extend='max')\n", + " cbar = plt.colorbar(im, extend='max')\n", + "\n", "\n", - " \n", - "plot_kernel_matrix(Kmat) " + "plot_kernel_matrix(Kmat)\n" ] }, { @@ -302,72 +311,83 @@ "from sklearn import svm\n", "from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score\n", "\n", - " \n", - "scores_types = ['Accuracy ', \n", - " 'f1 ', \n", - " 'Precision', \n", + "\n", + "scores_types = ['Accuracy ',\n", + " 'f1 ',\n", + " 'Precision',\n", " 'Recall ']\n", "\n", - "# Create and train a classifier from the Kernel matrix `Kmat`\n", - "# obtained from graphs of classes `classes_train`\n", - "def trained_classifier_from_Kmat(Kmat, classes_train): \n", + "\n", + "def trained_classifier_from_Kmat(Kmat, classes_train):\n", + " \"\"\"\n", + " Create and train a classifier from the Kernel matrix `Kmat`\n", + " obtained from graphs of classes `classes_train`\n", + " \"\"\"\n", " classifier = svm.SVC(kernel='precomputed')\n", " classifier.fit(Kmat, classes_train)\n", - " \n", + "\n", " return classifier\n", "\n", - "# Create and train a classifier from the probability\n", - "# distributions `p_dist_train` and the corresponding classes\n", - "# `classes_train`\n", - "def trained_classifier_pdist(p_dist_train, classes_train): \n", - " Kmat = kernel_matrix(p_dist_train,p_dist_train)\n", - " \n", + "\n", + "def trained_classifier_pdist(p_dist_train, classes_train):\n", + " \"\"\"\n", + " Create and train a classifier from the probability\n", + " distributions `p_dist_train` and the corresponding classes\n", + " `classes_train`\n", + " \"\"\"\n", + " Kmat = kernel_matrix(p_dist_train, p_dist_train)\n", + "\n", " return trained_classifier_from_Kmat(Kmat, classes_train)\n", "\n", - "# Test a trained classifier `classifier` from the probability \n", - "# distributions of the train and test data sets `p_dist_train` \n", - "# and `p_dist_test` respectively, and from the classes of the\n", - "# test set `classes_test`\n", - "def test_classifier(classifier, \n", - " p_dist_train, \n", - " p_dist_test, \n", - " classes_test, \n", + "def test_classifier(classifier,\n", + " p_dist_train,\n", + " p_dist_test,\n", + " classes_test,\n", " verbose=False):\n", - " X = kernel_matrix(p_dist_train,p_dist_test)\n", - " \n", + " \"\"\"\n", + " Test a trained classifier `classifier` from the probability\n", + " distributions of the train and test data sets `p_dist_train`\n", + " and `p_dist_test` respectively, and from the classes of the\n", + " test set `classes_test`\n", + " \"\"\"\n", + " X = kernel_matrix(p_dist_train, p_dist_test)\n", + "\n", " predicted_classes = classifier.predict(X)\n", "\n", - " scores = [accuracy_score(classes_test, \n", + " scores = [accuracy_score(classes_test,\n", " predicted_classes),\n", - " f1_score(classes_test, \n", - " predicted_classes, \n", + " f1_score(classes_test,\n", + " predicted_classes,\n", " average='weighted'),\n", - " precision_score(classes_test, \n", - " predicted_classes, \n", - " average='weighted', \n", + " precision_score(classes_test,\n", + " predicted_classes,\n", + " average='weighted',\n", " zero_division=0),\n", - " recall_score(classes_test, \n", - " predicted_classes, \n", + " recall_score(classes_test,\n", + " predicted_classes,\n", " average='weighted')]\n", "\n", " if verbose:\n", " for st, s in zip(scores_types, scores):\n", " print(f'{st} : {s:6.3}')\n", - " \n", + "\n", " return scores\n", "\n", - "# Train and test a classifier from test and \n", - "# train probability distributions and classes\n", - "def train_and_test_classifier(p_dist_train, \n", - " classes_train, \n", - " p_dist_test, \n", - " classes_test, \n", + "\n", + "def train_and_test_classifier(p_dist_train,\n", + " classes_train,\n", + " p_dist_test,\n", + " classes_test,\n", " verbose=False):\n", + " \"\"\"\n", + " Train and test a classifier from test and\n", + " train probability distributions and classes\n", + " \"\"\"\n", " classifier = trained_classifier_pdist(p_dist_train, classes_train)\n", - " \n", - " return classifier, test_classifier(classifier, p_dist_train, \n", - " p_dist_test, classes_test, \n", - " verbose=verbose)" + "\n", + " return classifier, test_classifier(classifier, p_dist_train,\n", + " p_dist_test, classes_test,\n", + " verbose=verbose)\n" ] }, { @@ -386,10 +406,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy : 0.88\n", - "f1 : 0.881\n", - "Precision : 0.907\n", - "Recall : 0.88\n" + "Accuracy : 0.92\n", + "f1 : 0.919\n", + "Precision : 0.931\n", + "Recall : 0.92\n" ] } ], @@ -397,14 +417,15 @@ "# Create a random training set\n", "graphs_train, classes_train, p_dist_train = create_random_graphs()\n", "# Create a random test set\n", - "graphs_test, classes_test, p_dist_test = create_random_graphs(n_graphs = 50)\n", + "graphs_test, classes_test, p_dist_test = create_random_graphs(n_graphs=50)\n", "\n", "# Compute the score of the classifier\n", - "classifier, scores = train_and_test_classifier(p_dist_train, \n", - " classes_train, \n", - " p_dist_test, \n", - " classes_test, \n", - " verbose=True)" + "classifier, scores = train_and_test_classifier(p_dist_train,\n", + " classes_train,\n", + " p_dist_test,\n", + " classes_test,\n", + " verbose=True\n", + " )\n" ] }, { @@ -422,33 +443,53 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using backend: pytorch\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset contains 2428 graphs of at most 12 nodes\n" - ] - } - ], + "outputs": [], "source": [ - "# package allowing for easy import of the dataset\n", - "import dgl\n", - "\n", - "# largest number of nodes in allowed graphs\n", - "Nmax = 12\n", - "\n", - "# Loading graphs smaller than Nmax\n", - "data = dgl.data.LegacyTUDataset('Fingerprint',max_allow_node=Nmax)\n", - "\n", - "print(f'Dataset contains {len(data)} graphs of at most {data.max_num_node} nodes')" + "prefix = './Fingerprint/Fingerprint_'\n", + "\n", + "graphs = {}\n", + "node_to_graph = {}\n", + "class_count = {}\n", + "\n", + "label_file = prefix + 'graph_labels' + '.txt'\n", + "with open(label_file) as f:\n", + " lines = f.readlines()\n", + " for i, line in enumerate(lines):\n", + " labl = int(line)\n", + " graphs[i+1] = nx.Graph(label=labl)\n", + " if labl in class_count.keys():\n", + " class_count[labl] += 1\n", + " else:\n", + " class_count[labl] = 1\n", + "\n", + "\n", + "node_to_graph_file = prefix + 'graph_indicator' + '.txt'\n", + "with open(node_to_graph_file) as f:\n", + " lines = f.readlines()\n", + " for i, line in enumerate(lines):\n", + " gi = int(line)\n", + " node_to_graph[i+1] = gi\n", + " graphs[gi].add_node(i+1)\n", + "\n", + "adjacency_file = prefix + 'A' + '.txt'\n", + "with open(adjacency_file) as f:\n", + " lines = f.readlines()\n", + " for line in lines:\n", + " Ind = line.split(',')\n", + " i = int(Ind[0])\n", + " j = int(Ind[1])\n", + " gi = node_to_graph[i]\n", + " graphs[gi].add_edge(i, j)\n", + "\n", + "coordinates_file = prefix + 'node_attributes' + '.txt'\n", + "with open(coordinates_file) as f:\n", + " lines = f.readlines()\n", + " for i,line in enumerate(lines):\n", + " Ind = line.split(',')\n", + " x = float(Ind[0])\n", + " y = float(Ind[1])\n", + " gi = node_to_graph[i+1]\n", + " nx.set_node_attributes(graphs[gi], {i+1: (x,y)}, \"coords\")" ] }, { @@ -472,51 +513,57 @@ "name": "stdout", "output_type": "stream", "text": [ - "After preprocessing, the dataset now contains 735 \n", + "After preprocessing, the dataset now contains 897 \n", "graphs of at least 5 and at most 12 nodes, distributed \n", - "across the different classes in the following way :\n", - "{0: 345, 4: 176, 5: 335, 6: 41}\n" + "across the different classes in the following way {0: 345, 4: 176, 5: 335, 6: 41}\n", + "\n" ] } ], "source": [ - "# Minimum number of nodes in a graph\n", + "# Minimum and maximum number of nodes in a graph\n", "Nmin = 5\n", + "Nmax = 12\n", "\n", "# Number of classes in the dataset\n", - "number_of_classes = data.num_labels\n", + "number_of_classes = len(class_count.keys())\n", "\n", "# Tally the number of graphs in each class\n", - "count = np.zeros(number_of_classes,dtype='int')\n", - "for G in data:\n", - " g, label = G\n", - " if Nmin <= g.num_nodes():\n", - " count[int(label)] += 1\n", + "count = {clas: 0 for clas in class_count.keys()}\n", + "for g in graphs.values():\n", + " if Nmin <= g.number_of_nodes() <= Nmax:\n", + " count[g.graph['label']] += 1\n", "\n", "# Number of graphs in the most represented class\n", - "size_of_largest_class = np.max(count)\n", - "# Include only classes with at least 10% of the \n", - "#size of the largest one\n", - "included_classes = {} \n", - "for clas, prop in enumerate(count):\n", + "size_of_largest_class = max(count.values())\n", + "# Include only classes with at least 10% of the size of the largest one\n", + "include_classes = {clas: False for clas in class_count.keys()}\n", + "for clas, prop in count.items():\n", " if prop > .1*size_of_largest_class:\n", - " included_classes[clas] = True\n", - " \n", + " include_classes[clas] = True\n", + "\n", + "\n", "data_preprocessed = []\n", - "for G in data:\n", - " g, label = G\n", - " if Nmin < g.num_nodes() and int(label) in included_classes.keys():\n", - " data_preprocessed.append(G)\n", - " \n", + "for g in graphs.values():\n", + " labl = g.graph['label']\n", + " if Nmin <= g.number_of_nodes() <= Nmax and include_classes[labl]:\n", + " mapping = {l: i for i, l in enumerate(g.nodes())}\n", + " g_shift = nx.relabel_nodes(g, mapping)\n", + " data_preprocessed.append(g_shift)\n", + "\n", "# size of the dataset\n", "n_graphs = len(data_preprocessed)\n", "\n", - "for clas in included_classes.keys():\n", - " included_classes[clas] = count[clas]\n", "\n", - "print(f'After preprocessing, the dataset now contains {len(data_preprocessed)} \\n'+\n", - " f'graphs of at least {Nmin} and at most {Nmax} nodes, distributed \\n'+\n", - " f'across the different classes in the following way :\\n{included_classes}')" + "included_classes = {}\n", + "for clas, icount in count.items():\n", + " if include_classes[clas]:\n", + " included_classes[clas] = icount\n", + "\n", + "print(f'After preprocessing, the dataset now contains {len(data_preprocessed)} \\n' +\n", + " f'graphs of at least {Nmin} and at most {Nmax} nodes, distributed \\n' +\n", + " f'across the different classes in the following way {included_classes}\\n')\n", + "\n" ] }, { @@ -535,31 +582,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "After preprocessing, the dataset now contains 443 graphs of at least 5 and at most 12 nodes, distributed across 2 different classes in the following way {0: 272, 4: 171}\n" + "After preprocessing, the dataset now contains 400 graphs of at least 5 and at most 12 nodes, distributed across 2 different classes in the following way {0: 246, 4: 154}\n" ] } ], "source": [ + "# We here only sample 400 graphs\n", + "dataset_targetsize = 400\n", + "\n", "kept_classes = {}\n", "for cls in list(included_classes.keys())[0:2]:\n", " kept_classes[cls] = 0\n", "\n", "\n", "data_reduced = []\n", - "for G in data_preprocessed:\n", - " g, label = G\n", - " cls = int(label)\n", - " if cls in kept_classes.keys():\n", - " kept_classes[cls] += 1\n", - " data_reduced.append(G)\n", - " \n", + "for g in data_preprocessed:\n", + " if(len(data_reduced) < dataset_targetsize):\n", + " cls = g.graph['label']\n", + " if cls in kept_classes.keys():\n", + " kept_classes[cls] += 1\n", + " data_reduced.append(g)\n", + "\n", "# size of the dataset\n", "n_graphs = len(data_reduced)\n", "\n", - "print(f'After preprocessing, the dataset now contains {len(data_reduced)} '+\n", - " f'graphs of at least {Nmin} and at most {Nmax} nodes, distributed '+\n", + "print(f'After preprocessing, the dataset now contains {len(data_reduced)} ' +\n", + " f'graphs of at least {Nmin} and at most {Nmax} nodes, distributed ' +\n", " f'across {len(kept_classes)} different classes in the following way ' +\n", - " f'{kept_classes}')" + " f'{kept_classes}')\n" ] }, { @@ -588,96 +638,75 @@ "metadata": {}, "outputs": [], "source": [ - "def unpack(G):\n", - " '''\n", - " from a graph in the TUI dataset, generates a dictionary \n", - " containing the coordinates of the nodes, and a list \n", - " containing all the edges\n", - " '''\n", - " # graph and label\n", - " graph, label = G\n", - " \n", - " # convert graph to networkx object\n", - " graph_nx = dgl.to_networkx(graph)\n", - " \n", - " # reshape graph using Fruchterman-Reingold algorithm, \n", - " # and store it as a dictionary\n", - " graph_dict = nx.spring_layout(graph_nx, iterations=35, center=(0,0), seed=1)\n", - " \n", - " # edges are represented as a tuple containing \n", - " # the connected nodes\n", - " node_1, node_2 = graph.edges()\n", - " edges = list(zip(np.array(node_1), np.array(node_2)))\n", - " \n", - " \n", - " # return graph dictionary and edges\n", - " return graph_dict, edges\n", - "\n", - "def max_distance(graph_dict):\n", - " '''\n", - " Computes the diameter of the register (i.e. the maximal \n", - " distance between two nodes)\n", - " '''\n", - " return np.max(np.linalg.norm(list(graph_dict.values()), axis=1))\n", + "from scipy.optimize import minimize\n", + "from scipy.spatial.distance import pdist\n", + "from scipy.optimize import NonlinearConstraint\n", "\n", "\n", - "def min_distance(graph_dict):\n", + "def correct_coordinates(g):\n", " '''\n", - " Computes the minimal distance between two nodes\n", + " Corrects the coordinates of the nodes so that the\n", + " atoms fit the hardware constraints.\n", " '''\n", - " min_dist = 1.e29\n", - " \n", - " for i in range(len(graph_dict)):\n", - " for j in range(i+1, len(graph_dict)):\n", - " dr = np.linalg.norm(graph_dict[i]-graph_dict[j])\n", - " min_dist = min(dr, min_dist)\n", - " \n", - " return min_dist\n", + " n = g.number_of_nodes()\n", "\n", + " # Coordinates given in the dataset\n", + " r_list = np.array([g.nodes[node][\"coords\"] for node in g.nodes()])\n", + " r_list += -np.mean(r_list, axis=0)\n", + " scale = np.max([np.sqrt(r.dot(r)) for r in r_list])\n", "\n", - "def max_edge_length(graph_dict, edges):\n", - " '''\n", - " Computes the maximal distance between nodes connected \n", - " by an edge of the graph\n", - " '''\n", - " max_length = 0\n", - " for n1, n2 in edges:\n", - " dr = np.linalg.norm(graph_dict[n1]-graph_dict[n2])\n", - " max_length = max(max_length, dr)\n", - " \n", - " return max_length\n", - " \n", - " \n", - "def upscale_graph(graph_dict, edges, device):\n", + " x0 = r_list.reshape(2*n)\n", + "\n", + " # Ensures the atoms are within range of the device\n", + " xmax = device.max_radial_distance/np.sqrt(2)\n", + " bounds = [(-xmax, xmax)] * (2*n)\n", + " x0 *= xmax/scale\n", + "\n", + " #Encode the constraint of a minimal distance bewteen atoms\n", + " def min_dist(params):\n", + " return np.min(pdist(params.reshape(n, 2)))\n", + "\n", + " dmin = 1.1*device.min_atom_distance\n", + " nlc = NonlinearConstraint(min_dist, dmin, np.inf)\n", + "\n", + " def cost_function(params):\n", + " return 1\n", + "\n", + " res = minimize(cost_function,\n", + " x0=x0,\n", + " bounds=bounds,\n", + " constraints=nlc,\n", + " method='SLSQP')\n", + " x = res.x\n", + " rmax = device.max_radial_distance\n", + " scale_diameter = .95 * rmax/np.max(pdist(x.reshape(n, 2))) \n", + " x *= max(scale_diameter,1.)\n", + " r_list = x.reshape(n, 2)\n", + " r_list += -np.mean(r_list, axis=0)\n", + "\n", + "\n", + " for node, r in zip(g.nodes(), r_list):\n", + " g.nodes[node][\"coords\"] = r\n", + "\n", + "\n", + "def max_edge_length(g):\n", " '''\n", - " Stretch the graph as much as possible so as to increase the\n", - " ratio between Omega and the couplings\n", + " Computes the maximal distance between nodes connected by an edge\n", + " of the graph\n", " '''\n", - " scale_max = device.max_radial_distance/max_distance(graph_dict)\n", - " for node in graph_dict:\n", - " graph_dict[node] *= .95*scale_max" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from pulser import Register\n", - "from pulser.devices import Chadoq2\n", + " n = g.number_of_nodes()\n", + " edges = np.array([1 if (i, j) in g.edges() else 0 for i in range(n) for j in range(i+1, n)])\n", + " r_list = np.array([g.nodes[node][\"coords\"] for node in g.nodes()])\n", "\n", - "device = Chadoq2\n", - "r_max = device.max_radial_distance\n", - "d_min = device.min_atom_distance\n", + " distances = pdist(r_list)\n", + " max_length = np.max(edges * distances)\n", "\n", - "omega_max = device.channels['rydberg_global'].max_amp\n", - "min_bond_length = device.rabi_from_blockade(omega_max)" + " return max_length" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -704,27 +733,24 @@ "\n", " # List of class of each graph\n", " label_list = []\n", - " for G in data_reduced:\n", - " g, label = G \n", - "\n", - " label_list.append(int(label))\n", + " for g in data_reduced:\n", "\n", - " graph_dict, edges = unpack(G)\n", + " label_list.append(g.graph['label'])\n", "\n", - " edges_list.append(edges)\n", + " correct_coordinates(g)\n", + " graph_dict= {i:g.nodes[i][\"coords\"] for i in g.nodes()}\n", "\n", - " # Rescale the graph to satisfy device constraints\n", - " upscale_graph(graph_dict, edges, device)\n", + " edges_list.append(g.edges)\n", "\n", " # Find the blockade radius and corresponding Rabi frequency\n", - " blockade_radius = max_edge_length(graph_dict, edges)\n", - " rabi = Chadoq2.rabi_from_blockade(blockade_radius)\n", + " blockade_radius = max_edge_length(g)\n", + " rabi = min(Chadoq2.rabi_from_blockade(blockade_radius), omega_max)\n", " rabi_list.append(rabi)\n", "\n", " # Create the register\n", " reg = Register(graph_dict)\n", " reg_list.append(reg)\n", - " \n", + "\n", " return reg_list, rabi_list, edges_list, label_list\n", "\n", "reg_list, rabi_list, edges_list, label_list = reg_from_data(data_reduced)" @@ -779,12 +805,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -796,20 +822,20 @@ "source": [ "from pulser import Pulse, Sequence, Simulation\n", "\n", - "def pulse_seqence(reg, \n", - " t_1=100, \n", + "def pulse_seqence(reg,\n", + " t_1=100,\n", " omega=omega_max, # amplitude of the initial and final pulses\n", " omega_g=0, # amplitude in the \"free evolution\" parts\n", " total_time=512): # total duration of the pulse\n", " seq = Sequence(reg, device)\n", " seq.declare_channel('Channel 0','rydberg_global')\n", - " \n", + "\n", " # making sure that the value of omega does not exceed the \n", " # maximal value, and that it doesn't lead to a pulse \n", " # duration that is too short\n", " omega = min([omega,1000*np.pi/2,omega_max])\n", - " \n", - " # Set the initial and final pulse times to the optimal value \n", + "\n", + " # Set the initial and final pulse times to the optimal value\n", " # be careful about the units : Omega(rad/μs) -> t (ns)\n", " t = 1000*np.pi/(2*omega)\n", " # Set the total_time\n", @@ -818,25 +844,26 @@ " delta = 0\n", " # We want the pulse to be along sigma_y\n", " phi=np.pi/2\n", - " \n", + "\n", " initial_pulse = Pulse.ConstantPulse(t,\n", - " omega, \n", - " delta, \n", + " omega,\n", + " delta,\n", " phase=phi)\n", " if total_time > t_1 + 2*t:\n", " Hg_pulse = Pulse.ConstantPulse(tau,\n", - " omega_g, \n", - " delta, \n", + " omega_g,\n", + " delta,\n", " phase=phi)\n", " if t_1 > 0:\n", - " middle_pulse = Pulse.ConstantPulse(t_1,omega, \n", - " delta, \n", + " middle_pulse = Pulse.ConstantPulse(t_1,\n", + " omega,\n", + " delta,\n", " phase=phi)\n", " final_pulse = Pulse.ConstantPulse(t,\n", - " omega, \n", - " delta, \n", + " omega,\n", + " delta,\n", " phase=phi)\n", - " \n", + "\n", " seq.add(initial_pulse, 'Channel 0')\n", " if total_time > t_1 + 2*t:\n", " seq.add(Hg_pulse, 'Channel 0')\n", @@ -845,14 +872,15 @@ " if total_time > t_1 + 2*t:\n", " seq.add(Hg_pulse, 'Channel 0')\n", " seq.add(final_pulse, 'Channel 0')\n", - " \n", + "\n", " seq.measure(basis='ground-rydberg')\n", - " \n", + "\n", " return seq\n", - " \n", + "\n", + "\n", "# Illustrate the pulse on a register containing a single atom\n", - "reg = Register.from_coordinates([(0,0)])\n", - "pulse_seqence(reg, t_1=160).draw()" + "reg = Register.from_coordinates([(0, 0)])\n", + "pulse_seqence(reg, t_1=160).draw()\n" ] }, { @@ -864,47 +892,54 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "def proba_distributions(t_1 = 100, #duration of the central pulse\n", - " omega = omega_max, # amplitude of the pulses\n", - " omega_g_factor = 1, # set to 1 if the Amplitude is non zero during the \"free evolution\"\n", - " total_time = 512, # total duration of the pulse\n", - " Nsamples = 1000,\n", - " indices = list(range(n_graphs))): #indices of the graphs used\n", - " \n", - " bins=np.linspace(0, Nmax*Nmax, Nmax*Nmax+1)\n", + "from tqdm.auto import tqdm\n", + "\n", + "\n", + "def proba_distributions(t_1=100, # duration of the central pulse\n", + " omega=omega_max, # amplitude of the pulses\n", + " omega_g_factor=1, # set to 1 if the Amplitude is non\n", + " # zero during the \"free evolution\"\n", + " total_time=512, # total duration of the pulse\n", + " Nsamples=1000,\n", + " indices=list(range(n_graphs))): # graphs to be used\n", + " '''\n", + " Compute the probability distributions for a given pulse\n", + " for all graphs in `indices`\n", + " '''\n", + "\n", + " bins = np.linspace(0, Nmax*Nmax, Nmax*Nmax + 1)\n", " histograms = []\n", - " for i in indices:\n", + " for i in tqdm(indices):\n", " reg, rabi, edges = reg_list[i], rabi_list[i], edges_list[i]\n", - " seq = pulse_seqence(reg, \n", - " t_1=t_1, \n", - " omega=omega, \n", - " omega_g=omega_g_factor*rabi, \n", + " seq = pulse_seqence(reg,\n", + " t_1=t_1,\n", + " omega=omega,\n", + " omega_g=omega_g_factor*rabi,\n", " total_time=total_time)\n", - " \n", + "\n", " # Simulate and sample\n", - " simul = Simulation(seq,evaluation_times=\"Full\")\n", + " simul = Simulation(seq, evaluation_times=.5, sampling_rate=.1)\n", " results = simul.run()\n", " sampling = results.sample_final_state(N_samples=Nsamples)\n", - " \n", + "\n", " # Create a list with the measurements of the ising energy\n", " ie_meas = []\n", " ie_weights = []\n", - " for bitstring,num in sampling.items():\n", + " for bitstring, num in sampling.items():\n", " ie_meas.append(compute_ising_energy(bitstring, edges))\n", " ie_weights.append(num)\n", - " \n", + "\n", " # Create histogram of the measurements and append to list\n", - " ncount, b = np.histogram(ie_meas, \n", - " bins=bins, \n", - " density=True, \n", + " ncount, b = np.histogram(ie_meas,\n", + " bins=bins,\n", + " density=True,\n", " weights=ie_weights)\n", " histograms.append(ncount)\n", " return histograms\n", - " \n", "\n", "\n", "def compute_ising_energy(outcome, edges):\n", @@ -914,16 +949,16 @@ " '''\n", " # split outcome string in a list\n", " outcome_ls = [char for char in outcome]\n", - " \n", + "\n", " energy = 0\n", - " \n", + "\n", " for edge in edges:\n", " i = int(edge[0])\n", " j = int(edge[1])\n", " if i < j:\n", " energy += int(outcome_ls[i])*int(outcome_ls[j])\n", - " \n", - " return energy" + "\n", + " return energy\n" ] }, { @@ -935,12 +970,40 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training in progress...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:10<00:00, 14.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing in progress...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:02<00:00, 20.60it/s]\n" + ] + }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -955,7 +1018,7 @@ "n_graphs = len(data_reduced)\n", "\n", "#sample 150 graphs and train on 100 of them\n", - "n_train = 100\n", + "n_train = 150\n", "n_test = 50\n", "\n", "# randomize graph order\n", @@ -969,40 +1032,41 @@ "\n", "\n", "# Probability distributions obtained after the pulse\n", - "\n", - "probas_train = proba_distributions(t_1 = 0, indices = indices_train)\n", - "probas_test = proba_distributions(t_1 = 0, indices = indices_test)\n", + "print('Training in progress...')\n", + "probas_train = proba_distributions(t_1=0, indices=indices_train)\n", + "print('Testing in progress...')\n", + "probas_test = proba_distributions(t_1=0, indices=indices_test)\n", "\n", "# Resulting kernel matrix\n", - "Kmat = kernel_matrix(probas_train,probas_train)\n", + "Kmat = kernel_matrix(probas_train, probas_train)\n", "\n", - "fig, ax = plt.subplots(figsize=(8,8))\n", - "im = ax.imshow(Kmat,cmap='OrRd')\n", - "cbar = plt.colorbar(im,extend='max')\n" + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "im = ax.imshow(Kmat, cmap='OrRd')\n", + "cbar = plt.colorbar(im, extend='max')\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy : 0.76\n", - "f1 : 0.755\n", - "Precision : 0.761\n", - "Recall : 0.76\n" + "Accuracy : 0.74\n", + "f1 : 0.661\n", + "Precision : 0.81\n", + "Recall : 0.74\n" ] } ], "source": [ - "classifier, scores = train_and_test_classifier(probas_train, \n", - " train_classes, \n", - " probas_test, \n", - " test_classes, \n", - " verbose=True)" + "classifier, scores = train_and_test_classifier(probas_train,\n", + " train_classes,\n", + " probas_test,\n", + " test_classes,\n", + " verbose=True)\n" ] }, { @@ -1024,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1034,81 +1098,83 @@ "N = 4\n", "M = 1\n", "\n", - "def score_function(t_1=100, \n", - " total_time=512, \n", - " repetitions=M, \n", + "\n", + "def score_function(t_1=100,\n", + " total_time=512,\n", + " repetitions=M,\n", " nblocks=N,\n", " label_list=label_list,\n", - " indices=list(range(n_graphs))): # list of graphs included\n", + " indices=list(range(n_graphs))): # list of graphs included\n", " '''\n", " Computes the accuracy, f1, precision and recall\n", " '''\n", - " \n", + "\n", " accuracy = []\n", " f1 = []\n", " precision = []\n", " recall = []\n", - " \n", + "\n", " n_g = len(indices)\n", - " \n", + "\n", " block_size = n_g//nblocks\n", - " \n", + "\n", "\n", " # Compute the probability distributions of all \n", " # graphs in the data set\n", " start_time = time.time()\n", - " probas_all = proba_distributions(t_1 = t_1, \n", - " total_time = total_time, \n", - " Nsamples = 1000,\n", - " indices = indices)\n", - " \n", + " probas_all = proba_distributions(t_1=t_1,\n", + " total_time=total_time,\n", + " Nsamples=1000,\n", + " indices=indices)\n", + "\n", " print(f' Probability lists were computed in {time.time() - start_time:4.1f} seconds')\n", - " \n", + "\n", " classes = np.array([label_list[i] for i in indices])\n", " start_time = time.time()\n", - " \n", + "\n", " for r in range(repetitions):\n", " #divide data in training set and test set\n", " indices_all = np.array(list(range(n_g)))\n", " np.random.shuffle(indices_all)\n", - " \n", - " mean_scores = np.zeros((4,))\n", + "\n", + " mean_scores = np.zeros((4, ))\n", " for iblock in range(nblocks):\n", - " indices_test = [indices_all[(iblock*block_size+i)%n_g] \n", + " indices_test = [indices_all[(iblock * block_size + i) % n_g]\n", " for i in range(block_size)]\n", - " indices_train =[indices_all[((iblock+1)*block_size+i)%n_g] \n", - " for i in range(n_g-block_size)]\n", - " \n", - " train_classes = np.array([label_list[indices[i]] \n", + " indices_train = [indices_all[((iblock + 1) * block_size + i) % n_g]\n", + " for i in range(n_g - block_size)]\n", + "\n", + " train_classes = np.array([label_list[indices[i]]\n", " for i in indices_train])\n", - " test_classes = np.array([label_list[indices[i]] \n", + " test_classes = np.array([label_list[indices[i]]\n", " for i in indices_test])\n", "\n", " # create probability histogram for train and test data \n", " probas_train = np.array([probas_all[i] for i in indices_train])\n", " probas_test = np.array([probas_all[i] for i in indices_test])\n", "\n", - " classifier, scores = train_and_test_classifier(probas_train, \n", - " train_classes, \n", - " probas_test, \n", + " classifier, scores = train_and_test_classifier(probas_train,\n", + " train_classes,\n", + " probas_test,\n", " test_classes,\n", - " verbose = False)\n", + " verbose=False)\n", " mean_scores += scores\n", - " \n", + "\n", " # calculate score metrics\n", " accuracy.append(mean_scores[0]/nblocks)\n", " f1.append(mean_scores[1]/nblocks)\n", " precision.append(mean_scores[2]/nblocks)\n", " recall.append(mean_scores[3]/nblocks)\n", - " \n", + "\n", " A = (np.mean(accuracy), np.std(accuracy))\n", " B = (np.mean(f1), np.std(f1))\n", " C = (np.mean(precision), np.std(precision))\n", " D = (np.mean(recall), np.std(recall))\n", "\n", " print(f' Kernel scores computed in {time.time() - start_time:4.1f} seconds')\n", - " \n", - " return A, B, C, D" + "\n", + " return A, B, C, D\n", + " " ] }, { @@ -1124,59 +1190,59 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def scan_scores(M=2,\n", " N=4,\n", " indices=list(range(n_graphs)),\n", - " durations = [512],\n", - " ):\n", + " durations=[512],\n", + " ):\n", "\n", " scores_dict = {}\n", "\n", " for s in scores_types:\n", " scores_dict[s] = []\n", "\n", - " print(f' ------------------------------------------------')\n", + " print(' ------------------------------------------------')\n", " print(f'| Max. duration of the middle pulse: {durations[-1]:4d} ns |')\n", " print(f'| Total duration of the pulse: {total_time:4d} ns |')\n", " print(f'| Using {N:2d} blocks of {len(indices)//N:4d} graphs each |')\n", - " print(f' ------------------------------------------------')\n", - " \n", - " \n", + " print(' ------------------------------------------------')\n", + "\n", " for t_1 in durations:\n", " print(f' Duration of the middle pulse: {t_1:4d} ns')\n", - " score_inst = score_function(t_1=t_1, \n", - " total_time=total_time, \n", - " repetitions=M, \n", + " score_inst = score_function(t_1=t_1,\n", + " total_time=total_time,\n", + " repetitions=M,\n", " nblocks=N,\n", " indices=indices_in)\n", "\n", - " for sc,st in zip(score_inst,scores_types):\n", + " for sc, st in zip(score_inst, scores_types):\n", " scores_dict[st].append(sc)\n", " print(f' > {st}: {sc[0]:6.3} +/- {sc[1]:6.3}')\n", " print()\n", " return scores_dict\n", - " \n", + "\n", "def plot_scores(scores_dict):\n", - " fig, ax = plt.subplots(figsize=(9,5))\n", + " fig, ax = plt.subplots(figsize=(9, 5))\n", " for k in scores_dict.keys():\n", - " ax.errorbar(list(durations),[s[0] for s in scores_dict[k]], \n", + " ax.errorbar(list(durations), [s[0] for s in scores_dict[k]],\n", " yerr=[s[1] for s in scores_dict[k]],\n", " label=k)\n", - " ax.set_title('Score vs duration $t_1$ of the central pulse',fontsize=16)\n", - " ax.set_ylabel(r'Score',fontsize=16)\n", - " ax.set_xlabel(r'$t_1$ (ns)',fontsize=16)\n", + " ax.set_title('Score vs duration $t_1$ of the central pulse', fontsize=16)\n", + " ax.set_ylabel(r'Score', fontsize=16)\n", + " ax.set_xlabel(r'$t_1$ (ns)', fontsize=16)\n", " ax.legend()\n", "\n", - " plt.show()" + " plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, + "id": "a88a7a1b", "metadata": {}, "outputs": [ { @@ -1186,63 +1252,154 @@ " ------------------------------------------------\n", "| Max. duration of the middle pulse: 192 ns |\n", "| Total duration of the pulse: 456 ns |\n", - "| Using 4 blocks of 25 graphs each |\n", + "| Using 8 blocks of 25 graphs each |\n", " ------------------------------------------------\n", - " Duration of the middle pulse: 0 ns\n", - " Probability lists were computed in 30.1 seconds\n", - " Kernel scores computed in 8.6 seconds\n", - " > Accuracy : 0.765 +/- 0.015\n", - " > f1 : 0.766 +/- 0.0145\n", - " > Precision: 0.785 +/- 0.00366\n", - " > Recall : 0.765 +/- 0.015\n", + " Duration of the middle pulse: 0 ns\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:12<00:00, 15.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Probability lists were computed in 13.0 seconds\n", + " Kernel scores computed in 153.0 seconds\n", + " > Accuracy : 0.727 +/- 0.00901\n", + " > f1 : 0.73 +/- 0.00858\n", + " > Precision: 0.765 +/- 0.0068\n", + " > Recall : 0.727 +/- 0.00901\n", "\n", - " Duration of the middle pulse: 32 ns\n", - " Probability lists were computed in 31.6 seconds\n", - " Kernel scores computed in 9.1 seconds\n", - " > Accuracy : 0.8 +/- 0.0\n", - " > f1 : 0.801 +/- 0.000395\n", - " > Precision: 0.81 +/- 0.00499\n", - " > Recall : 0.8 +/- 0.0\n", + " Duration of the middle pulse: 32 ns\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:13<00:00, 14.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Probability lists were computed in 13.5 seconds\n", + " Kernel scores computed in 159.0 seconds\n", + " > Accuracy : 0.74 +/- 0.00612\n", + " > f1 : 0.742 +/- 0.00667\n", + " > Precision: 0.78 +/- 0.00522\n", + " > Recall : 0.74 +/- 0.00612\n", "\n", - " Duration of the middle pulse: 64 ns\n", - " Probability lists were computed in 32.9 seconds\n", - " Kernel scores computed in 9.4 seconds\n", - " > Accuracy : 0.615 +/- 0.005\n", - " > f1 : 0.529 +/- 0.0113\n", - " > Precision: 0.602 +/- 0.0184\n", - " > Recall : 0.615 +/- 0.005\n", + " Duration of the middle pulse: 64 ns\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:14<00:00, 13.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Probability lists were computed in 14.4 seconds\n", + " Kernel scores computed in 162.8 seconds\n", + " > Accuracy : 0.672 +/- 0.0025\n", + " > f1 : 0.673 +/- 0.00165\n", + " > Precision: 0.692 +/- 0.0097\n", + " > Recall : 0.672 +/- 0.0025\n", "\n", - " Duration of the middle pulse: 96 ns\n", - " Probability lists were computed in 34.1 seconds\n", - " Kernel scores computed in 9.2 seconds\n", - " > Accuracy : 0.58 +/- 7.85e-17\n", - " > f1 : 0.427 +/- 0.000634\n", - " > Precision: 0.339 +/- 0.0012\n", - " > Recall : 0.58 +/- 7.85e-17\n", + " Duration of the middle pulse: 96 ns\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:14<00:00, 13.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Probability lists were computed in 14.4 seconds\n", + " Kernel scores computed in 158.8 seconds\n", + " > Accuracy : 0.674 +/- 0.0074\n", + " > f1 : 0.669 +/- 0.00745\n", + " > Precision: 0.701 +/- 0.0187\n", + " > Recall : 0.674 +/- 0.0074\n", "\n", - " Duration of the middle pulse: 128 ns\n", - " Probability lists were computed in 31.8 seconds\n", - " Kernel scores computed in 8.1 seconds\n", - " > Accuracy : 0.58 +/- 0.0\n", - " > f1 : 0.428 +/- 0.000439\n", - " > Precision: 0.341 +/- 0.0008\n", - " > Recall : 0.58 +/- 0.0\n", + " Duration of the middle pulse: 128 ns\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:13<00:00, 15.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Probability lists were computed in 13.3 seconds\n", + " Kernel scores computed in 149.7 seconds\n", + " > Accuracy : 0.619 +/- 0.0114\n", + " > f1 : 0.517 +/- 0.0204\n", + " > Precision: 0.608 +/- 0.0751\n", + " > Recall : 0.619 +/- 0.0114\n", "\n", - " Duration of the middle pulse: 160 ns\n", - " Probability lists were computed in 30.6 seconds\n", - " Kernel scores computed in 7.6 seconds\n", - " > Accuracy : 0.6 +/- 7.85e-17\n", - " > f1 : 0.485 +/- 0.00237\n", - " > Precision: 0.625 +/- 0.014\n", - " > Recall : 0.6 +/- 7.85e-17\n", + " Duration of the middle pulse: 160 ns\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:13<00:00, 15.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Probability lists were computed in 13.2 seconds\n", + " Kernel scores computed in 146.9 seconds\n", + " > Accuracy : 0.652 +/- 0.00559\n", + " > f1 : 0.616 +/- 0.00573\n", + " > Precision: 0.664 +/- 0.0146\n", + " > Recall : 0.652 +/- 0.00559\n", "\n", - " Duration of the middle pulse: 192 ns\n", - " Probability lists were computed in 30.8 seconds\n", - " Kernel scores computed in 7.3 seconds\n", - " > Accuracy : 0.74 +/- 0.01\n", - " > f1 : 0.73 +/- 0.0102\n", - " > Precision: 0.758 +/- 0.0058\n", - " > Recall : 0.74 +/- 0.01\n", + " Duration of the middle pulse: 192 ns\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:12<00:00, 15.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Probability lists were computed in 13.0 seconds\n", + " Kernel scores computed in 145.2 seconds\n", + " > Accuracy : 0.68 +/- 0.00866\n", + " > f1 : 0.67 +/- 0.00774\n", + " > Precision: 0.693 +/- 0.00482\n", + " > Recall : 0.68 +/- 0.00866\n", "\n" ] } @@ -1255,33 +1412,33 @@ "total_time = 2*t_1 + 256\n", "\n", "# duration of the middle pulse\n", - "durations = range(0, total_time-2*round(t_1)-32,32)\n", + "durations = range(0, total_time-2*round(t_1)-32, 32)\n", "\n", "\n", - "M = 2\n", - "N = 4\n", + "M = 4\n", + "N = 8\n", "\n", - "n_g = 100\n", + "n_g = 200\n", "indices_all = list(range(n_graphs))\n", "# Select a random subset of all graphs\n", "np.random.shuffle(indices_all)\n", "indices_in = indices_all[0:n_g]\n", "\n", - "scores_2layers = scan_scores(M = M,\n", - " N = N,\n", - " indices = indices_in,\n", - " durations = durations\n", - " )" + "scores_2layers = scan_scores(M=M,\n", + " N=N,\n", + " indices=indices_in,\n", + " durations=durations\n", + " )\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -1305,12 +1462,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following plot was obtained shows the same result, but using $n_g=400$ graphs and splitting them $M=5$ times into $N=10$ blocks. It took $\\sim 5$h to generate the data.\n", + "The following plot was obtained shows the same result, but using $n_g=400$ graphs and splitting them $M=5$ times into $N=10$ blocks. It took $\\sim 5$ h to generate the data.\n", "\n", "
\n", "\"opti_long.png\"\n", "
" ] + }, + { + "cell_type": "markdown", + "id": "c137acae", + "metadata": {}, + "source": [] } ], "metadata": { @@ -1329,7 +1492,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.2" } }, "nbformat": 4, From 1bb3502964c04a2b542013bc237423c3411d0307 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Thu, 2 Dec 2021 09:53:18 +0100 Subject: [PATCH 25/51] Bump to v0.4.0.dev --- pulser/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pulser/_version.py b/pulser/_version.py index 2ad5e58a2..1aa04d89f 100644 --- a/pulser/_version.py +++ b/pulser/_version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.4.0" +__version__ = "0.4.0.dev" From 4416b8531940e9820c9da02482a59638051ebdd7 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Thu, 9 Dec 2021 10:12:07 +0100 Subject: [PATCH 26/51] Bump to v0.4.1.dev --- pulser/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pulser/_version.py b/pulser/_version.py index e3bfeb8eb..d5872857d 100644 --- a/pulser/_version.py +++ b/pulser/_version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.4.1" +__version__ = "0.4.1.dev" From eb0081bb4e699d9d244703ce508fae6bd76fd735 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Mon, 3 Jan 2022 17:40:59 +0100 Subject: [PATCH 27/51] Bump to v0.4.2.dev --- pulser/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pulser/_version.py b/pulser/_version.py index d132a8478..b10f99188 100644 --- a/pulser/_version.py +++ b/pulser/_version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.4.2" +__version__ = "0.4.2.dev" From 39a3171d729992a48bf661d1e22aaf92136c82b1 Mon Sep 17 00:00:00 2001 From: Mauro D'Arcangelo <32898410+darcangelomauro@users.noreply.github.com> Date: Mon, 3 Jan 2022 17:51:54 +0100 Subject: [PATCH 28/51] Fixed harmless typo in create_random_graphs (#302) Co-authored-by: Mauro D'Arcangelo --- .../Quantum Evolution Kernel.ipynb | 45 ++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/tutorials/applications/Quantum Evolution Kernel.ipynb b/tutorials/applications/Quantum Evolution Kernel.ipynb index 143e3c6ac..6d10aa0de 100644 --- a/tutorials/applications/Quantum Evolution Kernel.ipynb +++ b/tutorials/applications/Quantum Evolution Kernel.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "f4015010", "metadata": {}, "source": [ "# Quantum Evolution Kernel with Rydberg atoms" @@ -9,6 +10,7 @@ }, { "cell_type": "markdown", + "id": "19d2d2bc", "metadata": {}, "source": [ "## Introduction\n", @@ -41,6 +43,7 @@ } }, "cell_type": "markdown", + "id": "e7e0a884", "metadata": {}, "source": [ "
\n", @@ -50,6 +53,7 @@ }, { "cell_type": "markdown", + "id": "380a14cd", "metadata": {}, "source": [ "### Jensen-Shannon divergence\n", @@ -63,6 +67,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "0b40c9f3", "metadata": {}, "outputs": [], "source": [ @@ -95,6 +100,7 @@ }, { "cell_type": "markdown", + "id": "e63351e8", "metadata": {}, "source": [ "## First example\n", @@ -104,6 +110,7 @@ }, { "cell_type": "markdown", + "id": "75883ac1", "metadata": {}, "source": [ "### Scheme\n", @@ -122,6 +129,7 @@ }, { "cell_type": "markdown", + "id": "6d180391", "metadata": {}, "source": [ "### Total occupation and Fourier transform\n", @@ -142,6 +150,7 @@ }, { "cell_type": "markdown", + "id": "dcb340a8", "metadata": {}, "source": [ "### Illustration on random graphs\n", @@ -152,6 +161,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "6ea03462", "metadata": {}, "outputs": [], "source": [ @@ -188,6 +198,7 @@ }, { "cell_type": "markdown", + "id": "c5861861", "metadata": {}, "source": [ "We now build an artificial set of graphs of two different Erdős–Rényi classes $\\rho=0.35$ and $\\rho=0.65$." @@ -196,6 +207,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "10fa3104", "metadata": {}, "outputs": [], "source": [ @@ -225,6 +237,7 @@ " rho = rho_high\n", " classes.append(1)\n", " G = nx.erdos_renyi_graph(N, rho)\n", + " graphs.append(G)\n", " pdist = pk(G)\n", " probability_distributions.append(pdist/np.sum(pdist))\n", "\n", @@ -233,6 +246,7 @@ }, { "cell_type": "markdown", + "id": "5612c6f5", "metadata": {}, "source": [ "From two graphs $\\mathcal{G}$ and $\\mathcal{G}'$, and their respective probability distributions $\\mathcal{P}=\\{p_k\\}_k$ constructed from the time evolution described above, the kernel can then be expressed as\n", @@ -247,6 +261,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "3b126a2f", "metadata": {}, "outputs": [], "source": [ @@ -264,6 +279,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "90740f18", "metadata": {}, "outputs": [ { @@ -293,6 +309,7 @@ }, { "cell_type": "markdown", + "id": "c581e23b", "metadata": {}, "source": [ "### Classification : Support Vector Machine\n", @@ -305,6 +322,7 @@ { "cell_type": "code", "execution_count": 6, + "id": "a8757521", "metadata": {}, "outputs": [], "source": [ @@ -392,6 +410,7 @@ }, { "cell_type": "markdown", + "id": "d759d39c", "metadata": {}, "source": [ "Given a new dataset, one first computes the kernel matrix between the new graphs and the old ones :" @@ -400,6 +419,7 @@ { "cell_type": "code", "execution_count": 7, + "id": "6afe8d74", "metadata": {}, "outputs": [ { @@ -430,6 +450,7 @@ }, { "cell_type": "markdown", + "id": "5c30755d", "metadata": {}, "source": [ "## Application on a benchmark dataset\n", @@ -442,6 +463,7 @@ { "cell_type": "code", "execution_count": 8, + "id": "e543bacc", "metadata": {}, "outputs": [], "source": [ @@ -494,6 +516,7 @@ }, { "cell_type": "markdown", + "id": "f031fc1f", "metadata": {}, "source": [ "### Preprocess dataset\n", @@ -507,6 +530,7 @@ { "cell_type": "code", "execution_count": 9, + "id": "6a16c2da", "metadata": {}, "outputs": [ { @@ -568,6 +592,7 @@ }, { "cell_type": "markdown", + "id": "b3c46389", "metadata": {}, "source": [ "In order to speed up the computations in this tutorial, we will artificialy reduce the number of classes to two, disregarding the others." @@ -576,6 +601,7 @@ { "cell_type": "code", "execution_count": 10, + "id": "fc36d32b", "metadata": {}, "outputs": [ { @@ -614,6 +640,7 @@ }, { "cell_type": "markdown", + "id": "8c745d4b", "metadata": {}, "source": [ "### Map graphs onto machine registers\n", @@ -635,6 +662,7 @@ { "cell_type": "code", "execution_count": 11, + "id": "101b61e5", "metadata": {}, "outputs": [], "source": [ @@ -707,6 +735,7 @@ { "cell_type": "code", "execution_count": 12, + "id": "74880999", "metadata": {}, "outputs": [], "source": [ @@ -763,6 +792,7 @@ } }, "cell_type": "markdown", + "id": "7e7bc165", "metadata": {}, "source": [ "### Optimized preparation of the equal superposition of Ising states\n", @@ -780,6 +810,7 @@ }, { "cell_type": "markdown", + "id": "0d4e0ca9", "metadata": {}, "source": [ "### Single parameter Pulse\n", @@ -806,6 +837,7 @@ { "cell_type": "code", "execution_count": 13, + "id": "6ec15d07", "metadata": {}, "outputs": [ { @@ -885,6 +917,7 @@ }, { "cell_type": "markdown", + "id": "6e5be79a", "metadata": {}, "source": [ "### Computing the probability distribution" @@ -893,6 +926,7 @@ { "cell_type": "code", "execution_count": 14, + "id": "df676daf", "metadata": {}, "outputs": [], "source": [ @@ -963,6 +997,7 @@ }, { "cell_type": "markdown", + "id": "2939040f", "metadata": {}, "source": [ "Let us first ignore the middle pulse and set $t_1=0$. " @@ -971,6 +1006,7 @@ { "cell_type": "code", "execution_count": 15, + "id": "4e860a36", "metadata": {}, "outputs": [ { @@ -1048,6 +1084,7 @@ { "cell_type": "code", "execution_count": 16, + "id": "9091dfc3", "metadata": {}, "outputs": [ { @@ -1071,6 +1108,7 @@ }, { "cell_type": "markdown", + "id": "20fad801", "metadata": {}, "source": [ "### Optimization of the pulse sequence\n", @@ -1089,6 +1127,7 @@ { "cell_type": "code", "execution_count": 17, + "id": "c6c76b5b", "metadata": {}, "outputs": [], "source": [ @@ -1179,6 +1218,7 @@ }, { "cell_type": "markdown", + "id": "0dee506e", "metadata": {}, "source": [ "We now look for the best pulse by varying the duration of the middle pulse. The total time is limited to a small value, and the data set is reduced to $n_g=100$ graphs for the sake of time in this tutorial.\n", @@ -1191,6 +1231,7 @@ { "cell_type": "code", "execution_count": 18, + "id": "b69d8710", "metadata": {}, "outputs": [], "source": [ @@ -1434,6 +1475,7 @@ { "cell_type": "code", "execution_count": 25, + "id": "6c012ba0", "metadata": {}, "outputs": [ { @@ -1460,6 +1502,7 @@ } }, "cell_type": "markdown", + "id": "6a1340ab", "metadata": {}, "source": [ "The following plot was obtained shows the same result, but using $n_g=400$ graphs and splitting them $M=5$ times into $N=10$ blocks. It took $\\sim 5$ h to generate the data.\n", @@ -1492,7 +1535,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.2" + "version": "3.9.1" } }, "nbformat": 4, From e0f7849a1747523e1503f008e5c2a050cbb22086 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Thu, 13 Jan 2022 16:07:38 +0100 Subject: [PATCH 29/51] Buffer times for retargeting and phase jumps (#305) * Adding buffer information to the Channel class * Adding the fixed retarget time * Adding phase jump buffers * Test and bug fixes * Mypy fix * Addressing review comments * Changing 'retarget_time' to 'min_retarget_interval' * Bug fix in Device specs string * Including suggestion --- pulser/channels.py | 45 ++++++++++++++++----- pulser/devices/_device_datacls.py | 5 ++- pulser/devices/_mock_device.py | 14 ++++++- pulser/sequence.py | 67 ++++++++++++++++++------------- pulser/tests/test_channels.py | 18 ++++++--- pulser/tests/test_devices.py | 2 +- pulser/tests/test_sequence.py | 2 +- 7 files changed, 103 insertions(+), 50 deletions(-) diff --git a/pulser/channels.py b/pulser/channels.py index 7e79e15f7..694779e0c 100644 --- a/pulser/channels.py +++ b/pulser/channels.py @@ -37,7 +37,11 @@ class Channel: max_abs_detuning: Maximum possible detuning (in rad/µs), in absolute value. max_amp: Maximum pulse amplitude (in rad/µs). - retarget_time: Maximum time to change the target (in ns). + phase_jump_time: Time taken to change the phase between consecutive + pulses (in ns). + min_retarget_interval: Minimum time required between the ends of two + target instructions (in ns). + fixed_retarget_t: Time taken to change the target (in ns). max_targets: How many qubits can be addressed at once by the same beam. clock_period: The duration of a clock cycle (in ns). The duration of a pulse or delay instruction is enforced to be a multiple of the @@ -55,7 +59,9 @@ class Channel: addressing: str max_abs_detuning: float max_amp: float - retarget_time: Optional[int] = None + phase_jump_time: int = 0 + min_retarget_interval: Optional[int] = None + fixed_retarget_t: Optional[int] = None max_targets: Optional[int] = None clock_period: int = 4 # ns min_duration: int = 16 # ns @@ -66,7 +72,9 @@ def Local( cls, max_abs_detuning: float, max_amp: float, - retarget_time: int = 220, + phase_jump_time: int = 0, + min_retarget_interval: int = 220, + fixed_retarget_t: int = 0, max_targets: int = 1, **kwargs: int, ) -> Channel: @@ -76,7 +84,11 @@ def Local( max_abs_detuning (float): Maximum possible detuning (in rad/µs), in absolute value. max_amp(float): Maximum pulse amplitude (in rad/µs). - retarget_time (int): Maximum time to change the target (in ns). + phase_jump_time (int): Time taken to change the phase between + consecutive pulses (in ns). + min_retarget_interval (int): Minimum time required between two + target instructions (in ns). + fixed_retarget_t (int): Time taken to change the target (in ns). max_targets (int): Maximum number of atoms the channel can target simultaneously. """ @@ -84,14 +96,20 @@ def Local( "Local", max_abs_detuning, max_amp, - retarget_time, + phase_jump_time, + min_retarget_interval, + fixed_retarget_t, max_targets, **kwargs, ) @classmethod def Global( - cls, max_abs_detuning: float, max_amp: float, **kwargs: int + cls, + max_abs_detuning: float, + max_amp: float, + phase_jump_time: int = 0, + **kwargs: int, ) -> Channel: """Initializes the channel with global addressing. @@ -99,8 +117,12 @@ def Global( max_abs_detuning (float): Maximum possible detuning (in rad/µs), in absolute value. max_amp(float): Maximum pulse amplitude (in rad/µs). + phase_jump_time (int): Time taken to change the phase between + consecutive pulses (in ns). """ - return cls("Global", max_abs_detuning, max_amp, **kwargs) + return cls( + "Global", max_abs_detuning, max_amp, phase_jump_time, **kwargs + ) def validate_duration(self, duration: int) -> int: """Validates and adapts the duration of an instruction on this channel. @@ -142,11 +164,14 @@ def validate_duration(self, duration: int) -> int: def __repr__(self) -> str: config = ( f".{self.addressing}(Max Absolute Detuning: " - f"{self.max_abs_detuning} rad/µs, Max Amplitude: " - f"{self.max_amp} rad/µs" + f"{self.max_abs_detuning} rad/µs, Max Amplitude: {self.max_amp}" + f" rad/µs, Phase Jump Time: {self.phase_jump_time} ns" ) if self.addressing == "Local": - config += f", Target time: {self.retarget_time} ns" + config += ( + f", Minimum retarget time: {self.min_retarget_interval} ns, " + f"Fixed retarget time: {self.fixed_retarget_t} ns" + ) if cast(int, self.max_targets) > 1: config += f", Max targets: {self.max_targets}" config += f", Basis: '{self.basis}'" diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index a1b4e0983..e441eb5a5 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -231,10 +231,13 @@ def _specs(self, for_docs: bool = False) -> str: + r"- Maximum :math:`|\delta|`:" + f" {ch.max_abs_detuning:.4g} rad/µs" ), + f"\t- Phase Jump Time: {ch.phase_jump_time} ns", ] if ch.addressing == "Local": ch_lines += [ - f"\t- Maximum time to retarget: {ch.retarget_time} ns", + "\t- Minimum time between retargets: " + f"{ch.min_retarget_interval} ns", + f"\t- Fixed retarget time: {ch.fixed_retarget_t} ns", f"\t- Maximum simultaneous targets: {ch.max_targets}", ] ch_lines += [ diff --git a/pulser/devices/_mock_device.py b/pulser/devices/_mock_device.py index ac4f8dcef..0b6e02216 100644 --- a/pulser/devices/_mock_device.py +++ b/pulser/devices/_mock_device.py @@ -31,7 +31,12 @@ ( "rydberg_local", Rydberg.Local( - 1000, 200, 0, max_targets=2000, clock_period=1, min_duration=1 + 1000, + 200, + min_retarget_interval=0, + max_targets=2000, + clock_period=1, + min_duration=1, ), ), ( @@ -41,7 +46,12 @@ ( "raman_local", Raman.Local( - 1000, 200, 0, max_targets=2000, clock_period=1, min_duration=1 + 1000, + 200, + min_retarget_interval=0, + max_targets=2000, + clock_period=1, + min_duration=1, ), ), ( diff --git a/pulser/sequence.py b/pulser/sequence.py index 23a9dd53a..b241b867c 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -622,6 +622,25 @@ def add( last = self._last(channel) t0 = last.tf # Preliminary ti basis = channel_obj.basis + + ph_refs = {self._phase_ref[basis][q].last_phase for q in last.targets} + if len(ph_refs) != 1: + raise ValueError( + "Cannot do a multiple-target pulse on qubits with different " + "phase references for the same basis." + ) + else: + phase_ref = ph_refs.pop() + + if phase_ref != 0: + # Has to recreate the original pulse with a new phase + pulse = Pulse( + pulse.amplitude, + pulse.detuning, + pulse.phase + phase_ref, + post_phase_shift=pulse.post_phase_shift, + ) + phase_barriers = [ self._phase_ref[basis][q].last_time for q in last.targets ] @@ -638,39 +657,27 @@ def add( if op.targets & last.targets or protocol == "wait-for-all": current_max_t = op.tf break - ti = current_max_t - if ti > t0: - # Insert a delay - delay_duration = ti - t0 - # Delay must not be shorter than the min duration for this channel - min_duration = self._channels[channel].min_duration - if delay_duration < min_duration: - ti += min_duration - delay_duration - delay_duration = min_duration + delay_duration = current_max_t - t0 + # Find last pulse and compare phase + for op in self._schedule[channel][::-1]: + if isinstance(op.type, Pulse): + if op.type.phase != pulse.phase: + delay_duration = max( + delay_duration, + # Considers that the last pulse might not be at t0 + channel_obj.phase_jump_time - (t0 - op.tf), + ) + break + if delay_duration > 0: + # Delay must not be shorter than the min duration for this channel + delay_duration = max(delay_duration, channel_obj.min_duration) self._delay(delay_duration, channel) + ti = t0 + delay_duration tf = ti + pulse.duration - prs = {self._phase_ref[basis][q].last_phase for q in last.targets} - if len(prs) != 1: - raise ValueError( - "Cannot do a multiple-target pulse on qubits with different " - "phase references for the same basis." - ) - else: - phase_ref = prs.pop() - - if phase_ref != 0: - # Has to recriate the original pulse with a new phase - pulse = Pulse( - pulse.amplitude, - pulse.detuning, - pulse.phase + phase_ref, - post_phase_shift=pulse.post_phase_shift, - ) - self._add_to_schedule(channel, _TimeSlot(pulse, ti, tf, last.targets)) for qubit in last.targets: @@ -1042,7 +1049,7 @@ def _target( if last.targets == qubits_set: return ti = last.tf - retarget = cast(int, self._channels[channel].retarget_time) + retarget = cast(int, self._channels[channel].min_retarget_interval) elapsed = ti - self._last_target[channel] delta = cast(int, np.clip(retarget - elapsed, 0, retarget)) if delta != 0: @@ -1051,7 +1058,9 @@ def _target( delta = self._channels[channel].validate_duration( 16 if delta < 16 else delta ) - tf = ti + delta + tf = ti + max( + delta, cast(int, self._channels[channel].fixed_retarget_t) + ) except ValueError: ti = -1 diff --git a/pulser/tests/test_channels.py b/pulser/tests/test_channels.py index 9961208c1..ce56cf1e9 100644 --- a/pulser/tests/test_channels.py +++ b/pulser/tests/test_channels.py @@ -33,8 +33,10 @@ def test_device_channels(): assert ch.clock_period >= 1 assert ch.min_duration >= 1 if ch.addressing == "Local": - assert ch.retarget_time >= 0 - assert ch.retarget_time == int(ch.retarget_time) + assert ch.min_retarget_interval >= 0 + assert ch.min_retarget_interval == int( + ch.min_retarget_interval + ) assert ch.max_targets >= 1 assert ch.max_targets == int(ch.max_targets) @@ -52,16 +54,20 @@ def test_validate_duration(): def test_repr(): - raman = Raman.Local(10, 2, retarget_time=1000, max_targets=4) + raman = Raman.Local( + 10, 2, min_retarget_interval=1000, fixed_retarget_t=200, max_targets=4 + ) r1 = ( "Raman.Local(Max Absolute Detuning: 10 rad/µs, Max Amplitude: " - "2 rad/µs, Target time: 1000 ns, Max targets: 4, Basis: 'digital')" + "2 rad/µs, Phase Jump Time: 0 ns, Minimum retarget time: 1000 ns, " + "Fixed retarget time: 200 ns, Max targets: 4, Basis: 'digital')" ) assert raman.__str__() == r1 - ryd = Rydberg.Global(50, 2.5) + ryd = Rydberg.Global(50, 2.5, phase_jump_time=300) r2 = ( "Rydberg.Global(Max Absolute Detuning: 50 rad/µs, " - "Max Amplitude: 2.5 rad/µs, Basis: 'ground-rydberg')" + "Max Amplitude: 2.5 rad/µs, Phase Jump Time: 300 ns, " + "Basis: 'ground-rydberg')" ) assert ryd.__str__() == r2 diff --git a/pulser/tests/test_devices.py b/pulser/tests/test_devices.py index d71807166..fcec79c57 100644 --- a/pulser/tests/test_devices.py +++ b/pulser/tests/test_devices.py @@ -61,7 +61,7 @@ def test_mock(): assert ch.max_abs_detuning >= 1000 assert ch.max_amp >= 200 if ch.addressing == "Local": - assert ch.retarget_time == 0 + assert ch.min_retarget_interval == 0 assert ch.max_targets > 1 assert ch.max_targets == int(ch.max_targets) diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 86a69aed8..a3d0bfadc 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -152,7 +152,7 @@ def test_target(): assert seq._schedule["ch0"][-1] == _TimeSlot("target", -1, 0, {"q1"}) seq.target("q4", "ch0") - retarget_t = seq.declared_channels["ch0"].retarget_time + retarget_t = seq.declared_channels["ch0"].min_retarget_interval assert seq._schedule["ch0"][-1] == _TimeSlot( "target", 0, retarget_t, {"q4"} ) From d964fdd78ad39f259b47a62dffde3e4289e45653 Mon Sep 17 00:00:00 2001 From: Codoscope <14247215+Codoscope@users.noreply.github.com> Date: Thu, 20 Jan 2022 14:01:40 +0100 Subject: [PATCH 30/51] Add Register3D _to_dict method and UTs (#306) * Add Register3D _to_dict method and UTs * Add missing tests * Apply remarks Co-authored-by: Codoscope --- pulser/_seq_drawer.py | 18 +++++++++--------- pulser/register.py | 25 +++++++++++++++++++++++-- pulser/sequence.py | 5 +++++ pulser/tests/test_json.py | 16 ++++++++++++++-- pulser/tests/test_register.py | 30 ++++++++++++++++++++++++++++++ 5 files changed, 81 insertions(+), 13 deletions(-) diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index 1dbb71973..4fa93502e 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -154,13 +154,13 @@ def phase_str(phi: float) -> str: area_ph_box = dict(boxstyle="round", facecolor="ghostwhite", alpha=0.7) slm_box = dict(boxstyle="round", alpha=0.4, facecolor="grey", hatch="//") - pos = np.array(seq._register._coords) + pos = np.array(seq.register._coords) # Draw masked register if draw_register: - if isinstance(seq._register, Register3D): + if isinstance(seq.register, Register3D): labels = "xyz" - fig_reg, axes_reg = seq._register._initialize_fig_axes_projection( + fig_reg, axes_reg = seq.register._initialize_fig_axes_projection( pos, blockade_radius=35, draw_half_radius=True, @@ -170,10 +170,10 @@ def phase_str(phi: float) -> str: for ax_reg, (ix, iy) in zip( axes_reg, combinations(np.arange(3), 2) ): - seq._register._draw_2D( + seq.register._draw_2D( ax=ax_reg, pos=pos, - ids=seq._register._ids, + ids=seq.register._ids, plane=(ix, iy), masked_qubits=seq._slm_mask_targets, ) @@ -184,16 +184,16 @@ def phase_str(phi: float) -> str: + "-plane" ) - elif isinstance(seq._register, Register): - fig_reg, ax_reg = seq._register._initialize_fig_axes( + elif isinstance(seq.register, Register): + fig_reg, ax_reg = seq.register._initialize_fig_axes( pos, blockade_radius=35, draw_half_radius=True, ) - seq._register._draw_2D( + seq.register._draw_2D( ax=ax_reg, pos=pos, - ids=seq._register._ids, + ids=seq.register._ids, masked_qubits=seq._slm_mask_targets, ) ax_reg.set_title("Masked register", pad=10) diff --git a/pulser/register.py b/pulser/register.py index 0ce466bda..d74996fb3 100644 --- a/pulser/register.py +++ b/pulser/register.py @@ -282,6 +282,25 @@ def _draw_checks( "Needs more than one atom to draw " "the blockade radius." ) + @abstractmethod + def _to_dict(self) -> dict[str, Any]: + qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) + return obj_to_dict(self, qs) + + def __eq__(self, other: Any) -> bool: + if type(other) is not type(self): + return False + + return set(self._ids) == set(other._ids) and all( + ( + np.array_equal( + self._coords[i], + other._coords[other._ids.index(id)], + ) + for i, id in enumerate(self._ids) + ) + ) + class Register(BaseRegister): """A 2D quantum register containing a set of qubits. @@ -736,8 +755,7 @@ def draw( plt.show() def _to_dict(self) -> dict[str, Any]: - qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) - return obj_to_dict(self, qs) + return super()._to_dict() class Register3D(BaseRegister): @@ -1077,3 +1095,6 @@ def draw( if fig_name is not None: plt.savefig(fig_name, **kwargs_savefig) plt.show() + + def _to_dict(self) -> dict[str, Any]: + return super()._to_dict() diff --git a/pulser/sequence.py b/pulser/sequence.py index b241b867c..aacbb6aa4 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -215,6 +215,11 @@ def qubit_info(self) -> dict[QubitId, np.ndarray]: """Dictionary with the qubit's IDs and positions.""" return self._register.qubits + @property + def register(self) -> BaseRegister: + """Register with the qubit's IDs and positions.""" + return self._register + @property def declared_channels(self) -> dict[str, Channel]: """Channels declared in this Sequence.""" diff --git a/pulser/tests/test_json.py b/pulser/tests/test_json.py index cf5468fe3..5eddc1dc7 100644 --- a/pulser/tests/test_json.py +++ b/pulser/tests/test_json.py @@ -17,8 +17,8 @@ import numpy as np import pytest -from pulser import Sequence, Register -from pulser.devices import Chadoq2 +from pulser import Sequence, Register, Register3D +from pulser.devices import Chadoq2, MockDevice from pulser.json.coders import PulserEncoder, PulserDecoder from pulser.parametrized.decorators import parametrize from pulser.waveforms import BlackmanWaveform @@ -43,6 +43,18 @@ def test_encoder(): encode(1j) +def test_register_2d(): + reg = Register({"c": (1, 2), "d": (8, 4)}) + seq = Sequence(reg, device=Chadoq2) + assert reg == encode_decode(seq).register + + +def test_register_3d(): + reg = Register3D({"a": (1, 2, 3), "b": (8, 5, 6)}) + seq = Sequence(reg, device=MockDevice) + assert reg == encode_decode(seq).register + + def test_rare_cases(): reg = Register.square(4) seq = Sequence(reg, Chadoq2) diff --git a/pulser/tests/test_register.py b/pulser/tests/test_register.py index d6368bf5c..ec153cc65 100644 --- a/pulser/tests/test_register.py +++ b/pulser/tests/test_register.py @@ -369,3 +369,33 @@ def test_to_2D(): reg = Register3D.cuboid(2, 2, 1) reg.to_2D() + + +def assert_eq(left, right): + assert left == right + assert right == left + + +def assert_ineq(left, right): + assert left != right + assert right != left + + +def test_equality_function(): + reg1 = Register({"c": (1, 2), "d": (8, 4)}) + assert_eq(reg1, reg1) + assert_eq(reg1, Register({"d": (8, 4), "c": (1, 2)})) + assert_ineq(reg1, Register({"c": (8, 4), "d": (1, 2)})) + assert_ineq(reg1, Register({"c": (1, 2), "d": (8, 4), "e": (8, 4)})) + assert_ineq(reg1, 10) + + reg2 = Register3D({"a": (1, 2, 3), "b": (8, 5, 6)}) + assert_eq(reg2, reg2) + assert_eq(reg2, Register3D({"a": (1, 2, 3), "b": (8, 5, 6)})) + assert_ineq(reg2, Register3D({"b": (1, 2, 3), "a": (8, 5, 6)})) + assert_ineq( + reg2, Register3D({"a": (1, 2, 3), "b": (8, 5, 6), "e": (8, 5, 6)}) + ) + assert_ineq(reg2, 10) + + assert_ineq(reg1, reg2) From a0f7381fdaa90cdaab561abc20a8d5d58337575e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Thu, 20 Jan 2022 14:34:21 +0100 Subject: [PATCH 31/51] Fixing mypy to 0.921 (#307) * Fixing mypy to 0.921 * Deleting unused import --- pulser/devices/_device_datacls.py | 6 ++---- requirements.txt | 2 +- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index e441eb5a5..13b4e1821 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -15,7 +15,7 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any, cast +from typing import Any import numpy as np from scipy.spatial.distance import pdist, squareform @@ -111,9 +111,7 @@ def rydberg_blockade_radius(self, rabi_frequency: float) -> float: Returns: float: The rydberg blockade radius, in μm. """ - return cast( - float, (self.interaction_coeff / rabi_frequency) ** (1 / 6) - ) + return (self.interaction_coeff / rabi_frequency) ** (1 / 6) def rabi_from_blockade(self, blockade_radius: float) -> float: """The maximum Rabi frequency value to enforce a given blockade radius. diff --git a/requirements.txt b/requirements.txt index 558fda947..c870d413d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,7 +12,7 @@ pytest pytest-cov flake8 flake8-docstrings -mypy +mypy == 0.921 black # tutorials From 7405d8cd7463782891cbbf40335f163dc8f284cc Mon Sep 17 00:00:00 2001 From: Louis Vignoli <97944962+lvignoli@users.noreply.github.com> Date: Fri, 21 Jan 2022 17:56:10 +0100 Subject: [PATCH 32/51] Fix broken link in QAOA tutorial (#309) --- tutorials/applications/Using QAOA to solve a MIS problem.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb index abc08e98d..cbc101d27 100644 --- a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb +++ b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb @@ -39,7 +39,7 @@ "source": [ "In this tutorial, we illustrate how to solve the Maximum Independent Set (MIS) problem using the Quantum Approximate Optimization Algorithm procedure on a platform of Rydberg atoms in analog mode, using Pulser. \n", "\n", - "For more details about this problem and how to encode it on a Rydberg atom quantum processor, see [Pichler, et al., 2018](https://arxiv.org/abs/1808.10816), [Henriet, 2020]( https://journals.aps.org/pra/abstract/10.1103/PhysRevA.101.012335) and [Dalyac, et al., 2020]( https://arxiv.org/abs/2012.14859])." + "For more details about this problem and how to encode it on a Rydberg atom quantum processor, see [Pichler, et al., 2018](https://arxiv.org/abs/1808.10816), [Henriet, 2020]( https://journals.aps.org/pra/abstract/10.1103/PhysRevA.101.012335) and [Dalyac, et al., 2020]( https://arxiv.org/abs/2012.14859)." ] }, { From b2ddd5e4969d7abc95ed2386b5fee6b310f8e72e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Wed, 26 Jan 2022 15:10:41 +0100 Subject: [PATCH 33/51] Decorator type hints (#311) * type hints for @parametrized following mypy docs * Returning full type hints to Pulse * Extending type hints for the other decorators Co-authored-by: Louis Vignoli --- pulser/_seq_drawer.py | 2 +- pulser/parametrized/decorators.py | 9 ++-- pulser/pulse.py | 73 ++++++++++++++++++------------- pulser/sequence.py | 16 +++---- 4 files changed, 57 insertions(+), 43 deletions(-) diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index 4fa93502e..2fa947740 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -40,7 +40,7 @@ def gather_data(seq: pulser.sequence.Sequence) -> dict: """ # The minimum time axis length is 100 ns total_duration = max(seq.get_duration(), 100) - data = {} + data: dict[str, Any] = {} for ch, sch in seq._schedule.items(): time = [-1] # To not break the "time[-1]" later on amp = [] diff --git a/pulser/parametrized/decorators.py b/pulser/parametrized/decorators.py index 273e2a7b7..ae27c70a2 100644 --- a/pulser/parametrized/decorators.py +++ b/pulser/parametrized/decorators.py @@ -18,12 +18,15 @@ from collections.abc import Callable from functools import wraps from itertools import chain -from typing import Any +from typing import Any, TypeVar, cast from pulser.parametrized import Parametrized, ParamObj -def parametrize(func: Callable) -> Callable: +F = TypeVar("F", bound=Callable) + + +def parametrize(func: F) -> F: """Makes a function support parametrized arguments. Note: @@ -38,4 +41,4 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: return ParamObj(func, *args, **kwargs) return func(*args, **kwargs) - return wrapper + return cast(F, wrapper) diff --git a/pulser/pulse.py b/pulser/pulse.py index 81d57e15c..db28249f6 100644 --- a/pulser/pulse.py +++ b/pulser/pulse.py @@ -15,10 +15,10 @@ from __future__ import annotations -from dataclasses import dataclass +from dataclasses import dataclass, field import functools import itertools -from typing import Any +from typing import Any, cast, Union import matplotlib.pyplot as plt import numpy as np @@ -29,7 +29,7 @@ from pulser.json.utils import obj_to_dict -@dataclass(repr=False, frozen=True) +@dataclass(init=False, repr=False, frozen=True) class Pulse: r"""A generic pulse. @@ -56,10 +56,11 @@ class Pulse: for enconding of arbitrary single-qubit gates into a single pulse (see ``Sequence.phase_shift()`` for more information). """ - amplitude: Waveform - detuning: Waveform - phase: float - post_phase_shift: float = 0.0 + + amplitude: Waveform = field(init=False) + detuning: Waveform = field(init=False) + phase: float = field(init=False) + post_phase_shift: float = field(default=0.0, init=False) def __new__(cls, *args, **kwargs): # type: ignore """Creates a Pulse instance or a ParamObj depending on the input.""" @@ -69,27 +70,35 @@ def __new__(cls, *args, **kwargs): # type: ignore else: return object.__new__(cls) - def __post_init__(self) -> None: + def __init__( + self, + amplitude: Union[Waveform, Parametrized], + detuning: Union[Waveform, Parametrized], + phase: Union[float, Parametrized], + post_phase_shift: Union[float, Parametrized] = 0.0, + ): """Initializes a new Pulse.""" if not ( - isinstance(self.amplitude, Waveform) - and isinstance(self.detuning, Waveform) + isinstance(amplitude, Waveform) and isinstance(detuning, Waveform) ): raise TypeError("'amplitude' and 'detuning' have to be waveforms.") - if self.detuning.duration != self.amplitude.duration: + if detuning.duration != amplitude.duration: raise ValueError( "The duration of detuning and amplitude waveforms must match." ) - if np.any(self.amplitude.samples < 0): + if np.any(amplitude.samples < 0): raise ValueError( "All samples of an amplitude waveform must be " "greater than or equal to zero." ) - - object.__setattr__(self, "phase", self.phase % (2 * np.pi)) + object.__setattr__(self, "amplitude", amplitude) + object.__setattr__(self, "detuning", detuning) + phase = cast(float, phase) + object.__setattr__(self, "phase", float(phase) % (2 * np.pi)) + post_phase_shift = cast(float, post_phase_shift) object.__setattr__( - self, "post_phase_shift", self.post_phase_shift % (2 * np.pi) + self, "post_phase_shift", float(post_phase_shift) % (2 * np.pi) ) @property @@ -101,10 +110,10 @@ def duration(self) -> int: @parametrize def ConstantDetuning( cls, - amplitude: Waveform, - detuning: float, - phase: float, - post_phase_shift: float = 0.0, + amplitude: Union[Waveform, Parametrized], + detuning: Union[float, Parametrized], + phase: Union[float, Parametrized], + post_phase_shift: Union[float, Parametrized] = 0.0, ) -> Pulse: """Creates a Pulse with an amplitude waveform and a constant detuning. @@ -115,17 +124,19 @@ def ConstantDetuning( post_phase_shift (float, default=0.): Optionally lets you add a phase shift (in rads) immediately after the end of the pulse. """ - detuning_wf = ConstantWaveform(amplitude.duration, detuning) + detuning_wf = ConstantWaveform( + cast(Waveform, amplitude).duration, detuning + ) return cls(amplitude, detuning_wf, phase, post_phase_shift) @classmethod @parametrize def ConstantAmplitude( cls, - amplitude: float, - detuning: Waveform, - phase: float, - post_phase_shift: float = 0.0, + amplitude: Union[float, Parametrized], + detuning: Union[Waveform, Parametrized], + phase: Union[float, Parametrized], + post_phase_shift: Union[float, Parametrized] = 0.0, ) -> Pulse: """Pulse with a constant amplitude and a detuning waveform. @@ -136,18 +147,20 @@ def ConstantAmplitude( post_phase_shift (float, default=0.): Optionally lets you add a phase shift (in rads) immediately after the end of the pulse. """ - amplitude_wf = ConstantWaveform(detuning.duration, amplitude) + amplitude_wf = ConstantWaveform( + cast(Waveform, detuning).duration, amplitude + ) return cls(amplitude_wf, detuning, phase, post_phase_shift) @classmethod @parametrize def ConstantPulse( cls, - duration: int, - amplitude: float, - detuning: float, - phase: float, - post_phase_shift: float = 0.0, + duration: Union[int, Parametrized], + amplitude: Union[float, Parametrized], + detuning: Union[float, Parametrized], + phase: Union[float, Parametrized], + post_phase_shift: Union[float, Parametrized] = 0.0, ) -> Pulse: """Pulse with a constant amplitude and a constant detuning. diff --git a/pulser/sequence.py b/pulser/sequence.py index aacbb6aa4..690cd217e 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -22,7 +22,7 @@ from itertools import chain import json from sys import version_info -from typing import Any, cast, NamedTuple, Optional, Tuple, Union +from typing import Any, cast, NamedTuple, Optional, Tuple, TypeVar, Union import warnings import os @@ -56,6 +56,7 @@ QubitId = Union[int, str] PROTOCOLS = Literal["min-delay", "no-delay", "wait-for-all"] +F = TypeVar("F", bound=Callable) class _TimeSlot(NamedTuple): @@ -71,7 +72,7 @@ class _TimeSlot(NamedTuple): _Call = namedtuple("_Call", ["name", "args", "kwargs"]) -def _screen(func: Callable) -> Callable: +def _screen(func: F) -> F: """Blocks the call to a function if the Sequence is parametrized.""" @wraps(func) @@ -83,10 +84,10 @@ def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: ) return func(self, *args, **kwargs) - return wrapper + return cast(F, wrapper) -def _store(func: Callable) -> Callable: +def _store(func: F) -> F: """Stores any Sequence building call for deferred execution.""" @wraps(func) @@ -122,7 +123,7 @@ def verify_variable(x: Any) -> None: func(self, *args, **kwargs) storage.append(_Call(func.__name__, args, kwargs)) - return wrapper + return cast(F, wrapper) class Sequence: @@ -419,10 +420,7 @@ def declare_channel( name: str, channel_id: str, initial_target: Optional[ - Union[ - Iterable[Union[QubitId, Parametrized]], - Union[QubitId, Parametrized], - ] + Union[QubitId, Iterable[QubitId], Parametrized] ] = None, ) -> None: """Declares a new channel to the Sequence. From 9656716db994571216ee2a565ec256e30d0b8e2e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Wed, 2 Feb 2022 16:01:26 +0100 Subject: [PATCH 34/51] Changes from black 22.1.0 (#314) --- pulser/_seq_drawer.py | 6 +++--- pulser/devices/_device_datacls.py | 4 ++-- pulser/json/utils.py | 2 +- pulser/register.py | 2 +- pulser/simulation/simresults.py | 10 +++++----- pulser/simulation/simulation.py | 19 ++++++++----------- pulser/tests/test_parametrized.py | 2 +- pulser/tests/test_simulation.py | 14 +++++++------- 8 files changed, 28 insertions(+), 31 deletions(-) diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index 2fa947740..c6f6a2f37 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -139,7 +139,7 @@ def phase_str(phi: float) -> str: elif value == 0: return "0" # pragma: no cover - just for safety else: - return fr"{value:.2g}$\pi$" + return rf"{value:.2g}$\pi$" n_channels = len(seq._channels) if not n_channels: @@ -343,12 +343,12 @@ def phase_str(phi: float) -> str: area_fmt = ( r"A: $\pi$" if round(area_val, 2) == 1 - else fr"A: {area_val:.2g}$\pi$" + else rf"A: {area_val:.2g}$\pi$" ) if not draw_phase: txt = area_fmt else: - phase_fmt = fr"$\phi$: {phase_str(phase_val)}" + phase_fmt = rf"$\phi$: {phase_str(phase_val)}" txt = "\n".join([phase_fmt, area_fmt]) a.text( x_plot, diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index 13b4e1821..f0a002bf2 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -122,7 +122,7 @@ def rabi_from_blockade(self, blockade_radius: float) -> float: Returns: float: The maximum rabi frequency value, in rad/µs. """ - return self.interaction_coeff / blockade_radius ** 6 + return self.interaction_coeff / blockade_radius**6 def validate_register(self, register: BaseRegister) -> None: """Checks if 'register' is compatible with this device. @@ -202,7 +202,7 @@ def _specs(self, for_docs: bool = False) -> str: lines = [ "\nRegister requirements:", f" - Dimensions: {self.dimensions}D", - fr" - Rydberg level: {self.rydberg_level}", + rf" - Rydberg level: {self.rydberg_level}", f" - Maximum number of atoms: {self.max_atom_num}", f" - Maximum distance from origin: {self.max_radial_distance} μm", ( diff --git a/pulser/json/utils.py b/pulser/json/utils.py index 0b04cb17f..1dbd43742 100644 --- a/pulser/json/utils.py +++ b/pulser/json/utils.py @@ -25,7 +25,7 @@ def obj_to_dict( _module: Optional[str] = None, _name: Optional[str] = None, _submodule: Optional[str] = None, - **kwargs: Any + **kwargs: Any, ) -> dict[str, Any]: """Encodes an object in a dictionary for serialization. diff --git a/pulser/register.py b/pulser/register.py index d74996fb3..7dd3e38a4 100644 --- a/pulser/register.py +++ b/pulser/register.py @@ -659,7 +659,7 @@ def max_connectivity( ) full_layers = int((-3.0 + np.sqrt(9 + 12 * (n_qubits - 1))) / 6.0) - atoms_left = n_qubits - 1 - (full_layers ** 2 + full_layers) * 3 + atoms_left = n_qubits - 1 - (full_layers**2 + full_layers) * 3 return cls._hexagon_helper(full_layers, atoms_left, spacing, prefix) diff --git a/pulser/simulation/simresults.py b/pulser/simulation/simresults.py index 099b209d2..fc30c0079 100644 --- a/pulser/simulation/simresults.py +++ b/pulser/simulation/simresults.py @@ -101,7 +101,7 @@ def expect( qobj_list = [] dim = self._dim if not self._use_pseudo_dens else 2 legal_dims = [[dim] * self._size] * 2 - legal_shape = (dim ** self._size, dim ** self._size) + legal_shape = (dim**self._size, dim**self._size) for obs in obs_list: if not ( isinstance(obs, np.ndarray) or isinstance(obs, qutip.Qobj) @@ -323,7 +323,7 @@ def get_final_state(self) -> qutip.Qobj: return self.get_state(self._sim_times[-1]) def _calc_weights(self, t_index: int) -> np.ndarray: - weights = np.zeros(2 ** self._size) + weights = np.zeros(2**self._size) for bin_rep, prob in self._results[t_index].items(): weights[int(bin_rep, base=2)] = prob return weights @@ -484,7 +484,7 @@ def get_state( ) ex_inds = [ i - for i in range(3 ** self._size) + for i in range(3**self._size) if ex_state in np.base_repr(i, base=3).zfill(self._size) ] ex_probs = np.abs(state.extract_states(ex_inds).full()) ** 2 @@ -563,8 +563,8 @@ def _calc_weights(self, t_index: int) -> np.ndarray: one_state = 2 # 1 = |h> ex_one = slice(0, 2) probs = probs.reshape([3] * n) - weights = np.zeros(2 ** n) - for dec_val in range(2 ** n): + weights = np.zeros(2**n) + for dec_val in range(2**n): ind: list[Union[int, slice]] = [] for v in np.binary_repr(dec_val, width=n): if v == "0": diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index 089c8e69f..aca3ecb19 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -263,7 +263,7 @@ def initial_state(self, state: Union[str, np.ndarray, qutip.Qobj]) -> None: else: state = cast(Union[np.ndarray, qutip.Qobj], state) shape = state.shape[0] - legal_shape = self.dim ** self._size + legal_shape = self.dim**self._size legal_dims = [[self.dim] * self._size, [1] * self._size] if shape != legal_shape: raise ValueError( @@ -645,7 +645,7 @@ def make_vdw_term(q1: QubitId, q2: QubitId) -> qutip.Qobj: 1/hbar factor. """ dist = np.linalg.norm(self._qdict[q1] - self._qdict[q2]) - U = 0.5 * self._seq._device.interaction_coeff / dist ** 6 + U = 0.5 * self._seq._device.interaction_coeff / dist**6 return U * self.build_operator([("sigma_rr", [q1, q2])]) def make_xy_term(q1: QubitId, q2: QubitId) -> qutip.Qobj: @@ -662,18 +662,15 @@ def make_xy_term(q1: QubitId, q2: QubitId) -> qutip.Qobj: if mag_norm < 1e-8: cosine = 0.0 else: - cosine = ( - np.dot( - (self._qdict[q1] - self._qdict[q2]), - self._seq.magnetic_field[:coords_dim], - ) - / (dist * mag_norm) - ) + cosine = np.dot( + (self._qdict[q1] - self._qdict[q2]), + self._seq.magnetic_field[:coords_dim], + ) / (dist * mag_norm) U = ( 0.5 * self._seq._device.interaction_coeff_xy - * (1 - 3 * cosine ** 2) - / dist ** 3 + * (1 - 3 * cosine**2) + / dist**3 ) return U * self.build_operator( [("sigma_du", [q1]), ("sigma_ud", [q2])] diff --git a/pulser/tests/test_parametrized.py b/pulser/tests/test_parametrized.py index 4d2646387..c0bee6d0f 100644 --- a/pulser/tests/test_parametrized.py +++ b/pulser/tests/test_parametrized.py @@ -113,6 +113,6 @@ def test_opsupport(): x = 8 * x # x = [-16, -8] x = -x // 3 # x = [5, 2] assert np.all(x.build() == [5.0, 2.0]) - assert (a ** a).build() == 0.25 + assert (a**a).build() == 0.25 assert abs(a).build() == 2.0 assert (3 % a).build() == -1.0 diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index 69458a30a..a3707909d 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -102,7 +102,7 @@ def test_initialization_and_construction_of_hamiltonian(): assert bool(set(sim._hamiltonian.tlist).intersection(sim.sampling_times)) for qobjevo in sim._hamiltonian.ops: for sh in qobjevo.qobj.shape: - assert sh == sim.dim ** sim._size + assert sh == sim.dim**sim._size def test_extraction_of_sequences(): @@ -285,7 +285,7 @@ def test_get_hamiltonian(): simple_sim.get_hamiltonian(-10) # Constant detuning, so |rr> Date: Thu, 3 Feb 2022 11:43:00 +0100 Subject: [PATCH 35/51] Spliting the `register` module into multiple files (#316) --- pulser/devices/_device_datacls.py | 2 +- pulser/register.py | 1100 ----------------------------- pulser/register/__init__.py | 20 + pulser/register/base_register.py | 299 ++++++++ pulser/register/register.py | 482 +++++++++++++ pulser/register/register3d.py | 370 ++++++++++ pulser/sequence.py | 2 +- 7 files changed, 1173 insertions(+), 1102 deletions(-) delete mode 100644 pulser/register.py create mode 100644 pulser/register/__init__.py create mode 100644 pulser/register/base_register.py create mode 100644 pulser/register/register.py create mode 100644 pulser/register/register3d.py diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index f0a002bf2..a904daa02 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -21,7 +21,7 @@ from scipy.spatial.distance import pdist, squareform from pulser import Pulse -from pulser.register import BaseRegister +from pulser.register.base_register import BaseRegister from pulser.channels import Channel from pulser.json.utils import obj_to_dict from pulser.devices.interaction_coefficients import c6_dict diff --git a/pulser/register.py b/pulser/register.py deleted file mode 100644 index 7dd3e38a4..000000000 --- a/pulser/register.py +++ /dev/null @@ -1,1100 +0,0 @@ -# Copyright 2020 Pulser Development Team -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Defines the configuration of an array of neutral atoms.""" - -from __future__ import annotations - -from abc import ABC, abstractmethod -from collections.abc import Mapping, Iterable -from collections.abc import Sequence as abcSequence -from typing import Any, cast, Optional, Union, TypeVar, Type -from itertools import combinations - -import matplotlib.pyplot as plt -from matplotlib import collections as mc -import numpy as np -from numpy.typing import ArrayLike -from scipy.spatial import KDTree - -import pulser -from pulser.json.utils import obj_to_dict - -QubitId = Union[int, str] - -T = TypeVar("T", bound="BaseRegister") - - -class BaseRegister(ABC): - """The abstract class for a register.""" - - @abstractmethod - def __init__(self, qubits: Mapping[Any, ArrayLike]): - """Initializes a custom Register.""" - if not isinstance(qubits, dict): - raise TypeError( - "The qubits have to be stored in a dictionary " - "matching qubit ids to position coordinates." - ) - if not qubits: - raise ValueError( - "Cannot create a Register with an empty qubit " "dictionary." - ) - self._ids = list(qubits.keys()) - self._coords = [np.array(v, dtype=float) for v in qubits.values()] - self._dim = 0 - - @property - def qubits(self) -> dict[QubitId, np.ndarray]: - """Dictionary of the qubit names and their position coordinates.""" - return dict(zip(self._ids, self._coords)) - - @classmethod - def from_coordinates( - cls: Type[T], - coords: np.ndarray, - center: bool = True, - prefix: Optional[str] = None, - labels: Optional[abcSequence[QubitId]] = None, - ) -> T: - """Creates the register from an array of coordinates. - - Args: - coords (ndarray): The coordinates of each qubit to include in the - register. - - Keyword args: - center(defaut=True): Whether or not to center the entire array - around the origin. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - labels (ArrayLike): The list of qubit ids. If defined, each qubit - id will be set to the corresponding value. - - Returns: - Register: A register with qubits placed on the given coordinates. - """ - if center: - coords = coords - np.mean(coords, axis=0) # Centers the array - if prefix is not None: - pre = str(prefix) - qubits = {pre + str(i): pos for i, pos in enumerate(coords)} - if labels is not None: - raise NotImplementedError( - "It is impossible to specify a prefix and " - "a set of labels at the same time" - ) - - elif labels is not None: - if len(coords) != len(labels): - raise ValueError( - f"Label length ({len(labels)}) does not" - f"match number of coordinates ({len(coords)})" - ) - qubits = dict(zip(cast(Iterable, labels), coords)) - else: - qubits = dict(cast(Iterable, enumerate(coords))) - return cls(qubits) - - @staticmethod - def _draw_2D( - ax: plt.axes._subplots.AxesSubplot, - pos: np.ndarray, - ids: list, - plane: tuple = (0, 1), - with_labels: bool = True, - blockade_radius: Optional[float] = None, - draw_graph: bool = True, - draw_half_radius: bool = False, - masked_qubits: set[QubitId] = set(), - ) -> None: - ix, iy = plane - - ax.scatter(pos[:, ix], pos[:, iy], s=30, alpha=0.7, c="darkgreen") - - # Draw square halo around masked qubits - if masked_qubits: - mask_pos = [] - for i, c in zip(ids, pos): - if i in masked_qubits: - mask_pos.append(c) - mask_arr = np.array(mask_pos) - ax.scatter( - mask_arr[:, ix], - mask_arr[:, iy], - marker="s", - s=1200, - alpha=0.2, - c="black", - ) - - axes = "xyz" - - ax.set_xlabel(axes[ix] + " (µm)") - ax.set_ylabel(axes[iy] + " (µm)") - ax.axis("equal") - ax.spines["right"].set_color("none") - ax.spines["top"].set_color("none") - - if with_labels: - # Determine which labels would overlap and merge those - plot_pos = list(pos[:, (ix, iy)]) - plot_ids: list[Union[list, str]] = [[f"{i}"] for i in ids] - # Threshold distance between points - epsilon = 1.0e-2 * np.diff(ax.get_xlim())[0] - - i = 0 - bbs = {} - while i < len(plot_ids): - r = plot_pos[i] - j = i + 1 - overlap = False - # Put in a list all qubits that overlap at position plot_pos[i] - while j < len(plot_ids): - r2 = plot_pos[j] - if np.max(np.abs(r - r2)) < epsilon: - plot_ids[i] = plot_ids[i] + plot_ids.pop(j) - plot_pos.pop(j) - overlap = True - else: - j += 1 - # Sort qubits in plot_ids[i] according to masked status - plot_ids[i] = sorted( - plot_ids[i], - key=lambda s: s in [str(q) for q in masked_qubits], - ) - # Merge all masked qubits - has_masked = False - for j in range(len(plot_ids[i])): - if plot_ids[i][j] in [str(q) for q in masked_qubits]: - plot_ids[i][j:] = [", ".join(plot_ids[i][j:])] - has_masked = True - break - # Add a square bracket that encloses all masked qubits - if has_masked: - plot_ids[i][-1] = "[" + plot_ids[i][-1] + "]" - # Merge what remains - plot_ids[i] = ", ".join(plot_ids[i]) - bbs[plot_ids[i]] = overlap - i += 1 - - for q, coords in zip(plot_ids, plot_pos): - bb = ( - dict(boxstyle="square", fill=False, ec="gray", ls="--") - if bbs[q] - else None - ) - v_al = "center" if bbs[q] else "bottom" - txt = ax.text( - coords[0], - coords[1], - q, - ha="left", - va=v_al, - wrap=True, - bbox=bb, - ) - txt._get_wrap_line_width = lambda: 50.0 - - if draw_half_radius and blockade_radius is not None: - for p in pos: - circle = plt.Circle( - tuple(p[[ix, iy]]), - blockade_radius / 2, - alpha=0.1, - color="darkgreen", - ) - ax.add_patch(circle) - ax.autoscale() - if draw_graph and blockade_radius is not None: - epsilon = 1e-9 # Accounts for rounding errors - edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) - bonds = pos[(tuple(edges),)] - if len(bonds) > 0: - lines = bonds[:, :, (ix, iy)] - else: - lines = [] - lc = mc.LineCollection(lines, linewidths=0.6, colors="grey") - ax.add_collection(lc) - - else: - # Only draw central axis lines when not drawing the graph - ax.axvline(0, c="grey", alpha=0.5, linestyle=":") - ax.axhline(0, c="grey", alpha=0.5, linestyle=":") - - @staticmethod - def _register_dims( - pos: np.ndarray, - blockade_radius: Optional[float] = None, - draw_half_radius: bool = False, - ) -> np.ndarray: - """Returns the dimensions of the register to be drawn.""" - diffs = np.ptp(pos, axis=0) - diffs[diffs < 9] *= 1.5 - diffs[diffs < 9] += 2 - if blockade_radius and draw_half_radius: - diffs[diffs < blockade_radius] = blockade_radius - - return np.array(diffs) - - def _draw_checks( - self, - blockade_radius: Optional[float] = None, - draw_graph: bool = True, - draw_half_radius: bool = False, - ) -> None: - """Checks common in all register drawings. - - Keyword Args: - blockade_radius(float, default=None): The distance (in μm) between - atoms below the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw the - half the blockade radius surrounding each atoms. If `True`, - requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the - interaction between atoms as edges in a graph. Will only draw - if the `blockade_radius` is defined. - """ - # Check spacing - if blockade_radius is not None and blockade_radius <= 0.0: - raise ValueError( - "Blockade radius (`blockade_radius` =" - f" {blockade_radius})" - " must be greater than 0." - ) - - if draw_half_radius: - if blockade_radius is None: - raise ValueError("Define 'blockade_radius' to draw.") - if len(self._ids) == 1: - raise NotImplementedError( - "Needs more than one atom to draw " "the blockade radius." - ) - - @abstractmethod - def _to_dict(self) -> dict[str, Any]: - qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) - return obj_to_dict(self, qs) - - def __eq__(self, other: Any) -> bool: - if type(other) is not type(self): - return False - - return set(self._ids) == set(other._ids) and all( - ( - np.array_equal( - self._coords[i], - other._coords[other._ids.index(id)], - ) - for i, id in enumerate(self._ids) - ) - ) - - -class Register(BaseRegister): - """A 2D quantum register containing a set of qubits. - - Args: - qubits (dict): Dictionary with the qubit names as keys and their - position coordinates (in μm) as values - (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). - """ - - def __init__(self, qubits: Mapping[Any, ArrayLike]): - """Initializes a custom Register.""" - super().__init__(qubits) - self._dim = self._coords[0].size - if any(c.shape != (self._dim,) for c in self._coords) or ( - self._dim != 2 - ): - raise ValueError( - "All coordinates must be specified as vectors of size 2." - ) - - @classmethod - def square( - cls, side: int, spacing: float = 4.0, prefix: Optional[str] = None - ) -> Register: - """Initializes the register with the qubits in a square array. - - Args: - side (int): Side of the square in number of qubits. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register: A register with qubits placed in a square array. - """ - # Check side - if side < 1: - raise ValueError( - f"The number of atoms per side (`side` = {side})" - " must be greater than or equal to 1." - ) - - return cls.rectangle(side, side, spacing=spacing, prefix=prefix) - - @classmethod - def rectangle( - cls, - rows: int, - columns: int, - spacing: float = 4.0, - prefix: Optional[str] = None, - ) -> Register: - """Initializes the register with the qubits in a rectangular array. - - Args: - rows (int): Number of rows. - columns (int): Number of columns. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) - - Returns: - Register: A register with qubits placed in a rectangular array. - """ - # Check rows - if rows < 1: - raise ValueError( - f"The number of rows (`rows` = {rows})" - " must be greater than or equal to 1." - ) - - # Check columns - if columns < 1: - raise ValueError( - f"The number of columns (`columns` = {columns})" - " must be greater than or equal to 1." - ) - - # Check spacing - if spacing <= 0.0: - raise ValueError( - f"Spacing between atoms (`spacing` = {spacing})" - " must be greater than 0." - ) - - coords = ( - np.array( - [(x, y) for y in range(rows) for x in range(columns)], - dtype=float, - ) - * spacing - ) - - return cls.from_coordinates(coords, center=True, prefix=prefix) - - @classmethod - def triangular_lattice( - cls, - rows: int, - atoms_per_row: int, - spacing: float = 4.0, - prefix: Optional[str] = None, - ) -> Register: - """Initializes the register with the qubits in a triangular lattice. - - Initializes the qubits in a triangular lattice pattern, more - specifically a triangular lattice with horizontal rows, meaning the - triangles are pointing up and down. - - Args: - rows (int): Number of rows. - atoms_per_row (int): Number of atoms per row. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register: A register with qubits placed in a triangular lattice. - """ - # Check rows - if rows < 1: - raise ValueError( - f"The number of rows (`rows` = {rows})" - " must be greater than or equal to 1." - ) - - # Check atoms per row - if atoms_per_row < 1: - raise ValueError( - "The number of atoms per row" - f" (`atoms_per_row` = {atoms_per_row})" - " must be greater than or equal to 1." - ) - - # Check spacing - if spacing <= 0.0: - raise ValueError( - f"Spacing between atoms (`spacing` = {spacing})" - " must be greater than 0." - ) - - coords = np.array( - [(x, y) for y in range(rows) for x in range(atoms_per_row)], - dtype=float, - ) - coords[:, 0] += 0.5 * np.mod(coords[:, 1], 2) - coords[:, 1] *= np.sqrt(3) / 2 - coords *= spacing - - return cls.from_coordinates(coords, center=True, prefix=prefix) - - @classmethod - def _hexagon_helper( - cls, - layers: int, - atoms_left: int, - spacing: float, - prefix: Optional[str] = None, - ) -> Register: - """Helper function for building hexagonal arrays. - - Args: - layers (int): Number of full layers around a central atom. - atoms_left (int): Number of atoms on the external layer. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register: A register with qubits placed in a hexagonal layout - with extra atoms on the outermost layer if needed. - """ - # y coordinates of the top vertex of a triangle - crest_y = np.sqrt(3) / 2.0 - - # Coordinates of vertices - start_x = [-1.0, -0.5, 0.5, 1.0, 0.5, -0.5] - start_y = [0.0, crest_y, crest_y, 0, -crest_y, -crest_y] - - # Steps to place atoms, starting from a vertex - delta_x = [0.5, 1.0, 0.5, -0.5, -1.0, -0.5] - delta_y = [crest_y, 0.0, -crest_y, -crest_y, 0.0, crest_y] - - coords = np.array( - [ - ( - start_x[side] * layer + atom * delta_x[side], - start_y[side] * layer + atom * delta_y[side], - ) - for layer in range(1, layers + 1) - for side in range(6) - for atom in range(1, layer + 1) - ], - dtype=float, - ) - - if atoms_left > 0: - layer = layers + 1 - min_atoms_per_side = atoms_left // 6 - # Extra atoms after balancing all sides - atoms_left %= 6 - - # Order for placing left atoms - # Top-Left, Top-Right, Bottom (C3 symmetry)... - # ...Top, Bottom-Right, Bottom-Left (C6 symmetry) - sides_order = [0, 3, 1, 4, 2, 5] - - coords2 = np.array( - [ - ( - start_x[side] * layer + atom * delta_x[side], - start_y[side] * layer + atom * delta_y[side], - ) - for side in range(6) - for atom in range( - 1, - min_atoms_per_side + 2 - if atoms_left > sides_order[side] - else min_atoms_per_side + 1, - ) - ], - dtype=float, - ) - - coords = np.concatenate((coords, coords2)) - - coords *= spacing - coords = np.concatenate(([(0.0, 0.0)], coords)) - - return cls.from_coordinates(coords, center=False, prefix=prefix) - - @classmethod - def hexagon( - cls, layers: int, spacing: float = 4.0, prefix: Optional[str] = None - ) -> Register: - """Initializes the register with the qubits in a hexagonal layout. - - Args: - layers (int): Number of layers around a central atom. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register: A register with qubits placed in a hexagonal layout. - """ - # Check layers - if layers < 1: - raise ValueError( - f"The number of layers (`layers` = {layers})" - " must be greater than or equal to 1." - ) - - # Check spacing - if spacing <= 0.0: - raise ValueError( - f"Spacing between atoms (`spacing` = {spacing})" - " must be greater than 0." - ) - - return cls._hexagon_helper(layers, 0, spacing, prefix) - - @classmethod - def max_connectivity( - cls, - n_qubits: int, - device: pulser.devices._device_datacls.Device, - spacing: float = None, - prefix: str = None, - ) -> Register: - """Initializes the register with maximum connectivity for a given device. - - In order to maximize connectivity, the basic pattern is the triangle. - Atoms are first arranged as layers of hexagons around a central atom. - Extra atoms are placed in such a manner that C3 and C6 rotational - symmetries are enforced as often as possible. - - Args: - n_qubits (int): Number of qubits. - device (Device): The device whose constraints must be obeyed. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - If omitted, the minimal distance for the device is used. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register: A register with qubits placed for maximum connectivity. - """ - # Check device - if not isinstance(device, pulser.devices._device_datacls.Device): - raise TypeError( - "'device' must be of type 'Device'. Import a valid" - " device from 'pulser.devices'." - ) - - # Check number of qubits (1 or above) - if n_qubits < 1: - raise ValueError( - f"The number of qubits (`n_qubits` = {n_qubits})" - " must be greater than or equal to 1." - ) - - # Check number of qubits (less than the max number of atoms) - if n_qubits > device.max_atom_num: - raise ValueError( - f"The number of qubits (`n_qubits` = {n_qubits})" - " must be less than or equal to the maximum" - " number of atoms supported by this device" - f" ({device.max_atom_num})." - ) - - # Default spacing or check minimal distance - if spacing is None: - spacing = device.min_atom_distance - elif spacing < device.min_atom_distance: - raise ValueError( - f"Spacing between atoms (`spacing = `{spacing})" - " must be greater than or equal to the minimal" - " distance supported by this device" - f" ({device.min_atom_distance})." - ) - - if n_qubits < 7: - crest_y = np.sqrt(3) / 2.0 - hex_coords = np.array( - [ - (0.0, 0.0), - (-0.5, crest_y), - (0.5, crest_y), - (1.0, 0.0), - (0.5, -crest_y), - (-0.5, -crest_y), - ] - ) - return cls.from_coordinates( - spacing * hex_coords[:n_qubits], prefix=prefix, center=False - ) - - full_layers = int((-3.0 + np.sqrt(9 + 12 * (n_qubits - 1))) / 6.0) - atoms_left = n_qubits - 1 - (full_layers**2 + full_layers) * 3 - - return cls._hexagon_helper(full_layers, atoms_left, spacing, prefix) - - def rotate(self, degrees: float) -> None: - """Rotates the array around the origin by the given angle. - - Args: - degrees (float): The angle of rotation in degrees. - """ - theta = np.deg2rad(degrees) - rot = np.array( - [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]] - ) - self._coords = [rot @ v for v in self._coords] - - def _initialize_fig_axes( - self, - pos: np.ndarray, - blockade_radius: Optional[float] = None, - draw_half_radius: bool = False, - ) -> tuple[plt.figure.Figure, plt.axes.Axes]: - """Creates the Figure and Axes for drawing the register.""" - diffs = super()._register_dims( - pos, - blockade_radius=blockade_radius, - draw_half_radius=draw_half_radius, - ) - big_side = max(diffs) - proportions = diffs / big_side - Ls = proportions * min( - big_side / 4, 10 - ) # Figsize is, at most, (10,10) - fig, axes = plt.subplots(figsize=Ls) - - return (fig, axes) - - def draw( - self, - with_labels: bool = True, - blockade_radius: Optional[float] = None, - draw_graph: bool = True, - draw_half_radius: bool = False, - fig_name: str = None, - kwargs_savefig: dict = {}, - ) -> None: - """Draws the entire register. - - Keyword Args: - with_labels(bool, default=True): If True, writes the qubit ID's - next to each qubit. - blockade_radius(float, default=None): The distance (in μm) between - atoms below the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw the - half the blockade radius surrounding each atoms. If `True`, - requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the - interaction between atoms as edges in a graph. Will only draw - if the `blockade_radius` is defined. - fig_name(str, default=None): The name on which to save the figure. - If None the figure will not be saved. - kwargs_savefig(dict, default={}): Keywords arguments for - ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` - is ``None``. - - Note: - When drawing half the blockade radius, we say there is a blockade - effect between atoms whenever their respective circles overlap. - This representation is preferred over drawing the full Rydberg - radius because it helps in seeing the interactions between atoms. - """ - super()._draw_checks( - blockade_radius=blockade_radius, - draw_graph=draw_graph, - draw_half_radius=draw_half_radius, - ) - pos = np.array(self._coords) - fig, ax = self._initialize_fig_axes( - pos, - blockade_radius=blockade_radius, - draw_half_radius=draw_half_radius, - ) - super()._draw_2D( - ax, - pos, - self._ids, - with_labels=with_labels, - blockade_radius=blockade_radius, - draw_graph=draw_graph, - draw_half_radius=draw_half_radius, - ) - if fig_name is not None: - plt.savefig(fig_name, **kwargs_savefig) - plt.show() - - def _to_dict(self) -> dict[str, Any]: - return super()._to_dict() - - -class Register3D(BaseRegister): - """A 3D quantum register containing a set of qubits. - - Args: - qubits (dict): Dictionary with the qubit names as keys and their - position coordinates (in μm) as values - (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). - """ - - def __init__(self, qubits: Mapping[Any, ArrayLike]): - """Initializes a custom Register.""" - super().__init__(qubits) - coords = [np.array(v, dtype=float) for v in qubits.values()] - self._dim = coords[0].size - if any(c.shape != (self._dim,) for c in coords) or (self._dim != 3): - raise ValueError( - "All coordinates must be specified as vectors of size 3." - ) - self._coords = coords - - @classmethod - def cubic( - cls, side: int, spacing: float = 4.0, prefix: Optional[str] = None - ) -> Register3D: - """Initializes the register with the qubits in a cubic array. - - Args: - side (int): Side of the cube in number of qubits. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register3D : A 3D register with qubits placed in a cubic array. - """ - # Check side - if side < 1: - raise ValueError( - f"The number of atoms per side (`side` = {side})" - " must be greater than or equal to 1." - ) - - return cls.cuboid(side, side, side, spacing=spacing, prefix=prefix) - - @classmethod - def cuboid( - cls, - rows: int, - columns: int, - layers: int, - spacing: float = 4.0, - prefix: Optional[str] = None, - ) -> Register3D: - """Initializes the register with the qubits in a cuboid array. - - Args: - rows (int): Number of rows. - columns (int): Number of columns. - layers (int): Number of layers. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) - - Returns: - Register3D : A 3D register with qubits placed in a cuboid array. - """ - # Check rows - if rows < 1: - raise ValueError( - f"The number of rows (`rows` = {rows})" - " must be greater than or equal to 1." - ) - - # Check columns - if columns < 1: - raise ValueError( - f"The number of columns (`columns` = {columns})" - " must be greater than or equal to 1." - ) - - # Check layers - if layers < 1: - raise ValueError( - f"The number of layers (`layers` = {layers})" - " must be greater than or equal to 1." - ) - - # Check spacing - if spacing <= 0.0: - raise ValueError( - f"Spacing between atoms (`spacing` = {spacing})" - " must be greater than 0." - ) - - coords = ( - np.array( - [ - (x, y, z) - for z in range(layers) - for y in range(rows) - for x in range(columns) - ], - dtype=float, - ) - * spacing - ) - - return cls.from_coordinates(coords, center=True, prefix=prefix) - - def to_2D(self, tol_width: float = 0.0) -> Register: - """Converts a Register3D into a Register (if possible). - - Args: - tol_width (float): The allowed transverse width of - the register to be projected. - - Returns: - Register: Returns a 2D register with the coordinates of the atoms - in a plane, if they are coplanar. - - Raises: - ValueError: If the atoms are not coplanar. - """ - coords = np.array(self._coords) - - barycenter = coords.sum(axis=0) / coords.shape[0] - # run SVD - u, s, vh = np.linalg.svd(coords - barycenter) - e_z = vh[2, :] - perp_extent = [e_z.dot(r) for r in coords] - width = np.ptp(perp_extent) - # A set of vector is coplanar if one of the Singular values is 0 - if width > tol_width: - raise ValueError( - f"Atoms are not coplanar (`width` = {width:#.2f} µm)" - ) - else: - e_x = vh[0, :] - e_y = vh[1, :] - coords_2D = np.array( - [np.array([e_x.dot(r), e_y.dot(r)]) for r in coords] - ) - return Register.from_coordinates(coords_2D, labels=self._ids) - - def _initialize_fig_axes_projection( - self, - pos: np.ndarray, - blockade_radius: Optional[float] = None, - draw_half_radius: bool = False, - ) -> tuple[plt.figure.Figure, plt.axes.Axes]: - """Creates the Figure and Axes for drawing the register projections.""" - diffs = super()._register_dims( - pos, - blockade_radius=blockade_radius, - draw_half_radius=draw_half_radius, - ) - - proportions = [] - for (ix, iy) in combinations(np.arange(3), 2): - big_side = max(diffs[[ix, iy]]) - Ls = diffs[[ix, iy]] / big_side - Ls *= max( - min(big_side / 4, 10), 4 - ) # Figsize is, at most, (10,10), and, at least (4,*) or (*,4) - proportions.append(Ls) - - fig_height = np.max([Ls[1] for Ls in proportions]) - - max_width = 0 - for i, (width, height) in enumerate(proportions): - proportions[i] = (width * fig_height / height, fig_height) - max_width = max(max_width, proportions[i][0]) - widths = [max(Ls[0], max_width / 5) for Ls in proportions] - fig_width = min(np.sum(widths), fig_height * 4) - - rescaling = 20 / max(max(fig_width, fig_height), 20) - figsize = (rescaling * fig_width, rescaling * fig_height) - - fig, axes = plt.subplots( - ncols=3, - figsize=figsize, - gridspec_kw=dict(width_ratios=widths), - ) - - return (fig, axes) - - def draw( - self, - with_labels: bool = False, - blockade_radius: Optional[float] = None, - draw_graph: bool = True, - draw_half_radius: bool = False, - projection: bool = False, - fig_name: str = None, - kwargs_savefig: dict = {}, - ) -> None: - """Draws the entire register. - - Keyword Args: - with_labels(bool, default=True): If True, writes the qubit ID's - next to each qubit. - blockade_radius(float, default=None): The distance (in μm) between - atoms below the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw the - half the blockade radius surrounding each atoms. If `True`, - requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the - interaction between atoms as edges in a graph. Will only draw - if the `blockade_radius` is defined. - projection(bool, default=False): Whether to draw a 2D projection - instead of a perspective view. - fig_name(str, default=None): The name on which to save the figure. - If None the figure will not be saved. - kwargs_savefig(dict, default={}): Keywords arguments for - ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` - is ``None``. - - Note: - When drawing half the blockade radius, we say there is a blockade - effect between atoms whenever their respective circles overlap. - This representation is preferred over drawing the full Rydberg - radius because it helps in seeing the interactions between atoms. - """ - super()._draw_checks( - blockade_radius=blockade_radius, - draw_graph=draw_graph, - draw_half_radius=draw_half_radius, - ) - - pos = np.array(self._coords) - - if draw_graph and blockade_radius is not None: - epsilon = 1e-9 # Accounts for rounding errors - edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) - - if projection: - labels = "xyz" - fig, axes = self._initialize_fig_axes_projection( - pos, - blockade_radius=blockade_radius, - draw_half_radius=draw_half_radius, - ) - fig.tight_layout(w_pad=6.5) - - for ax, (ix, iy) in zip(axes, combinations(np.arange(3), 2)): - super()._draw_2D( - ax, - pos, - self._ids, - plane=( - ix, - iy, - ), - with_labels=with_labels, - blockade_radius=blockade_radius, - draw_graph=draw_graph, - draw_half_radius=draw_half_radius, - ) - ax.set_title( - "Projection onto\n the " - + labels[ix] - + labels[iy] - + "-plane" - ) - - else: - fig = plt.figure(figsize=2 * plt.figaspect(0.5)) - - if draw_graph and blockade_radius is not None: - bonds = {} - for i, j in edges: - xi, yi, zi = pos[i] - xj, yj, zj = pos[j] - bonds[(i, j)] = [[xi, xj], [yi, yj], [zi, zj]] - - for i in range(1, 3): - ax = fig.add_subplot( - 1, 2, i, projection="3d", azim=-60 * (-1) ** i, elev=15 - ) - - ax.scatter( - pos[:, 0], - pos[:, 1], - pos[:, 2], - s=30, - alpha=0.7, - c="darkgreen", - ) - - if with_labels: - for q, coords in zip(self._ids, self._coords): - ax.text( - coords[0], - coords[1], - coords[2], - q, - fontsize=12, - ha="left", - va="bottom", - ) - - if draw_half_radius and blockade_radius is not None: - mesh_num = 20 if len(self._ids) > 10 else 40 - for r in pos: - x0, y0, z0 = r - radius = blockade_radius / 2 - - # Strange behavior pf mypy using "imaginary slice step" - # u, v = np.pi * np.mgrid[0:2:50j, 0:1:50j] - - v, u = np.meshgrid( - np.arccos(np.linspace(-1, 1, num=mesh_num)), - np.linspace(0, 2 * np.pi, num=mesh_num), - ) - x = radius * np.cos(u) * np.sin(v) + x0 - y = radius * np.sin(u) * np.sin(v) + y0 - z = radius * np.cos(v) + z0 - # alpha controls opacity - ax.plot_surface(x, y, z, color="darkgreen", alpha=0.1) - - if draw_graph and blockade_radius is not None: - for x, y, z in bonds.values(): - ax.plot(x, y, z, linewidth=1.5, color="grey") - - ax.set_xlabel("x (µm)") - ax.set_ylabel("y (µm)") - ax.set_zlabel("z (µm)") - - if fig_name is not None: - plt.savefig(fig_name, **kwargs_savefig) - plt.show() - - def _to_dict(self) -> dict[str, Any]: - return super()._to_dict() diff --git a/pulser/register/__init__.py b/pulser/register/__init__.py new file mode 100644 index 000000000..a57034208 --- /dev/null +++ b/pulser/register/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Classes for qubit register definition.""" + +from pulser.register.base_register import QubitId + +from pulser.register.register import Register + +from pulser.register.register3d import Register3D diff --git a/pulser/register/base_register.py b/pulser/register/base_register.py new file mode 100644 index 000000000..94ad37dae --- /dev/null +++ b/pulser/register/base_register.py @@ -0,0 +1,299 @@ +# Copyright 2021 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Defines the abstract register class.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections.abc import Mapping, Iterable +from collections.abc import Sequence as abcSequence +from typing import Any, cast, Optional, Union, TypeVar, Type + +import matplotlib.pyplot as plt +from matplotlib import collections as mc +import numpy as np +from numpy.typing import ArrayLike +from scipy.spatial import KDTree + +from pulser.json.utils import obj_to_dict + +T = TypeVar("T", bound="BaseRegister") +QubitId = Union[int, str] + + +class BaseRegister(ABC): + """The abstract class for a register.""" + + @abstractmethod + def __init__(self, qubits: Mapping[Any, ArrayLike]): + """Initializes a custom Register.""" + if not isinstance(qubits, dict): + raise TypeError( + "The qubits have to be stored in a dictionary " + "matching qubit ids to position coordinates." + ) + if not qubits: + raise ValueError( + "Cannot create a Register with an empty qubit " "dictionary." + ) + self._ids = list(qubits.keys()) + self._coords = [np.array(v, dtype=float) for v in qubits.values()] + self._dim = 0 + + @property + def qubits(self) -> dict[QubitId, np.ndarray]: + """Dictionary of the qubit names and their position coordinates.""" + return dict(zip(self._ids, self._coords)) + + @classmethod + def from_coordinates( + cls: Type[T], + coords: np.ndarray, + center: bool = True, + prefix: Optional[str] = None, + labels: Optional[abcSequence[QubitId]] = None, + ) -> T: + """Creates the register from an array of coordinates. + + Args: + coords (ndarray): The coordinates of each qubit to include in the + register. + + Keyword args: + center(defaut=True): Whether or not to center the entire array + around the origin. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + labels (ArrayLike): The list of qubit ids. If defined, each qubit + id will be set to the corresponding value. + + Returns: + Register: A register with qubits placed on the given coordinates. + """ + if center: + coords = coords - np.mean(coords, axis=0) # Centers the array + if prefix is not None: + pre = str(prefix) + qubits = {pre + str(i): pos for i, pos in enumerate(coords)} + if labels is not None: + raise NotImplementedError( + "It is impossible to specify a prefix and " + "a set of labels at the same time" + ) + + elif labels is not None: + if len(coords) != len(labels): + raise ValueError( + f"Label length ({len(labels)}) does not" + f"match number of coordinates ({len(coords)})" + ) + qubits = dict(zip(cast(Iterable, labels), coords)) + else: + qubits = dict(cast(Iterable, enumerate(coords))) + return cls(qubits) + + @staticmethod + def _draw_2D( + ax: plt.axes._subplots.AxesSubplot, + pos: np.ndarray, + ids: list, + plane: tuple = (0, 1), + with_labels: bool = True, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + masked_qubits: set[QubitId] = set(), + ) -> None: + ix, iy = plane + + ax.scatter(pos[:, ix], pos[:, iy], s=30, alpha=0.7, c="darkgreen") + + # Draw square halo around masked qubits + if masked_qubits: + mask_pos = [] + for i, c in zip(ids, pos): + if i in masked_qubits: + mask_pos.append(c) + mask_arr = np.array(mask_pos) + ax.scatter( + mask_arr[:, ix], + mask_arr[:, iy], + marker="s", + s=1200, + alpha=0.2, + c="black", + ) + + axes = "xyz" + + ax.set_xlabel(axes[ix] + " (µm)") + ax.set_ylabel(axes[iy] + " (µm)") + ax.axis("equal") + ax.spines["right"].set_color("none") + ax.spines["top"].set_color("none") + + if with_labels: + # Determine which labels would overlap and merge those + plot_pos = list(pos[:, (ix, iy)]) + plot_ids: list[Union[list, str]] = [[f"{i}"] for i in ids] + # Threshold distance between points + epsilon = 1.0e-2 * np.diff(ax.get_xlim())[0] + + i = 0 + bbs = {} + while i < len(plot_ids): + r = plot_pos[i] + j = i + 1 + overlap = False + # Put in a list all qubits that overlap at position plot_pos[i] + while j < len(plot_ids): + r2 = plot_pos[j] + if np.max(np.abs(r - r2)) < epsilon: + plot_ids[i] = plot_ids[i] + plot_ids.pop(j) + plot_pos.pop(j) + overlap = True + else: + j += 1 + # Sort qubits in plot_ids[i] according to masked status + plot_ids[i] = sorted( + plot_ids[i], + key=lambda s: s in [str(q) for q in masked_qubits], + ) + # Merge all masked qubits + has_masked = False + for j in range(len(plot_ids[i])): + if plot_ids[i][j] in [str(q) for q in masked_qubits]: + plot_ids[i][j:] = [", ".join(plot_ids[i][j:])] + has_masked = True + break + # Add a square bracket that encloses all masked qubits + if has_masked: + plot_ids[i][-1] = "[" + plot_ids[i][-1] + "]" + # Merge what remains + plot_ids[i] = ", ".join(plot_ids[i]) + bbs[plot_ids[i]] = overlap + i += 1 + + for q, coords in zip(plot_ids, plot_pos): + bb = ( + dict(boxstyle="square", fill=False, ec="gray", ls="--") + if bbs[q] + else None + ) + v_al = "center" if bbs[q] else "bottom" + txt = ax.text( + coords[0], + coords[1], + q, + ha="left", + va=v_al, + wrap=True, + bbox=bb, + ) + txt._get_wrap_line_width = lambda: 50.0 + + if draw_half_radius and blockade_radius is not None: + for p in pos: + circle = plt.Circle( + tuple(p[[ix, iy]]), + blockade_radius / 2, + alpha=0.1, + color="darkgreen", + ) + ax.add_patch(circle) + ax.autoscale() + if draw_graph and blockade_radius is not None: + epsilon = 1e-9 # Accounts for rounding errors + edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) + bonds = pos[(tuple(edges),)] + if len(bonds) > 0: + lines = bonds[:, :, (ix, iy)] + else: + lines = [] + lc = mc.LineCollection(lines, linewidths=0.6, colors="grey") + ax.add_collection(lc) + + else: + # Only draw central axis lines when not drawing the graph + ax.axvline(0, c="grey", alpha=0.5, linestyle=":") + ax.axhline(0, c="grey", alpha=0.5, linestyle=":") + + @staticmethod + def _register_dims( + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> np.ndarray: + """Returns the dimensions of the register to be drawn.""" + diffs = np.ptp(pos, axis=0) + diffs[diffs < 9] *= 1.5 + diffs[diffs < 9] += 2 + if blockade_radius and draw_half_radius: + diffs[diffs < blockade_radius] = blockade_radius + + return np.array(diffs) + + def _draw_checks( + self, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + ) -> None: + """Checks common in all register drawings. + + Keyword Args: + blockade_radius(float, default=None): The distance (in μm) between + atoms below the Rydberg blockade effect occurs. + draw_half_radius(bool, default=False): Whether or not to draw the + half the blockade radius surrounding each atoms. If `True`, + requires `blockade_radius` to be defined. + draw_graph(bool, default=True): Whether or not to draw the + interaction between atoms as edges in a graph. Will only draw + if the `blockade_radius` is defined. + """ + # Check spacing + if blockade_radius is not None and blockade_radius <= 0.0: + raise ValueError( + "Blockade radius (`blockade_radius` =" + f" {blockade_radius})" + " must be greater than 0." + ) + + if draw_half_radius: + if blockade_radius is None: + raise ValueError("Define 'blockade_radius' to draw.") + if len(self._ids) == 1: + raise NotImplementedError( + "Needs more than one atom to draw " "the blockade radius." + ) + + @abstractmethod + def _to_dict(self) -> dict[str, Any]: + qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) + return obj_to_dict(self, qs) + + def __eq__(self, other: Any) -> bool: + if type(other) is not type(self): + return False + + return set(self._ids) == set(other._ids) and all( + ( + np.array_equal( + self._coords[i], + other._coords[other._ids.index(id)], + ) + for i, id in enumerate(self._ids) + ) + ) diff --git a/pulser/register/register.py b/pulser/register/register.py new file mode 100644 index 000000000..7132c387e --- /dev/null +++ b/pulser/register/register.py @@ -0,0 +1,482 @@ +# Copyright 2020 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Defines the configuration of an array of neutral atoms in 2D.""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any, Optional + +import matplotlib.pyplot as plt +import numpy as np +from numpy.typing import ArrayLike + +import pulser +from pulser.register.base_register import BaseRegister + + +class Register(BaseRegister): + """A 2D quantum register containing a set of qubits. + + Args: + qubits (dict): Dictionary with the qubit names as keys and their + position coordinates (in μm) as values + (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). + """ + + def __init__(self, qubits: Mapping[Any, ArrayLike]): + """Initializes a custom Register.""" + super().__init__(qubits) + self._dim = self._coords[0].size + if any(c.shape != (self._dim,) for c in self._coords) or ( + self._dim != 2 + ): + raise ValueError( + "All coordinates must be specified as vectors of size 2." + ) + + @classmethod + def square( + cls, side: int, spacing: float = 4.0, prefix: Optional[str] = None + ) -> Register: + """Initializes the register with the qubits in a square array. + + Args: + side (int): Side of the square in number of qubits. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: A register with qubits placed in a square array. + """ + # Check side + if side < 1: + raise ValueError( + f"The number of atoms per side (`side` = {side})" + " must be greater than or equal to 1." + ) + + return cls.rectangle(side, side, spacing=spacing, prefix=prefix) + + @classmethod + def rectangle( + cls, + rows: int, + columns: int, + spacing: float = 4.0, + prefix: Optional[str] = None, + ) -> Register: + """Initializes the register with the qubits in a rectangular array. + + Args: + rows (int): Number of rows. + columns (int): Number of columns. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) + + Returns: + Register: A register with qubits placed in a rectangular array. + """ + # Check rows + if rows < 1: + raise ValueError( + f"The number of rows (`rows` = {rows})" + " must be greater than or equal to 1." + ) + + # Check columns + if columns < 1: + raise ValueError( + f"The number of columns (`columns` = {columns})" + " must be greater than or equal to 1." + ) + + # Check spacing + if spacing <= 0.0: + raise ValueError( + f"Spacing between atoms (`spacing` = {spacing})" + " must be greater than 0." + ) + + coords = ( + np.array( + [(x, y) for y in range(rows) for x in range(columns)], + dtype=float, + ) + * spacing + ) + + return cls.from_coordinates(coords, center=True, prefix=prefix) + + @classmethod + def triangular_lattice( + cls, + rows: int, + atoms_per_row: int, + spacing: float = 4.0, + prefix: Optional[str] = None, + ) -> Register: + """Initializes the register with the qubits in a triangular lattice. + + Initializes the qubits in a triangular lattice pattern, more + specifically a triangular lattice with horizontal rows, meaning the + triangles are pointing up and down. + + Args: + rows (int): Number of rows. + atoms_per_row (int): Number of atoms per row. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: A register with qubits placed in a triangular lattice. + """ + # Check rows + if rows < 1: + raise ValueError( + f"The number of rows (`rows` = {rows})" + " must be greater than or equal to 1." + ) + + # Check atoms per row + if atoms_per_row < 1: + raise ValueError( + "The number of atoms per row" + f" (`atoms_per_row` = {atoms_per_row})" + " must be greater than or equal to 1." + ) + + # Check spacing + if spacing <= 0.0: + raise ValueError( + f"Spacing between atoms (`spacing` = {spacing})" + " must be greater than 0." + ) + + coords = np.array( + [(x, y) for y in range(rows) for x in range(atoms_per_row)], + dtype=float, + ) + coords[:, 0] += 0.5 * np.mod(coords[:, 1], 2) + coords[:, 1] *= np.sqrt(3) / 2 + coords *= spacing + + return cls.from_coordinates(coords, center=True, prefix=prefix) + + @classmethod + def _hexagon_helper( + cls, + layers: int, + atoms_left: int, + spacing: float, + prefix: Optional[str] = None, + ) -> Register: + """Helper function for building hexagonal arrays. + + Args: + layers (int): Number of full layers around a central atom. + atoms_left (int): Number of atoms on the external layer. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: A register with qubits placed in a hexagonal layout + with extra atoms on the outermost layer if needed. + """ + # y coordinates of the top vertex of a triangle + crest_y = np.sqrt(3) / 2.0 + + # Coordinates of vertices + start_x = [-1.0, -0.5, 0.5, 1.0, 0.5, -0.5] + start_y = [0.0, crest_y, crest_y, 0, -crest_y, -crest_y] + + # Steps to place atoms, starting from a vertex + delta_x = [0.5, 1.0, 0.5, -0.5, -1.0, -0.5] + delta_y = [crest_y, 0.0, -crest_y, -crest_y, 0.0, crest_y] + + coords = np.array( + [ + ( + start_x[side] * layer + atom * delta_x[side], + start_y[side] * layer + atom * delta_y[side], + ) + for layer in range(1, layers + 1) + for side in range(6) + for atom in range(1, layer + 1) + ], + dtype=float, + ) + + if atoms_left > 0: + layer = layers + 1 + min_atoms_per_side = atoms_left // 6 + # Extra atoms after balancing all sides + atoms_left %= 6 + + # Order for placing left atoms + # Top-Left, Top-Right, Bottom (C3 symmetry)... + # ...Top, Bottom-Right, Bottom-Left (C6 symmetry) + sides_order = [0, 3, 1, 4, 2, 5] + + coords2 = np.array( + [ + ( + start_x[side] * layer + atom * delta_x[side], + start_y[side] * layer + atom * delta_y[side], + ) + for side in range(6) + for atom in range( + 1, + min_atoms_per_side + 2 + if atoms_left > sides_order[side] + else min_atoms_per_side + 1, + ) + ], + dtype=float, + ) + + coords = np.concatenate((coords, coords2)) + + coords *= spacing + coords = np.concatenate(([(0.0, 0.0)], coords)) + + return cls.from_coordinates(coords, center=False, prefix=prefix) + + @classmethod + def hexagon( + cls, layers: int, spacing: float = 4.0, prefix: Optional[str] = None + ) -> Register: + """Initializes the register with the qubits in a hexagonal layout. + + Args: + layers (int): Number of layers around a central atom. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: A register with qubits placed in a hexagonal layout. + """ + # Check layers + if layers < 1: + raise ValueError( + f"The number of layers (`layers` = {layers})" + " must be greater than or equal to 1." + ) + + # Check spacing + if spacing <= 0.0: + raise ValueError( + f"Spacing between atoms (`spacing` = {spacing})" + " must be greater than 0." + ) + + return cls._hexagon_helper(layers, 0, spacing, prefix) + + @classmethod + def max_connectivity( + cls, + n_qubits: int, + device: pulser.devices._device_datacls.Device, + spacing: float = None, + prefix: str = None, + ) -> Register: + """Initializes the register with maximum connectivity for a given device. + + In order to maximize connectivity, the basic pattern is the triangle. + Atoms are first arranged as layers of hexagons around a central atom. + Extra atoms are placed in such a manner that C3 and C6 rotational + symmetries are enforced as often as possible. + + Args: + n_qubits (int): Number of qubits. + device (Device): The device whose constraints must be obeyed. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + If omitted, the minimal distance for the device is used. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: A register with qubits placed for maximum connectivity. + """ + # Check device + if not isinstance(device, pulser.devices._device_datacls.Device): + raise TypeError( + "'device' must be of type 'Device'. Import a valid" + " device from 'pulser.devices'." + ) + + # Check number of qubits (1 or above) + if n_qubits < 1: + raise ValueError( + f"The number of qubits (`n_qubits` = {n_qubits})" + " must be greater than or equal to 1." + ) + + # Check number of qubits (less than the max number of atoms) + if n_qubits > device.max_atom_num: + raise ValueError( + f"The number of qubits (`n_qubits` = {n_qubits})" + " must be less than or equal to the maximum" + " number of atoms supported by this device" + f" ({device.max_atom_num})." + ) + + # Default spacing or check minimal distance + if spacing is None: + spacing = device.min_atom_distance + elif spacing < device.min_atom_distance: + raise ValueError( + f"Spacing between atoms (`spacing = `{spacing})" + " must be greater than or equal to the minimal" + " distance supported by this device" + f" ({device.min_atom_distance})." + ) + + if n_qubits < 7: + crest_y = np.sqrt(3) / 2.0 + hex_coords = np.array( + [ + (0.0, 0.0), + (-0.5, crest_y), + (0.5, crest_y), + (1.0, 0.0), + (0.5, -crest_y), + (-0.5, -crest_y), + ] + ) + return cls.from_coordinates( + spacing * hex_coords[:n_qubits], prefix=prefix, center=False + ) + + full_layers = int((-3.0 + np.sqrt(9 + 12 * (n_qubits - 1))) / 6.0) + atoms_left = n_qubits - 1 - (full_layers**2 + full_layers) * 3 + + return cls._hexagon_helper(full_layers, atoms_left, spacing, prefix) + + def rotate(self, degrees: float) -> None: + """Rotates the array around the origin by the given angle. + + Args: + degrees (float): The angle of rotation in degrees. + """ + theta = np.deg2rad(degrees) + rot = np.array( + [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]] + ) + self._coords = [rot @ v for v in self._coords] + + def _initialize_fig_axes( + self, + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> tuple[plt.figure.Figure, plt.axes.Axes]: + """Creates the Figure and Axes for drawing the register.""" + diffs = super()._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + big_side = max(diffs) + proportions = diffs / big_side + Ls = proportions * min( + big_side / 4, 10 + ) # Figsize is, at most, (10,10) + fig, axes = plt.subplots(figsize=Ls) + + return (fig, axes) + + def draw( + self, + with_labels: bool = True, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + fig_name: str = None, + kwargs_savefig: dict = {}, + ) -> None: + """Draws the entire register. + + Keyword Args: + with_labels(bool, default=True): If True, writes the qubit ID's + next to each qubit. + blockade_radius(float, default=None): The distance (in μm) between + atoms below the Rydberg blockade effect occurs. + draw_half_radius(bool, default=False): Whether or not to draw the + half the blockade radius surrounding each atoms. If `True`, + requires `blockade_radius` to be defined. + draw_graph(bool, default=True): Whether or not to draw the + interaction between atoms as edges in a graph. Will only draw + if the `blockade_radius` is defined. + fig_name(str, default=None): The name on which to save the figure. + If None the figure will not be saved. + kwargs_savefig(dict, default={}): Keywords arguments for + ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` + is ``None``. + + Note: + When drawing half the blockade radius, we say there is a blockade + effect between atoms whenever their respective circles overlap. + This representation is preferred over drawing the full Rydberg + radius because it helps in seeing the interactions between atoms. + """ + super()._draw_checks( + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) + pos = np.array(self._coords) + fig, ax = self._initialize_fig_axes( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + super()._draw_2D( + ax, + pos, + self._ids, + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) + if fig_name is not None: + plt.savefig(fig_name, **kwargs_savefig) + plt.show() + + def _to_dict(self) -> dict[str, Any]: + return super()._to_dict() diff --git a/pulser/register/register3d.py b/pulser/register/register3d.py new file mode 100644 index 000000000..04b5807b6 --- /dev/null +++ b/pulser/register/register3d.py @@ -0,0 +1,370 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Defines the configuration of an array of neutral atoms in 3D.""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any, Optional +from itertools import combinations + +import matplotlib.pyplot as plt +import numpy as np +from numpy.typing import ArrayLike +from scipy.spatial import KDTree + +from pulser.register.base_register import BaseRegister +from pulser.register.register import Register + + +class Register3D(BaseRegister): + """A 3D quantum register containing a set of qubits. + + Args: + qubits (dict): Dictionary with the qubit names as keys and their + position coordinates (in μm) as values + (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). + """ + + def __init__(self, qubits: Mapping[Any, ArrayLike]): + """Initializes a custom Register.""" + super().__init__(qubits) + coords = [np.array(v, dtype=float) for v in qubits.values()] + self._dim = coords[0].size + if any(c.shape != (self._dim,) for c in coords) or (self._dim != 3): + raise ValueError( + "All coordinates must be specified as vectors of size 3." + ) + self._coords = coords + + @classmethod + def cubic( + cls, side: int, spacing: float = 4.0, prefix: Optional[str] = None + ) -> Register3D: + """Initializes the register with the qubits in a cubic array. + + Args: + side (int): Side of the cube in number of qubits. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register3D : A 3D register with qubits placed in a cubic array. + """ + # Check side + if side < 1: + raise ValueError( + f"The number of atoms per side (`side` = {side})" + " must be greater than or equal to 1." + ) + + return cls.cuboid(side, side, side, spacing=spacing, prefix=prefix) + + @classmethod + def cuboid( + cls, + rows: int, + columns: int, + layers: int, + spacing: float = 4.0, + prefix: Optional[str] = None, + ) -> Register3D: + """Initializes the register with the qubits in a cuboid array. + + Args: + rows (int): Number of rows. + columns (int): Number of columns. + layers (int): Number of layers. + + Keyword args: + spacing(float): The distance between neighbouring qubits in μm. + prefix (str): The prefix for the qubit ids. If defined, each qubit + id starts with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) + + Returns: + Register3D : A 3D register with qubits placed in a cuboid array. + """ + # Check rows + if rows < 1: + raise ValueError( + f"The number of rows (`rows` = {rows})" + " must be greater than or equal to 1." + ) + + # Check columns + if columns < 1: + raise ValueError( + f"The number of columns (`columns` = {columns})" + " must be greater than or equal to 1." + ) + + # Check layers + if layers < 1: + raise ValueError( + f"The number of layers (`layers` = {layers})" + " must be greater than or equal to 1." + ) + + # Check spacing + if spacing <= 0.0: + raise ValueError( + f"Spacing between atoms (`spacing` = {spacing})" + " must be greater than 0." + ) + + coords = ( + np.array( + [ + (x, y, z) + for z in range(layers) + for y in range(rows) + for x in range(columns) + ], + dtype=float, + ) + * spacing + ) + + return cls.from_coordinates(coords, center=True, prefix=prefix) + + def to_2D(self, tol_width: float = 0.0) -> Register: + """Converts a Register3D into a Register (if possible). + + Args: + tol_width (float): The allowed transverse width of + the register to be projected. + + Returns: + Register: Returns a 2D register with the coordinates of the atoms + in a plane, if they are coplanar. + + Raises: + ValueError: If the atoms are not coplanar. + """ + coords = np.array(self._coords) + + barycenter = coords.sum(axis=0) / coords.shape[0] + # run SVD + u, s, vh = np.linalg.svd(coords - barycenter) + e_z = vh[2, :] + perp_extent = [e_z.dot(r) for r in coords] + width = np.ptp(perp_extent) + # A set of vector is coplanar if one of the Singular values is 0 + if width > tol_width: + raise ValueError( + f"Atoms are not coplanar (`width` = {width:#.2f} µm)" + ) + else: + e_x = vh[0, :] + e_y = vh[1, :] + coords_2D = np.array( + [np.array([e_x.dot(r), e_y.dot(r)]) for r in coords] + ) + return Register.from_coordinates(coords_2D, labels=self._ids) + + def _initialize_fig_axes_projection( + self, + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> tuple[plt.figure.Figure, plt.axes.Axes]: + """Creates the Figure and Axes for drawing the register projections.""" + diffs = super()._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + + proportions = [] + for (ix, iy) in combinations(np.arange(3), 2): + big_side = max(diffs[[ix, iy]]) + Ls = diffs[[ix, iy]] / big_side + Ls *= max( + min(big_side / 4, 10), 4 + ) # Figsize is, at most, (10,10), and, at least (4,*) or (*,4) + proportions.append(Ls) + + fig_height = np.max([Ls[1] for Ls in proportions]) + + max_width = 0 + for i, (width, height) in enumerate(proportions): + proportions[i] = (width * fig_height / height, fig_height) + max_width = max(max_width, proportions[i][0]) + widths = [max(Ls[0], max_width / 5) for Ls in proportions] + fig_width = min(np.sum(widths), fig_height * 4) + + rescaling = 20 / max(max(fig_width, fig_height), 20) + figsize = (rescaling * fig_width, rescaling * fig_height) + + fig, axes = plt.subplots( + ncols=3, + figsize=figsize, + gridspec_kw=dict(width_ratios=widths), + ) + + return (fig, axes) + + def draw( + self, + with_labels: bool = False, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + projection: bool = False, + fig_name: str = None, + kwargs_savefig: dict = {}, + ) -> None: + """Draws the entire register. + + Keyword Args: + with_labels(bool, default=True): If True, writes the qubit ID's + next to each qubit. + blockade_radius(float, default=None): The distance (in μm) between + atoms below the Rydberg blockade effect occurs. + draw_half_radius(bool, default=False): Whether or not to draw the + half the blockade radius surrounding each atoms. If `True`, + requires `blockade_radius` to be defined. + draw_graph(bool, default=True): Whether or not to draw the + interaction between atoms as edges in a graph. Will only draw + if the `blockade_radius` is defined. + projection(bool, default=False): Whether to draw a 2D projection + instead of a perspective view. + fig_name(str, default=None): The name on which to save the figure. + If None the figure will not be saved. + kwargs_savefig(dict, default={}): Keywords arguments for + ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` + is ``None``. + + Note: + When drawing half the blockade radius, we say there is a blockade + effect between atoms whenever their respective circles overlap. + This representation is preferred over drawing the full Rydberg + radius because it helps in seeing the interactions between atoms. + """ + super()._draw_checks( + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) + + pos = np.array(self._coords) + + if draw_graph and blockade_radius is not None: + epsilon = 1e-9 # Accounts for rounding errors + edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) + + if projection: + labels = "xyz" + fig, axes = self._initialize_fig_axes_projection( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + fig.tight_layout(w_pad=6.5) + + for ax, (ix, iy) in zip(axes, combinations(np.arange(3), 2)): + super()._draw_2D( + ax, + pos, + self._ids, + plane=( + ix, + iy, + ), + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) + ax.set_title( + "Projection onto\n the " + + labels[ix] + + labels[iy] + + "-plane" + ) + + else: + fig = plt.figure(figsize=2 * plt.figaspect(0.5)) + + if draw_graph and blockade_radius is not None: + bonds = {} + for i, j in edges: + xi, yi, zi = pos[i] + xj, yj, zj = pos[j] + bonds[(i, j)] = [[xi, xj], [yi, yj], [zi, zj]] + + for i in range(1, 3): + ax = fig.add_subplot( + 1, 2, i, projection="3d", azim=-60 * (-1) ** i, elev=15 + ) + + ax.scatter( + pos[:, 0], + pos[:, 1], + pos[:, 2], + s=30, + alpha=0.7, + c="darkgreen", + ) + + if with_labels: + for q, coords in zip(self._ids, self._coords): + ax.text( + coords[0], + coords[1], + coords[2], + q, + fontsize=12, + ha="left", + va="bottom", + ) + + if draw_half_radius and blockade_radius is not None: + mesh_num = 20 if len(self._ids) > 10 else 40 + for r in pos: + x0, y0, z0 = r + radius = blockade_radius / 2 + + # Strange behavior pf mypy using "imaginary slice step" + # u, v = np.pi * np.mgrid[0:2:50j, 0:1:50j] + + v, u = np.meshgrid( + np.arccos(np.linspace(-1, 1, num=mesh_num)), + np.linspace(0, 2 * np.pi, num=mesh_num), + ) + x = radius * np.cos(u) * np.sin(v) + x0 + y = radius * np.sin(u) * np.sin(v) + y0 + z = radius * np.cos(v) + z0 + # alpha controls opacity + ax.plot_surface(x, y, z, color="darkgreen", alpha=0.1) + + if draw_graph and blockade_radius is not None: + for x, y, z in bonds.values(): + ax.plot(x, y, z, linewidth=1.5, color="grey") + + ax.set_xlabel("x (µm)") + ax.set_ylabel("y (µm)") + ax.set_zlabel("z (µm)") + + if fig_name is not None: + plt.savefig(fig_name, **kwargs_savefig) + plt.show() + + def _to_dict(self) -> dict[str, Any]: + return super()._to_dict() diff --git a/pulser/sequence.py b/pulser/sequence.py index 690cd217e..78d09440d 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -38,7 +38,7 @@ from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, Variable from pulser.pulse import Pulse -from pulser.register import BaseRegister +from pulser.register.base_register import BaseRegister from pulser._seq_drawer import draw_sequence if version_info[:2] >= (3, 8): # pragma: no cover From 612cfd03ea3f2d1b43a0f4f681ac3eb73230cca2 Mon Sep 17 00:00:00 2001 From: Codoscope <14247215+Codoscope@users.noreply.github.com> Date: Mon, 7 Feb 2022 10:53:28 +0100 Subject: [PATCH 36/51] Ship typed hints to the library's users (#318) * Ship typed hints to the library's users * Fix comma syntax Co-authored-by: Codoscope --- pulser/py.typed | 0 setup.py | 2 ++ 2 files changed, 2 insertions(+) create mode 100644 pulser/py.typed diff --git a/pulser/py.typed b/pulser/py.typed new file mode 100644 index 000000000..e69de29bb diff --git a/setup.py b/setup.py index 378950d0b..33b94be63 100644 --- a/setup.py +++ b/setup.py @@ -33,6 +33,7 @@ ], }, packages=find_packages(), + package_data={"pulser": ["py.typed"]}, include_package_data=True, description="A pulse-level composer for neutral-atom quantum devices.", long_description=open("README.md").read(), @@ -45,4 +46,5 @@ "Programming Language :: Python :: 3", ], url="https://github.com/pasqal-io/Pulser", + zip_safe=False, ) From 7127a112613d77589312cce2184da7cb84de465d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Mon, 7 Feb 2022 18:25:05 +0100 Subject: [PATCH 37/51] Initial output modulation features (#312) * Adding modulation features to Channel * Adding "draw_modulation" option to "draw_sequence". * Adding method to calculate the minimal modulation buffers * Adding modulation information to Waveform and Pulse * Adding drawing of modulated waveform to Waveform.draw() * Making `mod_bandwidth` definition optional * Conditioning sequence creation on the pulse fall time * Type hinting * Preliminary definition of the IroiseMVP * Adjusting the IroiseMVP detuning range * Bug fixes + Accounting for fall time in `align` * Adding option to keep the ends constant on modulated samples * Preemptive validity checks on delay durations * Mypy fixes * Fix draw of interpolation points on waveform with output channel * Adding tests for modulation on Waveforms * Finishing unit tests * Fixing typos: 'bandwith' -> 'bandwidth' * Add option to consider fall time in 'get_duration()' and reusing method * Tests for fixed_retarget_t and phase_jump_time * Minor fixes * Adjust waveform drawing to show rise and fall * Moving the IroiseMVP definition to another branch * Fixing `lru_cache` on Python 3.7 * Black upgrade * Extra details on Pulse.fall_time() Co-authored-by: Codoscope <14247215+Codoscope@users.noreply.github.com> * Addressing review comments * Review suggestions * Changing condition for showing rise and fall in waveform drawing Co-authored-by: Codoscope <14247215+Codoscope@users.noreply.github.com> --- pulser/_seq_drawer.py | 71 ++++++-- pulser/channels.py | 106 ++++++++++++ pulser/devices/__init__.py | 6 +- pulser/pulse.py | 10 ++ pulser/sequence.py | 155 ++++++++++++++---- pulser/tests/test_channels.py | 39 ++++- pulser/tests/test_sequence.py | 104 ++++++++++++ pulser/tests/test_waveforms.py | 29 +++- pulser/waveforms.py | 137 ++++++++++++++-- .../Using QAOA to solve a MIS problem.ipynb | 2 +- 10 files changed, 586 insertions(+), 73 deletions(-) diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index c6f6a2f37..d65d52614 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -111,6 +111,8 @@ def draw_sequence( draw_interp_pts: bool = True, draw_phase_shifts: bool = False, draw_register: bool = False, + draw_input: bool = True, + draw_modulation: bool = False, ) -> tuple[Figure, Figure]: """Draws the entire sequence. @@ -129,6 +131,11 @@ def draw_sequence( draw_register (bool): Whether to draw the register before the pulse sequence, with a visual indication (square halo) around the qubits masked by the SLM, defaults to False. + draw_input(bool): Draws the programmed pulses on the channels, defaults + to True. + draw_modulation(bool): Draws the expected channel output, defaults to + False. If the channel does not have a defined 'mod_bandwidth', this + is skipped unless 'draw_input=False'. """ def phase_str(phi: float) -> str: @@ -256,9 +263,22 @@ def phase_str(phi: float) -> str: # Compare pulse with an interpolated pulse with 100 times more samples teff = np.arange(0, max(solver_time), delta_t / 100) + # Make sure the time axis of all channels are aligned + final_t = total_duration / time_scale + if draw_modulation: + for ch, ch_obj in seq._channels.items(): + final_t = max( + final_t, + (seq.get_duration(ch) + 2 * ch_obj.rise_time) / time_scale, + ) + t_min = -final_t * 0.03 + t_max = final_t * 1.05 + for ch, (a, b) in ch_axes.items(): - basis = seq._channels[ch].basis - t = np.array(data[ch]["time"]) / time_scale + ch_obj = seq._channels[ch] + basis = ch_obj.basis + times = np.array(data[ch]["time"]) + t = times / time_scale ya = data[ch]["amp"] yb = data[ch]["detuning"] if sampling_rate: @@ -276,8 +296,17 @@ def phase_str(phi: float) -> str: yaeff = cs_amp(teff) ybeff = cs_detuning(teff) - t_min = -t[-1] * 0.03 - t_max = t[-1] * 1.05 + draw_output = draw_modulation and ( + ch_obj.mod_bandwidth or not draw_input + ) + if draw_output: + t_diffs = np.diff(times) + input_a = np.repeat(ya[1:], t_diffs) + input_b = np.repeat(yb[1:], t_diffs) + end_index = int(final_t * time_scale) + ya_mod = ch_obj.modulate(input_a)[:end_index] + yb_mod = ch_obj.modulate(input_b, keep_ends=True)[:end_index] + a.set_xlim(t_min, t_max) b.set_xlim(t_min, t_max) @@ -294,16 +323,26 @@ def phase_str(phi: float) -> str: det_bottom = det_min - det_range * 0.05 b.set_ylim(det_bottom, det_top) - a.plot(t, ya, color="darkgreen", linewidth=0.8) - b.plot(t, yb, color="indigo", linewidth=0.8) + if draw_input: + a.plot(t, ya, color="darkgreen", linewidth=0.8) + b.plot(t, yb, color="indigo", linewidth=0.8) if sampling_rate: a.plot(teff, yaeff, color="darkgreen", linewidth=0.8) b.plot(teff, ybeff, color="indigo", linewidth=0.8, ls="-") a.fill_between(teff, 0, yaeff, color="darkgreen", alpha=0.3) b.fill_between(teff, 0, ybeff, color="indigo", alpha=0.3) - else: + elif draw_input: a.fill_between(t, 0, ya, color="darkgreen", alpha=0.3) b.fill_between(t, 0, yb, color="indigo", alpha=0.3) + if draw_output: + a.plot(ya_mod, color="darkred", linewidth=0.8) + b.plot(yb_mod, color="gold", linewidth=0.8) + a.fill_between( + np.arange(ya_mod.size), 0, ya_mod, color="darkred", alpha=0.3 + ) + b.fill_between( + np.arange(yb_mod.size), 0, yb_mod, color="gold", alpha=0.3 + ) a.set_ylabel(r"$\Omega$ (rad/µs)", fontsize=14, labelpad=10) b.set_ylabel(r"$\delta$ (rad/µs)", fontsize=14) @@ -367,7 +406,7 @@ def phase_str(phi: float) -> str: tgt_txt_y = max_amp * 1.1 - 0.25 * (len(targets) - 1) tgt_str = "\n".join(tgt_strs) if coords == "initial": - x = t_min + t[-1] * 0.005 + x = t_min + final_t * 0.005 target_regions.append([0, targets]) if seq._channels[ch].addressing == "Global": a.text( @@ -410,7 +449,7 @@ def phase_str(phi: float) -> str: a.axvspan(ti, tf, alpha=0.4, color="grey", hatch="//") b.axvspan(ti, tf, alpha=0.4, color="grey", hatch="//") a.text( - tf + t[-1] * 5e-3, + tf + final_t * 5e-3, tgt_txt_y, tgt_str, ha="left", @@ -420,7 +459,7 @@ def phase_str(phi: float) -> str: if phase and draw_phase_shifts: msg = r"$\phi=$" + phase_str(phase) wrd_len = len(max(tgt_strs, key=len)) - x = tf + t[-1] * 0.01 * (wrd_len + 1) + x = tf + final_t * 0.01 * (wrd_len + 1) a.text( x, max_amp * 1.1, @@ -431,7 +470,7 @@ def phase_str(phi: float) -> str: ) # Terminate the last open regions if target_regions: - target_regions[-1].append(t[-1]) + target_regions[-1].append(final_t) for start, targets_, end in ( target_regions if draw_phase_shifts else [] ): @@ -449,7 +488,7 @@ def phase_str(phi: float) -> str: b.axvline(t_, **conf) msg = "\u27F2 " + phase_str(delta) a.text( - t_ - t[-1] * 8e-3, + t_ - final_t * 8e-3, max_amp * 1.1, msg, ha="right", @@ -463,7 +502,7 @@ def phase_str(phi: float) -> str: a.axvspan(0, tf_m, color="black", alpha=0.1, zorder=-100) b.axvspan(0, tf_m, color="black", alpha=0.1, zorder=-100) tgt_strs = [str(q) for q in seq._slm_mask_targets] - tgt_txt_x = t[-1] * 0.005 + tgt_txt_x = final_t * 0.005 tgt_txt_y = b.get_ylim()[0] tgt_str = "\n".join(tgt_strs) b.text( @@ -478,7 +517,7 @@ def phase_str(phi: float) -> str: if "measurement" in data[ch]: msg = f"Basis: {data[ch]['measurement']}" b.text( - t[-1] * 1.025, + final_t * 1.025, det_top, msg, ha="center", @@ -487,8 +526,8 @@ def phase_str(phi: float) -> str: color="white", rotation=90, ) - a.axvspan(t[-1], t_max, color="midnightblue", alpha=1) - b.axvspan(t[-1], t_max, color="midnightblue", alpha=1) + a.axvspan(final_t, t_max, color="midnightblue", alpha=1) + b.axvspan(final_t, t_max, color="midnightblue", alpha=1) a.axhline(0, xmax=0.95, linestyle="-", linewidth=0.5, color="grey") b.axhline(0, xmax=0.95, linestyle=":", linewidth=0.5, color="grey") else: diff --git a/pulser/channels.py b/pulser/channels.py index 694779e0c..89015e9f6 100644 --- a/pulser/channels.py +++ b/pulser/channels.py @@ -19,9 +19,17 @@ from typing import cast, ClassVar, Optional import warnings +import numpy as np +from numpy.typing import ArrayLike +from scipy.fft import fft, ifft, fftfreq + # Warnings of adjusted waveform duration appear just once warnings.filterwarnings("once", "A duration of") +# Conversion factor from modulation bandwith to rise time +# For more info, see https://tinyurl.com/bdeumc8k +MODBW_TO_TR = 0.48 + @dataclass(init=True, repr=False, frozen=True) class Channel: @@ -48,6 +56,7 @@ class Channel: clock cycle. min_duration: The shortest duration an instruction can take. max_duration: The longest duration an instruction can take. + mod_bandwidth: The modulation bandwidth at -3dB (50% redution), in MHz. Example: To create a channel targeting the 'ground-rydberg' transition globally, @@ -66,6 +75,19 @@ class Channel: clock_period: int = 4 # ns min_duration: int = 16 # ns max_duration: int = 67108864 # ns + mod_bandwidth: Optional[float] = None # MHz + + @property + def rise_time(self) -> int: + """The rise time (in ns). + + Defined as the time taken to go from 10% to 90% output in response to + a step change in the input. + """ + if self.mod_bandwidth: + return int(MODBW_TO_TR / self.mod_bandwidth * 1e3) + else: + return 0 @classmethod def Local( @@ -161,6 +183,88 @@ def validate_duration(self, duration: int) -> int: ) return _duration + def modulate( + self, input_samples: np.ndarray, keep_ends: bool = False + ) -> np.ndarray: + """Modulates the input according to the channel's modulation bandwidth. + + Args: + input_samples (np.ndarray): The samples to modulate. + keep_ends (bool): Assume the end values of the samples were kept + constant (i.e. there is no ramp from zero on the ends). + + Returns: + np.ndarray: The modulated output signal. + """ + if not self.mod_bandwidth: + warnings.warn( + f"No modulation bandwidth defined for channel '{self}'," + " 'Channel.modulate()' returns the 'input_samples' unchanged.", + stacklevel=2, + ) + return input_samples + + # The cutoff frequency (fc) and the modulation transfer function + # are defined in https://tinyurl.com/bdeumc8k + fc = self.mod_bandwidth * 1e-3 / np.sqrt(np.log(2)) + if keep_ends: + samples = np.pad(input_samples, 2 * self.rise_time, mode="edge") + else: + samples = np.pad(input_samples, self.rise_time) + freqs = fftfreq(samples.size) + modulation = np.exp(-(freqs**2) / fc**2) + mod_samples = ifft(fft(samples) * modulation).real + if keep_ends: + # Cut off the extra ends + return cast( + np.ndarray, mod_samples[self.rise_time : -self.rise_time] + ) + return cast(np.ndarray, mod_samples) + + def calc_modulation_buffer( + self, + input_samples: ArrayLike, + mod_samples: ArrayLike, + max_allowed_diff: float = 1e-2, + ) -> tuple[int, int]: + """Calculates the minimal buffers needed around a modulated waveform. + + Args: + input_samples (ArrayLike): The input samples. + mod_samples (ArrayLike): The modulated samples. Must be of size + ``len(input_samples) + 2 * self.rise_time``. + max_allowed_diff (float): The maximum allowed difference between + the input and modulated samples at the end points. + + Returns: + tuple[int, int]: The minimum buffer times at the start and end of + the samples, in ns. + """ + if not self.mod_bandwidth: + raise TypeError( + f"The channel {self} doesn't have a modulation bandwidth." + ) + + tr = self.rise_time + samples = np.pad(input_samples, tr) + diffs = np.abs(samples - mod_samples) <= max_allowed_diff + try: + # Finds the last index in the start buffer that's below the max + # allowed diff. Considers that the waveform could start at the next + # indice (hence the -1, since we are subtracting from tr) + start = tr - np.argwhere(diffs[:tr])[-1][0] - 1 + except IndexError: + start = tr + try: + # Finds the first index in the end buffer that's below the max + # allowed diff. The index value found matches the minimum length + # for this end buffer. + end = np.argwhere(diffs[-tr:])[0][0] + except IndexError: + end = tr + + return start, end + def __repr__(self) -> str: config = ( f".{self.addressing}(Max Absolute Detuning: " @@ -175,6 +279,8 @@ def __repr__(self) -> str: if cast(int, self.max_targets) > 1: config += f", Max targets: {self.max_targets}" config += f", Basis: '{self.basis}'" + if self.mod_bandwidth: + config += f", Modulation Bandwidth: {self.mod_bandwidth} MHz" return self.name + config + ")" diff --git a/pulser/devices/__init__.py b/pulser/devices/__init__.py index 40c88be5a..0660fdcfc 100644 --- a/pulser/devices/__init__.py +++ b/pulser/devices/__init__.py @@ -13,12 +13,12 @@ # limitations under the License. """Valid devices for Pulser Sequence execution.""" -from pulser.devices._devices import ( - Chadoq2, -) +from pulser.devices._devices import Chadoq2 from pulser.devices._mock_device import MockDevice +from pulser.devices._device_datacls import Device + # Registers which devices can be used to avoid definition of custom devices _mock_devices = (MockDevice,) _valid_devices = (Chadoq2,) diff --git a/pulser/pulse.py b/pulser/pulse.py index db28249f6..c3a2a75e7 100644 --- a/pulser/pulse.py +++ b/pulser/pulse.py @@ -23,6 +23,7 @@ import matplotlib.pyplot as plt import numpy as np +from pulser.channels import Channel from pulser.parametrized import Parametrized, ParamObj from pulser.parametrized.decorators import parametrize from pulser.waveforms import Waveform, ConstantWaveform @@ -187,6 +188,15 @@ def draw(self) -> None: fig.tight_layout() plt.show() + def fall_time(self, channel: Channel) -> int: + """Calculates the extra time needed to ramp down to zero.""" + aligned_start_extra_time = channel.rise_time + end_extra_time = max( + self.amplitude.modulation_buffers(channel)[1], + self.detuning.modulation_buffers(channel)[1], + ) + return aligned_start_extra_time + end_extra_time + def _to_dict(self) -> dict[str, Any]: return obj_to_dict( self, diff --git a/pulser/sequence.py b/pulser/sequence.py index 78d09440d..cdd85f12a 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -282,27 +282,50 @@ def is_parametrized(self) -> bool: return not self._building @_screen - def get_duration(self, channel: Optional[str] = None) -> int: + def get_duration( + self, channel: Optional[str] = None, include_fall_time: bool = False + ) -> int: """Returns the current duration of a channel or the whole sequence. Keyword Args: channel (Optional[str]): A specific channel to return the duration of. If left as None, it will return the duration of the whole sequence. + include_fall_time (bool): Whether to include in the duration the + extra time needed by the last pulse to finish, if there is + modulation. Returns: int: The duration of the channel or sequence, in ns. """ if channel is None: - durations = [ - self._last(ch).tf - for ch in self._schedule - if self._schedule[ch] - ] - return 0 if not durations else max(durations) + channels = tuple(self._channels.keys()) + if not channels: + return 0 + else: + self._validate_channel(channel) + channels = (channel,) + last_ts = {} + for id in channels: + this_chobj = self._channels[id] + temp_tf = 0 + for i, op in enumerate(self._schedule[id][::-1]): + if i == 0: + # Start with the last slot found + temp_tf = op.tf + if not include_fall_time: + break + if isinstance(op.type, Pulse): + temp_tf = max( + temp_tf, op.tf + op.type.fall_time(this_chobj) + ) + break + elif temp_tf - op.tf >= 2 * this_chobj.rise_time: + # No pulse behind 'op' with a long enough fall time + break + last_ts[id] = temp_tf - self._validate_channel(channel) - return self._last(channel).tf if self._schedule[channel] else 0 + return max(last_ts.values()) @_screen def current_phase_ref( @@ -652,13 +675,20 @@ def add( for ch, seq in self._schedule.items(): if ch == channel: continue + this_chobj = self._channels[ch] for op in self._schedule[ch][::-1]: - if op.tf <= current_max_t: - break if not isinstance(op.type, Pulse): - continue - if op.targets & last.targets or protocol == "wait-for-all": - current_max_t = op.tf + if op.tf + 2 * this_chobj.rise_time <= current_max_t: + # No pulse behind 'op' needing a delay + break + elif ( + op.tf + op.type.fall_time(this_chobj) <= current_max_t + ): + break + elif ( + op.targets & last.targets or protocol == "wait-for-all" + ): + current_max_t = op.tf + op.type.fall_time(this_chobj) break delay_duration = current_max_t - t0 @@ -674,8 +704,13 @@ def add( break if delay_duration > 0: - # Delay must not be shorter than the min duration for this channel - delay_duration = max(delay_duration, channel_obj.min_duration) + # Delay must not be shorter than the min duration of this channel + # and a multiple of the clock period (forced by validate_duration) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + delay_duration = channel_obj.validate_duration( + max(delay_duration, channel_obj.min_duration) + ) self._delay(delay_duration, channel) ti = t0 + delay_duration @@ -683,9 +718,10 @@ def add( self._add_to_schedule(channel, _TimeSlot(pulse, ti, tf, last.targets)) + true_finish = tf + pulse.fall_time(channel_obj) for qubit in last.targets: - if self._last_used[basis][qubit] < tf: - self._last_used[basis][qubit] = tf + if self._last_used[basis][qubit] < true_finish: + self._last_used[basis][qubit] = true_finish if pulse.post_phase_shift: self._phase_shift( @@ -834,13 +870,23 @@ def align(self, *channels: Union[str, Parametrized]) -> None: if self.is_parametrized(): return - last_ts = {id: self._last(cast(str, id)).tf for id in channels} + channels = cast(Tuple[str], channels) + last_ts = { + id: self.get_duration(id, include_fall_time=True) + for id in channels + } tf = max(last_ts.values()) for id in channels: delta = tf - last_ts[id] if delta > 0: - self._delay(delta, cast(str, id)) + channel_obj = self._channels[id] + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + delta = channel_obj.validate_duration( + max(delta, channel_obj.min_duration) + ) + self._delay(delta, id) def build(self, **vars: Union[ArrayLike, float, int, str]) -> Sequence: """Builds a sequence from the programmed instructions. @@ -957,6 +1003,7 @@ def deserialize(obj: str, **kwargs: Any) -> Sequence: @_screen def draw( self, + mode: str = "input+output", draw_phase_area: bool = False, draw_interp_pts: bool = True, draw_phase_shifts: bool = False, @@ -967,11 +1014,18 @@ def draw( """Draws the sequence in its current state. Keyword Args: + mode (str, default="input+output"): The curves to draw. 'input' + draws only the programmed curves, 'output' the excepted curves + after modulation. 'input+output' will draw both curves except + for channels without a defined modulation bandwidth, in which + case only the input is drawn. draw_phase_area (bool): Whether phase and area values need to be - shown as text on the plot, defaults to False. + shown as text on the plot, defaults to False. Doesn't work in + 'output' mode. draw_interp_pts (bool): When the sequence has pulses with waveforms of type InterpolatedWaveform, draws the points of interpolation - on top of the respective waveforms (defaults to True). + on top of the respective input waveforms (defaults to True). + Doesn't work in 'output' mode. draw_phase_shifts (bool): Whether phase shift and reference information should be added to the plot, defaults to False. draw_register (bool): Whether to draw the register before the pulse @@ -991,12 +1045,34 @@ def draw( Simulation.draw(): Draws the provided sequence and the one used by the solver. """ + valid_modes = ("input", "output", "input+output") + if mode not in valid_modes: + raise ValueError( + f"'mode' must be one of {valid_modes}, not '{mode}'." + ) + if mode == "output": + if draw_phase_area: + warnings.warn( + "'draw_phase_area' doesn't work in 'output' mode, so it " + "will default to 'False'.", + stacklevel=2, + ) + draw_phase_area = False + if draw_interp_pts: + warnings.warn( + "'draw_interp_pts' doesn't work in 'output' mode, so it " + "will default to 'False'.", + stacklevel=2, + ) + draw_interp_pts = False fig_reg, fig = draw_sequence( self, draw_phase_area=draw_phase_area, draw_interp_pts=draw_interp_pts, draw_phase_shifts=draw_phase_shifts, draw_register=draw_register, + draw_input="input" in mode, + draw_modulation="output" in mode, ) if fig_name is not None and draw_register: name, ext = os.path.splitext(fig_name) @@ -1010,7 +1086,7 @@ def _target( self, qubits: Union[Iterable[QubitId], QubitId], channel: str ) -> None: self._validate_channel(channel) - + channel_obj = self._channels[channel] try: qubits_set = ( set(cast(Iterable, qubits)) @@ -1020,12 +1096,12 @@ def _target( except TypeError: qubits_set = {qubits} - if self._channels[channel].addressing != "Local": + if channel_obj.addressing != "Local": raise ValueError("Can only choose target of 'Local' channels.") - elif len(qubits_set) > cast(int, self._channels[channel].max_targets): + elif len(qubits_set) > cast(int, channel_obj.max_targets): raise ValueError( - "This channel can target at most " - f"{self._channels[channel].max_targets} qubits at a time" + f"This channel can target at most {channel_obj.max_targets} " + "qubits at a time." ) if self.is_parametrized(): @@ -1039,7 +1115,7 @@ def _target( elif not qubits_set.issubset(self._qids): raise ValueError("All given qubits must belong to the register.") - basis = self._channels[channel].basis + basis = channel_obj.basis phase_refs = {self._phase_ref[basis][q].last_phase for q in qubits_set} if len(phase_refs) != 1: raise ValueError( @@ -1048,22 +1124,33 @@ def _target( ) try: + fall_time = self.get_duration( + channel, include_fall_time=True + ) - self.get_duration(channel) + if fall_time > 0: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + self.delay( + max(fall_time, channel_obj.min_duration), + channel, + ) + last = self._last(channel) if last.targets == qubits_set: return ti = last.tf - retarget = cast(int, self._channels[channel].min_retarget_interval) + retarget = cast(int, channel_obj.min_retarget_interval) elapsed = ti - self._last_target[channel] delta = cast(int, np.clip(retarget - elapsed, 0, retarget)) + if channel_obj.fixed_retarget_t: + delta = max(delta, channel_obj.fixed_retarget_t) if delta != 0: with warnings.catch_warnings(): warnings.simplefilter("ignore") - delta = self._channels[channel].validate_duration( - 16 if delta < 16 else delta + delta = channel_obj.validate_duration( + max(delta, channel_obj.min_duration) ) - tf = ti + max( - delta, cast(int, self._channels[channel].fixed_retarget_t) - ) + tf = ti + delta except ValueError: ti = -1 diff --git a/pulser/tests/test_channels.py b/pulser/tests/test_channels.py index ce56cf1e9..4c25202df 100644 --- a/pulser/tests/test_channels.py +++ b/pulser/tests/test_channels.py @@ -12,10 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. +import numpy as np import pytest import pulser from pulser.channels import Raman, Rydberg +from pulser.waveforms import ConstantWaveform, BlackmanWaveform def test_device_channels(): @@ -64,10 +66,43 @@ def test_repr(): ) assert raman.__str__() == r1 - ryd = Rydberg.Global(50, 2.5, phase_jump_time=300) + ryd = Rydberg.Global(50, 2.5, phase_jump_time=300, mod_bandwidth=4) r2 = ( "Rydberg.Global(Max Absolute Detuning: 50 rad/µs, " "Max Amplitude: 2.5 rad/µs, Phase Jump Time: 300 ns, " - "Basis: 'ground-rydberg')" + "Basis: 'ground-rydberg', Modulation Bandwidth: 4 MHz)" ) assert ryd.__str__() == r2 + + +def test_modulation(): + rydberg_global = Rydberg.Global(2 * np.pi * 20, 2 * np.pi * 2.5) + + raman_local = Raman.Local( + 2 * np.pi * 20, + 2 * np.pi * 10, + mod_bandwidth=4, # MHz + ) + + wf = ConstantWaveform(100, 1) + assert rydberg_global.mod_bandwidth is None + with pytest.warns(UserWarning, match="No modulation bandwidth defined"): + out_samples = rydberg_global.modulate(wf.samples) + assert np.all(out_samples == wf.samples) + + with pytest.raises(TypeError, match="doesn't have a modulation bandwidth"): + rydberg_global.calc_modulation_buffer(wf.samples, out_samples) + + out_ = raman_local.modulate(wf.samples) + tr = raman_local.rise_time + assert len(out_) == wf.duration + 2 * tr + assert raman_local.calc_modulation_buffer(wf.samples, out_) == (tr, tr) + + wf2 = BlackmanWaveform(800, np.pi) + side_buffer_len = 45 + out_ = raman_local.modulate(wf2.samples) + assert len(out_) == wf2.duration + 2 * tr # modulate() does not truncate + assert raman_local.calc_modulation_buffer(wf2.samples, out_) == ( + side_buffer_len, + side_buffer_len, + ) diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index a3d0bfadc..1899b1ef3 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -19,6 +19,7 @@ import pulser from pulser import Sequence, Pulse, Register, Register3D +from pulser.channels import Rydberg, Raman from pulser.devices import Chadoq2, MockDevice from pulser.devices._device_datacls import Device from pulser.sequence import _TimeSlot @@ -554,3 +555,106 @@ def test_draw_register(): seq3d.config_slm_mask([6, 15]) with patch("matplotlib.pyplot.show"): seq3d.draw(draw_register=True) + + +def test_hardware_constraints(): + rydberg_global = Rydberg.Global( + 2 * np.pi * 20, + 2 * np.pi * 2.5, + phase_jump_time=120, # ns + mod_bandwidth=4, # MHz + ) + + raman_local = Raman.Local( + 2 * np.pi * 20, + 2 * np.pi * 10, + phase_jump_time=120, # ns + fixed_retarget_t=200, # ns + mod_bandwidth=7, # MHz + ) + + ConstrainedChadoq2 = Device( + name="ConstrainedChadoq2", + dimensions=2, + rydberg_level=70, + max_atom_num=100, + max_radial_distance=50, + min_atom_distance=4, + _channels=( + ("rydberg_global", rydberg_global), + ("raman_local", raman_local), + ), + ) + with pytest.warns( + UserWarning, match="should be imported from 'pulser.devices'" + ): + seq = Sequence(reg, ConstrainedChadoq2) + seq.declare_channel("ch0", "rydberg_global") + seq.declare_channel("ch1", "raman_local", initial_target="q1") + + const_pls = Pulse.ConstantPulse(100, 1, 0, np.pi) + seq.add(const_pls, "ch0") + black_wf = BlackmanWaveform(500, np.pi) + black_pls = Pulse.ConstantDetuning(black_wf, 0, 0) + seq.add(black_pls, "ch1") + blackman_slot = seq._last("ch1") + # The pulse accounts for the modulation buffer + assert ( + blackman_slot.ti == const_pls.duration + rydberg_global.rise_time * 2 + ) + seq.target("q0", "ch1") + target_slot = seq._last("ch1") + fall_time = black_pls.fall_time(raman_local) + assert ( + fall_time + == raman_local.rise_time + black_wf.modulation_buffers(raman_local)[1] + ) + fall_time += ( + raman_local.clock_period - fall_time % raman_local.clock_period + ) + assert target_slot.ti == blackman_slot.tf + fall_time + assert target_slot.tf == target_slot.ti + raman_local.fixed_retarget_t + + assert raman_local.min_retarget_interval > raman_local.fixed_retarget_t + seq.target("q2", "ch1") + assert ( + seq.get_duration("ch1") + == target_slot.tf + raman_local.min_retarget_interval + ) + + # Check for phase jump buffer + seq.add(black_pls, "ch0") # Phase = 0 + tf_ = seq.get_duration("ch0") + mid_delay = 40 + seq.delay(mid_delay, "ch0") + seq.add(const_pls, "ch0") # Phase = π + assert seq._last("ch0").ti - tf_ == rydberg_global.phase_jump_time + added_delay_slot = seq._schedule["ch0"][-2] + assert added_delay_slot.type == "delay" + assert ( + added_delay_slot.tf - added_delay_slot.ti + == rydberg_global.phase_jump_time - mid_delay + ) + + tf_ = seq.get_duration("ch0") + seq.align("ch0", "ch1") + fall_time = const_pls.fall_time(rydberg_global) + assert seq.get_duration() == tf_ + fall_time + + with pytest.raises(ValueError, match="'mode' must be one of"): + seq.draw(mode="all") + + with patch("matplotlib.pyplot.show"): + with pytest.warns( + UserWarning, + match="'draw_phase_area' doesn't work in 'output' mode", + ): + seq.draw( + mode="output", draw_interp_pts=False, draw_phase_area=True + ) + with pytest.warns( + UserWarning, + match="'draw_interp_pts' doesn't work in 'output' mode", + ): + seq.draw(mode="output") + seq.draw(mode="input+output") diff --git a/pulser/tests/test_waveforms.py b/pulser/tests/test_waveforms.py index c0487362b..fa10d51b3 100644 --- a/pulser/tests/test_waveforms.py +++ b/pulser/tests/test_waveforms.py @@ -21,6 +21,7 @@ from scipy.interpolate import interp1d, PchipInterpolator from pulser.json.coders import PulserEncoder, PulserDecoder +from pulser.channels import Rydberg from pulser.parametrized import Variable, ParamObj from pulser.waveforms import ( ConstantWaveform, @@ -98,10 +99,16 @@ def test_integral(): def test_draw(): + rydberg_global = Rydberg.Global( + 2 * np.pi * 20, + 2 * np.pi * 2.5, + phase_jump_time=120, # ns + mod_bandwidth=4, # MHz + ) with patch("matplotlib.pyplot.show"): composite.draw() - blackman.draw() - interp.draw() + blackman.draw(output_channel=rydberg_global) + interp.draw(output_channel=rydberg_global) def test_eq(): @@ -405,3 +412,21 @@ def test_get_item(): assert wf[duration * 2 :].size == 0 assert wf[duration * 2 : duration * 3].size == 0 assert wf[-duration * 3 : -duration * 2].size == 0 + + +def test_modulation(): + rydberg_global = Rydberg.Global( + 2 * np.pi * 20, + 2 * np.pi * 2.5, + phase_jump_time=120, # ns + mod_bandwidth=4, # MHz + ) + mod_samples = constant.modulated_samples(rydberg_global) + assert np.all(mod_samples == rydberg_global.modulate(constant.samples)) + assert constant.modulation_buffers(rydberg_global) == ( + rydberg_global.rise_time, + rydberg_global.rise_time, + ) + assert len(mod_samples) == constant.duration + 2 * rydberg_global.rise_time + assert np.isclose(np.sum(mod_samples) * 1e-3, constant.integral) + assert max(np.abs(mod_samples)) < np.abs(constant[0]) diff --git a/pulser/waveforms.py b/pulser/waveforms.py index b21ce7759..7daddbf0a 100644 --- a/pulser/waveforms.py +++ b/pulser/waveforms.py @@ -31,6 +31,7 @@ from numpy.typing import ArrayLike import scipy.interpolate as interpolate +from pulser.channels import Channel from pulser.parametrized import Parametrized, ParamObj from pulser.parametrized.decorators import parametrize from pulser.json.utils import obj_to_dict @@ -123,11 +124,28 @@ def integral(self) -> float: """Integral of the waveform (time in ns, value in rad/µs).""" return float(np.sum(self.samples)) * 1e-3 # ns * rad/µs = 1e-3 - def draw(self) -> None: - """Draws the waveform.""" - fig, ax = plt.subplots() - self._plot(ax, "rad/µs") + def draw(self, output_channel: Optional[Channel] = None) -> None: + """Draws the waveform. + Args: + output_channel: The output channel. If given, will draw the + modulated waveform on top of the input one. + """ + fig, ax = plt.subplots() + if not output_channel: + self._plot(ax, "rad/µs") + else: + self._plot( + ax, + "rad/µs", + label="Input", + start_t=self.modulation_buffers(output_channel)[0], + ) + self._plot( + ax, + channel=output_channel, + label="Output", + ) plt.show() def change_duration(self, new_duration: int) -> Waveform: @@ -141,6 +159,56 @@ def change_duration(self, new_duration: int) -> Waveform: " modifications to its duration." ) + def modulated_samples(self, channel: Channel) -> np.ndarray: + """The waveform samples as output of a given channel. + + This duration is adjusted according to the minimal buffer times. + + Args: + channel (Channel): The channel modulating the waveform. + + Returns: + numpy.ndarray: The array of samples after modulation. + """ + start, end = self.modulation_buffers(channel) + mod_samples = self._modulated_samples(channel) + tr = channel.rise_time + trim = slice(tr - start, len(mod_samples) - tr + end) + return cast(np.ndarray, mod_samples[trim]) + + @functools.lru_cache() + def modulation_buffers(self, channel: Channel) -> tuple[int, int]: + """The minimal buffers needed around a modulated waveform. + + Args: + channel (Channel): The channel modulating the waveform. + + Returns: + tuple[int, int]: The minimum buffer times at the start and end of + the samples, in ns. + """ + if not channel.mod_bandwidth: + return 0, 0 + + return channel.calc_modulation_buffer( + self._samples, self._modulated_samples(channel) + ) + + @functools.lru_cache() + def _modulated_samples(self, channel: Channel) -> np.ndarray: + """The waveform samples as output of a given channel. + + This is not adjusted to the minimal buffer times. Use + ``Waveform.modulated_samples()`` to get the output already truncated. + + Args: + channel (Channel): The channel modulating the waveform. + + Returns: + numpy.ndarray: The array of samples after modulation. + """ + return channel.modulate(self._samples) + @abstractmethod def _to_dict(self) -> dict[str, Any]: pass @@ -229,19 +297,42 @@ def __hash__(self) -> int: return hash(tuple(self.samples)) def _plot( - self, ax: Axes, ylabel: str, color: Optional[str] = None + self, + ax: Axes, + ylabel: Optional[str] = None, + color: Optional[str] = None, + channel: Optional[Channel] = None, + label: str = "", + start_t: int = 0, ) -> None: ax.set_xlabel("t (ns)") - ts = np.arange(self.duration) + samples = ( + self.samples + if channel is None + else self.modulated_samples(channel) + ) + ts = np.arange(len(samples)) + start_t + if not channel and start_t: + # Adds zero on both ends to show rise and fall + samples = np.pad(samples, 1) + # Repeats the times on the edges once + ts = np.pad(ts, 1, mode="edge") + if color: - ax.set_ylabel(ylabel, color=color, fontsize=14) - ax.plot(ts, self.samples, color=color) + color_dict = {"color": color} + hline_color = color ax.tick_params(axis="y", labelcolor=color) - ax.axhline(0, color=color, linestyle=":", linewidth=0.5) else: - ax.set_ylabel(ylabel, fontsize=14) - ax.plot(ts, self.samples) - ax.axhline(0, color="black", linestyle=":", linewidth=0.5) + color_dict = {} + hline_color = "black" + + if ylabel: + ax.set_ylabel(ylabel, fontsize=14, **color_dict) + ax.plot(ts, samples, label=label, **color_dict) + ax.axhline(0, color=hline_color, linestyle=":", linewidth=0.5) + + if label: + plt.legend() class CompositeWaveform(Waveform): @@ -723,10 +814,26 @@ def change_duration(self, new_duration: int) -> InterpolatedWaveform: return InterpolatedWaveform(new_duration, self._values, **self._kwargs) def _plot( - self, ax: Axes, ylabel: str, color: Optional[str] = None + self, + ax: Axes, + ylabel: Optional[str] = None, + color: Optional[str] = None, + channel: Optional[Channel] = None, + label: str = "", + start_t: int = 0, ) -> None: - super()._plot(ax, ylabel, color=color) - ax.scatter(self._data_pts[:, 0], self._data_pts[:, 1], c=color) + super()._plot( + ax, + ylabel, + color=color, + channel=channel, + label=label, + start_t=start_t, + ) + if not channel: + ax.scatter( + self._data_pts[:, 0] + start_t, self._data_pts[:, 1], c=color + ) def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, self._duration, self._values, **self._kwargs) diff --git a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb index cbc101d27..d2f72c265 100644 --- a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb +++ b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb @@ -61,7 +61,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For example, assume an ensemble of identical radio transmitters over French cities that each have the same radius of transmission. It was quickly realized that two transmitters with close or equal frequencies could interfere with one another, hence the necessity to assign non-interfering frequencies to overlapping transmiting towers. Because of the limited amount of bandwith space, some towers have to be assigned the same or close frequencies. The MIS of a graph of towers indicate the maximum number of towers that can have close or equal given frequency (red points). \n", + "For example, assume an ensemble of identical radio transmitters over French cities that each have the same radius of transmission. It was quickly realized that two transmitters with close or equal frequencies could interfere with one another, hence the necessity to assign non-interfering frequencies to overlapping transmiting towers. Because of the limited amount of bandwidth space, some towers have to be assigned the same or close frequencies. The MIS of a graph of towers indicate the maximum number of towers that can have close or equal given frequency (red points). \n", "\n", "
\n", "\"MIS\n", From 21903de97c3b158950e8ae10389462c1937edc01 Mon Sep 17 00:00:00 2001 From: Louis Vignoli <97944962+lvignoli@users.noreply.github.com> Date: Tue, 8 Feb 2022 10:37:19 +0100 Subject: [PATCH 38/51] Consistent formatting and style (#320) * Sort requirements.txt * Add black[jupyter] for notebook formatting * Format notebooks using black * Sort and add full black recommendations for flake8 * Add isort to requirements.txt with profile="black" * Sort imports with isort * Format markdown files using markdownlint https://github.com/DavidAnson/markdownlint https://marketplace.visualstudio.com/items?itemName=DavidAnson.vscode-markdownlint * .flake8: Removing max-line-length field * Sort imports of files touched by output modulation commits --- .flake8 | 12 +- CONTRIBUTING.md | 18 +- README.md | 5 +- docs/source/intro_rydberg_blockade.ipynb | 48 +- pulser/__init__.py | 4 - pulser/_seq_drawer.py | 10 +- pulser/channels.py | 6 +- pulser/devices/__init__.py | 4 +- pulser/devices/_device_datacls.py | 4 +- pulser/devices/_devices.py | 3 +- pulser/devices/_mock_device.py | 3 +- pulser/json/coders.py | 2 +- pulser/parametrized/__init__.py | 4 +- pulser/parametrized/decorators.py | 1 - pulser/parametrized/paramabc.py | 2 +- pulser/parametrized/paramobj.py | 8 +- pulser/parametrized/variable.py | 6 +- pulser/pulse.py | 8 +- pulser/register/__init__.py | 2 - pulser/register/base_register.py | 6 +- pulser/register/register3d.py | 2 +- pulser/sequence.py | 14 +- pulser/simulation/__init__.py | 2 +- pulser/simulation/simconfig.py | 4 +- pulser/simulation/simresults.py | 8 +- pulser/simulation/simulation.py | 19 +- pulser/tests/test_channels.py | 2 +- pulser/tests/test_json.py | 4 +- pulser/tests/test_parametrized.py | 1 - pulser/tests/test_paramseq.py | 2 +- pulser/tests/test_pulse.py | 2 +- pulser/tests/test_sequence.py | 6 +- pulser/tests/test_simresults.py | 9 +- pulser/tests/test_simulation.py | 7 +- pulser/tests/test_waveforms.py | 14 +- pulser/waveforms.py | 12 +- pyproject.toml | 3 + requirements.txt | 12 +- setup.py | 2 +- .../Composite Waveforms.ipynb | 9 +- .../Interpolated Waveforms.ipynb | 12 +- .../Parametrized Sequences.ipynb | 22 +- .../Phase Shifts and Virtual Z gates.ipynb | 47 +- .../advanced_features/Serialization.ipynb | 10 +- ...ting Sequences with Errors and Noise.ipynb | 110 ++-- .../State Preparation with the SLM Mask.ipynb | 8 +- .../Control-Z Gate Sequence.ipynb | 213 ++++--- .../Quantum Evolution Kernel.ipynb | 544 +++++++++--------- .../Using QAOA to solve a MIS problem.ipynb | 96 ++-- tutorials/creating_sequences.ipynb | 44 +- ... antiferromagnetic state preparation.ipynb | 175 +++--- .../Building 1D Rydberg Crystals.ipynb | 206 ++++--- ...iltonians in arrays of Rydberg atoms.ipynb | 261 +++++---- ...rromagnetic order in the Ising model.ipynb | 205 ++++--- .../Shadow estimation for VQS.ipynb | 383 +++++++----- .../Spin chain of 3 atoms in XY mode.ipynb | 59 +- tutorials/simulating_sequences.ipynb | 48 +- 57 files changed, 1549 insertions(+), 1184 deletions(-) diff --git a/.flake8 b/.flake8 index ce68d0eac..29fee19f4 100644 --- a/.flake8 +++ b/.flake8 @@ -1,6 +1,11 @@ [flake8] -exclude = ./build, ./docs docstring-convention = google +exclude = ./build, ./docs +extend-ignore = + # D105 Missing docstring in magic method + D105, + # E203 whitespace before ':' (for compliance with black) + E203, per-file-ignores = # D100 Missing docstring in public module # D103 Missing docstring in public function @@ -8,8 +13,3 @@ per-file-ignores = pulser/tests/*: D100, D103 __init__.py: F401 setup.py: D100 -extend-ignore = - # D105 Missing docstring in magic method - D105, - # E203 whitespace before ':' (for compliance with black) - E203, diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index bd8bc8131..c886f4327 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -8,10 +8,10 @@ The steps to take will depend on what you want to do, but generally you'll want 1. Do a quick search for keywords over the existing issues to ensure yours has not been added yet. 2. If you can't find your issue already listed, create a new one. Please try to be as clear and detailed as possible in your description. + - If you just want to give a suggestion or report a bug, that's already excellent and we thank you for it! Your issue will be listed and, hopefully, someone will take care of it at some point. - However, you may also want to be the one solving your issue, which would be even better! In these cases, you would proceed by preparing a [Pull Request](#making-a-pull-request). - ## Making a Pull Request We're thrilled that you want to contribute to Pulser! For general contributions, we use a combination of two Git workflows: the [Forking workflow](https://www.atlassian.com/git/tutorials/comparing-workflows/forking-workflow) and the [Gitflow workflow](https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow). If you don't know what any of this means, don't worry, you should still be able to make your contribution just by following the instructions detailed below. Nonetheless, in a nutshell, this workflow will have you making a fork from the main Pulser repository and working off a branch from `develop` (**not** `master`). Thus, you'll start your branch from `develop` and end with a pull request that merges your branch back to `develop`. The only exception to this rule is when making a `hotfix`, but in these cases the Pulser development team will take care of it for you. @@ -19,12 +19,15 @@ We're thrilled that you want to contribute to Pulser! For general contributions, Here are the steps you should follow to make your contribution: 0. Fork the Pulser repository and add the main Pulser repository as the `upstream`. You only have to do this once and you do so by clicking the "Fork" button at the upper right corner of the [repo page](https://github.com/pasqal-io/Pulser). This will create a new GitHub repo at `https://github.com/USERNAME/Pulser`, where `USERNAME` is your GitHub ID. Then, `cd` into the folder where you would like to place your new fork and clone it by doing: + ```bash git clone https://github.com/USERNAME/Pulser.git ``` + **Note**: `USERNAME` should be replaced by your own GitHub ID. Then, you'll want to go into the directory of your brand new Pulser fork and add the main Pulser repository as the `upstream` by running: + ```bash git remote add upstream https://github.com/pasqal-io/Pulser.git ``` @@ -32,10 +35,12 @@ Here are the steps you should follow to make your contribution: 1. Have the related issue assigned to you. We suggest that you work only on issues that have been assigned to you; by doing this, you make sure to be the only one working on this and we prevent everyone from doing duplicate work. If a related issue does not exist yet, consult the [section above](#reporting-a-bug-or-suggesting-a-feature) to see how to proceed. 2. You'll want to create a new branch where you will do your changes. The starting point will be `upstream/develop`, which is where you'll ultimately merge your changes. Inside your fork's root folder, run: + ```bash git fetch upstream git checkout -b branch-name-here upstream/develop ``` + This will create and checkout the new branch, where you will do your changes. **Note**: `branch-name-here` should be replaced by the name you'll give your branch. Try to be descriptive, pick a name that identifies your new feature. @@ -43,12 +48,15 @@ Here are the steps you should follow to make your contribution: 3. Do your work and commit the changes to this new branch. Try to make the first line of your commit messages short but informative; in case you want to go into more detail, you have the option to do so in the next lines. 4. At this point, your branch might have drifted out of sync with Pulser's `develop` branch (the `upstream`). By running + ```shell git pull upstream develop ``` + you will fetch the latest changes in `upstream/develop` and merge them with your working branch, at which point you'll have to solve any merge conflicts that may arise. This will keep your working branch in sync with `upstream/develop`. 5. Finally, you push your code to your local branch: + ```bash git push origin branch-name-here ``` @@ -66,29 +74,37 @@ pip install -r requirements.txt ``` - **Tests**: We use [`pytest`](https://docs.pytest.org/en/latest/) to run unit tests on our code. If your changes break existing tests, you'll have to update these tests accordingly. Additionally, we aim for 100% coverage over our code. Try to cover all the new lines of code with simple tests, which should be placed in the `Pulser/pulser/tests` folder. To run all tests and check coverage, run: + ```bash pytest --cov pulser ``` + All lines that are not meant to be tested must be tagged with `# pragma: no cover`. Use it sparingly, every decision to leave a line uncovered must be well justified. - **Style**: We use [`flake8`](https://flake8.pycqa.org/en/latest/) and the `flake8-docstrings` extension to enforce PEP8 style guidelines. To lint your code with `flake8`, simply run: + ```bash flake8 . ``` + To help you keep your code compliant with PEP8 guidelines effortlessly, we suggest you look into installing a linter for your text editor of choice. - **Format**: We use the [`black`](https://black.readthedocs.io/en/stable/index.html) auto-formatter to enforce a consistent style throughout the entire code base. It will also ensure your code is compliant with the formatting enforced by `flake8` for you. To automatically format your code with black, just run: + ```bash black . ``` + Note that some IDE's and text editors support plug-ins which auto-format your code with `black` upon saving, so you don't have to worry about code format at all. - **Type hints**: We use [mypy](http://mypy-lang.org/) to type check the code. Your code should have type annotations and pass the type checks from running: + ```bash mypy ``` + In case `mypy` produces a false positive, you can ignore the respective line by adding the `# type: ignore` annotation. **Note**: Type hints for `numpy` have only been added in version 1.20. Make sure you have `numpy >= 1.20` diff --git a/README.md b/README.md index ebd2dc48e..eb6cceed2 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,9 @@ Pulser is a framework for composing, simulating and executing **pulse** sequences for neutral-atom quantum devices. -**Documentation** for the [latest release](https://pypi.org/project/pulser/) of `pulser` is available at https://pulser.readthedocs.io (for the docs tracking the `develop` branch of this repository, visit https://pulser.readthedocs.io/en/latest instead). +**Documentation** for the [latest release](https://pypi.org/project/pulser/) of `pulser` is available at (for the docs tracking the `develop` branch of this repository, visit instead). -The source code can be found at https://github.com/pasqal-io/Pulser. +The source code can be found at . ## Overview of Pulser @@ -51,6 +51,7 @@ Then, you can do the following to run the test suite and report test coverage: ```bash pytest --cov pulser ``` + ## Contributing Want to contribute to Pulser? Great! See [How to Contribute][contributing] for information on how you can do so. diff --git a/docs/source/intro_rydberg_blockade.ipynb b/docs/source/intro_rydberg_blockade.ipynb index 2e91174e5..ba7eb8459 100644 --- a/docs/source/intro_rydberg_blockade.ipynb +++ b/docs/source/intro_rydberg_blockade.ipynb @@ -88,8 +88,10 @@ "from pulser import Pulse\n", "from pulser.waveforms import RampWaveform, BlackmanWaveform\n", "\n", - "duration = 1000 # Typical: ~1 µsec\n", - "pulse = Pulse(BlackmanWaveform(duration, np.pi), RampWaveform(duration, -5., 10.), 0)\n", + "duration = 1000 # Typical: ~1 µsec\n", + "pulse = Pulse(\n", + " BlackmanWaveform(duration, np.pi), RampWaveform(duration, -5.0, 10.0), 0\n", + ")\n", "pulse.draw()" ] }, @@ -131,18 +133,18 @@ "source": [ "from pulser import Sequence\n", "\n", - "reg = Register.rectangle(1, 2, spacing=8, prefix='atom')\n", + "reg = Register.rectangle(1, 2, spacing=8, prefix=\"atom\")\n", "reg.draw()\n", "\n", - "pi_pulse = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0., 0.)\n", + "pi_pulse = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0.0, 0.0)\n", "\n", "seq = Sequence(reg, Chadoq2)\n", "\n", - "seq.declare_channel('ryd','rydberg_local','atom0')\n", + "seq.declare_channel(\"ryd\", \"rydberg_local\", \"atom0\")\n", "\n", - "seq.add(pi_pulse,'ryd')\n", - "seq.target('atom1', 'ryd')\n", - "seq.add(pi_pulse,'ryd')\n", + "seq.add(pi_pulse, \"ryd\")\n", + "seq.target(\"atom1\", \"ryd\")\n", + "seq.add(pi_pulse, \"ryd\")\n", "\n", "seq.draw()" ] @@ -199,25 +201,27 @@ "data = []\n", "distances = np.linspace(6.5, 14, 7)\n", "\n", - "r = [1,0] # |r>\n", - "rr = np.kron(r,r) # |rr>\n", + "r = [1, 0] # |r>\n", + "rr = np.kron(r, r) # |rr>\n", "occup = [np.outer(rr, np.conj(rr))] # |rr> dict: diff --git a/pulser/channels.py b/pulser/channels.py index 89015e9f6..1dd230b62 100644 --- a/pulser/channels.py +++ b/pulser/channels.py @@ -15,13 +15,13 @@ from __future__ import annotations -from dataclasses import dataclass -from typing import cast, ClassVar, Optional import warnings +from dataclasses import dataclass +from typing import ClassVar, Optional, cast import numpy as np from numpy.typing import ArrayLike -from scipy.fft import fft, ifft, fftfreq +from scipy.fft import fft, fftfreq, ifft # Warnings of adjusted waveform duration appear just once warnings.filterwarnings("once", "A duration of") diff --git a/pulser/devices/__init__.py b/pulser/devices/__init__.py index 0660fdcfc..bf6d17fca 100644 --- a/pulser/devices/__init__.py +++ b/pulser/devices/__init__.py @@ -13,12 +13,10 @@ # limitations under the License. """Valid devices for Pulser Sequence execution.""" +from pulser.devices._device_datacls import Device from pulser.devices._devices import Chadoq2 - from pulser.devices._mock_device import MockDevice -from pulser.devices._device_datacls import Device - # Registers which devices can be used to avoid definition of custom devices _mock_devices = (MockDevice,) _valid_devices = (Chadoq2,) diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index a904daa02..5003eef21 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -21,10 +21,10 @@ from scipy.spatial.distance import pdist, squareform from pulser import Pulse -from pulser.register.base_register import BaseRegister from pulser.channels import Channel -from pulser.json.utils import obj_to_dict from pulser.devices.interaction_coefficients import c6_dict +from pulser.json.utils import obj_to_dict +from pulser.register.base_register import BaseRegister @dataclass(frozen=True, repr=False) diff --git a/pulser/devices/_devices.py b/pulser/devices/_devices.py index 0fa9e50e0..9a757ec91 100644 --- a/pulser/devices/_devices.py +++ b/pulser/devices/_devices.py @@ -14,9 +14,8 @@ """Definitions of real devices.""" import numpy as np -from pulser.devices._device_datacls import Device from pulser.channels import Raman, Rydberg - +from pulser.devices._device_datacls import Device Chadoq2 = Device( name="Chadoq2", diff --git a/pulser/devices/_mock_device.py b/pulser/devices/_mock_device.py index 0b6e02216..01bf385e5 100644 --- a/pulser/devices/_mock_device.py +++ b/pulser/devices/_mock_device.py @@ -12,9 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +from pulser.channels import Microwave, Raman, Rydberg from pulser.devices._device_datacls import Device -from pulser.channels import Rydberg, Raman, Microwave - MockDevice = Device( name="MockDevice", diff --git a/pulser/json/coders.py b/pulser/json/coders.py index d4a79a999..011140d01 100644 --- a/pulser/json/coders.py +++ b/pulser/json/coders.py @@ -17,7 +17,7 @@ import importlib import inspect -from json import JSONEncoder, JSONDecoder +from json import JSONDecoder, JSONEncoder from typing import Any, cast import numpy as np diff --git a/pulser/parametrized/__init__.py b/pulser/parametrized/__init__.py index 5bb0d5cd5..6f0775cf3 100644 --- a/pulser/parametrized/__init__.py +++ b/pulser/parametrized/__init__.py @@ -14,7 +14,5 @@ """Classes for parametrized pulse-sequence building.""" from pulser.parametrized.paramabc import Parametrized - -from pulser.parametrized.variable import Variable - from pulser.parametrized.paramobj import ParamObj +from pulser.parametrized.variable import Variable diff --git a/pulser/parametrized/decorators.py b/pulser/parametrized/decorators.py index ae27c70a2..a77df039a 100644 --- a/pulser/parametrized/decorators.py +++ b/pulser/parametrized/decorators.py @@ -22,7 +22,6 @@ from pulser.parametrized import Parametrized, ParamObj - F = TypeVar("F", bound=Callable) diff --git a/pulser/parametrized/paramabc.py b/pulser/parametrized/paramabc.py index fff36e656..f050ab736 100644 --- a/pulser/parametrized/paramabc.py +++ b/pulser/parametrized/paramabc.py @@ -16,7 +16,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Any, TYPE_CHECKING +from typing import TYPE_CHECKING, Any if TYPE_CHECKING: from pulser.parametrized import Variable # pragma: no cover diff --git a/pulser/parametrized/paramobj.py b/pulser/parametrized/paramobj.py index 33d173f73..0bcf0ad4a 100644 --- a/pulser/parametrized/paramobj.py +++ b/pulser/parametrized/paramobj.py @@ -15,13 +15,13 @@ from __future__ import annotations -from collections.abc import Callable -from functools import partialmethod -from itertools import chain import inspect import operator import warnings -from typing import Any, Union, TYPE_CHECKING +from collections.abc import Callable +from functools import partialmethod +from itertools import chain +from typing import TYPE_CHECKING, Any, Union from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized diff --git a/pulser/parametrized/variable.py b/pulser/parametrized/variable.py index 43bcd2914..743c337bb 100644 --- a/pulser/parametrized/variable.py +++ b/pulser/parametrized/variable.py @@ -16,16 +16,16 @@ from __future__ import annotations import collections.abc # To use collections.abc.Sequence -from collections.abc import Iterable import dataclasses -from typing import Union, Any, cast +from collections.abc import Iterable +from typing import Any, Union, cast import numpy as np from numpy.typing import ArrayLike +from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized from pulser.parametrized.paramobj import OpSupport -from pulser.json.utils import obj_to_dict @dataclasses.dataclass(frozen=True, eq=False) diff --git a/pulser/pulse.py b/pulser/pulse.py index c3a2a75e7..e800bc7a5 100644 --- a/pulser/pulse.py +++ b/pulser/pulse.py @@ -15,19 +15,19 @@ from __future__ import annotations -from dataclasses import dataclass, field import functools import itertools -from typing import Any, cast, Union +from dataclasses import dataclass, field +from typing import Any, Union, cast import matplotlib.pyplot as plt import numpy as np from pulser.channels import Channel +from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, ParamObj from pulser.parametrized.decorators import parametrize -from pulser.waveforms import Waveform, ConstantWaveform -from pulser.json.utils import obj_to_dict +from pulser.waveforms import ConstantWaveform, Waveform @dataclass(init=False, repr=False, frozen=True) diff --git a/pulser/register/__init__.py b/pulser/register/__init__.py index a57034208..084859366 100644 --- a/pulser/register/__init__.py +++ b/pulser/register/__init__.py @@ -14,7 +14,5 @@ """Classes for qubit register definition.""" from pulser.register.base_register import QubitId - from pulser.register.register import Register - from pulser.register.register3d import Register3D diff --git a/pulser/register/base_register.py b/pulser/register/base_register.py index 94ad37dae..d9437f192 100644 --- a/pulser/register/base_register.py +++ b/pulser/register/base_register.py @@ -16,13 +16,13 @@ from __future__ import annotations from abc import ABC, abstractmethod -from collections.abc import Mapping, Iterable +from collections.abc import Iterable, Mapping from collections.abc import Sequence as abcSequence -from typing import Any, cast, Optional, Union, TypeVar, Type +from typing import Any, Optional, Type, TypeVar, Union, cast import matplotlib.pyplot as plt -from matplotlib import collections as mc import numpy as np +from matplotlib import collections as mc from numpy.typing import ArrayLike from scipy.spatial import KDTree diff --git a/pulser/register/register3d.py b/pulser/register/register3d.py index 04b5807b6..cbc5d7763 100644 --- a/pulser/register/register3d.py +++ b/pulser/register/register3d.py @@ -16,8 +16,8 @@ from __future__ import annotations from collections.abc import Mapping -from typing import Any, Optional from itertools import combinations +from typing import Any, Optional import matplotlib.pyplot as plt import numpy as np diff --git a/pulser/sequence.py b/pulser/sequence.py index cdd85f12a..97df719c5 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -15,31 +15,31 @@ from __future__ import annotations +import copy +import json +import os +import warnings from collections import namedtuple from collections.abc import Callable, Generator, Iterable -import copy from functools import wraps from itertools import chain -import json from sys import version_info -from typing import Any, cast, NamedTuple, Optional, Tuple, TypeVar, Union -import warnings -import os +from typing import Any, NamedTuple, Optional, Tuple, TypeVar, Union, cast import matplotlib.pyplot as plt import numpy as np from numpy.typing import ArrayLike import pulser +from pulser._seq_drawer import draw_sequence from pulser.channels import Channel from pulser.devices import MockDevice from pulser.devices._device_datacls import Device -from pulser.json.coders import PulserEncoder, PulserDecoder +from pulser.json.coders import PulserDecoder, PulserEncoder from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, Variable from pulser.pulse import Pulse from pulser.register.base_register import BaseRegister -from pulser._seq_drawer import draw_sequence if version_info[:2] >= (3, 8): # pragma: no cover from typing import Literal, get_args diff --git a/pulser/simulation/__init__.py b/pulser/simulation/__init__.py index c32b17714..3d2ae5021 100644 --- a/pulser/simulation/__init__.py +++ b/pulser/simulation/__init__.py @@ -13,5 +13,5 @@ # limitations under the License. """Classes for classical emulation of a Sequence.""" -from pulser.simulation.simulation import Simulation from pulser.simulation.simconfig import SimConfig +from pulser.simulation.simulation import Simulation diff --git a/pulser/simulation/simconfig.py b/pulser/simulation/simconfig.py index 0222fd9f1..8d175541f 100644 --- a/pulser/simulation/simconfig.py +++ b/pulser/simulation/simconfig.py @@ -15,9 +15,9 @@ from __future__ import annotations -from sys import version_info from dataclasses import dataclass, field -from typing import Union, Any +from sys import version_info +from typing import Any, Union import numpy as np import qutip diff --git a/pulser/simulation/simresults.py b/pulser/simulation/simresults.py index fc30c0079..f8c689462 100644 --- a/pulser/simulation/simresults.py +++ b/pulser/simulation/simresults.py @@ -15,17 +15,17 @@ from __future__ import annotations -from collections import Counter import collections.abc from abc import ABC, abstractmethod +from collections import Counter from functools import lru_cache -from typing import Optional, Union, cast, Tuple, Mapping +from typing import Mapping, Optional, Tuple, Union, cast import matplotlib.pyplot as plt -import qutip -from qutip.piqs import isdiagonal import numpy as np +import qutip from numpy.typing import ArrayLike +from qutip.piqs import isdiagonal class SimulationResults(ABC): diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index aca3ecb19..a480eb3a0 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -15,30 +15,29 @@ from __future__ import annotations -from typing import Optional, Union, cast, Any -from collections.abc import Mapping import itertools +import warnings from collections import Counter +from collections.abc import Mapping from copy import deepcopy from dataclasses import asdict -import warnings +from typing import Any, Optional, Union, cast -import qutip +import matplotlib.pyplot as plt import numpy as np +import qutip from numpy.typing import ArrayLike -import matplotlib.pyplot as plt from pulser import Pulse, Sequence +from pulser._seq_drawer import draw_sequence from pulser.register import QubitId +from pulser.sequence import _TimeSlot +from pulser.simulation.simconfig import SimConfig from pulser.simulation.simresults import ( - SimulationResults, CoherentResults, NoisyResults, + SimulationResults, ) -from pulser.simulation.simconfig import SimConfig -from pulser._seq_drawer import draw_sequence -from pulser.sequence import _TimeSlot - SUPPORTED_NOISE = { "ising": {"dephasing", "doppler", "amplitude", "SPAM"}, diff --git a/pulser/tests/test_channels.py b/pulser/tests/test_channels.py index 4c25202df..532be943f 100644 --- a/pulser/tests/test_channels.py +++ b/pulser/tests/test_channels.py @@ -17,7 +17,7 @@ import pulser from pulser.channels import Raman, Rydberg -from pulser.waveforms import ConstantWaveform, BlackmanWaveform +from pulser.waveforms import BlackmanWaveform, ConstantWaveform def test_device_channels(): diff --git a/pulser/tests/test_json.py b/pulser/tests/test_json.py index 5eddc1dc7..482b87c00 100644 --- a/pulser/tests/test_json.py +++ b/pulser/tests/test_json.py @@ -17,9 +17,9 @@ import numpy as np import pytest -from pulser import Sequence, Register, Register3D +from pulser import Register, Register3D, Sequence from pulser.devices import Chadoq2, MockDevice -from pulser.json.coders import PulserEncoder, PulserDecoder +from pulser.json.coders import PulserDecoder, PulserEncoder from pulser.parametrized.decorators import parametrize from pulser.waveforms import BlackmanWaveform diff --git a/pulser/tests/test_parametrized.py b/pulser/tests/test_parametrized.py index c0bee6d0f..233869a6f 100644 --- a/pulser/tests/test_parametrized.py +++ b/pulser/tests/test_parametrized.py @@ -21,7 +21,6 @@ from pulser.parametrized import Variable from pulser.waveforms import BlackmanWaveform, CompositeWaveform - a = Variable("a", float) b = Variable("b", int, size=2) b._assign([-1.5, 1.5]) diff --git a/pulser/tests/test_paramseq.py b/pulser/tests/test_paramseq.py index f210cbedc..04bbf527b 100644 --- a/pulser/tests/test_paramseq.py +++ b/pulser/tests/test_paramseq.py @@ -17,7 +17,7 @@ import numpy as np import pytest -from pulser import Sequence, Register, Pulse +from pulser import Pulse, Register, Sequence from pulser.devices import Chadoq2, MockDevice from pulser.parametrized import Variable from pulser.waveforms import BlackmanWaveform diff --git a/pulser/tests/test_pulse.py b/pulser/tests/test_pulse.py index b63793d77..595ce469e 100644 --- a/pulser/tests/test_pulse.py +++ b/pulser/tests/test_pulse.py @@ -18,7 +18,7 @@ import pytest from pulser import Pulse -from pulser.waveforms import ConstantWaveform, BlackmanWaveform, RampWaveform +from pulser.waveforms import BlackmanWaveform, ConstantWaveform, RampWaveform cwf = ConstantWaveform(100, -10) bwf = BlackmanWaveform(200, 3) diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 1899b1ef3..0fac5dfbc 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -18,16 +18,16 @@ import pytest import pulser -from pulser import Sequence, Pulse, Register, Register3D -from pulser.channels import Rydberg, Raman +from pulser import Pulse, Register, Register3D, Sequence +from pulser.channels import Raman, Rydberg from pulser.devices import Chadoq2, MockDevice from pulser.devices._device_datacls import Device from pulser.sequence import _TimeSlot from pulser.waveforms import ( BlackmanWaveform, CompositeWaveform, - RampWaveform, InterpolatedWaveform, + RampWaveform, ) reg = Register.triangular_lattice(4, 7, spacing=5, prefix="q") diff --git a/pulser/tests/test_simresults.py b/pulser/tests/test_simresults.py index f8219c884..ec0aecadf 100644 --- a/pulser/tests/test_simresults.py +++ b/pulser/tests/test_simresults.py @@ -11,20 +11,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from copy import deepcopy - from collections import Counter +from copy import deepcopy import numpy as np import pytest import qutip from qutip.piqs import isdiagonal -from pulser import Sequence, Pulse, Register +from pulser import Pulse, Register, Sequence from pulser.devices import Chadoq2, MockDevice -from pulser.waveforms import BlackmanWaveform -from pulser.simulation import Simulation, SimConfig +from pulser.simulation import SimConfig, Simulation from pulser.simulation.simresults import CoherentResults, NoisyResults +from pulser.waveforms import BlackmanWaveform np.random.seed(123) q_dict = { diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index a3707909d..9db3e9f08 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -12,18 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. -from unittest.mock import patch - from collections import Counter +from unittest.mock import patch import numpy as np import pytest import qutip -from pulser import Sequence, Pulse, Register +from pulser import Pulse, Register, Sequence from pulser.devices import Chadoq2, MockDevice -from pulser.waveforms import BlackmanWaveform, RampWaveform, ConstantWaveform from pulser.simulation import SimConfig, Simulation +from pulser.waveforms import BlackmanWaveform, ConstantWaveform, RampWaveform q_dict = { "control1": np.array([-4.0, 0.0]), diff --git a/pulser/tests/test_waveforms.py b/pulser/tests/test_waveforms.py index fa10d51b3..3c140ddb3 100644 --- a/pulser/tests/test_waveforms.py +++ b/pulser/tests/test_waveforms.py @@ -18,19 +18,19 @@ import numpy as np import pytest -from scipy.interpolate import interp1d, PchipInterpolator +from scipy.interpolate import PchipInterpolator, interp1d -from pulser.json.coders import PulserEncoder, PulserDecoder from pulser.channels import Rydberg -from pulser.parametrized import Variable, ParamObj +from pulser.json.coders import PulserDecoder, PulserEncoder +from pulser.parametrized import ParamObj, Variable from pulser.waveforms import ( - ConstantWaveform, - KaiserWaveform, - RampWaveform, BlackmanWaveform, - CustomWaveform, CompositeWaveform, + ConstantWaveform, + CustomWaveform, InterpolatedWaveform, + KaiserWaveform, + RampWaveform, ) np.random.seed(20201105) diff --git a/pulser/waveforms.py b/pulser/waveforms.py index 7daddbf0a..5a2757594 100644 --- a/pulser/waveforms.py +++ b/pulser/waveforms.py @@ -15,26 +15,26 @@ from __future__ import annotations -from abc import ABC, abstractmethod import functools import inspect import itertools import sys +import warnings +from abc import ABC, abstractmethod from sys import version_info from types import FunctionType -from typing import Any, cast, Optional, Tuple, Union -import warnings +from typing import Any, Optional, Tuple, Union, cast -from matplotlib.axes import Axes import matplotlib.pyplot as plt import numpy as np -from numpy.typing import ArrayLike import scipy.interpolate as interpolate +from matplotlib.axes import Axes +from numpy.typing import ArrayLike from pulser.channels import Channel +from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, ParamObj from pulser.parametrized.decorators import parametrize -from pulser.json.utils import obj_to_dict if version_info[:2] >= (3, 8): # pragma: no cover from functools import cached_property diff --git a/pyproject.toml b/pyproject.toml index a8f43fefd..1e75c2fbd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,2 +1,5 @@ [tool.black] line-length = 79 + +[tool.isort] +profile = "black" diff --git a/requirements.txt b/requirements.txt index c870d413d..df9ab926a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,18 +4,20 @@ qutip scipy # version specific -typing-extensions; python_version == '3.7' backports.cached-property; python_version == '3.7' +typing-extensions; python_version == '3.7' # tests -pytest -pytest-cov +black +black[jupyter] flake8 flake8-docstrings +isort mypy == 0.921 -black +pytest +pytest-cov # tutorials notebook -scikit-optimize python-igraph +scikit-optimize diff --git a/setup.py b/setup.py index 33b94be63..db2211c6c 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from setuptools import setup, find_packages +from setuptools import find_packages, setup __version__ = "" exec(open("pulser/_version.py").read()) diff --git a/tutorials/advanced_features/Composite Waveforms.ipynb b/tutorials/advanced_features/Composite Waveforms.ipynb index 3cb38b815..fa26b46b3 100644 --- a/tutorials/advanced_features/Composite Waveforms.ipynb +++ b/tutorials/advanced_features/Composite Waveforms.ipynb @@ -16,7 +16,12 @@ "import numpy as np\n", "\n", "from pulser import Pulse\n", - "from pulser.waveforms import BlackmanWaveform, RampWaveform, CompositeWaveform, ConstantWaveform" + "from pulser.waveforms import (\n", + " BlackmanWaveform,\n", + " RampWaveform,\n", + " CompositeWaveform,\n", + " ConstantWaveform,\n", + ")" ] }, { @@ -37,7 +42,7 @@ "outputs": [], "source": [ "# Defining simple waveforms\n", - "pi_pulse = BlackmanWaveform(1000, np.pi) # Blackman pi-pulse of 1us\n", + "pi_pulse = BlackmanWaveform(1000, np.pi) # Blackman pi-pulse of 1us\n", "up = RampWaveform(500, 0, 5)\n", "down = RampWaveform(500, 5, 0)\n", "\n", diff --git a/tutorials/advanced_features/Interpolated Waveforms.ipynb b/tutorials/advanced_features/Interpolated Waveforms.ipynb index fdf8ea3f6..c3987ceb2 100644 --- a/tutorials/advanced_features/Interpolated Waveforms.ipynb +++ b/tutorials/advanced_features/Interpolated Waveforms.ipynb @@ -64,7 +64,7 @@ "metadata": {}, "outputs": [], "source": [ - "ts = np.r_[np.linspace(0., 0.5, num=len(values) - 1), 1]\n", + "ts = np.r_[np.linspace(0.0, 0.5, num=len(values) - 1), 1]\n", "int_wf_t = InterpolatedWaveform(duration, values, times=ts)\n", "int_wf_t.draw()" ] @@ -103,7 +103,9 @@ }, "outputs": [], "source": [ - "int_wf3 = InterpolatedWaveform(duration, values, interpolator=\"interp1d\", kind=\"cubic\")\n", + "int_wf3 = InterpolatedWaveform(\n", + " duration, values, interpolator=\"interp1d\", kind=\"cubic\"\n", + ")\n", "int_wf3.draw()" ] }, @@ -163,7 +165,9 @@ "det_vals = param_seq.declare_variable(\"det_vals\", size=4, dtype=float)\n", "\n", "amp_wf = InterpolatedWaveform(1000, amp_vals)\n", - "det_wf = InterpolatedWaveform(1000, det_vals, interpolator=\"interp1d\", kind=\"cubic\")\n", + "det_wf = InterpolatedWaveform(\n", + " 1000, det_vals, interpolator=\"interp1d\", kind=\"cubic\"\n", + ")\n", "pls = Pulse(amp_wf, det_wf, 0)\n", "\n", "param_seq.add(pls, \"rydberg_global\")" @@ -182,7 +186,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq1 = param_seq.build(amp_vals=[0, 2, 1, 2, 0], det_vals = [0, -5, 5, -5])\n", + "seq1 = param_seq.build(amp_vals=[0, 2, 1, 2, 0], det_vals=[0, -5, 5, -5])\n", "seq1.draw()" ] }, diff --git a/tutorials/advanced_features/Parametrized Sequences.ipynb b/tutorials/advanced_features/Parametrized Sequences.ipynb index df69949b5..e0c2273b1 100644 --- a/tutorials/advanced_features/Parametrized Sequences.ipynb +++ b/tutorials/advanced_features/Parametrized Sequences.ipynb @@ -35,10 +35,10 @@ "metadata": {}, "outputs": [], "source": [ - "reg = Register.square(2, prefix='q')\n", + "reg = Register.square(2, prefix=\"q\")\n", "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('rydberg', 'rydberg_global')\n", - "seq.declare_channel('raman', 'raman_local')" + "seq.declare_channel(\"rydberg\", \"rydberg_global\")\n", + "seq.declare_channel(\"raman\", \"raman_local\")" ] }, { @@ -61,9 +61,9 @@ "metadata": {}, "outputs": [], "source": [ - "Omega_max = seq.declare_variable('Omega_max')\n", - "ts = seq.declare_variable('ts', size=2, dtype=int)\n", - "last_target = seq.declare_variable('last_target', dtype=str)" + "Omega_max = seq.declare_variable(\"Omega_max\")\n", + "ts = seq.declare_variable(\"ts\", size=2, dtype=int)\n", + "last_target = seq.declare_variable(\"last_target\", dtype=str)" ] }, { @@ -85,7 +85,7 @@ "U = Omega_max / 2.3\n", "delta_0 = -6 * U\n", "delta_f = 2 * U\n", - "t_sweep = (delta_f - delta_0)/(2 * np.pi * 10) * 1000" + "t_sweep = (delta_f - delta_0) / (2 * np.pi * 10) * 1000" ] }, { @@ -177,7 +177,7 @@ "metadata": {}, "outputs": [], "source": [ - "generic_pulse = Pulse.ConstantPulse(100, 2*np.pi, 2, 0.)\n", + "generic_pulse = Pulse.ConstantPulse(100, 2 * np.pi, 2, 0.0)\n", "seq.add(generic_pulse, \"rydberg\")\n", "seq.target(\"q0\", \"raman\")\n", "seq.add(generic_pulse, \"raman\")\n", @@ -256,7 +256,9 @@ "metadata": {}, "outputs": [], "source": [ - "built_seq = seq.build(Omega_max = 2.3 * 2*np.pi, ts = [200, 500], last_target=\"q3\")\n", + "built_seq = seq.build(\n", + " Omega_max=2.3 * 2 * np.pi, ts=[200, 500], last_target=\"q3\"\n", + ")\n", "built_seq.draw()" ] }, @@ -273,7 +275,7 @@ "metadata": {}, "outputs": [], "source": [ - "alt_seq = seq.build(Omega_max = 2*np.pi, ts = [400, 100], last_target=\"q2\")\n", + "alt_seq = seq.build(Omega_max=2 * np.pi, ts=[400, 100], last_target=\"q2\")\n", "alt_seq.draw()" ] } diff --git a/tutorials/advanced_features/Phase Shifts and Virtual Z gates.ipynb b/tutorials/advanced_features/Phase Shifts and Virtual Z gates.ipynb index ff724dcec..3422c18e6 100644 --- a/tutorials/advanced_features/Phase Shifts and Virtual Z gates.ipynb +++ b/tutorials/advanced_features/Phase Shifts and Virtual Z gates.ipynb @@ -124,7 +124,7 @@ "metadata": {}, "outputs": [], "source": [ - "reg = Register({'q0': (0, 0)})\n", + "reg = Register({\"q0\": (0, 0)})\n", "device = MockDevice\n", "seq = Sequence(reg, device)\n", "seq.available_channels" @@ -136,7 +136,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.declare_channel('ch0', 'raman_local', initial_target = 'q0')" + "seq.declare_channel(\"ch0\", \"raman_local\", initial_target=\"q0\")" ] }, { @@ -147,7 +147,8 @@ "source": [ "# Defining the waveform for a pi/2 pulse\n", "from pulser.waveforms import BlackmanWaveform\n", - "pi2_wf = BlackmanWaveform(1000, np.pi/2) # Duration: 1us, Area: pi/2\n", + "\n", + "pi2_wf = BlackmanWaveform(1000, np.pi / 2) # Duration: 1us, Area: pi/2\n", "pi2_wf.draw()" ] }, @@ -160,7 +161,7 @@ "outputs": [], "source": [ "# 2. Create the pi/2 pulse\n", - "pi_2 = Pulse.ConstantDetuning(pi2_wf, detuning=0, phase=np.pi/2)\n", + "pi_2 = Pulse.ConstantDetuning(pi2_wf, detuning=0, phase=np.pi / 2)\n", "pi_2.draw()" ] }, @@ -172,11 +173,11 @@ }, "outputs": [], "source": [ - "#3. Applying the H gate\n", + "# 3. Applying the H gate\n", "\n", - "seq.add(pi_2, 'ch0') # The first pi/2-pulse\n", + "seq.add(pi_2, \"ch0\") # The first pi/2-pulse\n", "# Now the phase shift of pi on 'q0', for the 'digital' basis, which is usually where phase shifts are useful\n", - "seq.phase_shift(np.pi, 'q0', basis='digital') \n", + "seq.phase_shift(np.pi, \"q0\", basis=\"digital\")\n", "\n", "seq.draw(draw_phase_shifts=True)" ] @@ -198,9 +199,11 @@ "metadata": {}, "outputs": [], "source": [ - "h = Pulse.ConstantDetuning(pi2_wf, detuning=0, phase=np.pi/2, post_phase_shift=np.pi)\n", + "h = Pulse.ConstantDetuning(\n", + " pi2_wf, detuning=0, phase=np.pi / 2, post_phase_shift=np.pi\n", + ")\n", "\n", - "seq.add(h, 'ch0')\n", + "seq.add(h, \"ch0\")\n", "seq.draw(draw_phase_shifts=True)" ] }, @@ -233,7 +236,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.add(h, 'ch0')\n", + "seq.add(h, \"ch0\")\n", "print(seq)" ] }, @@ -264,12 +267,12 @@ "metadata": {}, "outputs": [], "source": [ - "reg = Register({'q0': (0, 0), 'q1': (5, 5)})\n", + "reg = Register({\"q0\": (0, 0), \"q1\": (5, 5)})\n", "device = MockDevice\n", "seq = Sequence(reg, device)\n", - "seq.declare_channel('raman', 'raman_local', initial_target = 'q0')\n", - "seq.declare_channel('ryd1', 'rydberg_local', initial_target = 'q0')\n", - "seq.declare_channel('ryd2', 'rydberg_local', initial_target = 'q0')\n", + "seq.declare_channel(\"raman\", \"raman_local\", initial_target=\"q0\")\n", + "seq.declare_channel(\"ryd1\", \"rydberg_local\", initial_target=\"q0\")\n", + "seq.declare_channel(\"ryd2\", \"rydberg_local\", initial_target=\"q0\")\n", "seq.declared_channels" ] }, @@ -286,8 +289,8 @@ "metadata": {}, "outputs": [], "source": [ - "seq.add(h, 'raman')\n", - "seq.add(h, 'ryd1')\n", + "seq.add(h, \"raman\")\n", + "seq.add(h, \"ryd1\")\n", "seq.draw(draw_phase_shifts=True)" ] }, @@ -320,7 +323,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.add(pi_2, 'ryd2')\n", + "seq.add(pi_2, \"ryd2\")\n", "print(seq)" ] }, @@ -339,10 +342,10 @@ "metadata": {}, "outputs": [], "source": [ - "seq.target('q1', 'raman')\n", - "seq.add(h, 'raman')\n", - "seq.target('q1', 'ryd1')\n", - "seq.add(h, 'ryd1')\n", + "seq.target(\"q1\", \"raman\")\n", + "seq.add(h, \"raman\")\n", + "seq.target(\"q1\", \"ryd1\")\n", + "seq.add(h, \"ryd1\")\n", "print(seq)\n", "seq.draw(draw_phase_shifts=True)" ] @@ -362,7 +365,7 @@ }, "outputs": [], "source": [ - "seq.target('q1', 'ryd2')\n", + "seq.target(\"q1\", \"ryd2\")\n", "seq.draw(draw_phase_shifts=True)" ] }, diff --git a/tutorials/advanced_features/Serialization.ipynb b/tutorials/advanced_features/Serialization.ipynb index 295ed5082..c9883f621 100644 --- a/tutorials/advanced_features/Serialization.ipynb +++ b/tutorials/advanced_features/Serialization.ipynb @@ -42,10 +42,12 @@ "seq.declare_channel(\"digital\", \"raman_local\", initial_target=\"control\")\n", "seq.declare_channel(\"rydberg\", \"rydberg_local\", initial_target=\"control\")\n", "\n", - "half_pi_wf = BlackmanWaveform(200, np.pi/2)\n", + "half_pi_wf = BlackmanWaveform(200, np.pi / 2)\n", "\n", - "ry = Pulse.ConstantDetuning(amplitude=half_pi_wf,detuning=0,phase=-np.pi/2)\n", - "ry_dag = Pulse.ConstantDetuning(amplitude=half_pi_wf,detuning=0,phase=np.pi/2)\n", + "ry = Pulse.ConstantDetuning(amplitude=half_pi_wf, detuning=0, phase=-np.pi / 2)\n", + "ry_dag = Pulse.ConstantDetuning(\n", + " amplitude=half_pi_wf, detuning=0, phase=np.pi / 2\n", + ")\n", "\n", "seq.add(ry, \"digital\")\n", "seq.target(\"target\", \"digital\")\n", @@ -55,7 +57,7 @@ "pi_pulse = Pulse.ConstantDetuning(pi_wf, 0, 0)\n", "\n", "max_val = Chadoq2.rabi_from_blockade(8)\n", - "two_pi_wf = BlackmanWaveform.from_max_val(max_val, 2*np.pi)\n", + "two_pi_wf = BlackmanWaveform.from_max_val(max_val, 2 * np.pi)\n", "two_pi_pulse = Pulse.ConstantDetuning(two_pi_wf, 0, 0)\n", "\n", "seq.align(\"digital\", \"rydberg\")\n", diff --git a/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb index fecbad9e7..155822b7f 100644 --- a/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb +++ b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb @@ -69,7 +69,7 @@ "metadata": {}, "outputs": [], "source": [ - "reg = Register.from_coordinates([(0,0)], prefix='q')" + "reg = Register.from_coordinates([(0, 0)], prefix=\"q\")" ] }, { @@ -99,10 +99,10 @@ ], "source": [ "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ch0', 'rydberg_global')\n", + "seq.declare_channel(\"ch0\", \"rydberg_global\")\n", "duration = 2500\n", - "pulse = Pulse.ConstantPulse(duration, 2*np.pi, 0., 0.)\n", - "seq.add(pulse, 'ch0')\n", + "pulse = Pulse.ConstantPulse(duration, 2 * np.pi, 0.0, 0.0)\n", + "seq.add(pulse, \"ch0\")\n", "seq.draw()" ] }, @@ -136,7 +136,7 @@ "metadata": {}, "outputs": [], "source": [ - "obs = qutip.basis(2,0).proj()" + "obs = qutip.basis(2, 0).proj()" ] }, { @@ -216,7 +216,7 @@ "metadata": {}, "outputs": [], "source": [ - "config_spam = SimConfig(noise=('SPAM'), runs = 30, samples_per_run = 5)\n", + "config_spam = SimConfig(noise=(\"SPAM\"), runs=30, samples_per_run=5)\n", "sim.set_config(config_spam)" ] }, @@ -284,7 +284,12 @@ } ], "source": [ - "cfg2 = SimConfig(noise=('SPAM', 'dephasing', 'doppler'), eta=0.8, temperature=1000, runs=10000)\n", + "cfg2 = SimConfig(\n", + " noise=(\"SPAM\", \"dephasing\", \"doppler\"),\n", + " eta=0.8,\n", + " temperature=1000,\n", + " runs=10000,\n", + ")\n", "sim.add_config(cfg2)\n", "sim.show_config()" ] @@ -316,7 +321,7 @@ "metadata": {}, "outputs": [], "source": [ - "sim.evaluation_times = .8" + "sim.evaluation_times = 0.8" ] }, { @@ -401,7 +406,7 @@ } ], "source": [ - "res.plot(obs, fmt='.')\n", + "res.plot(obs, fmt=\".\")\n", "plt.show()" ] }, @@ -424,7 +429,7 @@ } ], "source": [ - "res.plot(obs, error_bars=False, fmt='.')" + "res.plot(obs, error_bars=False, fmt=\".\")" ] }, { @@ -467,7 +472,7 @@ "res_spam = sim.run()\n", "res_spam.plot(obs)\n", "sim.reset_config()\n", - "sim.eval_times = 'Full'\n", + "sim.eval_times = \"Full\"\n", "res_clean = sim.run()\n", "res_clean.plot(obs)\n", "plt.show()" @@ -499,7 +504,7 @@ } ], "source": [ - "config_spam_mod = SimConfig(noise=('SPAM'), eta=0.4, runs = 100)\n", + "config_spam_mod = SimConfig(noise=(\"SPAM\"), eta=0.4, runs=100)\n", "sim.set_config(config_spam_mod)\n", "sim.evaluation_times = 0.5\n", "res_large_eta = sim.run()\n", @@ -546,12 +551,14 @@ } ], "source": [ - "plt.figure(figsize=(10,5))\n", + "plt.figure(figsize=(10, 5))\n", "res_clean.plot(obs)\n", - "for eta in np.linspace(0,0.99,4):\n", - " config_spam_eta = SimConfig(noise = 'SPAM', eta=eta, runs = 50, epsilon=0, epsilon_prime=0)\n", + "for eta in np.linspace(0, 0.99, 4):\n", + " config_spam_eta = SimConfig(\n", + " noise=\"SPAM\", eta=eta, runs=50, epsilon=0, epsilon_prime=0\n", + " )\n", " sim.set_config(config_spam_eta)\n", - " sim.run().plot(obs, label=f'eta = {eta}')\n", + " sim.run().plot(obs, label=f\"eta = {eta}\")\n", "plt.legend()\n", "plt.show()" ] @@ -596,12 +603,14 @@ } ], "source": [ - "plt.figure(figsize=(10,5))\n", + "plt.figure(figsize=(10, 5))\n", "res_clean.plot(obs)\n", - "for eps in np.linspace(0,.99,4):\n", - " config_spam_eps = SimConfig(noise = 'SPAM', eta=0, runs = 50, epsilon=eps, epsilon_prime=0)\n", + "for eps in np.linspace(0, 0.99, 4):\n", + " config_spam_eps = SimConfig(\n", + " noise=\"SPAM\", eta=0, runs=50, epsilon=eps, epsilon_prime=0\n", + " )\n", " sim.set_config(config_spam_eps)\n", - " sim.run().plot(obs, label=f'epsilon = {eps}')\n", + " sim.run().plot(obs, label=f\"epsilon = {eps}\")\n", "plt.legend()\n", "plt.show()" ] @@ -646,12 +655,14 @@ } ], "source": [ - "plt.figure(figsize=(10,5))\n", + "plt.figure(figsize=(10, 5))\n", "res_clean.plot(obs)\n", - "for eps_p in np.linspace(0,.99,4):\n", - " config_spam_eps_p = SimConfig(noise = 'SPAM', eta=0, runs = 50, epsilon=0, epsilon_prime=eps_p)\n", + "for eps_p in np.linspace(0, 0.99, 4):\n", + " config_spam_eps_p = SimConfig(\n", + " noise=\"SPAM\", eta=0, runs=50, epsilon=0, epsilon_prime=eps_p\n", + " )\n", " sim.set_config(config_spam_eps_p)\n", - " sim.run().plot(obs, label=f'epsilon = {eps_p}')\n", + " sim.run().plot(obs, label=f\"epsilon = {eps_p}\")\n", "plt.legend()\n", "plt.show()" ] @@ -703,7 +714,9 @@ } ], "source": [ - "config_doppler = SimConfig(noise='doppler', runs=100, temperature = 5000, samples_per_run=1)\n", + "config_doppler = SimConfig(\n", + " noise=\"doppler\", runs=100, temperature=5000, samples_per_run=1\n", + ")\n", "sim.set_config(config_doppler)\n", "sim.show_config()" ] @@ -763,28 +776,34 @@ "outputs": [], "source": [ "# Parameters in rad/µs and ns\n", - "Omega_max = 2.3 * 2*np.pi \n", + "Omega_max = 2.3 * 2 * np.pi\n", "U = Omega_max / 2.3\n", "delta_0 = -6 * U\n", "delta_f = 2 * U\n", "t_rise = 252\n", "t_fall = 500\n", - "t_sweep = (delta_f - delta_0)/(2 * np.pi * 10) * 1000\n", + "t_sweep = (delta_f - delta_0) / (2 * np.pi * 10) * 1000\n", "R_interatomic = Chadoq2.rydberg_blockade_radius(U)\n", "\n", "N_side = 3\n", - "reg = Register.rectangle(N_side, N_side, R_interatomic, prefix='q')\n", + "reg = Register.rectangle(N_side, N_side, R_interatomic, prefix=\"q\")\n", "\n", - "rise = Pulse.ConstantDetuning(RampWaveform(t_rise, 0., Omega_max), delta_0, 0.)\n", - "sweep = Pulse.ConstantAmplitude(Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.)\n", - "fall = Pulse.ConstantDetuning(RampWaveform(t_fall, Omega_max, 0.), delta_f, 0.)\n", + "rise = Pulse.ConstantDetuning(\n", + " RampWaveform(t_rise, 0.0, Omega_max), delta_0, 0.0\n", + ")\n", + "sweep = Pulse.ConstantAmplitude(\n", + " Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.0\n", + ")\n", + "fall = Pulse.ConstantDetuning(\n", + " RampWaveform(t_fall, Omega_max, 0.0), delta_f, 0.0\n", + ")\n", "\n", "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ising', 'rydberg_global')\n", + "seq.declare_channel(\"ising\", \"rydberg_global\")\n", "\n", - "seq.add(rise, 'ising')\n", - "seq.add(sweep, 'ising')\n", - "seq.add(fall, 'ising')" + "seq.add(rise, \"ising\")\n", + "seq.add(sweep, \"ising\")\n", + "seq.add(fall, \"ising\")" ] }, { @@ -793,9 +812,12 @@ "metadata": {}, "outputs": [], "source": [ - "config_all_noise = SimConfig(noise=('SPAM', 'doppler', 'amplitude'),\n", - " runs=100, samples_per_run=10)\n", - "simul = Simulation(seq, sampling_rate=0.05, evaluation_times=0.2, config=config_all_noise)\n", + "config_all_noise = SimConfig(\n", + " noise=(\"SPAM\", \"doppler\", \"amplitude\"), runs=100, samples_per_run=10\n", + ")\n", + "simul = Simulation(\n", + " seq, sampling_rate=0.05, evaluation_times=0.2, config=config_all_noise\n", + ")\n", "spam_results = simul.run()\n", "simul.reset_config()\n", "clean_results = simul.run()" @@ -827,17 +849,19 @@ } ], "source": [ - "plt.figure(figsize=(20,5))\n", + "plt.figure(figsize=(20, 5))\n", "spam_count = spam_results.sample_final_state(N_samples=1e5)\n", "clean_count = clean_results.sample_final_state(N_samples=1e5)\n", "\n", - "clean_most_freq = {k:v for k,v in clean_count.items() if v>500}\n", - "spam_most_freq = {k:v for k,v in spam_count.items() if v>500}\n", + "clean_most_freq = {k: v for k, v in clean_count.items() if v > 500}\n", + "spam_most_freq = {k: v for k, v in spam_count.items() if v > 500}\n", "\n", - "plt.bar(list(clean_most_freq.keys()), list(clean_most_freq.values()), width=0.9)\n", + "plt.bar(\n", + " list(clean_most_freq.keys()), list(clean_most_freq.values()), width=0.9\n", + ")\n", "plt.bar(list(spam_most_freq.keys()), list(spam_most_freq.values()), width=0.5)\n", "\n", - "plt.xticks(rotation='vertical')\n", + "plt.xticks(rotation=\"vertical\")\n", "plt.show()" ] }, diff --git a/tutorials/advanced_features/State Preparation with the SLM Mask.ipynb b/tutorials/advanced_features/State Preparation with the SLM Mask.ipynb index cbe7cb6e8..6f32fd09a 100644 --- a/tutorials/advanced_features/State Preparation with the SLM Mask.ipynb +++ b/tutorials/advanced_features/State Preparation with the SLM Mask.ipynb @@ -36,7 +36,7 @@ "from pulser.simulation import Simulation\n", "\n", "# Qubit register\n", - "qubits = {\"q0\": (-5,0), \"q1\": (0,0), \"q2\": (5,0)}\n", + "qubits = {\"q0\": (-5, 0), \"q1\": (0, 0), \"q2\": (5, 0)}\n", "reg = Register(qubits)\n", "reg.draw()" ] @@ -58,9 +58,9 @@ "seq = Sequence(reg, MockDevice)\n", "\n", "# Declare a global XY channel and add the pi pulse\n", - "seq.declare_channel('ch', 'mw_global')\n", + "seq.declare_channel(\"ch\", \"mw_global\")\n", "pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi), 0, 0)\n", - "seq.add(pulse, 'ch')" + "seq.add(pulse, \"ch\")" ] }, { @@ -193,7 +193,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.add(pulse, 'ch')" + "seq.add(pulse, \"ch\")" ] }, { diff --git a/tutorials/applications/Control-Z Gate Sequence.ipynb b/tutorials/applications/Control-Z Gate Sequence.ipynb index 438a2812f..8e0aaad92 100644 --- a/tutorials/applications/Control-Z Gate Sequence.ipynb +++ b/tutorials/applications/Control-Z Gate Sequence.ipynb @@ -58,7 +58,7 @@ "from pulser import Pulse, Sequence, Register\n", "from pulser.devices import Chadoq2\n", "from pulser.simulation import Simulation\n", - "from pulser.waveforms import BlackmanWaveform,ConstantWaveform" + "from pulser.waveforms import BlackmanWaveform, ConstantWaveform" ] }, { @@ -86,10 +86,12 @@ "outputs": [], "source": [ "Rabi = np.linspace(1, 10, 10)\n", - "R_blockade = [Chadoq2.rydberg_blockade_radius(2.*np.pi*rabi) for rabi in Rabi]\n", + "R_blockade = [\n", + " Chadoq2.rydberg_blockade_radius(2.0 * np.pi * rabi) for rabi in Rabi\n", + "]\n", "\n", "plt.figure()\n", - "plt.plot(Rabi, R_blockade,'--o')\n", + "plt.plot(Rabi, R_blockade, \"--o\")\n", "plt.xlabel(r\"$\\Omega/(2\\pi)$ [MHz]\", fontsize=14)\n", "plt.ylabel(r\"$R_b$ [$\\mu\\.m$]\", fontsize=14)\n", "plt.show()" @@ -109,9 +111,10 @@ "outputs": [], "source": [ "# Atom Register and Device\n", - "q_dict = {\"control\":np.array([-2,0.]),\n", - " \"target\": np.array([2,0.]),\n", - " }\n", + "q_dict = {\n", + " \"control\": np.array([-2, 0.0]),\n", + " \"target\": np.array([2, 0.0]),\n", + "}\n", "reg = Register(q_dict)\n", "reg.draw()" ] @@ -144,22 +147,24 @@ "outputs": [], "source": [ "def build_state_from_id(s_id, basis_name):\n", - " if len(s_id) not in {2,3}:\n", + " if len(s_id) not in {2, 3}:\n", " raise ValueError(\"Not a valid state ID string\")\n", - " \n", - " ids = {'digital': 'gh', 'ground-rydberg': 'rg', 'all': 'rgh'}\n", + "\n", + " ids = {\"digital\": \"gh\", \"ground-rydberg\": \"rg\", \"all\": \"rgh\"}\n", " if basis_name not in ids:\n", - " raise ValueError('Not a valid basis')\n", - " \n", - " pool = {''.join(x) for x in product(ids[basis_name], repeat=len(s_id))}\n", + " raise ValueError(\"Not a valid basis\")\n", + "\n", + " pool = {\"\".join(x) for x in product(ids[basis_name], repeat=len(s_id))}\n", " if s_id not in pool:\n", - " raise ValueError('Not a valid state id for the given basis.')\n", + " raise ValueError(\"Not a valid state id for the given basis.\")\n", "\n", - " ket = {op: qutip.basis(len(ids[basis_name]), i) \n", - " for i, op in enumerate(ids[basis_name])}\n", + " ket = {\n", + " op: qutip.basis(len(ids[basis_name]), i)\n", + " for i, op in enumerate(ids[basis_name])\n", + " }\n", " if len(s_id) == 3:\n", - " #Recall that s_id = 'C1'+'C2'+'T' while in the register reg_id = 'C1'+'T'+'C2'.\n", - " reg_id = s_id[0]+s_id[2]+s_id[1] \n", + " # Recall that s_id = 'C1'+'C2'+'T' while in the register reg_id = 'C1'+'T'+'C2'.\n", + " reg_id = s_id[0] + s_id[2] + s_id[1]\n", " return qutip.tensor([ket[x] for x in reg_id])\n", " else:\n", " return qutip.tensor([ket[x] for x in s_id])" @@ -178,7 +183,7 @@ "metadata": {}, "outputs": [], "source": [ - "build_state_from_id('hg','digital')" + "build_state_from_id(\"hg\", \"digital\")" ] }, { @@ -194,8 +199,10 @@ "metadata": {}, "outputs": [], "source": [ - "duration = 300 \n", - "pi_Y = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0., -np.pi/2)\n", + "duration = 300\n", + "pi_Y = Pulse.ConstantDetuning(\n", + " BlackmanWaveform(duration, np.pi), 0.0, -np.pi / 2\n", + ")\n", "pi_Y.draw()" ] }, @@ -214,31 +221,37 @@ "source": [ "def preparation_sequence(state_id, reg):\n", " global seq\n", - " \n", - " if not set(state_id) <= {'g','h'} or len(state_id) != len(reg.qubits):\n", - " raise ValueError('Not a valid state ID')\n", + "\n", + " if not set(state_id) <= {\"g\", \"h\"} or len(state_id) != len(reg.qubits):\n", + " raise ValueError(\"Not a valid state ID\")\n", "\n", " if len(reg.qubits) == 2:\n", - " seq_dict = {'1':'target', '0':'control'}\n", + " seq_dict = {\"1\": \"target\", \"0\": \"control\"}\n", " elif len(reg.qubits) == 3:\n", - " seq_dict = {'2':'target', '1':'control2', '0':'control1'}\n", + " seq_dict = {\"2\": \"target\", \"1\": \"control2\", \"0\": \"control1\"}\n", "\n", " seq = Sequence(reg, Chadoq2)\n", - " if set(state_id) == {'g'}:\n", - " basis = 'ground-rydberg'\n", - " print(f'Warning: {state_id} state does not require a preparation sequence.')\n", + " if set(state_id) == {\"g\"}:\n", + " basis = \"ground-rydberg\"\n", + " print(\n", + " f\"Warning: {state_id} state does not require a preparation sequence.\"\n", + " )\n", " else:\n", - " basis = 'all'\n", + " basis = \"all\"\n", " for k in range(len(reg.qubits)):\n", - " if state_id[k] == 'h':\n", - " if 'raman' not in seq.declared_channels:\n", - " seq.declare_channel('raman','raman_local', seq_dict[str(k)])\n", + " if state_id[k] == \"h\":\n", + " if \"raman\" not in seq.declared_channels:\n", + " seq.declare_channel(\n", + " \"raman\", \"raman_local\", seq_dict[str(k)]\n", + " )\n", " else:\n", - " seq.target(seq_dict[str(k)],'raman')\n", - " seq.add(pi_Y,'raman')\n", + " seq.target(seq_dict[str(k)], \"raman\")\n", + " seq.add(pi_Y, \"raman\")\n", + "\n", + " prep_state = build_state_from_id(\n", + " state_id, basis\n", + " ) # Raises error if not a valid `state_id` for the register\n", "\n", - " prep_state = build_state_from_id(state_id, basis) # Raises error if not a valid `state_id` for the register\n", - " \n", " return prep_state" ] }, @@ -256,7 +269,7 @@ "outputs": [], "source": [ "# Define sequence and Set channels\n", - "prep_state = preparation_sequence('hh', reg)\n", + "prep_state = preparation_sequence(\"hh\", reg)\n", "seq.draw(draw_phase_area=True)" ] }, @@ -280,8 +293,10 @@ "metadata": {}, "outputs": [], "source": [ - "pi_pulse = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0., 0)\n", - "twopi_pulse = Pulse.ConstantDetuning(BlackmanWaveform(duration, 2*np.pi), 0., 0)" + "pi_pulse = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0.0, 0)\n", + "twopi_pulse = Pulse.ConstantDetuning(\n", + " BlackmanWaveform(duration, 2 * np.pi), 0.0, 0\n", + ")" ] }, { @@ -291,21 +306,25 @@ "outputs": [], "source": [ "def CZ_sequence(initial_id):\n", - " \n", + "\n", " # Prepare State\n", - " prep_state = preparation_sequence(initial_id, reg) \n", - " prep_time = max((seq._last(ch).tf for ch in seq.declared_channels), default=0)\n", - " \n", + " prep_state = preparation_sequence(initial_id, reg)\n", + " prep_time = max(\n", + " (seq._last(ch).tf for ch in seq.declared_channels), default=0\n", + " )\n", + "\n", " # Declare Rydberg channel\n", - " seq.declare_channel('ryd', 'rydberg_local', 'control')\n", - " \n", + " seq.declare_channel(\"ryd\", \"rydberg_local\", \"control\")\n", + "\n", " # Write CZ sequence:\n", - " seq.add(pi_pulse, 'ryd', 'wait-for-all') # Wait for state preparation to finish.\n", - " seq.target('target', 'ryd') # Changes to target qubit\n", - " seq.add(twopi_pulse, 'ryd')\n", - " seq.target('control', 'ryd') # Changes back to control qubit\n", - " seq.add(pi_pulse, 'ryd') \n", - " \n", + " seq.add(\n", + " pi_pulse, \"ryd\", \"wait-for-all\"\n", + " ) # Wait for state preparation to finish.\n", + " seq.target(\"target\", \"ryd\") # Changes to target qubit\n", + " seq.add(twopi_pulse, \"ryd\")\n", + " seq.target(\"control\", \"ryd\") # Changes back to control qubit\n", + " seq.add(pi_pulse, \"ryd\")\n", + "\n", " return prep_state, prep_time" ] }, @@ -315,10 +334,12 @@ "metadata": {}, "outputs": [], "source": [ - "prep_state, prep_time = CZ_sequence('gh') # constructs seq, prep_state and prep_time\n", + "prep_state, prep_time = CZ_sequence(\n", + " \"gh\"\n", + ") # constructs seq, prep_state and prep_time\n", "seq.draw(draw_phase_area=True)\n", - "print(f'Prepared state: {prep_state}')\n", - "print(f'Preparation time: {prep_time}ns')" + "print(f\"Prepared state: {prep_state}\")\n", + "print(f\"Preparation time: {prep_time}ns\")" ] }, { @@ -335,25 +356,27 @@ "outputs": [], "source": [ "CZ = {}\n", - "for state_id in {'gg','hg','gh','hh'}:\n", + "for state_id in {\"gg\", \"hg\", \"gh\", \"hh\"}:\n", " # Get CZ sequence\n", - " prep_state, prep_time = CZ_sequence(state_id) # constructs seq, prep_state and prep_time\n", - " \n", + " prep_state, prep_time = CZ_sequence(\n", + " state_id\n", + " ) # constructs seq, prep_state and prep_time\n", + "\n", " # Construct Simulation instance\n", " simul = Simulation(seq)\n", " res = simul.run()\n", - " \n", - " data=[st.overlap(prep_state) for st in res.states]\n", - " \n", + "\n", + " data = [st.overlap(prep_state) for st in res.states]\n", + "\n", " final_st = res.states[-1]\n", " CZ[state_id] = final_st.overlap(prep_state)\n", - " \n", + "\n", " plt.figure()\n", " plt.plot(np.real(data))\n", " plt.xlabel(r\"Time [ns]\")\n", - " plt.ylabel(fr'$ \\langle\\,{state_id} |\\, \\psi(t)\\rangle$')\n", - " plt.axvspan(0, prep_time, alpha=0.06, color='royalblue')\n", - " plt.title(fr\"Action of gate on state $|${state_id}$\\rangle$\")" + " plt.ylabel(rf\"$ \\langle\\,{state_id} |\\, \\psi(t)\\rangle$\")\n", + " plt.axvspan(0, prep_time, alpha=0.06, color=\"royalblue\")\n", + " plt.title(rf\"Action of gate on state $|${state_id}$\\rangle$\")" ] }, { @@ -386,9 +409,11 @@ "outputs": [], "source": [ "# Atom Register and Device\n", - "q_dict = {\"control1\":np.array([-2.0, 0.]),\n", - " \"target\": np.array([0., 2*np.sqrt(3.001)]),\n", - " \"control2\": np.array([2.0, 0.])}\n", + "q_dict = {\n", + " \"control1\": np.array([-2.0, 0.0]),\n", + " \"target\": np.array([0.0, 2 * np.sqrt(3.001)]),\n", + " \"control2\": np.array([2.0, 0.0]),\n", + "}\n", "reg = Register(q_dict)\n", "reg.draw()" ] @@ -399,7 +424,7 @@ "metadata": {}, "outputs": [], "source": [ - "preparation_sequence('hhh', reg)\n", + "preparation_sequence(\"hhh\", reg)\n", "seq.draw(draw_phase_area=True)" ] }, @@ -412,22 +437,26 @@ "def CCZ_sequence(initial_id):\n", " # Prepare State\n", " prep_state = preparation_sequence(initial_id, reg)\n", - " prep_time = max((seq._last(ch).tf for ch in seq.declared_channels), default=0)\n", - " \n", + " prep_time = max(\n", + " (seq._last(ch).tf for ch in seq.declared_channels), default=0\n", + " )\n", + "\n", " # Declare Rydberg channel\n", - " seq.declare_channel('ryd', 'rydberg_local', 'control1')\n", - " \n", + " seq.declare_channel(\"ryd\", \"rydberg_local\", \"control1\")\n", + "\n", " # Write CCZ sequence:\n", - " seq.add(pi_pulse, 'ryd', protocol='wait-for-all') # Wait for state preparation to finish.\n", - " seq.target('control2', 'ryd')\n", - " seq.add(pi_pulse, 'ryd')\n", - " seq.target('target','ryd')\n", - " seq.add(twopi_pulse, 'ryd')\n", - " seq.target('control2','ryd')\n", - " seq.add(pi_pulse, 'ryd')\n", - " seq.target('control1','ryd')\n", - " seq.add(pi_pulse,'ryd')\n", - " \n", + " seq.add(\n", + " pi_pulse, \"ryd\", protocol=\"wait-for-all\"\n", + " ) # Wait for state preparation to finish.\n", + " seq.target(\"control2\", \"ryd\")\n", + " seq.add(pi_pulse, \"ryd\")\n", + " seq.target(\"target\", \"ryd\")\n", + " seq.add(twopi_pulse, \"ryd\")\n", + " seq.target(\"control2\", \"ryd\")\n", + " seq.add(pi_pulse, \"ryd\")\n", + " seq.target(\"control1\", \"ryd\")\n", + " seq.add(pi_pulse, \"ryd\")\n", + "\n", " return prep_state, prep_time" ] }, @@ -437,7 +466,7 @@ "metadata": {}, "outputs": [], "source": [ - "CCZ_sequence('hhh')\n", + "CCZ_sequence(\"hhh\")\n", "seq.draw(draw_phase_area=True)" ] }, @@ -450,25 +479,25 @@ "outputs": [], "source": [ "CCZ = {}\n", - "for state_id in {''.join(x) for x in product('gh', repeat=3)}:\n", + "for state_id in {\"\".join(x) for x in product(\"gh\", repeat=3)}:\n", " # Get CCZ sequence\n", " prep_state, prep_time = CCZ_sequence(state_id)\n", - " \n", + "\n", " # Construct Simulation instance\n", " simul = Simulation(seq)\n", - " \n", + "\n", " res = simul.run()\n", - " \n", - " data=[st.overlap(prep_state) for st in res.states]\n", + "\n", + " data = [st.overlap(prep_state) for st in res.states]\n", " final_st = res.states[-1]\n", " CCZ[state_id] = final_st.overlap(prep_state)\n", - " \n", + "\n", " plt.figure()\n", " plt.plot(np.real(data))\n", " plt.xlabel(r\"Time [ns]\")\n", - " plt.ylabel(fr'$ \\langle\\,{state_id} | \\psi(t)\\rangle$')\n", - " plt.axvspan(0, prep_time, alpha=0.06, color='royalblue')\n", - " plt.title(fr\"Action of gate on state $|${state_id}$\\rangle$\")" + " plt.ylabel(rf\"$ \\langle\\,{state_id} | \\psi(t)\\rangle$\")\n", + " plt.axvspan(0, prep_time, alpha=0.06, color=\"royalblue\")\n", + " plt.title(rf\"Action of gate on state $|${state_id}$\\rangle$\")" ] }, { diff --git a/tutorials/applications/Quantum Evolution Kernel.ipynb b/tutorials/applications/Quantum Evolution Kernel.ipynb index 6d10aa0de..bc89f817e 100644 --- a/tutorials/applications/Quantum Evolution Kernel.ipynb +++ b/tutorials/applications/Quantum Evolution Kernel.ipynb @@ -76,26 +76,27 @@ "\n", "def JSdiv(p1, p2):\n", " \"\"\"Compute the Jensen-Shannon divergence between two distributions.\"\"\"\n", - " q1 = np.array(p1)/np.sum(p1)\n", - " q2 = np.array(p2)/np.sum(p2)\n", + " q1 = np.array(p1) / np.sum(p1)\n", + " q2 = np.array(p2) / np.sum(p2)\n", " # Alowing for distributions to have different sizes\n", " delta = len(q1) - len(q2)\n", " if delta < 0:\n", " q1 = np.concatenate((q1, np.array([0 for i in range(-delta)])))\n", " elif delta > 0:\n", " q2 = np.concatenate((q2, np.array([0 for i in range(delta)])))\n", - " pq = (q1 + q2)/2\n", + " pq = (q1 + q2) / 2\n", "\n", " def entropy(pl_unscaled):\n", " # Making sure the probability distributions are similarly normalized\n", - " pl = np.array(pl_unscaled)/np.sum(pl_unscaled)\n", + " pl = np.array(pl_unscaled) / np.sum(pl_unscaled)\n", " res = 0\n", " for p in pl:\n", " if p > 0:\n", - " res += p*np.log(p)\n", + " res += p * np.log(p)\n", " return -res\n", - " out = entropy(pq)-(entropy(q1)+entropy(q2))/2\n", - " return out\n" + "\n", + " out = entropy(pq) - (entropy(q1) + entropy(q2)) / 2\n", + " return out" ] }, { @@ -168,32 +169,32 @@ "import matplotlib.pyplot as plt\n", "from IPython.display import Latex\n", "import scipy.special\n", + "\n", "# Load graph package\n", "import networkx as nx\n", "\n", "\n", - "def pk(G, theta=np.pi/4):\n", + "def pk(G, theta=np.pi / 4):\n", " cnt = nx.degree_histogram(G)\n", " kappamax = len(cnt)\n", "\n", - " c = np.cos(theta)**2\n", - " s = 1-c\n", - " t = np.tan(theta)**2\n", + " c = np.cos(theta) ** 2\n", + " s = 1 - c\n", + " t = np.tan(theta) ** 2\n", " sp = 2 * c * s\n", "\n", " res0 = 0\n", " for kappa, m in enumerate(cnt):\n", - " res0 += m * (1-c**kappa)\n", + " res0 += m * (1 - c**kappa)\n", " res = [(sp * res0)]\n", " for k in range(1, kappamax):\n", " res0 = 0\n", " for kappa in range(k, kappamax):\n", " m_kappa = cnt[kappa]\n", " binom = scipy.special.comb(kappa, k, exact=True)\n", - " res0 += m_kappa * binom * (c**(kappa+1-k))\n", - " res.append(((s**(1+k)) * res0))\n", - " return res\n", - " " + " res0 += m_kappa * binom * (c ** (kappa + 1 - k))\n", + " res.append(((s ** (1 + k)) * res0))\n", + " return res" ] }, { @@ -217,10 +218,7 @@ "n_graphs = 100\n", "\n", "\n", - "def create_random_graphs(N_max=100,\n", - " n_graphs=100,\n", - " rho_low=0.35,\n", - " rho_high=0.65):\n", + "def create_random_graphs(N_max=100, n_graphs=100, rho_low=0.35, rho_high=0.65):\n", " # Dataset with graphs of two different Erdős–Rényi classes\n", " graphs = []\n", " # Classes of these graphs\n", @@ -229,8 +227,8 @@ " probability_distributions = []\n", " for _ in range(n_graphs):\n", " # Number of nodes in the graph in [N_max/2,N_max]\n", - " N = np.random.randint(N_max//2, N_max+1)\n", - " if np.random.rand() < .5:\n", + " N = np.random.randint(N_max // 2, N_max + 1)\n", + " if np.random.rand() < 0.5:\n", " rho = rho_low\n", " classes.append(0)\n", " else:\n", @@ -239,9 +237,9 @@ " G = nx.erdos_renyi_graph(N, rho)\n", " graphs.append(G)\n", " pdist = pk(G)\n", - " probability_distributions.append(pdist/np.sum(pdist))\n", + " probability_distributions.append(pdist / np.sum(pdist))\n", "\n", - " return graphs, classes, probability_distributions\n" + " return graphs, classes, probability_distributions" ] }, { @@ -266,14 +264,15 @@ "outputs": [], "source": [ "def kernel_matrix(pdist1, pdist2, mu=1):\n", - " Kmat = np.array([[np.exp(-mu * JSdiv(p1, p2)) for p1 in pdist1]\n", - " for p2 in pdist2])\n", + " Kmat = np.array(\n", + " [[np.exp(-mu * JSdiv(p1, p2)) for p1 in pdist1] for p2 in pdist2]\n", + " )\n", " return Kmat\n", "\n", "\n", "graphs, classes, proba_dists = create_random_graphs()\n", "\n", - "Kmat = kernel_matrix(proba_dists, proba_dists)\n" + "Kmat = kernel_matrix(proba_dists, proba_dists)" ] }, { @@ -298,13 +297,13 @@ "source": [ "def plot_kernel_matrix(Kmat):\n", " fig, ax = plt.subplots(figsize=(8, 8))\n", - " im = ax.imshow(Kmat, cmap='OrRd')\n", - " ax.set_xlabel('Graph #', fontsize=18)\n", - " ax.set_ylabel('Graph #', fontsize=18)\n", - " cbar = plt.colorbar(im, extend='max')\n", + " im = ax.imshow(Kmat, cmap=\"OrRd\")\n", + " ax.set_xlabel(\"Graph #\", fontsize=18)\n", + " ax.set_ylabel(\"Graph #\", fontsize=18)\n", + " cbar = plt.colorbar(im, extend=\"max\")\n", "\n", "\n", - "plot_kernel_matrix(Kmat)\n" + "plot_kernel_matrix(Kmat)" ] }, { @@ -327,13 +326,15 @@ "outputs": [], "source": [ "from sklearn import svm\n", - "from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score\n", + "from sklearn.metrics import (\n", + " f1_score,\n", + " accuracy_score,\n", + " recall_score,\n", + " precision_score,\n", + ")\n", "\n", "\n", - "scores_types = ['Accuracy ',\n", - " 'f1 ',\n", - " 'Precision',\n", - " 'Recall ']\n", + "scores_types = [\"Accuracy \", \"f1 \", \"Precision\", \"Recall \"]\n", "\n", "\n", "def trained_classifier_from_Kmat(Kmat, classes_train):\n", @@ -341,7 +342,7 @@ " Create and train a classifier from the Kernel matrix `Kmat`\n", " obtained from graphs of classes `classes_train`\n", " \"\"\"\n", - " classifier = svm.SVC(kernel='precomputed')\n", + " classifier = svm.SVC(kernel=\"precomputed\")\n", " classifier.fit(Kmat, classes_train)\n", "\n", " return classifier\n", @@ -357,11 +358,10 @@ "\n", " return trained_classifier_from_Kmat(Kmat, classes_train)\n", "\n", - "def test_classifier(classifier,\n", - " p_dist_train,\n", - " p_dist_test,\n", - " classes_test,\n", - " verbose=False):\n", + "\n", + "def test_classifier(\n", + " classifier, p_dist_train, p_dist_test, classes_test, verbose=False\n", + "):\n", " \"\"\"\n", " Test a trained classifier `classifier` from the probability\n", " distributions of the train and test data sets `p_dist_train`\n", @@ -372,40 +372,37 @@ "\n", " predicted_classes = classifier.predict(X)\n", "\n", - " scores = [accuracy_score(classes_test,\n", - " predicted_classes),\n", - " f1_score(classes_test,\n", - " predicted_classes,\n", - " average='weighted'),\n", - " precision_score(classes_test,\n", - " predicted_classes,\n", - " average='weighted',\n", - " zero_division=0),\n", - " recall_score(classes_test,\n", - " predicted_classes,\n", - " average='weighted')]\n", + " scores = [\n", + " accuracy_score(classes_test, predicted_classes),\n", + " f1_score(classes_test, predicted_classes, average=\"weighted\"),\n", + " precision_score(\n", + " classes_test,\n", + " predicted_classes,\n", + " average=\"weighted\",\n", + " zero_division=0,\n", + " ),\n", + " recall_score(classes_test, predicted_classes, average=\"weighted\"),\n", + " ]\n", "\n", " if verbose:\n", " for st, s in zip(scores_types, scores):\n", - " print(f'{st} : {s:6.3}')\n", + " print(f\"{st} : {s:6.3}\")\n", "\n", " return scores\n", "\n", "\n", - "def train_and_test_classifier(p_dist_train,\n", - " classes_train,\n", - " p_dist_test,\n", - " classes_test,\n", - " verbose=False):\n", + "def train_and_test_classifier(\n", + " p_dist_train, classes_train, p_dist_test, classes_test, verbose=False\n", + "):\n", " \"\"\"\n", " Train and test a classifier from test and\n", " train probability distributions and classes\n", " \"\"\"\n", " classifier = trained_classifier_pdist(p_dist_train, classes_train)\n", "\n", - " return classifier, test_classifier(classifier, p_dist_train,\n", - " p_dist_test, classes_test,\n", - " verbose=verbose)\n" + " return classifier, test_classifier(\n", + " classifier, p_dist_train, p_dist_test, classes_test, verbose=verbose\n", + " )" ] }, { @@ -440,12 +437,9 @@ "graphs_test, classes_test, p_dist_test = create_random_graphs(n_graphs=50)\n", "\n", "# Compute the score of the classifier\n", - "classifier, scores = train_and_test_classifier(p_dist_train,\n", - " classes_train,\n", - " p_dist_test,\n", - " classes_test,\n", - " verbose=True\n", - " )\n" + "classifier, scores = train_and_test_classifier(\n", + " p_dist_train, classes_train, p_dist_test, classes_test, verbose=True\n", + ")" ] }, { @@ -467,51 +461,51 @@ "metadata": {}, "outputs": [], "source": [ - "prefix = './Fingerprint/Fingerprint_'\n", + "prefix = \"./Fingerprint/Fingerprint_\"\n", "\n", "graphs = {}\n", "node_to_graph = {}\n", - "class_count = {}\n", + "class_count = {}\n", "\n", - "label_file = prefix + 'graph_labels' + '.txt'\n", + "label_file = prefix + \"graph_labels\" + \".txt\"\n", "with open(label_file) as f:\n", " lines = f.readlines()\n", " for i, line in enumerate(lines):\n", " labl = int(line)\n", - " graphs[i+1] = nx.Graph(label=labl)\n", + " graphs[i + 1] = nx.Graph(label=labl)\n", " if labl in class_count.keys():\n", " class_count[labl] += 1\n", " else:\n", " class_count[labl] = 1\n", "\n", "\n", - "node_to_graph_file = prefix + 'graph_indicator' + '.txt'\n", + "node_to_graph_file = prefix + \"graph_indicator\" + \".txt\"\n", "with open(node_to_graph_file) as f:\n", " lines = f.readlines()\n", " for i, line in enumerate(lines):\n", " gi = int(line)\n", - " node_to_graph[i+1] = gi\n", - " graphs[gi].add_node(i+1)\n", + " node_to_graph[i + 1] = gi\n", + " graphs[gi].add_node(i + 1)\n", "\n", - "adjacency_file = prefix + 'A' + '.txt'\n", + "adjacency_file = prefix + \"A\" + \".txt\"\n", "with open(adjacency_file) as f:\n", " lines = f.readlines()\n", " for line in lines:\n", - " Ind = line.split(',')\n", + " Ind = line.split(\",\")\n", " i = int(Ind[0])\n", " j = int(Ind[1])\n", " gi = node_to_graph[i]\n", " graphs[gi].add_edge(i, j)\n", "\n", - "coordinates_file = prefix + 'node_attributes' + '.txt'\n", + "coordinates_file = prefix + \"node_attributes\" + \".txt\"\n", "with open(coordinates_file) as f:\n", " lines = f.readlines()\n", - " for i,line in enumerate(lines):\n", - " Ind = line.split(',')\n", + " for i, line in enumerate(lines):\n", + " Ind = line.split(\",\")\n", " x = float(Ind[0])\n", " y = float(Ind[1])\n", - " gi = node_to_graph[i+1]\n", - " nx.set_node_attributes(graphs[gi], {i+1: (x,y)}, \"coords\")" + " gi = node_to_graph[i + 1]\n", + " nx.set_node_attributes(graphs[gi], {i + 1: (x, y)}, \"coords\")" ] }, { @@ -556,20 +550,20 @@ "count = {clas: 0 for clas in class_count.keys()}\n", "for g in graphs.values():\n", " if Nmin <= g.number_of_nodes() <= Nmax:\n", - " count[g.graph['label']] += 1\n", + " count[g.graph[\"label\"]] += 1\n", "\n", "# Number of graphs in the most represented class\n", "size_of_largest_class = max(count.values())\n", "# Include only classes with at least 10% of the size of the largest one\n", "include_classes = {clas: False for clas in class_count.keys()}\n", "for clas, prop in count.items():\n", - " if prop > .1*size_of_largest_class:\n", + " if prop > 0.1 * size_of_largest_class:\n", " include_classes[clas] = True\n", "\n", "\n", "data_preprocessed = []\n", "for g in graphs.values():\n", - " labl = g.graph['label']\n", + " labl = g.graph[\"label\"]\n", " if Nmin <= g.number_of_nodes() <= Nmax and include_classes[labl]:\n", " mapping = {l: i for i, l in enumerate(g.nodes())}\n", " g_shift = nx.relabel_nodes(g, mapping)\n", @@ -584,10 +578,11 @@ " if include_classes[clas]:\n", " included_classes[clas] = icount\n", "\n", - "print(f'After preprocessing, the dataset now contains {len(data_preprocessed)} \\n' +\n", - " f'graphs of at least {Nmin} and at most {Nmax} nodes, distributed \\n' +\n", - " f'across the different classes in the following way {included_classes}\\n')\n", - "\n" + "print(\n", + " f\"After preprocessing, the dataset now contains {len(data_preprocessed)} \\n\"\n", + " + f\"graphs of at least {Nmin} and at most {Nmax} nodes, distributed \\n\"\n", + " + f\"across the different classes in the following way {included_classes}\\n\"\n", + ")" ] }, { @@ -623,8 +618,8 @@ "\n", "data_reduced = []\n", "for g in data_preprocessed:\n", - " if(len(data_reduced) < dataset_targetsize):\n", - " cls = g.graph['label']\n", + " if len(data_reduced) < dataset_targetsize:\n", + " cls = g.graph[\"label\"]\n", " if cls in kept_classes.keys():\n", " kept_classes[cls] += 1\n", " data_reduced.append(g)\n", @@ -632,10 +627,12 @@ "# size of the dataset\n", "n_graphs = len(data_reduced)\n", "\n", - "print(f'After preprocessing, the dataset now contains {len(data_reduced)} ' +\n", - " f'graphs of at least {Nmin} and at most {Nmax} nodes, distributed ' +\n", - " f'across {len(kept_classes)} different classes in the following way ' +\n", - " f'{kept_classes}')\n" + "print(\n", + " f\"After preprocessing, the dataset now contains {len(data_reduced)} \"\n", + " + f\"graphs of at least {Nmin} and at most {Nmax} nodes, distributed \"\n", + " + f\"across {len(kept_classes)} different classes in the following way \"\n", + " + f\"{kept_classes}\"\n", + ")" ] }, { @@ -672,10 +669,10 @@ "\n", "\n", "def correct_coordinates(g):\n", - " '''\n", + " \"\"\"\n", " Corrects the coordinates of the nodes so that the\n", " atoms fit the hardware constraints.\n", - " '''\n", + " \"\"\"\n", " n = g.number_of_nodes()\n", "\n", " # Coordinates given in the dataset\n", @@ -683,47 +680,50 @@ " r_list += -np.mean(r_list, axis=0)\n", " scale = np.max([np.sqrt(r.dot(r)) for r in r_list])\n", "\n", - " x0 = r_list.reshape(2*n)\n", + " x0 = r_list.reshape(2 * n)\n", "\n", " # Ensures the atoms are within range of the device\n", - " xmax = device.max_radial_distance/np.sqrt(2)\n", - " bounds = [(-xmax, xmax)] * (2*n)\n", - " x0 *= xmax/scale\n", + " xmax = device.max_radial_distance / np.sqrt(2)\n", + " bounds = [(-xmax, xmax)] * (2 * n)\n", + " x0 *= xmax / scale\n", "\n", - " #Encode the constraint of a minimal distance bewteen atoms\n", + " # Encode the constraint of a minimal distance bewteen atoms\n", " def min_dist(params):\n", " return np.min(pdist(params.reshape(n, 2)))\n", "\n", - " dmin = 1.1*device.min_atom_distance\n", + " dmin = 1.1 * device.min_atom_distance\n", " nlc = NonlinearConstraint(min_dist, dmin, np.inf)\n", "\n", " def cost_function(params):\n", " return 1\n", "\n", - " res = minimize(cost_function,\n", - " x0=x0,\n", - " bounds=bounds,\n", - " constraints=nlc,\n", - " method='SLSQP')\n", + " res = minimize(\n", + " cost_function, x0=x0, bounds=bounds, constraints=nlc, method=\"SLSQP\"\n", + " )\n", " x = res.x\n", " rmax = device.max_radial_distance\n", - " scale_diameter = .95 * rmax/np.max(pdist(x.reshape(n, 2))) \n", - " x *= max(scale_diameter,1.)\n", + " scale_diameter = 0.95 * rmax / np.max(pdist(x.reshape(n, 2)))\n", + " x *= max(scale_diameter, 1.0)\n", " r_list = x.reshape(n, 2)\n", " r_list += -np.mean(r_list, axis=0)\n", "\n", - "\n", " for node, r in zip(g.nodes(), r_list):\n", " g.nodes[node][\"coords\"] = r\n", "\n", "\n", "def max_edge_length(g):\n", - " '''\n", + " \"\"\"\n", " Computes the maximal distance between nodes connected by an edge\n", " of the graph\n", - " '''\n", + " \"\"\"\n", " n = g.number_of_nodes()\n", - " edges = np.array([1 if (i, j) in g.edges() else 0 for i in range(n) for j in range(i+1, n)])\n", + " edges = np.array(\n", + " [\n", + " 1 if (i, j) in g.edges() else 0\n", + " for i in range(n)\n", + " for j in range(i + 1, n)\n", + " ]\n", + " )\n", " r_list = np.array([g.nodes[node][\"coords\"] for node in g.nodes()])\n", "\n", " distances = pdist(r_list)\n", @@ -746,9 +746,10 @@ "r_max = device.max_radial_distance\n", "d_min = device.min_atom_distance\n", "\n", - "omega_max = device.channels['rydberg_global'].max_amp\n", + "omega_max = device.channels[\"rydberg_global\"].max_amp\n", "min_bond_length = device.rabi_from_blockade(omega_max)\n", "\n", + "\n", "def reg_from_data(data_reduced):\n", " # The list of registers for each graph\n", " reg_list = []\n", @@ -764,10 +765,10 @@ " label_list = []\n", " for g in data_reduced:\n", "\n", - " label_list.append(g.graph['label'])\n", + " label_list.append(g.graph[\"label\"])\n", "\n", " correct_coordinates(g)\n", - " graph_dict= {i:g.nodes[i][\"coords\"] for i in g.nodes()}\n", + " graph_dict = {i: g.nodes[i][\"coords\"] for i in g.nodes()}\n", "\n", " edges_list.append(g.edges)\n", "\n", @@ -782,6 +783,7 @@ "\n", " return reg_list, rabi_list, edges_list, label_list\n", "\n", + "\n", "reg_list, rabi_list, edges_list, label_list = reg_from_data(data_reduced)" ] }, @@ -854,65 +856,56 @@ "source": [ "from pulser import Pulse, Sequence, Simulation\n", "\n", - "def pulse_seqence(reg,\n", - " t_1=100,\n", - " omega=omega_max, # amplitude of the initial and final pulses\n", - " omega_g=0, # amplitude in the \"free evolution\" parts\n", - " total_time=512): # total duration of the pulse\n", + "\n", + "def pulse_seqence(\n", + " reg,\n", + " t_1=100,\n", + " omega=omega_max, # amplitude of the initial and final pulses\n", + " omega_g=0, # amplitude in the \"free evolution\" parts\n", + " total_time=512,\n", + "): # total duration of the pulse\n", " seq = Sequence(reg, device)\n", - " seq.declare_channel('Channel 0','rydberg_global')\n", + " seq.declare_channel(\"Channel 0\", \"rydberg_global\")\n", "\n", - " # making sure that the value of omega does not exceed the \n", - " # maximal value, and that it doesn't lead to a pulse \n", + " # making sure that the value of omega does not exceed the\n", + " # maximal value, and that it doesn't lead to a pulse\n", " # duration that is too short\n", - " omega = min([omega,1000*np.pi/2,omega_max])\n", + " omega = min([omega, 1000 * np.pi / 2, omega_max])\n", "\n", " # Set the initial and final pulse times to the optimal value\n", " # be careful about the units : Omega(rad/μs) -> t (ns)\n", - " t = 1000*np.pi/(2*omega)\n", + " t = 1000 * np.pi / (2 * omega)\n", " # Set the total_time\n", - " tau = (total_time - 2*t - t_1)/2\n", + " tau = (total_time - 2 * t - t_1) / 2\n", " # No detuning needed here\n", " delta = 0\n", " # We want the pulse to be along sigma_y\n", - " phi=np.pi/2\n", - "\n", - " initial_pulse = Pulse.ConstantPulse(t,\n", - " omega,\n", - " delta,\n", - " phase=phi)\n", - " if total_time > t_1 + 2*t:\n", - " Hg_pulse = Pulse.ConstantPulse(tau,\n", - " omega_g,\n", - " delta,\n", - " phase=phi)\n", + " phi = np.pi / 2\n", + "\n", + " initial_pulse = Pulse.ConstantPulse(t, omega, delta, phase=phi)\n", + " if total_time > t_1 + 2 * t:\n", + " Hg_pulse = Pulse.ConstantPulse(tau, omega_g, delta, phase=phi)\n", " if t_1 > 0:\n", - " middle_pulse = Pulse.ConstantPulse(t_1,\n", - " omega,\n", - " delta,\n", - " phase=phi)\n", - " final_pulse = Pulse.ConstantPulse(t,\n", - " omega,\n", - " delta,\n", - " phase=phi)\n", - "\n", - " seq.add(initial_pulse, 'Channel 0')\n", - " if total_time > t_1 + 2*t:\n", - " seq.add(Hg_pulse, 'Channel 0')\n", + " middle_pulse = Pulse.ConstantPulse(t_1, omega, delta, phase=phi)\n", + " final_pulse = Pulse.ConstantPulse(t, omega, delta, phase=phi)\n", + "\n", + " seq.add(initial_pulse, \"Channel 0\")\n", + " if total_time > t_1 + 2 * t:\n", + " seq.add(Hg_pulse, \"Channel 0\")\n", " if t_1 > 0:\n", - " seq.add(middle_pulse, 'Channel 0')\n", - " if total_time > t_1 + 2*t:\n", - " seq.add(Hg_pulse, 'Channel 0')\n", - " seq.add(final_pulse, 'Channel 0')\n", + " seq.add(middle_pulse, \"Channel 0\")\n", + " if total_time > t_1 + 2 * t:\n", + " seq.add(Hg_pulse, \"Channel 0\")\n", + " seq.add(final_pulse, \"Channel 0\")\n", "\n", - " seq.measure(basis='ground-rydberg')\n", + " seq.measure(basis=\"ground-rydberg\")\n", "\n", " return seq\n", "\n", "\n", "# Illustrate the pulse on a register containing a single atom\n", "reg = Register.from_coordinates([(0, 0)])\n", - "pulse_seqence(reg, t_1=160).draw()\n" + "pulse_seqence(reg, t_1=160).draw()" ] }, { @@ -933,30 +926,34 @@ "from tqdm.auto import tqdm\n", "\n", "\n", - "def proba_distributions(t_1=100, # duration of the central pulse\n", - " omega=omega_max, # amplitude of the pulses\n", - " omega_g_factor=1, # set to 1 if the Amplitude is non\n", - " # zero during the \"free evolution\"\n", - " total_time=512, # total duration of the pulse\n", - " Nsamples=1000,\n", - " indices=list(range(n_graphs))): # graphs to be used\n", - " '''\n", + "def proba_distributions(\n", + " t_1=100, # duration of the central pulse\n", + " omega=omega_max, # amplitude of the pulses\n", + " omega_g_factor=1, # set to 1 if the Amplitude is non\n", + " # zero during the \"free evolution\"\n", + " total_time=512, # total duration of the pulse\n", + " Nsamples=1000,\n", + " indices=list(range(n_graphs)),\n", + "): # graphs to be used\n", + " \"\"\"\n", " Compute the probability distributions for a given pulse\n", " for all graphs in `indices`\n", - " '''\n", + " \"\"\"\n", "\n", - " bins = np.linspace(0, Nmax*Nmax, Nmax*Nmax + 1)\n", + " bins = np.linspace(0, Nmax * Nmax, Nmax * Nmax + 1)\n", " histograms = []\n", " for i in tqdm(indices):\n", " reg, rabi, edges = reg_list[i], rabi_list[i], edges_list[i]\n", - " seq = pulse_seqence(reg,\n", - " t_1=t_1,\n", - " omega=omega,\n", - " omega_g=omega_g_factor*rabi,\n", - " total_time=total_time)\n", + " seq = pulse_seqence(\n", + " reg,\n", + " t_1=t_1,\n", + " omega=omega,\n", + " omega_g=omega_g_factor * rabi,\n", + " total_time=total_time,\n", + " )\n", "\n", " # Simulate and sample\n", - " simul = Simulation(seq, evaluation_times=.5, sampling_rate=.1)\n", + " simul = Simulation(seq, evaluation_times=0.5, sampling_rate=0.1)\n", " results = simul.run()\n", " sampling = results.sample_final_state(N_samples=Nsamples)\n", "\n", @@ -968,19 +965,18 @@ " ie_weights.append(num)\n", "\n", " # Create histogram of the measurements and append to list\n", - " ncount, b = np.histogram(ie_meas,\n", - " bins=bins,\n", - " density=True,\n", - " weights=ie_weights)\n", + " ncount, b = np.histogram(\n", + " ie_meas, bins=bins, density=True, weights=ie_weights\n", + " )\n", " histograms.append(ncount)\n", " return histograms\n", "\n", "\n", "def compute_ising_energy(outcome, edges):\n", - " '''\n", + " \"\"\"\n", " Computes the Ising energy (i.e. the observable\n", " used by the kernel) from a measure bitstgring/state\n", - " '''\n", + " \"\"\"\n", " # split outcome string in a list\n", " outcome_ls = [char for char in outcome]\n", "\n", @@ -990,9 +986,9 @@ " i = int(edge[0])\n", " j = int(edge[1])\n", " if i < j:\n", - " energy += int(outcome_ls[i])*int(outcome_ls[j])\n", + " energy += int(outcome_ls[i]) * int(outcome_ls[j])\n", "\n", - " return energy\n" + " return energy" ] }, { @@ -1053,14 +1049,14 @@ "source": [ "n_graphs = len(data_reduced)\n", "\n", - "#sample 150 graphs and train on 100 of them\n", + "# sample 150 graphs and train on 100 of them\n", "n_train = 150\n", "n_test = 50\n", "\n", "# randomize graph order\n", "indices_all = list(range(n_graphs))\n", "indices_train = indices_all[0:n_train]\n", - "indices_test = indices_all[n_train:n_train+n_test]\n", + "indices_test = indices_all[n_train : n_train + n_test]\n", "\n", "# Labels of the sampled graphs\n", "train_classes = np.array([label_list[i] for i in indices_train])\n", @@ -1068,17 +1064,17 @@ "\n", "\n", "# Probability distributions obtained after the pulse\n", - "print('Training in progress...')\n", + "print(\"Training in progress...\")\n", "probas_train = proba_distributions(t_1=0, indices=indices_train)\n", - "print('Testing in progress...')\n", + "print(\"Testing in progress...\")\n", "probas_test = proba_distributions(t_1=0, indices=indices_test)\n", "\n", "# Resulting kernel matrix\n", "Kmat = kernel_matrix(probas_train, probas_train)\n", "\n", "fig, ax = plt.subplots(figsize=(8, 8))\n", - "im = ax.imshow(Kmat, cmap='OrRd')\n", - "cbar = plt.colorbar(im, extend='max')\n" + "im = ax.imshow(Kmat, cmap=\"OrRd\")\n", + "cbar = plt.colorbar(im, extend=\"max\")" ] }, { @@ -1099,11 +1095,9 @@ } ], "source": [ - "classifier, scores = train_and_test_classifier(probas_train,\n", - " train_classes,\n", - " probas_test,\n", - " test_classes,\n", - " verbose=True)\n" + "classifier, scores = train_and_test_classifier(\n", + " probas_train, train_classes, probas_test, test_classes, verbose=True\n", + ")" ] }, { @@ -1138,15 +1132,17 @@ "M = 1\n", "\n", "\n", - "def score_function(t_1=100,\n", - " total_time=512,\n", - " repetitions=M,\n", - " nblocks=N,\n", - " label_list=label_list,\n", - " indices=list(range(n_graphs))): # list of graphs included\n", - " '''\n", + "def score_function(\n", + " t_1=100,\n", + " total_time=512,\n", + " repetitions=M,\n", + " nblocks=N,\n", + " label_list=label_list,\n", + " indices=list(range(n_graphs)),\n", + "): # list of graphs included\n", + " \"\"\"\n", " Computes the accuracy, f1, precision and recall\n", - " '''\n", + " \"\"\"\n", "\n", " accuracy = []\n", " f1 = []\n", @@ -1155,65 +1151,74 @@ "\n", " n_g = len(indices)\n", "\n", - " block_size = n_g//nblocks\n", - "\n", + " block_size = n_g // nblocks\n", "\n", - " # Compute the probability distributions of all \n", + " # Compute the probability distributions of all\n", " # graphs in the data set\n", " start_time = time.time()\n", - " probas_all = proba_distributions(t_1=t_1,\n", - " total_time=total_time,\n", - " Nsamples=1000,\n", - " indices=indices)\n", + " probas_all = proba_distributions(\n", + " t_1=t_1, total_time=total_time, Nsamples=1000, indices=indices\n", + " )\n", "\n", - " print(f' Probability lists were computed in {time.time() - start_time:4.1f} seconds')\n", + " print(\n", + " f\" Probability lists were computed in {time.time() - start_time:4.1f} seconds\"\n", + " )\n", "\n", " classes = np.array([label_list[i] for i in indices])\n", " start_time = time.time()\n", "\n", " for r in range(repetitions):\n", - " #divide data in training set and test set\n", + " # divide data in training set and test set\n", " indices_all = np.array(list(range(n_g)))\n", " np.random.shuffle(indices_all)\n", "\n", - " mean_scores = np.zeros((4, ))\n", + " mean_scores = np.zeros((4,))\n", " for iblock in range(nblocks):\n", - " indices_test = [indices_all[(iblock * block_size + i) % n_g]\n", - " for i in range(block_size)]\n", - " indices_train = [indices_all[((iblock + 1) * block_size + i) % n_g]\n", - " for i in range(n_g - block_size)]\n", - "\n", - " train_classes = np.array([label_list[indices[i]]\n", - " for i in indices_train])\n", - " test_classes = np.array([label_list[indices[i]]\n", - " for i in indices_test])\n", - "\n", - " # create probability histogram for train and test data \n", + " indices_test = [\n", + " indices_all[(iblock * block_size + i) % n_g]\n", + " for i in range(block_size)\n", + " ]\n", + " indices_train = [\n", + " indices_all[((iblock + 1) * block_size + i) % n_g]\n", + " for i in range(n_g - block_size)\n", + " ]\n", + "\n", + " train_classes = np.array(\n", + " [label_list[indices[i]] for i in indices_train]\n", + " )\n", + " test_classes = np.array(\n", + " [label_list[indices[i]] for i in indices_test]\n", + " )\n", + "\n", + " # create probability histogram for train and test data\n", " probas_train = np.array([probas_all[i] for i in indices_train])\n", " probas_test = np.array([probas_all[i] for i in indices_test])\n", "\n", - " classifier, scores = train_and_test_classifier(probas_train,\n", - " train_classes,\n", - " probas_test,\n", - " test_classes,\n", - " verbose=False)\n", + " classifier, scores = train_and_test_classifier(\n", + " probas_train,\n", + " train_classes,\n", + " probas_test,\n", + " test_classes,\n", + " verbose=False,\n", + " )\n", " mean_scores += scores\n", "\n", " # calculate score metrics\n", - " accuracy.append(mean_scores[0]/nblocks)\n", - " f1.append(mean_scores[1]/nblocks)\n", - " precision.append(mean_scores[2]/nblocks)\n", - " recall.append(mean_scores[3]/nblocks)\n", + " accuracy.append(mean_scores[0] / nblocks)\n", + " f1.append(mean_scores[1] / nblocks)\n", + " precision.append(mean_scores[2] / nblocks)\n", + " recall.append(mean_scores[3] / nblocks)\n", "\n", " A = (np.mean(accuracy), np.std(accuracy))\n", " B = (np.mean(f1), np.std(f1))\n", " C = (np.mean(precision), np.std(precision))\n", " D = (np.mean(recall), np.std(recall))\n", "\n", - " print(f' Kernel scores computed in {time.time() - start_time:4.1f} seconds')\n", + " print(\n", + " f\" Kernel scores computed in {time.time() - start_time:4.1f} seconds\"\n", + " )\n", "\n", - " return A, B, C, D\n", - " " + " return A, B, C, D" ] }, { @@ -1235,49 +1240,58 @@ "metadata": {}, "outputs": [], "source": [ - "def scan_scores(M=2,\n", - " N=4,\n", - " indices=list(range(n_graphs)),\n", - " durations=[512],\n", - " ):\n", + "def scan_scores(\n", + " M=2,\n", + " N=4,\n", + " indices=list(range(n_graphs)),\n", + " durations=[512],\n", + "):\n", "\n", " scores_dict = {}\n", "\n", " for s in scores_types:\n", " scores_dict[s] = []\n", "\n", - " print(' ------------------------------------------------')\n", - " print(f'| Max. duration of the middle pulse: {durations[-1]:4d} ns |')\n", - " print(f'| Total duration of the pulse: {total_time:4d} ns |')\n", - " print(f'| Using {N:2d} blocks of {len(indices)//N:4d} graphs each |')\n", - " print(' ------------------------------------------------')\n", + " print(\" ------------------------------------------------\")\n", + " print(f\"| Max. duration of the middle pulse: {durations[-1]:4d} ns |\")\n", + " print(f\"| Total duration of the pulse: {total_time:4d} ns |\")\n", + " print(\n", + " f\"| Using {N:2d} blocks of {len(indices)//N:4d} graphs each |\"\n", + " )\n", + " print(\" ------------------------------------------------\")\n", "\n", " for t_1 in durations:\n", - " print(f' Duration of the middle pulse: {t_1:4d} ns')\n", - " score_inst = score_function(t_1=t_1,\n", - " total_time=total_time,\n", - " repetitions=M,\n", - " nblocks=N,\n", - " indices=indices_in)\n", + " print(f\" Duration of the middle pulse: {t_1:4d} ns\")\n", + " score_inst = score_function(\n", + " t_1=t_1,\n", + " total_time=total_time,\n", + " repetitions=M,\n", + " nblocks=N,\n", + " indices=indices_in,\n", + " )\n", "\n", " for sc, st in zip(score_inst, scores_types):\n", " scores_dict[st].append(sc)\n", - " print(f' > {st}: {sc[0]:6.3} +/- {sc[1]:6.3}')\n", + " print(f\" > {st}: {sc[0]:6.3} +/- {sc[1]:6.3}\")\n", " print()\n", " return scores_dict\n", "\n", + "\n", "def plot_scores(scores_dict):\n", " fig, ax = plt.subplots(figsize=(9, 5))\n", " for k in scores_dict.keys():\n", - " ax.errorbar(list(durations), [s[0] for s in scores_dict[k]],\n", - " yerr=[s[1] for s in scores_dict[k]],\n", - " label=k)\n", - " ax.set_title('Score vs duration $t_1$ of the central pulse', fontsize=16)\n", - " ax.set_ylabel(r'Score', fontsize=16)\n", - " ax.set_xlabel(r'$t_1$ (ns)', fontsize=16)\n", + " ax.errorbar(\n", + " list(durations),\n", + " [s[0] for s in scores_dict[k]],\n", + " yerr=[s[1] for s in scores_dict[k]],\n", + " label=k,\n", + " )\n", + " ax.set_title(\"Score vs duration $t_1$ of the central pulse\", fontsize=16)\n", + " ax.set_ylabel(r\"Score\", fontsize=16)\n", + " ax.set_xlabel(r\"$t_1$ (ns)\", fontsize=16)\n", " ax.legend()\n", "\n", - " plt.show()\n" + " plt.show()" ] }, { @@ -1447,13 +1461,13 @@ ], "source": [ "# Duration of the initial and final pulses\n", - "t_1 = 4*round(1000*np.pi/(4*2*omega_max))\n", + "t_1 = 4 * round(1000 * np.pi / (4 * 2 * omega_max))\n", "\n", "# Total duration of the pulse\n", - "total_time = 2*t_1 + 256\n", + "total_time = 2 * t_1 + 256\n", "\n", "# duration of the middle pulse\n", - "durations = range(0, total_time-2*round(t_1)-32, 32)\n", + "durations = range(0, total_time - 2 * round(t_1) - 32, 32)\n", "\n", "\n", "M = 4\n", @@ -1465,11 +1479,7 @@ "np.random.shuffle(indices_all)\n", "indices_in = indices_all[0:n_g]\n", "\n", - "scores_2layers = scan_scores(M=M,\n", - " N=N,\n", - " indices=indices_in,\n", - " durations=durations\n", - " )\n" + "scores_2layers = scan_scores(M=M, N=N, indices=indices_in, durations=durations)" ] }, { diff --git a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb index d2f72c265..e5cc7fe06 100644 --- a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb +++ b/tutorials/applications/Using QAOA to solve a MIS problem.ipynb @@ -126,10 +126,14 @@ "outputs": [], "source": [ "def pos_to_graph(pos):\n", - " rb = Chadoq2.rydberg_blockade_radius(1.)\n", + " rb = Chadoq2.rydberg_blockade_radius(1.0)\n", " g = igraph.Graph()\n", " N = len(pos)\n", - " edges = [[m,n] for m,n in combinations(range(N), r=2) if np.linalg.norm(pos[m] - pos[n]) < rb] \n", + " edges = [\n", + " [m, n]\n", + " for m, n in combinations(range(N), r=2)\n", + " if np.linalg.norm(pos[m] - pos[n]) < rb\n", + " ]\n", " g.add_vertices(N)\n", " g.add_edges(edges)\n", " return g" @@ -161,13 +165,17 @@ } ], "source": [ - "pos = np.array([[0., 0.], [-4, -7], [4,-7], [8,6], [-8,6]])\n", + "pos = np.array([[0.0, 0.0], [-4, -7], [4, -7], [8, 6], [-8, 6]])\n", "\n", - "G = pos_to_graph(pos) \n", + "G = pos_to_graph(pos)\n", "qubits = dict(enumerate(pos))\n", "\n", "reg = Register(qubits)\n", - "reg.draw(blockade_radius=Chadoq2.rydberg_blockade_radius(1.), draw_graph=True, draw_half_radius=True)" + "reg.draw(\n", + " blockade_radius=Chadoq2.rydberg_blockade_radius(1.0),\n", + " draw_graph=True,\n", + " draw_half_radius=True,\n", + ")" ] }, { @@ -203,23 +211,23 @@ "\n", "# Parametrized sequence\n", "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ch0','rydberg_global')\n", + "seq.declare_channel(\"ch0\", \"rydberg_global\")\n", "\n", - "t_list = seq.declare_variable('t_list', size=LAYERS)\n", - "s_list = seq.declare_variable('s_list', size=LAYERS)\n", + "t_list = seq.declare_variable(\"t_list\", size=LAYERS)\n", + "s_list = seq.declare_variable(\"s_list\", size=LAYERS)\n", "\n", "if LAYERS == 1:\n", " t_list = [t_list]\n", " s_list = [s_list]\n", - " \n", - "for t, s in zip(t_list, s_list): \n", - " pulse_1 = Pulse.ConstantPulse(1000*t, 1., 0., 0) \n", - " pulse_2 = Pulse.ConstantPulse(1000*s, 1., 1., 0)\n", "\n", - " seq.add(pulse_1, 'ch0')\n", - " seq.add(pulse_2, 'ch0')\n", - " \n", - "seq.measure('ground-rydberg')" + "for t, s in zip(t_list, s_list):\n", + " pulse_1 = Pulse.ConstantPulse(1000 * t, 1.0, 0.0, 0)\n", + " pulse_2 = Pulse.ConstantPulse(1000 * s, 1.0, 1.0, 0)\n", + "\n", + " seq.add(pulse_1, \"ch0\")\n", + " seq.add(pulse_2, \"ch0\")\n", + "\n", + "seq.measure(\"ground-rydberg\")" ] }, { @@ -246,10 +254,10 @@ " params = np.array(parameters)\n", " t_params, s_params = np.reshape(params.astype(int), (2, LAYERS))\n", " assigned_seq = seq.build(t_list=t_params, s_list=s_params)\n", - " simul = Simulation(assigned_seq, sampling_rate=.01)\n", + " simul = Simulation(assigned_seq, sampling_rate=0.01)\n", " results = simul.run()\n", - " count_dict = results.sample_final_state() #sample from the state vector \n", - " return count_dict " + " count_dict = results.sample_final_state() # sample from the state vector\n", + " return count_dict" ] }, { @@ -258,8 +266,10 @@ "metadata": {}, "outputs": [], "source": [ - "guess = {'t': np.random.uniform(8, 10, LAYERS),\n", - " 's': np.random.uniform(1, 3, LAYERS)}" + "guess = {\n", + " \"t\": np.random.uniform(8, 10, LAYERS),\n", + " \"s\": np.random.uniform(1, 3, LAYERS),\n", + "}" ] }, { @@ -268,7 +278,7 @@ "metadata": {}, "outputs": [], "source": [ - "example_dict = quantum_loop(np.r_[guess['t'], guess['s']])" + "example_dict = quantum_loop(np.r_[guess[\"t\"], guess[\"s\"]])" ] }, { @@ -286,13 +296,13 @@ "source": [ "def plot_distribution(C):\n", " C = dict(sorted(C.items(), key=lambda item: item[1], reverse=True))\n", - " indexes = ['01011', '00111'] # MIS indexes\n", - " color_dict = {key:'r' if key in indexes else 'g' for key in C}\n", - " plt.figure(figsize=(12,6))\n", + " indexes = [\"01011\", \"00111\"] # MIS indexes\n", + " color_dict = {key: \"r\" if key in indexes else \"g\" for key in C}\n", + " plt.figure(figsize=(12, 6))\n", " plt.xlabel(\"bitstrings\")\n", " plt.ylabel(\"counts\")\n", - " plt.bar(C.keys(), C.values(), width=0.5, color = color_dict.values())\n", - " plt.xticks(rotation='vertical')\n", + " plt.bar(C.keys(), C.values(), width=0.5, color=color_dict.values())\n", + " plt.xticks(rotation=\"vertical\")\n", " plt.show()" ] }, @@ -357,12 +367,13 @@ " z = np.array(list(bitstring), dtype=int)\n", " A = np.array(G.get_adjacency().data)\n", " # Add penalty and bias:\n", - " cost = penalty*(z.T @ np.triu(A) @ z) - np.sum(z)\n", - " return cost \n", + " cost = penalty * (z.T @ np.triu(A) @ z) - np.sum(z)\n", + " return cost\n", + "\n", "\n", - "def get_cost(counter,G):\n", - " cost = sum(counter[key] * get_cost_colouring(key,G) for key in counter) \n", - " return cost / sum(counter.values()) # Divide by total samples" + "def get_cost(counter, G):\n", + " cost = sum(counter[key] * get_cost_colouring(key, G) for key in counter)\n", + " return cost / sum(counter.values()) # Divide by total samples" ] }, { @@ -384,7 +395,7 @@ } ], "source": [ - "get_cost_colouring('00111', G)" + "get_cost_colouring(\"00111\", G)" ] }, { @@ -413,10 +424,10 @@ "metadata": {}, "outputs": [], "source": [ - "def func(param,*args):\n", + "def func(param, *args):\n", " G = args[0]\n", " C = quantum_loop(param)\n", - " cost = get_cost(C,G)\n", + " cost = get_cost(C, G)\n", " return cost" ] }, @@ -440,13 +451,14 @@ "metadata": {}, "outputs": [], "source": [ - "res = minimize(func, \n", - " args=G,\n", - " x0=np.r_[guess['t'], guess['s']],\n", - " method='Nelder-Mead',\n", - " tol=1e-5,\n", - " options = {'maxiter': 100}\n", - " )" + "res = minimize(\n", + " func,\n", + " args=G,\n", + " x0=np.r_[guess[\"t\"], guess[\"s\"]],\n", + " method=\"Nelder-Mead\",\n", + " tol=1e-5,\n", + " options={\"maxiter\": 100},\n", + ")" ] }, { diff --git a/tutorials/creating_sequences.ipynb b/tutorials/creating_sequences.ipynb index 923b79958..206d8b048 100644 --- a/tutorials/creating_sequences.ipynb +++ b/tutorials/creating_sequences.ipynb @@ -66,7 +66,7 @@ "reg1 = Register(qubits) # Copy of 'reg' to keep the original intact\n", "print(\"The original array:\")\n", "reg1.draw()\n", - "reg1.rotate(45) # Rotate by 45 degrees\n", + "reg1.rotate(45) # Rotate by 45 degrees\n", "print(\"The rotated array:\")\n", "reg1.draw()" ] @@ -84,7 +84,9 @@ "metadata": {}, "outputs": [], "source": [ - "reg2 = Register.from_coordinates(square, prefix='q') # All qubit IDs will start with 'q'\n", + "reg2 = Register.from_coordinates(\n", + " square, prefix=\"q\"\n", + ") # All qubit IDs will start with 'q'\n", "reg2.draw()" ] }, @@ -105,7 +107,7 @@ "metadata": {}, "outputs": [], "source": [ - "reg3 = Register.square(4, spacing=5) # 4x4 array with atoms 5 um apart\n", + "reg3 = Register.square(4, spacing=5) # 4x4 array with atoms 5 um apart\n", "reg3.draw()" ] }, @@ -174,11 +176,11 @@ }, "outputs": [], "source": [ - "seq.declare_channel('ch0', 'raman_local')\n", + "seq.declare_channel(\"ch0\", \"raman_local\")\n", "print(\"Available channels after declaring 'ch0':\")\n", "pprint(seq.available_channels)\n", "\n", - "seq.declare_channel('ch1', 'rydberg_local', initial_target=4)\n", + "seq.declare_channel(\"ch1\", \"rydberg_local\", initial_target=4)\n", "print(\"\\nAvailable channels after declaring 'ch1':\")\n", "pprint(seq.available_channels)" ] @@ -221,7 +223,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.target(1, 'ch0')" + "seq.target(1, \"ch0\")" ] }, { @@ -253,7 +255,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.add(simple_pulse, 'ch0')" + "seq.add(simple_pulse, \"ch0\")" ] }, { @@ -269,7 +271,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.delay(100, 'ch1')" + "seq.delay(100, \"ch1\")" ] }, { @@ -288,8 +290,10 @@ "from pulser.waveforms import RampWaveform, BlackmanWaveform\n", "\n", "duration = 1000\n", - "amp_wf = BlackmanWaveform(duration, np.pi/2) # Duration: 1000 ns, Area: pi/2\n", - "detuning_wf = RampWaveform(duration, -20, 20) # Duration: 1000ns, linear sweep from -20 to 20 rad/µs" + "amp_wf = BlackmanWaveform(duration, np.pi / 2) # Duration: 1000 ns, Area: pi/2\n", + "detuning_wf = RampWaveform(\n", + " duration, -20, 20\n", + ") # Duration: 1000ns, linear sweep from -20 to 20 rad/µs" ] }, { @@ -323,7 +327,7 @@ }, "outputs": [], "source": [ - "amp_wf.integral # dimensionless" + "amp_wf.integral # dimensionless" ] }, { @@ -356,7 +360,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.add(complex_pulse, 'ch1')" + "seq.add(complex_pulse, \"ch1\")" ] }, { @@ -406,8 +410,8 @@ "metadata": {}, "outputs": [], "source": [ - "seq.target(4, 'ch0')\n", - "seq.add(complex_pulse, 'ch0')\n", + "seq.target(4, \"ch0\")\n", + "seq.add(complex_pulse, \"ch0\")\n", "\n", "print(\"Current Schedule:\")\n", "print(seq)\n", @@ -436,9 +440,9 @@ "metadata": {}, "outputs": [], "source": [ - "seq.target(0, 'ch1')\n", - "seq.add(simple_pulse, 'ch1', protocol='min-delay')\n", - "seq.add(simple_pulse, 'ch1', protocol='wait-for-all')\n", + "seq.target(0, \"ch1\")\n", + "seq.add(simple_pulse, \"ch1\", protocol=\"min-delay\")\n", + "seq.add(simple_pulse, \"ch1\", protocol=\"wait-for-all\")\n", "\n", "print(\"Current Schedule:\")\n", "print(seq)\n", @@ -465,8 +469,8 @@ "metadata": {}, "outputs": [], "source": [ - "seq.target(0, 'ch0')\n", - "seq.add(complex_pulse, 'ch0', protocol='no-delay')\n", + "seq.target(0, \"ch0\")\n", + "seq.add(complex_pulse, \"ch0\", protocol=\"no-delay\")\n", "\n", "print(\"Current Schedule:\")\n", "print(seq)\n", @@ -500,7 +504,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq.measure(basis='ground-rydberg')" + "seq.measure(basis=\"ground-rydberg\")" ] }, { diff --git a/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb b/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb index 85a792908..551633ded 100644 --- a/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb +++ b/tutorials/quantum_simulation/Bayesian Optimisation for antiferromagnetic state preparation.ipynb @@ -87,26 +87,29 @@ "source": [ "# Parameters in rad/µs and ns\n", "\n", - "T = 1000 # duration\n", + "T = 1000 # duration\n", "U = 2 * np.pi * 5.0\n", "\n", - "Omega_max = 0.5 * U \n", + "Omega_max = 0.5 * U\n", "\n", - "delta_0 = -1.0 * U \n", - "delta_f = 1.0 * U \n", + "delta_0 = -1.0 * U\n", + "delta_f = 1.0 * U\n", "\n", "R_interatomic = Chadoq2.rydberg_blockade_radius(Omega_max) / 1.2\n", - "print(f'Interatomic Radius is: {R_interatomic}µm.')\n", + "print(f\"Interatomic Radius is: {R_interatomic}µm.\")\n", "\n", "N_side = 4\n", "coords = (\n", - " [R_interatomic * np.r_[x,0] for x in range(N_side-1)] \n", - " + [R_interatomic * np.r_[N_side-1,y] for y in range(N_side-1)]\n", - " + [R_interatomic * np.r_[N_side-1-x,N_side-1] for x in range(N_side-1)] \n", - " + [R_interatomic * np.r_[0,N_side-1-y] for y in range(N_side-1)]\n", + " [R_interatomic * np.r_[x, 0] for x in range(N_side - 1)]\n", + " + [R_interatomic * np.r_[N_side - 1, y] for y in range(N_side - 1)]\n", + " + [\n", + " R_interatomic * np.r_[N_side - 1 - x, N_side - 1]\n", + " for x in range(N_side - 1)\n", + " ]\n", + " + [R_interatomic * np.r_[0, N_side - 1 - y] for y in range(N_side - 1)]\n", ")\n", - "reg = Register.from_coordinates(coords, prefix='q')\n", - "N=len(coords)\n", + "reg = Register.from_coordinates(coords, prefix=\"q\")\n", + "N = len(coords)\n", "N_samples = 1000\n", "reg.draw()" ] @@ -133,15 +136,15 @@ ], "source": [ "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ising', 'rydberg_global')\n", + "seq.declare_channel(\"ising\", \"rydberg_global\")\n", "\n", "tol = 1e-6\n", - "max_amp = seq.declared_channels['ising'].max_amp * (1-tol)\n", - "max_det = seq.declared_channels['ising'].max_abs_detuning * (1-tol)\n", - "Omega_max=min(max_amp, Omega_max)\n", - "delta_0=np.sign(delta_0)*min(max_det, abs(delta_0))\n", - "delta_f=np.sign(delta_f)*min(max_det, abs(delta_f))\n", - "print(Omega_max/U, np.round(delta_0/U,2), delta_f/U)" + "max_amp = seq.declared_channels[\"ising\"].max_amp * (1 - tol)\n", + "max_det = seq.declared_channels[\"ising\"].max_abs_detuning * (1 - tol)\n", + "Omega_max = min(max_amp, Omega_max)\n", + "delta_0 = np.sign(delta_0) * min(max_det, abs(delta_0))\n", + "delta_f = np.sign(delta_f) * min(max_det, abs(delta_f))\n", + "print(Omega_max / U, np.round(delta_0 / U, 2), delta_f / U)" ] }, { @@ -178,8 +181,8 @@ "m = 3\n", "\n", "# Random instance of the parameter space\n", - "amp_params = np.random.uniform(0,Omega_max,m)\n", - "det_params = np.random.uniform(delta_0,delta_f,m)" + "amp_params = np.random.uniform(0, Omega_max, m)\n", + "det_params = np.random.uniform(delta_0, delta_f, m)" ] }, { @@ -197,7 +200,7 @@ "source": [ "def create_interp_pulse(amp_params, det_params):\n", " return Pulse(\n", - " InterpolatedWaveform(T, [1e-9, *amp_params, 1e-9]), \n", + " InterpolatedWaveform(T, [1e-9, *amp_params, 1e-9]),\n", " InterpolatedWaveform(T, [delta_0, *det_params, delta_f]),\n", " 0,\n", " )" @@ -228,8 +231,8 @@ ], "source": [ "seq = Sequence(reg, Chadoq2)\n", - "seq.declare_channel('ising', 'rydberg_global')\n", - "seq.add(create_interp_pulse(amp_params, det_params),'ising')\n", + "seq.declare_channel(\"ising\", \"rydberg_global\")\n", + "seq.add(create_interp_pulse(amp_params, det_params), \"ising\")\n", "seq.draw()" ] }, @@ -279,37 +282,43 @@ "outputs": [], "source": [ "def occupation(j, N):\n", - " up = qutip.basis(2,0)\n", + " up = qutip.basis(2, 0)\n", " prod = [qutip.qeye(2) for _ in range(N)]\n", " prod[j] = up * up.dag()\n", " return qutip.tensor(prod)\n", "\n", + "\n", "def get_corr_pairs(k, N):\n", - " corr_pairs = [[i,(i+k)%N] for i in range(N)]\n", + " corr_pairs = [[i, (i + k) % N] for i in range(N)]\n", " return corr_pairs\n", "\n", + "\n", "def get_corr_function(k, N, state):\n", " corr_pairs = get_corr_pairs(k, N)\n", " operators = [occupation(j, N) for j in range(N)]\n", " covariance = 0\n", " for qi, qj in corr_pairs:\n", - " covariance += qutip.expect(operators[qi]*operators[qj], state)\n", - " covariance -= qutip.expect(operators[qi], state)*qutip.expect(operators[qj], state)\n", - " return covariance/len(corr_pairs) \n", + " covariance += qutip.expect(operators[qi] * operators[qj], state)\n", + " covariance -= qutip.expect(operators[qi], state) * qutip.expect(\n", + " operators[qj], state\n", + " )\n", + " return covariance / len(corr_pairs)\n", + "\n", "\n", "def get_full_corr_function(reg, state):\n", " N = len(reg.qubits)\n", " correlation_function = {}\n", - " for k in range(-N//2, N//2+1):\n", + " for k in range(-N // 2, N // 2 + 1):\n", " correlation_function[k] = get_corr_function(k, N, state)\n", " return correlation_function\n", "\n", + "\n", "def get_neel_structure_factor(reg, state):\n", " N = len(reg.qubits)\n", " st_fac = 0\n", - " for k in range(-N//2, N//2+1):\n", + " for k in range(-N // 2, N // 2 + 1):\n", " kk = np.abs(k)\n", - " st_fac += 4 * (-1)**kk * get_corr_function(k, N, state)\n", + " st_fac += 4 * (-1) ** kk * get_corr_function(k, N, state)\n", " return st_fac" ] }, @@ -328,7 +337,11 @@ "source": [ "def proba_from_state(results, min_p=0.1):\n", " sampling = results.sample_final_state(N_samples=N_samples)\n", - " return {k: f'{100*v/N_samples}%' for k, v in sampling.items() if v/N_samples > min_p}" + " return {\n", + " k: f\"{100*v/N_samples}%\"\n", + " for k, v in sampling.items()\n", + " if v / N_samples > min_p\n", + " }" ] }, { @@ -358,11 +371,11 @@ "AF2 = qutip.tensor([qutip.basis(2, (k + 1) % 2) for k in range(N)])\n", "AF_state = (AF1 + AF2).unit()\n", "\n", - "t1=time.process_time()\n", + "t1 = time.process_time()\n", "S_max = get_neel_structure_factor(reg, AF_state)\n", - "print('S_Neel(AF state) =', S_max)\n", - "t2=time.process_time()\n", - "print('computed in', (t2-t1),'sec')" + "print(\"S_Neel(AF state) =\", S_max)\n", + "t2 = time.process_time()\n", + "print(\"computed in\", (t2 - t1), \"sec\")" ] }, { @@ -387,16 +400,21 @@ "source": [ "def score(params):\n", " seq = Sequence(reg, Chadoq2)\n", - " seq.declare_channel('ising', 'rydberg_global')\n", - " seq.add(create_interp_pulse(params[:m], params[m:]),'ising')\n", - " \n", + " seq.declare_channel(\"ising\", \"rydberg_global\")\n", + " seq.add(create_interp_pulse(params[:m], params[m:]), \"ising\")\n", + "\n", " simul = Simulation(seq, sampling_rate=0.5)\n", " results = simul.run()\n", "\n", " sampling = results.sample_final_state(N_samples=N_samples)\n", - " sampled_state = sum([np.sqrt(sampling[k]/N_samples)*qutip.ket(k) for k in sampling.keys()])\n", + " sampled_state = sum(\n", + " [\n", + " np.sqrt(sampling[k] / N_samples) * qutip.ket(k)\n", + " for k in sampling.keys()\n", + " ]\n", + " )\n", "\n", - " F = get_neel_structure_factor(reg, sampled_state)/S_max\n", + " F = get_neel_structure_factor(reg, sampled_state) / S_max\n", "\n", " return 1 - F" ] @@ -472,7 +490,9 @@ "n_r = 30\n", "n_c = 120\n", "\n", - "RESULT = gp_minimize(score, bounds, n_random_starts=n_r, n_calls=n_c, verbose=False)" + "RESULT = gp_minimize(\n", + " score, bounds, n_random_starts=n_r, n_calls=n_c, verbose=False\n", + ")" ] }, { @@ -498,10 +518,10 @@ "def sort_improv(RESULT):\n", " score_vals = RESULT.func_vals\n", " min = score_vals[0]\n", - " score_list=[]\n", + " score_list = []\n", " for s in score_vals:\n", - " if s10}\n", + "most_freq = {k: v for k, v in count.items() if v > 10}\n", "plt.bar(list(most_freq.keys()), list(most_freq.values()))\n", - "plt.xticks(rotation='vertical')\n", + "plt.xticks(rotation=\"vertical\")\n", "plt.show()" ] }, @@ -227,8 +233,8 @@ "metadata": {}, "outputs": [], "source": [ - "def occupation(j,N):\n", - " up = qutip.basis(2,0)\n", + "def occupation(j, N):\n", + " up = qutip.basis(2, 0)\n", " prod = [qutip.qeye(2) for _ in range(N)]\n", " prod[j] = up * up.dag()\n", " return qutip.tensor(prod)" @@ -240,7 +246,7 @@ "metadata": {}, "outputs": [], "source": [ - "occup_list = [occupation(j, N_side*N_side) for j in range(N_side*N_side)]" + "occup_list = [occupation(j, N_side * N_side) for j in range(N_side * N_side)]" ] }, { @@ -260,8 +266,8 @@ " corr_pairs = []\n", " for i, qi in enumerate(register.qubits):\n", " for j, qj in enumerate(register.qubits):\n", - " r_ij = register.qubits[qi]-register.qubits[qj]\n", - " distance = np.linalg.norm(r_ij - R_interatomic*np.array([k, l]))\n", + " r_ij = register.qubits[qi] - register.qubits[qj]\n", + " distance = np.linalg.norm(r_ij - R_interatomic * np.array([k, l]))\n", " if distance < 1:\n", " corr_pairs.append([i, j])\n", " return corr_pairs" @@ -283,22 +289,27 @@ "def get_corr_function(k, l, reg, R_interatomic, state):\n", " N_qubits = len(reg.qubits)\n", " corr_pairs = get_corr_pairs(k, l, reg, R_interatomic)\n", - " \n", + "\n", " operators = [occupation(j, N_qubits) for j in range(N_qubits)]\n", " covariance = 0\n", " for qi, qj in corr_pairs:\n", - " covariance += qutip.expect(operators[qi]*operators[qj], state)\n", - " covariance -= qutip.expect(operators[qi], state)*qutip.expect(operators[qj], state)\n", - " return covariance/len(corr_pairs)\n", - " \n", + " covariance += qutip.expect(operators[qi] * operators[qj], state)\n", + " covariance -= qutip.expect(operators[qi], state) * qutip.expect(\n", + " operators[qj], state\n", + " )\n", + " return covariance / len(corr_pairs)\n", + "\n", + "\n", "def get_full_corr_function(reg, state):\n", " N_qubits = len(reg.qubits)\n", - " \n", + "\n", " correlation_function = {}\n", " N_side = int(np.sqrt(N_qubits))\n", - " for k in range(-N_side+1, N_side):\n", - " for l in range(-N_side+1, N_side):\n", - " correlation_function[(k, l)] = get_corr_function(k, l, reg, R_interatomic, state)\n", + " for k in range(-N_side + 1, N_side):\n", + " for l in range(-N_side + 1, N_side):\n", + " correlation_function[(k, l)] = get_corr_function(\n", + " k, l, reg, R_interatomic, state\n", + " )\n", " return correlation_function" ] }, @@ -326,12 +337,14 @@ "outputs": [], "source": [ "expected_corr_function = {}\n", - "xi = 1 # Estimated Correlation Length\n", - "for k in range(-N_side+1,N_side):\n", - " for l in range(-N_side+1,N_side):\n", + "xi = 1 # Estimated Correlation Length\n", + "for k in range(-N_side + 1, N_side):\n", + " for l in range(-N_side + 1, N_side):\n", " kk = np.abs(k)\n", " ll = np.abs(l)\n", - " expected_corr_function[(k, l)] = (-1)**(kk + ll) * np.exp(-np.sqrt(k**2 + l**2)/xi)" + " expected_corr_function[(k, l)] = (-1) ** (kk + ll) * np.exp(\n", + " -np.sqrt(k**2 + l**2) / xi\n", + " )" ] }, { @@ -342,22 +355,34 @@ }, "outputs": [], "source": [ - "A = 4*np.reshape(list(correlation_function.values()), (2*N_side-1, 2*N_side-1))\n", - "A = A/np.max(A)\n", - "B = np.reshape(list(expected_corr_function.values()), (2*N_side-1, 2*N_side-1))\n", - "B = B*np.max(A)\n", - "\n", - "for i, M in enumerate([A.copy(),B.copy()]):\n", - " M[N_side-1, N_side-1] = None\n", - " plt.figure(figsize=(3.5,3.5))\n", - " plt.imshow(M, cmap='coolwarm', vmin=-.6, vmax=.6)\n", - " plt.xticks(range(len(M)), [f'{x}' for x in range(-N_side + 1, N_side)])\n", - " plt.xlabel(r'$\\mathscr{k}$', fontsize=22)\n", - " plt.yticks(range(len(M)), [f'{-y}' for y in range(-N_side + 1, N_side)])\n", - " plt.ylabel(r'$\\mathscr{l}$', rotation=0, fontsize=22, labelpad=10)\n", + "A = 4 * np.reshape(\n", + " list(correlation_function.values()), (2 * N_side - 1, 2 * N_side - 1)\n", + ")\n", + "A = A / np.max(A)\n", + "B = np.reshape(\n", + " list(expected_corr_function.values()), (2 * N_side - 1, 2 * N_side - 1)\n", + ")\n", + "B = B * np.max(A)\n", + "\n", + "for i, M in enumerate([A.copy(), B.copy()]):\n", + " M[N_side - 1, N_side - 1] = None\n", + " plt.figure(figsize=(3.5, 3.5))\n", + " plt.imshow(M, cmap=\"coolwarm\", vmin=-0.6, vmax=0.6)\n", + " plt.xticks(range(len(M)), [f\"{x}\" for x in range(-N_side + 1, N_side)])\n", + " plt.xlabel(r\"$\\mathscr{k}$\", fontsize=22)\n", + " plt.yticks(range(len(M)), [f\"{-y}\" for y in range(-N_side + 1, N_side)])\n", + " plt.ylabel(r\"$\\mathscr{l}$\", rotation=0, fontsize=22, labelpad=10)\n", " plt.colorbar(fraction=0.047, pad=0.02)\n", - " if i == 0 :plt.title(r'$4\\times\\.g^{(2)}(\\mathscr{k},\\mathscr{l})$ after simulation', fontsize=14)\n", - " if i == 1 :plt.title(r'Exponential $g^{(2)}(\\mathscr{k},\\mathscr{l})$ expected', fontsize=14)\n", + " if i == 0:\n", + " plt.title(\n", + " r\"$4\\times\\.g^{(2)}(\\mathscr{k},\\mathscr{l})$ after simulation\",\n", + " fontsize=14,\n", + " )\n", + " if i == 1:\n", + " plt.title(\n", + " r\"Exponential $g^{(2)}(\\mathscr{k},\\mathscr{l})$ expected\",\n", + " fontsize=14,\n", + " )\n", " plt.show()" ] }, @@ -411,13 +436,17 @@ " N_side = int(np.sqrt(N_qubits))\n", "\n", " st_fac = 0\n", - " for k in range(-N_side+1, N_side):\n", - " for l in range(-N_side+1, N_side):\n", + " for k in range(-N_side + 1, N_side):\n", + " for l in range(-N_side + 1, N_side):\n", " kk = np.abs(k)\n", " ll = np.abs(l)\n", " if not (k == 0 and l == 0):\n", - " st_fac += 4 * (-1)**(kk + ll) * get_corr_function(k, l, reg, R_interatomic, state)\n", - " return st_fac " + " st_fac += (\n", + " 4\n", + " * (-1) ** (kk + ll)\n", + " * get_corr_function(k, l, reg, R_interatomic, state)\n", + " )\n", + " return st_fac" ] }, { @@ -426,36 +455,44 @@ "metadata": {}, "outputs": [], "source": [ - "def calculate_neel(det, N, Omega_max = 2.3 * 2 * np.pi):\n", - " #Setup:\n", + "def calculate_neel(det, N, Omega_max=2.3 * 2 * np.pi):\n", + " # Setup:\n", " U = Omega_max / 2.3\n", " delta_0 = -6 * U\n", - " delta_f = det * U \n", - " \n", + " delta_f = det * U\n", + "\n", " t_rise = 252\n", " t_fall = 500\n", - " t_sweep = int((delta_f - delta_0)/(2 * np.pi * 10) * 1000)\n", - " t_sweep += 4 - t_sweep % 4 # To be a multiple of the clock period of Chadoq2 (4ns)\n", - " \n", - " R_interatomic = Chadoq2.rydberg_blockade_radius(U) \n", + " t_sweep = int((delta_f - delta_0) / (2 * np.pi * 10) * 1000)\n", + " t_sweep += (\n", + " 4 - t_sweep % 4\n", + " ) # To be a multiple of the clock period of Chadoq2 (4ns)\n", + "\n", + " R_interatomic = Chadoq2.rydberg_blockade_radius(U)\n", " reg = Register.rectangle(N, N, R_interatomic)\n", "\n", - " #Pulse Sequence\n", - " rise = Pulse.ConstantDetuning(RampWaveform(t_rise, 0., Omega_max), delta_0, 0.)\n", - " sweep = Pulse.ConstantAmplitude(Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.)\n", - " fall = Pulse.ConstantDetuning(RampWaveform(t_fall, Omega_max, 0.), delta_f, 0.)\n", + " # Pulse Sequence\n", + " rise = Pulse.ConstantDetuning(\n", + " RampWaveform(t_rise, 0.0, Omega_max), delta_0, 0.0\n", + " )\n", + " sweep = Pulse.ConstantAmplitude(\n", + " Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.0\n", + " )\n", + " fall = Pulse.ConstantDetuning(\n", + " RampWaveform(t_fall, Omega_max, 0.0), delta_f, 0.0\n", + " )\n", "\n", " seq = Sequence(reg, Chadoq2)\n", - " seq.declare_channel('ising', 'rydberg_global')\n", - " seq.add(rise, 'ising')\n", - " seq.add(sweep, 'ising')\n", - " seq.add(fall, 'ising')\n", + " seq.declare_channel(\"ising\", \"rydberg_global\")\n", + " seq.add(rise, \"ising\")\n", + " seq.add(sweep, \"ising\")\n", + " seq.add(fall, \"ising\")\n", "\n", " simul = Simulation(seq, sampling_rate=0.2)\n", " results = simul.run()\n", - " \n", + "\n", " final = results.states[-1]\n", - " return get_neel_structure_factor(reg, R_interatomic, final) " + " return get_neel_structure_factor(reg, R_interatomic, final)" ] }, { @@ -467,19 +504,21 @@ "outputs": [], "source": [ "N_side = 3\n", - "occup_list = [occupation(j, N_side*N_side) for j in range(N_side*N_side)]\n", + "occup_list = [occupation(j, N_side * N_side) for j in range(N_side * N_side)]\n", "\n", "detunings = np.linspace(-1, 5, 20)\n", - "results=[]\n", + "results = []\n", "for det in detunings:\n", - " print(f'Detuning = {np.round(det,3)} x 2π Mhz.')\n", + " print(f\"Detuning = {np.round(det,3)} x 2π Mhz.\")\n", " results.append(calculate_neel(det, N_side))\n", - "plt.xlabel(r'$\\hbar\\delta_{final}/U$')\n", - "plt.ylabel(r'Néel Structure Factor $S_{Neel}$')\n", - "plt.plot(detunings, results, 'o', ls='solid')\n", + "plt.xlabel(r\"$\\hbar\\delta_{final}/U$\")\n", + "plt.ylabel(r\"Néel Structure Factor $S_{Neel}$\")\n", + "plt.plot(detunings, results, \"o\", ls=\"solid\")\n", "plt.show()\n", "max_index = results.index(max(results))\n", - "print(f'Max S_Neel {np.round(max(results),2)} at detuning = {np.round(detunings[max_index],2)} x 2π Mhz.')" + "print(\n", + " f\"Max S_Neel {np.round(max(results),2)} at detuning = {np.round(detunings[max_index],2)} x 2π Mhz.\"\n", + ")" ] } ], diff --git a/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb b/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb index d8ad3c4f2..b7bd17a06 100644 --- a/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb +++ b/tutorials/quantum_simulation/Shadow estimation for VQS.ipynb @@ -115,13 +115,13 @@ "num_qubits = 4\n", "zero_state = qutip.basis(2, 0).proj()\n", "one_state = qutip.basis(2, 1).proj()\n", - "hadamard = 1/np.sqrt(2) * qutip.Qobj([[1., 1.], [1., -1.]])\n", - "h_mul_phase = qutip.Qobj(np.array([[1., 1], [1.j, -1.j]])) / np.sqrt(2)\n", + "hadamard = 1 / np.sqrt(2) * qutip.Qobj([[1.0, 1.0], [1.0, -1.0]])\n", + "h_mul_phase = qutip.Qobj(np.array([[1.0, 1], [1.0j, -1.0j]])) / np.sqrt(2)\n", "unitary_ensemble = [hadamard, h_mul_phase, qutip.qeye(2)]\n", "\n", - "g = qutip.basis(2,1)\n", - "r = qutip.basis(2,0)\n", - "n = r*r.dag()\n", + "g = qutip.basis(2, 1)\n", + "r = qutip.basis(2, 0)\n", + "n = r * r.dag()\n", "\n", "sx = qutip.sigmax()\n", "sy = qutip.sigmay()\n", @@ -175,11 +175,11 @@ "source": [ "def compute_shadow_size(delta, epsilon, observables):\n", " \"\"\"Helper function.\n", - " \n", - " Computes both the number of shadows needed as well as the size of blocks needed \n", + "\n", + " Computes both the number of shadows needed as well as the size of blocks needed\n", " for the median_of_means method in order to approximate the expectation value of M\n", " (linear) observables with additive error epsilon and fail probability delta.\n", - " \n", + "\n", " Args:\n", " delta (float): Failure probability.\n", " epsilon (float): Additive error on expectation values.\n", @@ -190,8 +190,9 @@ " shadow_norm = (\n", " lambda op: np.linalg.norm(\n", " op - np.trace(op) / 2 ** int(np.log2(op.shape[0])), ord=np.inf\n", - " ) ** 2\n", " )\n", + " ** 2\n", + " )\n", " # Theoretical number of shadows per cluster in the median of means procedure :\n", " # N = 34 * max(shadow_norm(o) for o in observables) / epsilon ** 2\n", " # We use N = 20 here to allow for quick simulation\n", @@ -225,7 +226,9 @@ " unitary_ids = np.random.randint(0, 3, size=(shadow_size, num_qubits))\n", " outcomes = []\n", " for ns in range(shadow_size):\n", - " unitmat = qutip.tensor([unitary_ensemble[unitary_ids[ns, i]] for i in range(num_qubits)])\n", + " unitmat = qutip.tensor(\n", + " [unitary_ensemble[unitary_ids[ns, i]] for i in range(num_qubits)]\n", + " )\n", " outcomes.append(measure_bitstring(unitmat.dag() * rho * unitmat))\n", "\n", " # combine the computational basis outcomes and the sampled unitaries\n", @@ -257,18 +260,18 @@ "\n", " Args:\n", " outcome_ns: Bitstring at ns\n", - " unitary_ids_ns: Rotation applied at ns. \n", + " unitary_ids_ns: Rotation applied at ns.\n", "\n", " Returns:\n", " Reconstructed snapshot.\n", " \"\"\"\n", " state_list = []\n", - " \n", + "\n", " for k in range(num_qubits):\n", " op = unitary_ensemble[unitary_ids_ns[k]]\n", - " b = zero_state if outcome_ns[k] == '0' else one_state\n", + " b = zero_state if outcome_ns[k] == \"0\" else one_state\n", " state_list.append(3 * op * b * op.dag() - qutip.qeye(2))\n", - " \n", + "\n", " return qutip.tensor(state_list)" ] }, @@ -294,7 +297,7 @@ " # computing and saving mean per block\n", " means = []\n", " for block in range(K):\n", - " states = [snap_list[i] for i in np.where(indic==block)[0]]\n", + " states = [snap_list[i] for i in np.where(indic == block)[0]]\n", " exp = qutip.expect(obs, states)\n", " means.append(np.mean(exp))\n", " return np.median(means)" @@ -351,11 +354,20 @@ "source": [ "num_qubits = 2\n", "shadow_size = 10000\n", - "rho_1 = (qutip.tensor([qutip.basis(2,0), qutip.basis(2,0)]) + qutip.tensor([qutip.basis(2,0), qutip.basis(2,1)])).proj().unit()\n", + "rho_1 = (\n", + " (\n", + " qutip.tensor([qutip.basis(2, 0), qutip.basis(2, 0)])\n", + " + qutip.tensor([qutip.basis(2, 0), qutip.basis(2, 1)])\n", + " )\n", + " .proj()\n", + " .unit()\n", + ")\n", "print(\"Original density matrix :\")\n", "print(rho_1.full())\n", "outcomes, unitary_ids = calculate_classical_shadow(rho_1, shadow_size)\n", - "snapshots = [snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)]\n", + "snapshots = [\n", + " snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)\n", + "]\n", "print(\"Shadow reconstruction :\")\n", "print(np.around(state_reconstruction(snapshots).full(), 2))\n", "\n", @@ -363,7 +375,10 @@ "shadow_sizes = [100, 1000, 2000, 5000, 10000]\n", "for i, shadow_size in enumerate(shadow_sizes):\n", " outcomes, unitary_ids = calculate_classical_shadow(rho_1, shadow_size)\n", - " snapshots = [snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)]\n", + " snapshots = [\n", + " snapshot_state(outcomes[ns], unitary_ids[ns])\n", + " for ns in range(shadow_size)\n", + " ]\n", " dist[i] = qutip.tracedist(state_reconstruction(snapshots), rho_1)\n", "num_qubits = 4" ] @@ -447,7 +462,7 @@ " if end != -1:\n", " o = o[:end]\n", " for i, x in enumerate(o):\n", - " if not(x == p[i] or x == \"1\"):\n", + " if not (x == p[i] or x == \"1\"):\n", " return False\n", " return True" ] @@ -486,7 +501,7 @@ "def cond_conf(o, P_sharp):\n", " \"\"\"Returns the (modified) conditionned expectation value of the cost function depending\n", " on already chosen Paulis in P_sharp.\n", - " \n", + "\n", " Args:\n", " o (list[str]): list of Pauli strings to be measured\n", " P_sharp (list[str]): list of already chosen Paulis\n", @@ -495,14 +510,20 @@ " eta = 0.9\n", " nu = 1 - np.exp(-eta / 2)\n", " L = len(o)\n", - " m = len(P_sharp) - 1 # index of last chosen Pauli string\n", - " k = len(P_sharp[-1]) - 1 # index of last chosen Pauli matrix in mth Pauli string\n", + " m = len(P_sharp) - 1 # index of last chosen Pauli string\n", + " k = (\n", + " len(P_sharp[-1]) - 1\n", + " ) # index of last chosen Pauli matrix in mth Pauli string\n", " result = 0\n", " for l in range(0, L):\n", " v = 0\n", - " for m_prime in range(0,m):\n", + " for m_prime in range(0, m):\n", " v += (eta / 2) * int(hits(P_sharp[m_prime], o[l]))\n", - " v -= np.log(1 - (nu / 3**(weight(o[l], start=k+1))) * hits(P_sharp[m], o[l], end=k+1))\n", + " v -= np.log(\n", + " 1\n", + " - (nu / 3 ** (weight(o[l], start=k + 1)))\n", + " * hits(P_sharp[m], o[l], end=k + 1)\n", + " )\n", " result += np.exp(-v)\n", " return result" ] @@ -523,7 +544,7 @@ "def derandomization(M, o):\n", " \"\"\"Derandomization algorithm returning best Pauli indices according to a greedy algorithm\n", " that aims at minimizing the cost function above.\n", - " \n", + "\n", " Args:\n", " M (int): number of measurements\n", " n (int): number of qubits (size of Pauli strings)\n", @@ -593,7 +614,7 @@ " assuming the state is already rotated in the needed eigenbases of all single-qubit Paulis.\n", "\n", " NB : Faster than using qutip.measure due to not returning the eigenstates...\n", - " \n", + "\n", " Args:\n", " x (str): input bitstring\n", " sigma (str): input Pauli string to be measured on |x>\n", @@ -615,9 +636,9 @@ "source": [ "def classical_shadow_derand(rho, measurements):\n", " \"\"\"Returns the n-strings of ±1 corresponding to measurements in the input list on state rho.\n", - " \n", + "\n", " Args:\n", - " rho (qutip.Qobj): input state as a density matrix \n", + " rho (qutip.Qobj): input state as a density matrix\n", " measurements (list[str]): derandomized measurement bases in which to measure state rho\n", "\n", " Returns:\n", @@ -629,7 +650,12 @@ " outcomes = []\n", " for ns in range(shadow_size):\n", " # multi-qubit change of basis\n", - " unitmat = qutip.tensor([unitary_ensemble[_pauli_index(measurements[ns][i])]for i in range(num_qubits)])\n", + " unitmat = qutip.tensor(\n", + " [\n", + " unitary_ensemble[_pauli_index(measurements[ns][i])]\n", + " for i in range(num_qubits)\n", + " ]\n", + " )\n", " x = measure_bitstring(unitmat.dag() * rho * unitmat)\n", " outcomes.append(pauli_string_value(x, measurements[ns]))\n", " # ±1 strings\n", @@ -658,7 +684,8 @@ " break\n", " if pauli != \"1\":\n", " product *= single_measurement[i][1]\n", - " if not_match: continue\n", + " if not_match:\n", + " continue\n", "\n", " sum_product += product\n", " cnt_match += 1\n", @@ -719,7 +746,10 @@ "outputs": [], "source": [ "def pauli(positions=[], operators=[]):\n", - " op_list = [operators[positions.index(j)] if j in positions else qutip.qeye(2) for j in range(num_qubits)]\n", + " op_list = [\n", + " operators[positions.index(j)] if j in positions else qutip.qeye(2)\n", + " for j in range(num_qubits)\n", + " ]\n", " return qutip.tensor(op_list)" ] }, @@ -729,24 +759,30 @@ "metadata": {}, "outputs": [], "source": [ - "coeff_fact = [0.81261,\n", - " 0.171201,\n", - " 0.2227965,\n", - " 0.16862325,\n", - " 0.174349,\n", - " 0.12054625,\n", - " 0.165868,\n", - " 0.04532175]\n", + "coeff_fact = [\n", + " 0.81261,\n", + " 0.171201,\n", + " 0.2227965,\n", + " 0.16862325,\n", + " 0.174349,\n", + " 0.12054625,\n", + " 0.165868,\n", + " 0.04532175,\n", + "]\n", "\n", - "paulis = [pauli(),\n", - " pauli([0], [sz]) + pauli([1], [sz]),\n", - " pauli([2], [sz]) + pauli([3], [sz]),\n", - " pauli([1, 0], [sz, sz]),\n", - " pauli([3, 2], [sz, sz]),\n", - " pauli([2, 0], [sz, sz]) + pauli([3, 1], [sz, sz]),\n", - " pauli([2, 1], [sz, sz]) + pauli([3, 0], [sz, sz]),\n", - " pauli([3, 2, 1, 0], [sx, sx, sy, sy]) + pauli([3, 2, 1, 0], [sy, sy, sx, sx]),\n", - " pauli([3, 2, 1, 0], [sx, sy, sy, sx]) + pauli([3, 2, 1, 0], [sy, sx, sx, sy])]" + "paulis = [\n", + " pauli(),\n", + " pauli([0], [sz]) + pauli([1], [sz]),\n", + " pauli([2], [sz]) + pauli([3], [sz]),\n", + " pauli([1, 0], [sz, sz]),\n", + " pauli([3, 2], [sz, sz]),\n", + " pauli([2, 0], [sz, sz]) + pauli([3, 1], [sz, sz]),\n", + " pauli([2, 1], [sz, sz]) + pauli([3, 0], [sz, sz]),\n", + " pauli([3, 2, 1, 0], [sx, sx, sy, sy])\n", + " + pauli([3, 2, 1, 0], [sy, sy, sx, sx]),\n", + " pauli([3, 2, 1, 0], [sx, sy, sy, sx])\n", + " + pauli([3, 2, 1, 0], [sy, sx, sx, sy]),\n", + "]" ] }, { @@ -772,7 +808,14 @@ "source": [ "# H2 Molecule : 4 qubits in Jordan-Wigner mapping of the Hamiltonian\n", "a = 10\n", - "reg = Register.from_coordinates([[0, 0], [a, 0], [0.5*a, a*np.sqrt(3)/2], [0.5*a, -a*np.sqrt(3)/2]])\n", + "reg = Register.from_coordinates(\n", + " [\n", + " [0, 0],\n", + " [a, 0],\n", + " [0.5 * a, a * np.sqrt(3) / 2],\n", + " [0.5 * a, -a * np.sqrt(3) / 2],\n", + " ]\n", + ")\n", "reg.draw()" ] }, @@ -798,17 +841,20 @@ ], "source": [ "def cost_hamiltonian_JW():\n", - " H = - coeff_fact[0] * paulis[0] \\\n", - " + coeff_fact[1] * paulis[1] \\\n", - " - coeff_fact[2] * paulis[2] \\\n", - " + coeff_fact[3] * paulis[3] \\\n", - " + coeff_fact[4] * paulis[4] \\\n", - " + coeff_fact[5] * paulis[5] \\\n", - " + coeff_fact[6] * paulis[6] \\\n", - " - coeff_fact[7] * paulis[7] \\\n", + " H = (\n", + " -coeff_fact[0] * paulis[0]\n", + " + coeff_fact[1] * paulis[1]\n", + " - coeff_fact[2] * paulis[2]\n", + " + coeff_fact[3] * paulis[3]\n", + " + coeff_fact[4] * paulis[4]\n", + " + coeff_fact[5] * paulis[5]\n", + " + coeff_fact[6] * paulis[6]\n", + " - coeff_fact[7] * paulis[7]\n", " + coeff_fact[7] * paulis[8]\n", + " )\n", " return H\n", "\n", + "\n", "global H\n", "H = cost_hamiltonian_JW()\n", "exact_energy, ground_state = cost_hamiltonian_JW().groundstate()\n", @@ -851,25 +897,31 @@ " in_state (qubit.Qobj): initial state.\n", " \"\"\"\n", " seq = Sequence(r, Chadoq2)\n", - " seq.declare_channel('ch0','rydberg_global')\n", - " middle = len(param)//2\n", - " \n", + " seq.declare_channel(\"ch0\", \"rydberg_global\")\n", + " middle = len(param) // 2\n", + "\n", " for tau, t in zip(param[middle:], param[:middle]):\n", - " pulse_1 = Pulse.ConstantPulse(tau, 1., 0, 0) \n", - " pulse_2 = Pulse.ConstantPulse(t, 1., 1., 0) \n", - " seq.add(pulse_1, 'ch0')\n", - " seq.add(pulse_2, 'ch0')\n", - " \n", - " seq.measure('ground-rydberg')\n", - " simul = Simulation(seq, sampling_rate=.05)\n", + " pulse_1 = Pulse.ConstantPulse(tau, 1.0, 0, 0)\n", + " pulse_2 = Pulse.ConstantPulse(t, 1.0, 1.0, 0)\n", + " seq.add(pulse_1, \"ch0\")\n", + " seq.add(pulse_2, \"ch0\")\n", + "\n", + " seq.measure(\"ground-rydberg\")\n", + " simul = Simulation(seq, sampling_rate=0.05)\n", " simul.initial_state = in_state\n", " results = simul.run()\n", " return results.expect([H])[-1][-1]\n", "\n", + "\n", "def loop_JW(param, in_state):\n", - " res = minimize(quantum_loop, param, method='Nelder-Mead', args=in_state,\n", - " options={'return_all':True, 'maxiter':200, 'adaptive':True})\n", - " return(res)" + " res = minimize(\n", + " quantum_loop,\n", + " param,\n", + " method=\"Nelder-Mead\",\n", + " args=in_state,\n", + " options={\"return_all\": True, \"maxiter\": 200, \"adaptive\": True},\n", + " )\n", + " return res" ] }, { @@ -887,7 +939,7 @@ "source": [ "# Setup for VQS\n", "layers = 5\n", - "param = [2000]*layers + [4000]*layers" + "param = [2000] * layers + [4000] * layers" ] }, { @@ -912,6 +964,7 @@ ], "source": [ "import warnings\n", + "\n", "# Ignore the warnings\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", "\n", @@ -957,8 +1010,10 @@ } ], "source": [ - "plt.plot([quantum_loop(pars, gggg) for pars in loop_ising_results.allvecs], 'k')\n", - "plt.axhline(exact_energy, color='red')" + "plt.plot(\n", + " [quantum_loop(pars, gggg) for pars in loop_ising_results.allvecs], \"k\"\n", + ")\n", + "plt.axhline(exact_energy, color=\"red\")" ] }, { @@ -997,15 +1052,17 @@ "outputs": [], "source": [ "def exp_value_JW(exp_values):\n", - " return (- coeff_fact[0] * exp_values[0] \\\n", - " + coeff_fact[1] * exp_values[1] \\\n", - " - coeff_fact[2] * exp_values[2] \\\n", - " + coeff_fact[3] * exp_values[3] \\\n", - " + coeff_fact[4] * exp_values[4] \\\n", - " + coeff_fact[5] * exp_values[5] \\\n", - " + coeff_fact[6] * exp_values[6] \\\n", - " - coeff_fact[7] * exp_values[7] \\\n", - " + coeff_fact[7] * exp_values[8])" + " return (\n", + " -coeff_fact[0] * exp_values[0]\n", + " + coeff_fact[1] * exp_values[1]\n", + " - coeff_fact[2] * exp_values[2]\n", + " + coeff_fact[3] * exp_values[3]\n", + " + coeff_fact[4] * exp_values[4]\n", + " + coeff_fact[5] * exp_values[5]\n", + " + coeff_fact[6] * exp_values[6]\n", + " - coeff_fact[7] * exp_values[7]\n", + " + coeff_fact[7] * exp_values[8]\n", + " )" ] }, { @@ -1021,19 +1078,19 @@ " in_state (qubit.Qobj): initial state.\n", " \"\"\"\n", " seq = Sequence(r, Chadoq2)\n", - " seq.declare_channel('ch0','rydberg_global')\n", - " middle = len(param)//2\n", - " \n", + " seq.declare_channel(\"ch0\", \"rydberg_global\")\n", + " middle = len(param) // 2\n", + "\n", " for tau, t in zip(param[middle:], param[:middle]):\n", - " pulse_1 = Pulse.ConstantPulse(tau, 1., 0, 0) \n", - " pulse_2 = Pulse.ConstantPulse(t, 1., 1., 0)\n", - " seq.add(pulse_1, 'ch0')\n", - " seq.add(pulse_2, 'ch0')\n", - " \n", - " seq.measure('ground-rydberg')\n", - " simul = Simulation(seq, sampling_rate=.01)\n", + " pulse_1 = Pulse.ConstantPulse(tau, 1.0, 0, 0)\n", + " pulse_2 = Pulse.ConstantPulse(t, 1.0, 1.0, 0)\n", + " seq.add(pulse_1, \"ch0\")\n", + " seq.add(pulse_2, \"ch0\")\n", + "\n", + " seq.measure(\"ground-rydberg\")\n", + " simul = Simulation(seq, sampling_rate=0.01)\n", " simul.initial_state = in_state\n", - " \n", + "\n", " # Classical shadow estimation\n", " # Theoretical shadow size and number of clusters :\n", " # shadow_size, K = compute_shadow_size(0.1, 0.5, paulis)\n", @@ -1041,14 +1098,23 @@ " K = 4\n", " rho = simul.run().get_final_state().proj()\n", " outcomes, unitary_ids = calculate_classical_shadow(rho, shadow_size)\n", - " snapshots = [snapshot_state(outcomes[ns], unitary_ids[ns]) for ns in range(shadow_size)]\n", + " snapshots = [\n", + " snapshot_state(outcomes[ns], unitary_ids[ns])\n", + " for ns in range(shadow_size)\n", + " ]\n", " meds = [_median_of_means(obs, snapshots, K) for obs in paulis]\n", " return exp_value_JW(meds)\n", "\n", + "\n", "def loop_JW_shadows(param, in_state, shadow_size=20):\n", - " res = minimize(quantum_loop_shadows, param, method='Nelder-Mead', args=(in_state, shadow_size),\n", - " options={'return_all':True, 'maxiter':100, 'adaptive':True})\n", - " return(res)" + " res = minimize(\n", + " quantum_loop_shadows,\n", + " param,\n", + " method=\"Nelder-Mead\",\n", + " args=(in_state, shadow_size),\n", + " options={\"return_all\": True, \"maxiter\": 100, \"adaptive\": True},\n", + " )\n", + " return res" ] }, { @@ -1057,10 +1123,15 @@ "metadata": {}, "outputs": [], "source": [ - "shadow_sizes = [10,20,40,60,80,100]\n", + "shadow_sizes = [10, 20, 40, 60, 80, 100]\n", "energies = []\n", "for shadow_size in shadow_sizes:\n", - " energies.append(abs(loop_JW_shadows(param, gggg, shadow_size=shadow_size).fun - exact_energy))" + " energies.append(\n", + " abs(\n", + " loop_JW_shadows(param, gggg, shadow_size=shadow_size).fun\n", + " - exact_energy\n", + " )\n", + " )" ] }, { @@ -1092,10 +1163,10 @@ } ], "source": [ - "plt.figure(figsize=(8,5))\n", + "plt.figure(figsize=(8, 5))\n", "plt.xlabel(\"Shadow size\", fontsize=15)\n", "plt.ylabel(r\"$|\\frac{E - E_{ground}}{E_{ground}}|$\", fontsize=20)\n", - "plt.plot(shadow_sizes, [-e/exact_energy for e in energies])" + "plt.plot(shadow_sizes, [-e / exact_energy for e in energies])" ] }, { @@ -1125,24 +1196,41 @@ "metadata": {}, "outputs": [], "source": [ - "coeff_non_fact = [-0.81261,\n", - " 0.171201,\n", - " 0.171201,\n", - " -0.2227965,\n", - " -0.2227965,\n", - " 0.16862325,\n", - " 0.174349,\n", - " 0.12054625,\n", - " 0.12054625,\n", - " 0.165868,\n", - " 0.165868,\n", - " -0.04532175,\n", - " -0.04532175,\n", - " 0.04532175,\n", - " 0.04532175]\n", + "coeff_non_fact = [\n", + " -0.81261,\n", + " 0.171201,\n", + " 0.171201,\n", + " -0.2227965,\n", + " -0.2227965,\n", + " 0.16862325,\n", + " 0.174349,\n", + " 0.12054625,\n", + " 0.12054625,\n", + " 0.165868,\n", + " 0.165868,\n", + " -0.04532175,\n", + " -0.04532175,\n", + " 0.04532175,\n", + " 0.04532175,\n", + "]\n", "\n", - "paulis_str = [\"1111\", \"Z111\", \"1Z11\", \"11Z1\", \"111Z\", \"ZZ11\", \"11ZZ\", \"Z1Z1\", \"1Z1Z\", \"1ZZ1\",\n", - " \"Z11Z\", \"YYXX\", \"XXYY\", \"XYYX\", \"YXXY\"]" + "paulis_str = [\n", + " \"1111\",\n", + " \"Z111\",\n", + " \"1Z11\",\n", + " \"11Z1\",\n", + " \"111Z\",\n", + " \"ZZ11\",\n", + " \"11ZZ\",\n", + " \"Z1Z1\",\n", + " \"1Z1Z\",\n", + " \"1ZZ1\",\n", + " \"Z11Z\",\n", + " \"YYXX\",\n", + " \"XXYY\",\n", + " \"XYYX\",\n", + " \"YXXY\",\n", + "]" ] }, { @@ -1152,7 +1240,12 @@ "outputs": [], "source": [ "def exp_value_JW_non_fact(outcomes):\n", - " return sum([c*exp_value(sigma, outcomes) for c, sigma in zip(coeff_non_fact, paulis_str)])" + " return sum(\n", + " [\n", + " c * exp_value(sigma, outcomes)\n", + " for c, sigma in zip(coeff_non_fact, paulis_str)\n", + " ]\n", + " )" ] }, { @@ -1177,9 +1270,11 @@ ], "source": [ "measurements = derandomization(60, paulis_str)\n", - "print(f\"ZZZZ measurements : {measurements.count('ZZZZ')}, XXYY measurements : {measurements.count('XXYY')}, \" +\n", - " f\"YXXY measurements : {measurements.count('YXXY')}, XYYX measurements : {measurements.count('XYYX')}, \" +\n", - " f\"YYXX measurements : {measurements.count('YYXX')} : total = 60 measurements\")" + "print(\n", + " f\"ZZZZ measurements : {measurements.count('ZZZZ')}, XXYY measurements : {measurements.count('XXYY')}, \"\n", + " + f\"YXXY measurements : {measurements.count('YXXY')}, XYYX measurements : {measurements.count('XYYX')}, \"\n", + " + f\"YYXX measurements : {measurements.count('YYXX')} : total = 60 measurements\"\n", + ")" ] }, { @@ -1202,28 +1297,34 @@ " in_state (qubit.Qobj): initial state.\n", " \"\"\"\n", " seq = Sequence(r, Chadoq2)\n", - " seq.declare_channel('ch0','rydberg_global')\n", - " middle = len(param)//2\n", - " \n", + " seq.declare_channel(\"ch0\", \"rydberg_global\")\n", + " middle = len(param) // 2\n", + "\n", " for tau, t in zip(param[middle:], param[:middle]):\n", - " pulse_1 = Pulse.ConstantPulse(tau, 1., 0, 0) \n", - " pulse_2 = Pulse.ConstantPulse(t, 1., 1., 0)\n", - " seq.add(pulse_1, 'ch0')\n", - " seq.add(pulse_2, 'ch0')\n", - " \n", - " seq.measure('ground-rydberg')\n", - " simul = Simulation(seq, sampling_rate=.05)\n", + " pulse_1 = Pulse.ConstantPulse(tau, 1.0, 0, 0)\n", + " pulse_2 = Pulse.ConstantPulse(t, 1.0, 1.0, 0)\n", + " seq.add(pulse_1, \"ch0\")\n", + " seq.add(pulse_2, \"ch0\")\n", + "\n", + " seq.measure(\"ground-rydberg\")\n", + " simul = Simulation(seq, sampling_rate=0.05)\n", " simul.initial_state = in_state\n", - " \n", + "\n", " # Classical shadow estimation\n", " rho = simul.run().get_final_state().proj()\n", " outcomes = classical_shadow_derand(rho, measurements)\n", " return exp_value_JW_non_fact(outcomes)\n", "\n", + "\n", "def loop_JW_derand(param, in_state):\n", - " res = minimize(quantum_loop_derand, param, method='Nelder-Mead', args=in_state,\n", - " options={'return_all':True, 'maxiter':150, 'adaptive':True})\n", - " return(res)" + " res = minimize(\n", + " quantum_loop_derand,\n", + " param,\n", + " method=\"Nelder-Mead\",\n", + " args=in_state,\n", + " options={\"return_all\": True, \"maxiter\": 150, \"adaptive\": True},\n", + " )\n", + " return res" ] }, { @@ -1232,11 +1333,13 @@ "metadata": {}, "outputs": [], "source": [ - "measurement_sizes = [20,30,40,60,80,100]\n", + "measurement_sizes = [20, 30, 40, 60, 80, 100]\n", "energies_derand = []\n", "for meas_size in measurement_sizes:\n", - " measurements=derandomization(meas_size, paulis_str)\n", - " energies_derand.append(abs(loop_JW_derand(param, gggg).fun - exact_energy) / abs(exact_energy))" + " measurements = derandomization(meas_size, paulis_str)\n", + " energies_derand.append(\n", + " abs(loop_JW_derand(param, gggg).fun - exact_energy) / abs(exact_energy)\n", + " )" ] }, { @@ -1268,7 +1371,7 @@ } ], "source": [ - "plt.figure(figsize=(8,5))\n", + "plt.figure(figsize=(8, 5))\n", "plt.xlabel(\"Measurement size\", fontsize=15)\n", "plt.ylabel(r\"$|\\frac{E - E_{ground}}{E_{ground}}|$\", fontsize=20)\n", "plt.plot(measurement_sizes, energies_derand)" diff --git a/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb index e805f0b50..4c23f0381 100644 --- a/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb +++ b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb @@ -74,7 +74,7 @@ "\n", "reg = Register(qubits)\n", "seq = Sequence(reg, MockDevice)\n", - "seq.declare_channel('MW', 'mw_global')" + "seq.declare_channel(\"MW\", \"mw_global\")" ] }, { @@ -90,9 +90,9 @@ "metadata": {}, "outputs": [], "source": [ - "simple_pulse = Pulse.ConstantPulse(4000, 2*np.pi*4.6, 0, 0)\n", - "seq.add(simple_pulse, 'MW')\n", - "seq.measure(basis='XY')\n", + "simple_pulse = Pulse.ConstantPulse(4000, 2 * np.pi * 4.6, 0, 0)\n", + "seq.add(simple_pulse, \"MW\")\n", + "seq.measure(basis=\"XY\")\n", "\n", "sim = Simulation(seq)\n", "\n", @@ -117,11 +117,12 @@ " prod[j] = (qutip.sigmaz() + qutip.qeye(2)) / 2\n", " return qutip.tensor(prod)\n", "\n", + "\n", "magn = magnetization(0, 1)\n", "plt.figure(figsize=[16, 6])\n", "results.plot(magn)\n", - "plt.xlabel('Pulse duration (ns)', fontsize='x-large')\n", - "plt.ylabel('Excitation of the atom', fontsize='x-large')\n", + "plt.xlabel(\"Pulse duration (ns)\", fontsize=\"x-large\")\n", + "plt.ylabel(\"Excitation of the atom\", fontsize=\"x-large\")\n", "plt.show()" ] }, @@ -145,24 +146,24 @@ "metadata": {}, "outputs": [], "source": [ - "coords = np.array([[-8., 0], [0, 0], [8., 0]])\n", + "coords = np.array([[-8.0, 0], [0, 0], [8.0, 0]])\n", "qubits = dict(enumerate(coords))\n", "\n", "reg = Register(qubits)\n", "seq = Sequence(reg, MockDevice)\n", - "seq.declare_channel('ch0', 'mw_global')\n", + "seq.declare_channel(\"ch0\", \"mw_global\")\n", "reg.draw()\n", "\n", "# State preparation using SLM mask\n", "masked_qubits = [1, 2]\n", "seq.config_slm_mask(masked_qubits)\n", "masked_pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi), 0, 0)\n", - "seq.add(masked_pulse, 'ch0')\n", + "seq.add(masked_pulse, \"ch0\")\n", "\n", "# Simulation pulse\n", "simple_pulse = Pulse.ConstantPulse(7000, 0, 0, 0)\n", - "seq.add(simple_pulse, 'ch0')\n", - "seq.measure(basis='XY')\n", + "seq.add(simple_pulse, \"ch0\")\n", + "seq.measure(basis=\"XY\")\n", "\n", "sim = Simulation(seq, sampling_rate=1)\n", "results = sim.run(nsteps=5000)" @@ -181,17 +182,17 @@ "plt.figure(figsize=[16, 18])\n", "plt.subplot(311)\n", "plt.plot(expectations[0])\n", - "plt.ylabel('Excitation of atom 0', fontsize='x-large')\n", - "plt.xlabel('Time (ns)', fontsize='x-large')\n", + "plt.ylabel(\"Excitation of atom 0\", fontsize=\"x-large\")\n", + "plt.xlabel(\"Time (ns)\", fontsize=\"x-large\")\n", "plt.subplot(312)\n", "plt.plot(expectations[1])\n", - "plt.ylabel('Excitation of atom 1', fontsize='x-large')\n", - "plt.xlabel('Time (ns)', fontsize='x-large')\n", + "plt.ylabel(\"Excitation of atom 1\", fontsize=\"x-large\")\n", + "plt.xlabel(\"Time (ns)\", fontsize=\"x-large\")\n", "plt.ylim([0, 1])\n", "plt.subplot(313)\n", "plt.plot(expectations[2])\n", - "plt.ylabel('Excitation of atom 2', fontsize='x-large')\n", - "plt.xlabel('Time (ns)', fontsize='x-large')\n", + "plt.ylabel(\"Excitation of atom 2\", fontsize=\"x-large\")\n", + "plt.xlabel(\"Time (ns)\", fontsize=\"x-large\")\n", "plt.show()" ] }, @@ -234,13 +235,13 @@ "metadata": {}, "outputs": [], "source": [ - "coords = np.array([[-1., 0], [0, 0], [np.sqrt(2/3), np.sqrt(1/3)]]) * 8.\n", + "coords = np.array([[-1.0, 0], [0, 0], [np.sqrt(2 / 3), np.sqrt(1 / 3)]]) * 8.0\n", "qubits = dict(enumerate(coords))\n", "\n", "reg = Register(qubits)\n", "seq = Sequence(reg, MockDevice)\n", - "seq.declare_channel('ch0', 'mw_global')\n", - "seq.set_magnetic_field(0., 1., 0)\n", + "seq.declare_channel(\"ch0\", \"mw_global\")\n", + "seq.set_magnetic_field(0.0, 1.0, 0)\n", "reg.draw()" ] }, @@ -261,12 +262,12 @@ "masked_qubits = [1, 2]\n", "seq.config_slm_mask(masked_qubits)\n", "masked_pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi), 0, 0)\n", - "seq.add(masked_pulse, 'ch0')\n", + "seq.add(masked_pulse, \"ch0\")\n", "\n", "# Simulation pulse\n", "simple_pulse = Pulse.ConstantPulse(7000, 0, 0, 0)\n", - "seq.add(simple_pulse, 'ch0')\n", - "seq.measure(basis='XY')\n", + "seq.add(simple_pulse, \"ch0\")\n", + "seq.measure(basis=\"XY\")\n", "\n", "sim = Simulation(seq, sampling_rate=1)\n", "results = sim.run(progress_bar=True, nsteps=5000)" @@ -285,18 +286,18 @@ "plt.figure(figsize=[16, 18])\n", "plt.subplot(311)\n", "plt.plot(expectations[0])\n", - "plt.ylabel('Excitation of atom 0', fontsize='x-large')\n", - "plt.xlabel('Time ($\\mu$s)', fontsize='x-large')\n", + "plt.ylabel(\"Excitation of atom 0\", fontsize=\"x-large\")\n", + "plt.xlabel(\"Time ($\\mu$s)\", fontsize=\"x-large\")\n", "plt.ylim([0, 1])\n", "plt.subplot(312)\n", "plt.plot(expectations[1])\n", - "plt.ylabel('Excitation of atom 1', fontsize='x-large')\n", - "plt.xlabel('Time ($\\mu$s)', fontsize='x-large')\n", + "plt.ylabel(\"Excitation of atom 1\", fontsize=\"x-large\")\n", + "plt.xlabel(\"Time ($\\mu$s)\", fontsize=\"x-large\")\n", "plt.ylim([0, 1])\n", "plt.subplot(313)\n", "plt.plot(expectations[2])\n", - "plt.ylabel('Excitation of atom 2', fontsize='x-large')\n", - "plt.xlabel('Time ($\\mu$s)', fontsize='x-large')\n", + "plt.ylabel(\"Excitation of atom 2\", fontsize=\"x-large\")\n", + "plt.xlabel(\"Time ($\\mu$s)\", fontsize=\"x-large\")\n", "plt.ylim([0, 1])" ] }, diff --git a/tutorials/simulating_sequences.ipynb b/tutorials/simulating_sequences.ipynb index 367c62590..03fa994e7 100644 --- a/tutorials/simulating_sequences.ipynb +++ b/tutorials/simulating_sequences.ipynb @@ -39,7 +39,7 @@ "# Setup\n", "L = 14\n", "\n", - "Omega_max = 2.3 * 2*np.pi \n", + "Omega_max = 2.3 * 2 * np.pi\n", "U = Omega_max / 2.3\n", "\n", "delta_0 = -3 * U\n", @@ -47,15 +47,24 @@ "\n", "t_rise = 2000\n", "t_fall = 2000\n", - "t_sweep = (delta_f - delta_0)/(2 * np.pi * 10) * 5000\n", + "t_sweep = (delta_f - delta_0) / (2 * np.pi * 10) * 5000\n", "\n", "# Define a ring of atoms distanced by a blockade radius distance:\n", "R_interatomic = MockDevice.rydberg_blockade_radius(U)\n", - "coords = R_interatomic/(2*np.tan(np.pi/L)) * np.array([(np.cos(theta*2*np.pi/L), np.sin(theta*2*np.pi/L)) for theta in range(L)])\n", - " \n", - "reg = Register.from_coordinates(coords, prefix='atom')\n", + "coords = (\n", + " R_interatomic\n", + " / (2 * np.tan(np.pi / L))\n", + " * np.array(\n", + " [\n", + " (np.cos(theta * 2 * np.pi / L), np.sin(theta * 2 * np.pi / L))\n", + " for theta in range(L)\n", + " ]\n", + " )\n", + ")\n", "\n", - "reg.draw(blockade_radius=R_interatomic, draw_half_radius=True, draw_graph = True)" + "reg = Register.from_coordinates(coords, prefix=\"atom\")\n", + "\n", + "reg.draw(blockade_radius=R_interatomic, draw_half_radius=True, draw_graph=True)" ] }, { @@ -78,16 +87,22 @@ "metadata": {}, "outputs": [], "source": [ - "rise = Pulse.ConstantDetuning(RampWaveform(t_rise, 0., Omega_max), delta_0, 0.)\n", - "sweep = Pulse.ConstantAmplitude(Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.)\n", - "fall = Pulse.ConstantDetuning(RampWaveform(t_fall, Omega_max, 0.), delta_f, 0.)\n", + "rise = Pulse.ConstantDetuning(\n", + " RampWaveform(t_rise, 0.0, Omega_max), delta_0, 0.0\n", + ")\n", + "sweep = Pulse.ConstantAmplitude(\n", + " Omega_max, RampWaveform(t_sweep, delta_0, delta_f), 0.0\n", + ")\n", + "fall = Pulse.ConstantDetuning(\n", + " RampWaveform(t_fall, Omega_max, 0.0), delta_f, 0.0\n", + ")\n", "\n", "seq = Sequence(reg, MockDevice)\n", - "seq.declare_channel('ising', 'rydberg_global')\n", + "seq.declare_channel(\"ising\", \"rydberg_global\")\n", "\n", - "seq.add(rise, 'ising')\n", - "seq.add(sweep, 'ising')\n", - "seq.add(fall, 'ising')\n", + "seq.add(rise, \"ising\")\n", + "seq.add(sweep, \"ising\")\n", + "seq.add(fall, \"ising\")\n", "\n", "seq.draw()" ] @@ -158,7 +173,7 @@ "metadata": {}, "outputs": [], "source": [ - "results.states[23] # Given as a `qutip.Qobj` object" + "results.states[23] # Given as a `qutip.Qobj` object" ] }, { @@ -176,9 +191,9 @@ "source": [ "counts = results.sample_final_state(N_samples=1000)\n", "\n", - "large_counts = {k:v for k,v in counts.items() if v > 5}\n", + "large_counts = {k: v for k, v in counts.items() if v > 5}\n", "\n", - "plt.figure(figsize=(15,4))\n", + "plt.figure(figsize=(15, 4))\n", "plt.xticks(rotation=90, fontsize=14)\n", "plt.title(\"Most frequent observations\")\n", "plt.bar(large_counts.keys(), large_counts.values())" @@ -209,6 +224,7 @@ " prod[j] = qutip.sigmaz()\n", " return qutip.tensor(prod)\n", "\n", + "\n", "magn_list = [magnetization(j, L) for j in range(L)]" ] }, From 66eec5ad26fdd921477c2df87cca37ca7433f8ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Tue, 8 Feb 2022 11:35:53 +0100 Subject: [PATCH 39/51] New CI checks (#323) * Adding `isort` check to GitHub actions * Attempt to add `black[jupyter]` to CI checks * Attempt to fix broken `black[jupyter]` CI test * Explicitly writing the `isort` CI check * Updating the CONTRIBUTING guidelines to feature the new CI checks --- .github/workflows/ci.yml | 27 ++++++++++++++++++++++++++- CONTRIBUTING.md | 10 ++++++++-- 2 files changed, 34 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index edac902b6..c2a0419f3 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -32,8 +32,33 @@ jobs: steps: - name: Check out Pulser uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: 3.8 + - name: Install black + run: | + python -m pip install --upgrade pip + pip install black + pip install 'black[jupyter]' - name: Check formatting with black - uses: psf/black@stable + run: black --check --diff . + isort: + runs-on: ubuntu-latest + steps: + - name: Check out Pulser + uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: 3.8 + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + pip install -e . + pip install -r requirements.txt + - name: Check import sorting with isort + run: isort --check-only --diff . typing: runs-on: ubuntu-latest steps: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c886f4327..f4db89303 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -90,7 +90,7 @@ pip install -r requirements.txt To help you keep your code compliant with PEP8 guidelines effortlessly, we suggest you look into installing a linter for your text editor of choice. -- **Format**: We use the [`black`](https://black.readthedocs.io/en/stable/index.html) auto-formatter to enforce a consistent style throughout the entire code base. It will also ensure your code is compliant with the formatting enforced by `flake8` for you. To automatically format your code with black, just run: +- **Format**: We use the [`black`](https://black.readthedocs.io/en/stable/index.html) auto-formatter to enforce a consistent style throughout the entire code base, including the Jupyter notebooks (so make sure to install `black[jupyter]`). It will also ensure your code is compliant with the formatting enforced by `flake8` for you. To automatically format your code with black, just run: ```bash black . @@ -98,7 +98,13 @@ pip install -r requirements.txt Note that some IDE's and text editors support plug-ins which auto-format your code with `black` upon saving, so you don't have to worry about code format at all. -- **Type hints**: We use [mypy](http://mypy-lang.org/) to type check the code. Your code should have type +- **Import sorting**: We use [`isort`](https://pycqa.github.io/isort/) to automatically sort all library imports. You can do the same by running: + + ```bash + isort . + ``` + +- **Type hints**: We use [`mypy`](http://mypy-lang.org/) to type check the code. Your code should have type annotations and pass the type checks from running: ```bash From a60d1261899f59df822886ee5e3a2235085757dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Tue, 8 Feb 2022 13:42:36 +0100 Subject: [PATCH 40/51] Changing the line length for the `isort` config (#324) --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 1e75c2fbd..d84cc51b8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,3 +3,4 @@ line-length = 79 [tool.isort] profile = "black" +line_length = 79 From 5f4516809eb76a738f3a2946232d797fda8feb52 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Thu, 10 Feb 2022 10:48:29 +0100 Subject: [PATCH 41/51] Removing restriction on numpy version following qutip's update (#326) * Removing numpy restriction to <1.22, adding qutip restriction to >=4.63 * Fixing mypy errors --- pulser/register/register.py | 2 +- pulser/waveforms.py | 6 ++++-- requirements.txt | 4 ++-- setup.py | 4 ++-- 4 files changed, 9 insertions(+), 7 deletions(-) diff --git a/pulser/register/register.py b/pulser/register/register.py index 7132c387e..14addc018 100644 --- a/pulser/register/register.py +++ b/pulser/register/register.py @@ -265,7 +265,7 @@ def _hexagon_helper( coords = np.concatenate((coords, coords2)) coords *= spacing - coords = np.concatenate(([(0.0, 0.0)], coords)) + coords = np.concatenate((np.zeros((1, 2)), coords)) return cls.from_coordinates(coords, center=False, prefix=prefix) diff --git a/pulser/waveforms.py b/pulser/waveforms.py index 5a2757594..ccf5ef0c3 100644 --- a/pulser/waveforms.py +++ b/pulser/waveforms.py @@ -174,7 +174,7 @@ def modulated_samples(self, channel: Channel) -> np.ndarray: mod_samples = self._modulated_samples(channel) tr = channel.rise_time trim = slice(tr - start, len(mod_samples) - tr + end) - return cast(np.ndarray, mod_samples[trim]) + return mod_samples[trim] @functools.lru_cache() def modulation_buffers(self, channel: Channel) -> tuple[int, int]: @@ -226,7 +226,7 @@ def __getitem__( ) -> Union[float, np.ndarray]: if isinstance(index_or_slice, slice): s: slice = self._check_slice(index_or_slice) - return cast(np.ndarray, self._samples[s]) + return self._samples[s] else: index: int = self._check_index(index_or_slice) return cast(float, self._samples[index]) @@ -726,6 +726,7 @@ def __init__( super().__init__(duration) self._values = np.array(values, dtype=float) if times is not None: + times = cast(ArrayLike, times) times_ = np.array(times, dtype=float) if len(times_) != len(self._values): raise ValueError( @@ -932,6 +933,7 @@ def from_max_val( """ max_val = cast(float, max_val) area = cast(float, area) + beta = cast(float, beta) if np.sign(max_val) != np.sign(area): raise ValueError( diff --git a/requirements.txt b/requirements.txt index df9ab926a..d0b13f486 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ matplotlib -numpy >= 1.20, < 1.22 -qutip +numpy >= 1.20 +qutip >= 4.6.3 scipy # version specific diff --git a/setup.py b/setup.py index db2211c6c..5caa815e3 100644 --- a/setup.py +++ b/setup.py @@ -22,9 +22,9 @@ version=__version__, install_requires=[ "matplotlib", - "numpy>=1.20, <1.22", + "numpy>=1.20", "scipy", - "qutip", + "qutip>=4.6.3", ], extras_require={ ":python_version == '3.7'": [ From a72792639fd5da0dff666fc07732c915eac9eb0b Mon Sep 17 00:00:00 2001 From: Codoscope <14247215+Codoscope@users.noreply.github.com> Date: Tue, 15 Feb 2022 14:05:18 +0100 Subject: [PATCH 42/51] Generalize variable (#327) * Add new variable system * Fix tests * Reformat * Fix imports * Try to fix coverage replacing colon with 'pass' * Allow mypy to detect Variable as an Iterable * Allow mypy to detect operators * Implement statically methods of OpSupport * Fix coverage and import * Apply remark to test VariableItem * Fix flake8 Co-authored-by: Codoscope --- pulser/parametrized/paramobj.py | 63 ++++++++++++++++++++----------- pulser/parametrized/variable.py | 28 +++++++------- pulser/sequence.py | 44 ++++++++++++++++++--- pulser/tests/test_json.py | 2 +- pulser/tests/test_parametrized.py | 25 ++++++++++-- pulser/tests/test_paramseq.py | 9 ++++- 6 files changed, 123 insertions(+), 48 deletions(-) diff --git a/pulser/parametrized/paramobj.py b/pulser/parametrized/paramobj.py index 0bcf0ad4a..f5fc6849e 100644 --- a/pulser/parametrized/paramobj.py +++ b/pulser/parametrized/paramobj.py @@ -19,7 +19,6 @@ import operator import warnings from collections.abc import Callable -from functools import partialmethod from itertools import chain from typing import TYPE_CHECKING, Any, Union @@ -29,39 +28,57 @@ if TYPE_CHECKING: from pulser.parametrized import Variable # pragma: no cover -# Available operations on parametrized objects with OpSupport -reversible_ops = [ - "__add__", - "__sub__", - "__mul__", - "__truediv__", - "__floordiv__", - "__pow__", - "__mod__", -] - class OpSupport: """Methods for supporting operators on parametrized objects.""" - def _do_op(self, op_name: str, other: Union[int, float]) -> ParamObj: - return ParamObj(getattr(operator, op_name), self, other) - - def _do_rop(self, op_name: str, other: Union[int, float]) -> ParamObj: - return ParamObj(getattr(operator, op_name), other, self) - def __neg__(self) -> ParamObj: return ParamObj(operator.neg, self) def __abs__(self) -> ParamObj: return ParamObj(operator.abs, self) + def __add__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__add__, self, other) + + def __radd__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__add__, other, self) + + def __sub__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__sub__, self, other) + + def __rsub__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__sub__, other, self) + + def __mul__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__mul__, self, other) + + def __rmul__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__mul__, other, self) + + def __truediv__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__truediv__, self, other) + + def __rtruediv__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__truediv__, other, self) + + def __floordiv__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__floordiv__, self, other) + + def __rfloordiv__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__floordiv__, other, self) + + def __pow__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__pow__, self, other) + + def __rpow__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__pow__, other, self) + + def __mod__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__mod__, self, other) -# Inject operator magic methods into OpSupport -for method in reversible_ops: - rmethod = "__r" + method[2:] - setattr(OpSupport, method, partialmethod(OpSupport._do_op, method)) - setattr(OpSupport, rmethod, partialmethod(OpSupport._do_rop, method)) + def __rmod__(self, other: Union[int, float]) -> ParamObj: + return ParamObj(operator.__mod__, other, self) class ParamObj(Parametrized, OpSupport): diff --git a/pulser/parametrized/variable.py b/pulser/parametrized/variable.py index 743c337bb..8238d1e64 100644 --- a/pulser/parametrized/variable.py +++ b/pulser/parametrized/variable.py @@ -18,7 +18,7 @@ import collections.abc # To use collections.abc.Sequence import dataclasses from collections.abc import Iterable -from typing import Any, Union, cast +from typing import Any, Iterator, Optional, Union, cast import numpy as np from numpy.typing import ArrayLike @@ -63,7 +63,7 @@ def variables(self) -> dict[str, Variable]: return {self.name: self} def _clear(self) -> None: - object.__setattr__(self, "value", None) + object.__setattr__(self, "value", None) # TODO rename _value? object.__setattr__(self, "_count", self._count + 1) def _assign(self, value: Union[ArrayLike, str, float, int]) -> None: @@ -78,22 +78,19 @@ def _assign(self, value: Union[ArrayLike, str, float, int]) -> None: "must be of type 'str'." ) - val = np.array(value, dtype=self.dtype) + val = np.array(value, dtype=self.dtype, ndmin=1) if val.size != self.size: raise ValueError( f"Can't assign array of size {val.size} to " + f"variable of size {self.size}." ) - if self.size == 1: - object.__setattr__(self, "value", self.dtype(val)) - else: - object.__setattr__(self, "value", val) + object.__setattr__(self, "value", val) object.__setattr__(self, "_count", self._count + 1) - def build(self) -> Union[ArrayLike, str, float, int]: + def build(self) -> ArrayLike: """Returns the variable's current value.""" - self.value: Union[ArrayLike, str, float, int] + self.value: Optional[ArrayLike] if self.value is None: raise ValueError(f"No value assigned to variable '{self.name}'.") return self.value @@ -109,20 +106,22 @@ def __str__(self) -> str: def __len__(self) -> int: return self.size - def __getitem__(self, key: Union[int, slice]) -> _VariableItem: + def __getitem__(self, key: Union[int, slice]) -> VariableItem: if not isinstance(key, (int, slice)): raise TypeError(f"Invalid key type {type(key)} for '{self.name}'.") - if self.size == 1: - raise TypeError(f"Variable '{self.name}' is not subscriptable.") if isinstance(key, int): if not -self.size <= key < self.size: raise IndexError(f"{key} outside of range for '{self.name}'.") - return _VariableItem(self, key) + return VariableItem(self, key) + + def __iter__(self) -> Iterator[VariableItem]: + for i in range(len(self)): + yield self[i] @dataclasses.dataclass(frozen=True) -class _VariableItem(Parametrized, OpSupport): +class VariableItem(Parametrized, OpSupport): """Stores access to items of a variable with multiple values.""" var: Variable @@ -130,6 +129,7 @@ class _VariableItem(Parametrized, OpSupport): @property def variables(self) -> dict[str, Variable]: + """All the variables involved with this object.""" return self.var.variables def build(self) -> Union[ArrayLike, str, float, int]: diff --git a/pulser/sequence.py b/pulser/sequence.py index 97df719c5..41b697353 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -24,7 +24,16 @@ from functools import wraps from itertools import chain from sys import version_info -from typing import Any, NamedTuple, Optional, Tuple, TypeVar, Union, cast +from typing import ( + Any, + NamedTuple, + Optional, + Tuple, + TypeVar, + Union, + cast, + overload, +) import matplotlib.pyplot as plt import numpy as np @@ -38,6 +47,7 @@ from pulser.json.coders import PulserDecoder, PulserEncoder from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, Variable +from pulser.parametrized.variable import VariableItem from pulser.pulse import Pulse from pulser.register.base_register import BaseRegister @@ -538,12 +548,31 @@ def declare_channel( ) ) + @overload def declare_variable( self, name: str, - size: int = 1, + *, + dtype: Union[type[int], type[float], type[str]] = float, + ) -> VariableItem: + pass + + @overload + def declare_variable( + self, + name: str, + *, + size: int, dtype: Union[type[int], type[float], type[str]] = float, ) -> Variable: + pass + + def declare_variable( + self, + name: str, + size: Optional[int] = None, + dtype: Union[type[int], type[float], type[str]] = float, + ) -> Union[Variable, VariableItem]: """Declare a new variable within this Sequence. The declared variables can be used to create parametrized versions of @@ -570,9 +599,14 @@ def declare_variable( """ if name in self._variables: raise ValueError("Name for variable is already being used.") - var = Variable(name, dtype, size=size) - self._variables[name] = var - return var + + if size is None: + var = self.declare_variable(name, size=1, dtype=dtype) + return var[0] + else: + var = Variable(name, dtype, size=size) + self._variables[name] = var + return var @_store def add( diff --git a/pulser/tests/test_json.py b/pulser/tests/test_json.py index 482b87c00..9fb61a02a 100644 --- a/pulser/tests/test_json.py +++ b/pulser/tests/test_json.py @@ -69,7 +69,7 @@ def test_rare_cases(): with pytest.warns(UserWarning, match="not encode a Sequence"): wf_ = Sequence.deserialize(s) - var._assign(-10) + seq._variables["var"]._assign(-10) with pytest.raises(ValueError, match="No value assigned"): wf_.build() diff --git a/pulser/tests/test_parametrized.py b/pulser/tests/test_parametrized.py index 233869a6f..041ef6313 100644 --- a/pulser/tests/test_parametrized.py +++ b/pulser/tests/test_parametrized.py @@ -25,6 +25,8 @@ b = Variable("b", int, size=2) b._assign([-1.5, 1.5]) c = Variable("c", str) +d = Variable("d", float, size=1) +d._assign([0.5]) t = Variable("t", int) bwf = BlackmanWaveform(t, a) pulse = Pulse.ConstantDetuning(bwf, b[0], b[1]) @@ -69,20 +71,23 @@ def test_var(): with pytest.raises(TypeError, match="Invalid key type"): b[[0, 1]] - with pytest.raises(TypeError, match="not subscriptable"): - a[0] with pytest.raises(IndexError): b[2] def test_varitem(): + a0 = a[0] b1 = b[1] b01 = b[100::-1] + d0 = d[0] assert b01.variables == {"b": b} + assert str(a0) == "a[0]" assert str(b1) == "b[1]" assert str(b01) == "b[100::-1]" - assert b1.build() == 1 + assert str(d0) == "d[0]" + assert b1.build() == 1 # TODO should be 1.5 assert np.all(b01.build() == np.array([1, -1])) + assert d0.build() == 0.5 with pytest.raises(FrozenInstanceError): b1.key = 0 @@ -115,3 +120,17 @@ def test_opsupport(): assert (a**a).build() == 0.25 assert abs(a).build() == 2.0 assert (3 % a).build() == -1.0 + assert (-a).build() == 2.0 + + x = a + 11 + assert x.build() == 9 + x = x % 6 + assert x.build() == 3 + x = 2 - x + assert x.build() == -1 + x = 4 / x + assert x.build() == -4 + x = 9 // x + assert x.build() == -3 + x = 2**x + assert x.build() == 0.125 diff --git a/pulser/tests/test_paramseq.py b/pulser/tests/test_paramseq.py index 04bbf527b..364b915e5 100644 --- a/pulser/tests/test_paramseq.py +++ b/pulser/tests/test_paramseq.py @@ -20,6 +20,7 @@ from pulser import Pulse, Register, Sequence from pulser.devices import Chadoq2, MockDevice from pulser.parametrized import Variable +from pulser.parametrized.variable import VariableItem from pulser.waveforms import BlackmanWaveform reg = Register.rectangle(4, 3) @@ -29,7 +30,7 @@ def test_var_declarations(): sb = Sequence(reg, device) assert sb.declared_variables == {} - var = sb.declare_variable("var") + var = sb.declare_variable("var", size=1) assert sb.declared_variables == {"var": var} assert isinstance(var, Variable) assert var.dtype == float @@ -39,6 +40,9 @@ def test_var_declarations(): var2 = sb.declare_variable("var2", 4, str) assert var2.dtype == str assert len(var2) == 4 + var3 = sb.declare_variable("var3") + assert sb.declared_variables["var3"] == var3.var + assert isinstance(var3, VariableItem) def test_stored_calls(): @@ -175,7 +179,8 @@ def test_str(): sb.add(pls, "ch1") s = ( f"Prelude\n-------\n{str(seq)}Stored calls\n------------\n\n" - + "1. add(Pulse.ConstantPulse(mul(var, 100), var, -1, var), ch1)" + + "1. add(Pulse.ConstantPulse(mul(var[0], 100), var[0]," + + " -1, var[0]), ch1)" ) assert s == str(sb) From 7976013add4bb642d9b5e6e4bc78586f522e728f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Tue, 15 Feb 2022 15:42:06 +0100 Subject: [PATCH 43/51] Adding CI unit tests in Python 3.9 and 3.10 (#328) * Adding coverage tests for Python 3.9 and 3.10 * Fix attempt for 3.10 --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index c2a0419f3..073b63888 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -80,7 +80,7 @@ jobs: strategy: matrix: os: [ubuntu-latest] - python-version: [3.7, 3.8] + python-version: [3.7, 3.8, 3.9, "3.10"] steps: - name: Check out Pulser uses: actions/checkout@v2 From 81a4312e62acaad0266c3fa420cff17f95a0041e Mon Sep 17 00:00:00 2001 From: Louis Vignoli <97944962+lvignoli@users.noreply.github.com> Date: Fri, 18 Feb 2022 16:37:50 +0100 Subject: [PATCH 44/51] Adding CI checks with git hooks and pre-commit (#331) * add pre-commit hooks * update contributing guidelines with pre-commit usage * fix a typo * Remove superflous args from isort hooks Running isort hook with --show-config arg and the verbose: true flag shows that the pyproject.toml configuration is read and used by the hook: ```terminal ... { "profile": "black", "line_length": 79, "source": "/Users/manta/Desktop/Pulser/pyproject.toml" } ... ``` --- .mypy_script | 2 ++ .pre-commit-config.yaml | 26 ++++++++++++++++++++++++++ CONTRIBUTING.md | 20 ++++++++++++++++++++ requirements.txt | 3 +++ 4 files changed, 51 insertions(+) create mode 100755 .mypy_script create mode 100644 .pre-commit-config.yaml diff --git a/.mypy_script b/.mypy_script new file mode 100755 index 000000000..69889334b --- /dev/null +++ b/.mypy_script @@ -0,0 +1,2 @@ +#!/usr/bin/env bash +mypy --config-file .mypy.ini diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..0afee19c3 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + - repo: https://github.com/psf/black + rev: 22.1.0 + hooks: + - id: black-jupyter + + - repo: https://github.com/PyCQA/flake8 + rev: 4.0.1 + hooks: + - id: flake8 + + - repo: https://github.com/pycqa/isort + rev: 5.10.1 + hooks: + - id: isort + name: isort (python) + + # Calling mypy from a bash script, as I cannot figure out how to pass the + # .mypy.ini config file when using the hook at + # https://github.com/pre-commit/mirrors-mypy + - repo: local + hooks: + - id: mypy + name: mypy + entry: ./.mypy_script + language: script diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f4db89303..f1d15872c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -115,3 +115,23 @@ annotations and pass the type checks from running: **Note**: Type hints for `numpy` have only been added in version 1.20. Make sure you have `numpy >= 1.20` installed before running the type checks. + +### Use `pre-commit` to automate CI checks + +[`pre-commit`](https://pre-commit.com/) is a tool to easily setup and manage [git hooks](https://git-scm.com/docs/githooks). + +Run + +```bash +pre-commit install --hook-type pre-push +``` + +to install `black`, `isort`, `flake8` and `mypy` hooks in your local repository (at `.git/hooks/` by defaults) +and run them automatically before any push to a remote git repository. +If an issue is found by these tools, the git hook will abort the push. `black` and `isort` hooks may reformat guilty files. + +Disable the hooks with + +```bash +pre-commit uninstall --hook-type pre-push +``` diff --git a/requirements.txt b/requirements.txt index d0b13f486..0ba7b5d10 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,6 +17,9 @@ mypy == 0.921 pytest pytest-cov +# CI +pre-commit + # tutorials notebook python-igraph From 4eaf3ff9e32fd7eede7b021f2be143b3348bb918 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Tue, 22 Feb 2022 11:01:39 +0100 Subject: [PATCH 45/51] Adding Register Layouts (#333) * Starting the Register Layout file * WIP: RegisterLayout definition * Moving Register drawing functions to dedicated class * Finishing the preliminary RegisterLayout * Finishing the first RegisterLayout version * Moving 3D drawing to RegDrawer * Trap sorting in 3D * Enabling layout drawing in 3D * Adding unit tests * Separate coordinate checker in register validation * Adding RegisterLayout device validation * Missing type hints * Adding option to declare pre-calibrated layouts on Device * Adding method to get trap_ids from coords * Moving 2D pattern creation to separate functions * Validating the RegisterLayout within register validation if possible * Blocking `Register.rotate()` call for registers coming from a layout * Created the special register layout classes * Completing unit tests for register layout * Moving layout info to Register.__init__() * Adding serialization to RegisterLayout * Finishing unit tests * Import sorting * Addressing review comments --- pulser/devices/_device_datacls.py | 106 +++++--- pulser/register/_patterns.py | 132 +++++++++ pulser/register/_reg_drawer.py | 386 +++++++++++++++++++++++++++ pulser/register/base_register.py | 228 ++++------------ pulser/register/register.py | 160 ++--------- pulser/register/register3d.py | 164 ++---------- pulser/register/register_layout.py | 269 +++++++++++++++++++ pulser/register/special_layouts.py | 168 ++++++++++++ pulser/tests/test_devices.py | 69 ++++- pulser/tests/test_json.py | 12 + pulser/tests/test_register.py | 5 + pulser/tests/test_register_layout.py | 185 +++++++++++++ 12 files changed, 1388 insertions(+), 496 deletions(-) create mode 100644 pulser/register/_patterns.py create mode 100644 pulser/register/_reg_drawer.py create mode 100644 pulser/register/register_layout.py create mode 100644 pulser/register/special_layouts.py create mode 100644 pulser/tests/test_register_layout.py diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index 5003eef21..6b21ff826 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -14,7 +14,7 @@ from __future__ import annotations -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import Any import numpy as np @@ -24,7 +24,8 @@ from pulser.channels import Channel from pulser.devices.interaction_coefficients import c6_dict from pulser.json.utils import obj_to_dict -from pulser.register.base_register import BaseRegister +from pulser.register.base_register import BaseRegister, QubitId +from pulser.register.register_layout import RegisterLayout @dataclass(frozen=True, repr=False) @@ -55,10 +56,15 @@ class Device: _channels: tuple[tuple[str, Channel], ...] # Ising interaction coeff interaction_coeff_xy: float = 3700.0 + pre_calibrated_layouts: tuple[RegisterLayout, ...] = field( + default_factory=tuple + ) def __post_init__(self) -> None: # Hack to override the docstring of an instance object.__setattr__(self, "__doc__", self._specs(for_docs=True)) + for layout in self.pre_calibrated_layouts: + self.validate_layout(layout) @property def channels(self) -> dict[str, Channel]: @@ -75,6 +81,11 @@ def interaction_coeff(self) -> float: r""":math:`C_6/\hbar` coefficient of chosen Rydberg level.""" return float(c6_dict[self.rydberg_level]) + @property + def calibrated_register_layouts(self) -> dict[str, RegisterLayout]: + """Register layouts already calibrated on this device.""" + return {str(layout): layout for layout in self.pre_calibrated_layouts} + def print_specs(self) -> None: """Prints the device specifications.""" title = f"{self.name} Specifications" @@ -128,53 +139,47 @@ def validate_register(self, register: BaseRegister) -> None: """Checks if 'register' is compatible with this device. Args: - register(pulser.Register): The Register to validate. + register(BaseRegister): The Register to validate. """ - if not (isinstance(register, BaseRegister)): + if not isinstance(register, BaseRegister): raise TypeError( - "register has to be a pulser.Register or " + "'register' must be a pulser.Register or " "a pulser.Register3D instance." ) - ids = list(register.qubits.keys()) - atoms = list(register.qubits.values()) - if len(atoms) > self.max_atom_num: - raise ValueError( - f"The number of atoms ({len(atoms)})" - " must be less than or equal to the maximum" - " number of atoms supported by this device" - f" ({self.max_atom_num})." - ) - if register._dim > self.dimensions: raise ValueError( f"All qubit positions must be at most {self.dimensions}D " "vectors." ) + self._validate_coords(register.qubits, kind="atoms") - if len(atoms) > 1: - distances = pdist(atoms) # Pairwise distance between atoms - if np.any(distances < self.min_atom_distance): - sq_dists = squareform(distances) - mask = np.triu(np.ones(len(atoms), dtype=bool), k=1) - bad_pairs = np.argwhere( - np.logical_and(sq_dists < self.min_atom_distance, mask) - ) - bad_qbt_pairs = [(ids[i], ids[j]) for i, j in bad_pairs] + if register._layout_info is not None: + try: + self.validate_layout(register._layout_info.layout) + except (ValueError, TypeError): raise ValueError( - "The minimal distance between atoms in this device " - f"({self.min_atom_distance} µm) is not respected for the " - f"pairs: {bad_qbt_pairs}" + "The 'register' is associated with an incompatible " + "register layout." ) - too_far = np.linalg.norm(atoms, axis=1) > self.max_radial_distance - if np.any(too_far): + def validate_layout(self, layout: RegisterLayout) -> None: + """Checks if a register layout is compatible with this device. + + Args: + layout(RegisterLayout): The RegisterLayout to validate. + """ + if not isinstance(layout, RegisterLayout): + raise TypeError("'layout' must be a RegisterLayout instance.") + + if layout.dimensionality > self.dimensions: raise ValueError( - f"All qubits must be at most {self.max_radial_distance} μm " - f"away from the center of the array, which is not the case " - f"for: {[ids[int(i)] for i in np.where(too_far)[0]]}" + "The device supports register layouts of at most " + f"{self.dimensions} dimensions." ) + self._validate_coords(layout.traps_dict, kind="traps") + def validate_pulse(self, pulse: Pulse, channel_id: str) -> None: """Checks if a pulse can be executed on a specific device channel. @@ -247,6 +252,43 @@ def _specs(self, for_docs: bool = False) -> str: return "\n".join(lines + ch_lines) + def _validate_coords( + self, coords_dict: dict[QubitId, np.ndarray], kind: str = "atoms" + ) -> None: + ids = list(coords_dict.keys()) + coords = list(coords_dict.values()) + max_number = self.max_atom_num * (2 if kind == "traps" else 1) + if len(coords) > max_number: + raise ValueError( + f"The number of {kind} ({len(coords)})" + " must be less than or equal to the maximum" + f" number of {kind} supported by this device" + f" ({max_number})." + ) + + if len(coords) > 1: + distances = pdist(coords) # Pairwise distance between atoms + if np.any(distances < self.min_atom_distance): + sq_dists = squareform(distances) + mask = np.triu(np.ones(len(coords), dtype=bool), k=1) + bad_pairs = np.argwhere( + np.logical_and(sq_dists < self.min_atom_distance, mask) + ) + bad_qbt_pairs = [(ids[i], ids[j]) for i, j in bad_pairs] + raise ValueError( + f"The minimal distance between {kind} in this device " + f"({self.min_atom_distance} µm) is not respected for the " + f"pairs: {bad_qbt_pairs}" + ) + + too_far = np.linalg.norm(coords, axis=1) > self.max_radial_distance + if np.any(too_far): + raise ValueError( + f"All {kind} must be at most {self.max_radial_distance} μm " + f"away from the center of the array, which is not the case " + f"for: {[ids[int(i)] for i in np.where(too_far)[0]]}" + ) + def _to_dict(self) -> dict[str, Any]: return obj_to_dict( self, _build=False, _module="pulser.devices", _name=self.name diff --git a/pulser/register/_patterns.py b/pulser/register/_patterns.py new file mode 100644 index 000000000..afb45025f --- /dev/null +++ b/pulser/register/_patterns.py @@ -0,0 +1,132 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import numpy as np + + +def square_rect(rows: int, columns: int) -> np.ndarray: + """A square lattice pattern in a rectangular shape. + + Args: + rows(int): Number of rows. + columns(int): Number of columns. + + Returns: + np.ndarray: The coordinates of the points in the pattern. + """ + points = np.mgrid[:columns, :rows].transpose().reshape(-1, 2) + # Centering + points = points - np.ceil([columns / 2, rows / 2]) + 1 + return points + + +def triangular_rect(rows: int, columns: int) -> np.ndarray: + """A triangular lattice pattern in a rectangular shape. + + Args: + rows(int): Number of rows. + columns(int): Number of columns. + + Returns: + np.ndarray: The coordinates of the points in the pattern. + """ + points = square_rect(rows, columns) + points[:, 0] += 0.5 * np.mod(points[:, 1], 2) + points[:, 1] *= np.sqrt(3) / 2 + return points + + +def triangular_hex(n_points: int) -> np.ndarray: + """A triangular lattice pattern in an hexagonal shape. + + Args: + n_points(int): The number of points in the pattern. + + + Returns: + np.ndarray: The coordinates of the points in the pattern. + """ + # y coordinates of the top vertex of a triangle + crest_y = np.sqrt(3) / 2.0 + + if n_points < 7: + hex_coords = np.array( + [ + (0.0, 0.0), + (-0.5, crest_y), + (0.5, crest_y), + (1.0, 0.0), + (0.5, -crest_y), + (-0.5, -crest_y), + ] + ) + return hex_coords[:n_points] + + layers = int((-3.0 + np.sqrt(9 + 12 * (n_points - 1))) / 6.0) + points_left = n_points - 1 - (layers**2 + layers) * 3 + + # Coordinates of vertices + start_x = [-1.0, -0.5, 0.5, 1.0, 0.5, -0.5] + start_y = [0.0, crest_y, crest_y, 0, -crest_y, -crest_y] + + # Steps to place atoms, starting from a vertex + delta_x = [0.5, 1.0, 0.5, -0.5, -1.0, -0.5] + delta_y = [crest_y, 0.0, -crest_y, -crest_y, 0.0, crest_y] + + coords = np.array( + [ + ( + start_x[side] * layer + atom * delta_x[side], + start_y[side] * layer + atom * delta_y[side], + ) + for layer in range(1, layers + 1) + for side in range(6) + for atom in range(1, layer + 1) + ], + dtype=float, + ) + + if points_left > 0: + layer = layers + 1 + min_atoms_per_side = points_left // 6 + # Extra atoms after balancing all sides + points_left %= 6 + + # Order for placing left atoms + # Top-Left, Top-Right, Bottom (C3 symmetry)... + # ...Top, Bottom-Right, Bottom-Left (C6 symmetry) + sides_order = [0, 3, 1, 4, 2, 5] + + coords2 = np.array( + [ + ( + start_x[side] * layer + atom * delta_x[side], + start_y[side] * layer + atom * delta_y[side], + ) + for side in range(6) + for atom in range( + 1, + min_atoms_per_side + 2 + if points_left > sides_order[side] + else min_atoms_per_side + 1, + ) + ], + dtype=float, + ) + + coords = np.concatenate((coords, coords2)) + + coords = np.concatenate((np.zeros((1, 2)), coords)) + return coords diff --git a/pulser/register/_reg_drawer.py b/pulser/register/_reg_drawer.py new file mode 100644 index 000000000..e36b38bbc --- /dev/null +++ b/pulser/register/_reg_drawer.py @@ -0,0 +1,386 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from itertools import combinations +from typing import Optional, Union + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib import collections as mc +from scipy.spatial import KDTree + +from pulser.register.base_register import QubitId + + +class RegDrawer: + """Helper functions for Register drawing.""" + + @staticmethod + def _draw_2D( + ax: plt.axes._subplots.AxesSubplot, + pos: np.ndarray, + ids: list, + plane: tuple = (0, 1), + with_labels: bool = True, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + masked_qubits: set[QubitId] = set(), + are_traps: bool = False, + ) -> None: + ix, iy = plane + + if are_traps: + params = dict(s=50, edgecolors="black", facecolors="none") + else: + params = dict(s=30, c="darkgreen") + + ax.scatter(pos[:, ix], pos[:, iy], alpha=0.7, **params) + + # Draw square halo around masked qubits + if masked_qubits: + mask_pos = [] + for i, c in zip(ids, pos): + if i in masked_qubits: + mask_pos.append(c) + mask_arr = np.array(mask_pos) + ax.scatter( + mask_arr[:, ix], + mask_arr[:, iy], + marker="s", + s=1200, + alpha=0.2, + c="black", + ) + + axes = "xyz" + + ax.set_xlabel(axes[ix] + " (µm)") + ax.set_ylabel(axes[iy] + " (µm)") + ax.axis("equal") + ax.spines["right"].set_color("none") + ax.spines["top"].set_color("none") + + if with_labels: + # Determine which labels would overlap and merge those + plot_pos = list(pos[:, (ix, iy)]) + plot_ids: list[Union[str, list[str]]] = [[f"{i}"] for i in ids] + # Threshold distance between points + epsilon = 1.0e-2 * np.diff(ax.get_xlim())[0] + + i = 0 + bbs = {} + while i < len(plot_ids): + r = plot_pos[i] + j = i + 1 + overlap = False + # Put in a list all qubits that overlap at position plot_pos[i] + while j < len(plot_ids): + r2 = plot_pos[j] + if np.max(np.abs(r - r2)) < epsilon: + plot_ids[i] = plot_ids[i] + plot_ids.pop(j) + plot_pos.pop(j) + overlap = True + else: + j += 1 + # Sort qubits in plot_ids[i] according to masked status + plot_ids[i] = sorted( + plot_ids[i], + key=lambda s: s in [str(q) for q in masked_qubits], + ) + # Merge all masked qubits + has_masked = False + for j in range(len(plot_ids[i])): + if plot_ids[i][j] in [str(q) for q in masked_qubits]: + plot_ids[i][j:] = [", ".join(plot_ids[i][j:])] + has_masked = True + break + # Add a square bracket that encloses all masked qubits + if has_masked: + plot_ids[i][-1] = "[" + plot_ids[i][-1] + "]" + # Merge what remains + plot_ids[i] = ", ".join(plot_ids[i]) + bbs[plot_ids[i]] = overlap + i += 1 + + for q, coords in zip(plot_ids, plot_pos): + bb = ( + dict(boxstyle="square", fill=False, ec="gray", ls="--") + if bbs[q] + else None + ) + v_al = "center" if bbs[q] else "bottom" + txt = ax.text( + coords[0], + coords[1], + q, + ha="left", + va=v_al, + wrap=True, + bbox=bb, + fontsize=12, + multialignment="right", + ) + txt._get_wrap_line_width = lambda: 50.0 + + if draw_half_radius and blockade_radius is not None: + for p in pos: + circle = plt.Circle( + tuple(p[[ix, iy]]), + blockade_radius / 2, + alpha=0.1, + color="darkgreen", + ) + ax.add_patch(circle) + ax.autoscale() + if draw_graph and blockade_radius is not None: + epsilon = 1e-9 # Accounts for rounding errors + edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) + bonds = pos[(tuple(edges),)] + if len(bonds) > 0: + lines = bonds[:, :, (ix, iy)] + else: + lines = [] + lc = mc.LineCollection(lines, linewidths=0.6, colors="grey") + ax.add_collection(lc) + + else: + # Only draw central axis lines when not drawing the graph + ax.axvline(0, c="grey", alpha=0.5, linestyle=":") + ax.axhline(0, c="grey", alpha=0.5, linestyle=":") + + @staticmethod + def _draw_3D( + pos: np.ndarray, + ids: list, + projection: bool = False, + with_labels: bool = True, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + are_traps: bool = False, + ) -> None: + if draw_graph and blockade_radius is not None: + epsilon = 1e-9 # Accounts for rounding errors + edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) + + if projection: + labels = "xyz" + fig, axes = RegDrawer._initialize_fig_axes_projection( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + fig.tight_layout(w_pad=6.5) + + for ax, (ix, iy) in zip(axes, combinations(np.arange(3), 2)): + RegDrawer._draw_2D( + ax, + pos, + ids, + plane=( + ix, + iy, + ), + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + are_traps=are_traps, + ) + ax.set_title( + "Projection onto\n the " + + labels[ix] + + labels[iy] + + "-plane" + ) + + else: + fig = plt.figure(figsize=2 * plt.figaspect(0.5)) + + if draw_graph and blockade_radius is not None: + bonds = {} + for i, j in edges: + xi, yi, zi = pos[i] + xj, yj, zj = pos[j] + bonds[(i, j)] = [[xi, xj], [yi, yj], [zi, zj]] + + if are_traps: + params = dict(s=50, c="white", edgecolors="black") + else: + params = dict(s=30, c="darkgreen") + + for i in range(1, 3): + ax = fig.add_subplot( + 1, 2, i, projection="3d", azim=-60 * (-1) ** i, elev=15 + ) + + ax.scatter( + pos[:, 0], pos[:, 1], pos[:, 2], alpha=0.7, **params + ) + + if with_labels: + for q, coords in zip(ids, pos): + ax.text( + coords[0], + coords[1], + coords[2], + q, + fontsize=12, + ha="left", + va="bottom", + ) + + if draw_half_radius and blockade_radius is not None: + mesh_num = 20 if len(ids) > 10 else 40 + for r in pos: + x0, y0, z0 = r + radius = blockade_radius / 2 + + # Strange behavior pf mypy using "imaginary slice step" + # u, v = np.pi * np.mgrid[0:2:50j, 0:1:50j] + + v, u = np.meshgrid( + np.arccos(np.linspace(-1, 1, num=mesh_num)), + np.linspace(0, 2 * np.pi, num=mesh_num), + ) + x = radius * np.cos(u) * np.sin(v) + x0 + y = radius * np.sin(u) * np.sin(v) + y0 + z = radius * np.cos(v) + z0 + # alpha controls opacity + ax.plot_surface(x, y, z, color="darkgreen", alpha=0.1) + + if draw_graph and blockade_radius is not None: + for x, y, z in bonds.values(): + ax.plot(x, y, z, linewidth=1.5, color="grey") + + ax.set_xlabel("x (µm)") + ax.set_ylabel("y (µm)") + ax.set_zlabel("z (µm)") + + @staticmethod + def _register_dims( + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> np.ndarray: + """Returns the dimensions of the register to be drawn.""" + diffs = np.ptp(pos, axis=0) + diffs[diffs < 9] *= 1.5 + diffs[diffs < 9] += 2 + if blockade_radius and draw_half_radius: + diffs[diffs < blockade_radius] = blockade_radius + + return np.array(diffs) + + @staticmethod + def _initialize_fig_axes( + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> tuple[plt.figure.Figure, plt.axes.Axes]: + """Creates the Figure and Axes for drawing the register.""" + diffs = RegDrawer._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + big_side = max(diffs) + proportions = diffs / big_side + Ls = proportions * min( + big_side / 4, 10 + ) # Figsize is, at most, (10,10) + fig, axes = plt.subplots(figsize=Ls) + + return (fig, axes) + + @staticmethod + def _initialize_fig_axes_projection( + pos: np.ndarray, + blockade_radius: Optional[float] = None, + draw_half_radius: bool = False, + ) -> tuple[plt.figure.Figure, plt.axes.Axes]: + """Creates the Figure and Axes for drawing the register projections.""" + diffs = RegDrawer._register_dims( + pos, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + + proportions = [] + for (ix, iy) in combinations(np.arange(3), 2): + big_side = max(diffs[[ix, iy]]) + Ls = diffs[[ix, iy]] / big_side + Ls *= max( + min(big_side / 4, 10), 4 + ) # Figsize is, at most, (10,10), and, at least (4,*) or (*,4) + proportions.append(Ls) + + fig_height = np.max([Ls[1] for Ls in proportions]) + + max_width = 0 + for i, (width, height) in enumerate(proportions): + proportions[i] = (width * fig_height / height, fig_height) + max_width = max(max_width, proportions[i][0]) + widths = [max(Ls[0], max_width / 5) for Ls in proportions] + fig_width = min(np.sum(widths), fig_height * 4) + + rescaling = 20 / max(max(fig_width, fig_height), 20) + figsize = (rescaling * fig_width, rescaling * fig_height) + + fig, axes = plt.subplots( + ncols=3, + figsize=figsize, + gridspec_kw=dict(width_ratios=widths), + ) + + return (fig, axes) + + @staticmethod + def _draw_checks( + n_atoms: int, + blockade_radius: Optional[float] = None, + draw_graph: bool = True, + draw_half_radius: bool = False, + ) -> None: + """Checks common in all register drawings. + + Args: + n_atoms(int): Number of atoms in the register. + blockade_radius(float, default=None): The distance (in μm) between + atoms below the Rydberg blockade effect occurs. + draw_half_radius(bool, default=False): Whether or not to draw the + half the blockade radius surrounding each atoms. If `True`, + requires `blockade_radius` to be defined. + draw_graph(bool, default=True): Whether or not to draw the + interaction between atoms as edges in a graph. Will only draw + if the `blockade_radius` is defined. + """ + # Check spacing + if blockade_radius is not None and blockade_radius <= 0.0: + raise ValueError( + "Blockade radius (`blockade_radius` =" + f" {blockade_radius})" + " must be greater than 0." + ) + + if draw_half_radius: + if blockade_radius is None: + raise ValueError("Define 'blockade_radius' to draw.") + if n_atoms < 2: + raise NotImplementedError( + "Needs more than one atom to draw the blockade radius." + ) diff --git a/pulser/register/base_register.py b/pulser/register/base_register.py index d9437f192..bc505489a 100644 --- a/pulser/register/base_register.py +++ b/pulser/register/base_register.py @@ -18,25 +18,41 @@ from abc import ABC, abstractmethod from collections.abc import Iterable, Mapping from collections.abc import Sequence as abcSequence -from typing import Any, Optional, Type, TypeVar, Union, cast +from typing import ( + TYPE_CHECKING, + Any, + NamedTuple, + Optional, + Type, + TypeVar, + Union, + cast, +) -import matplotlib.pyplot as plt import numpy as np -from matplotlib import collections as mc from numpy.typing import ArrayLike -from scipy.spatial import KDTree from pulser.json.utils import obj_to_dict +if TYPE_CHECKING: # pragma: no cover + from pulser.register.register_layout import RegisterLayout + T = TypeVar("T", bound="BaseRegister") QubitId = Union[int, str] +class _LayoutInfo(NamedTuple): + """Auxiliary class to store the register layout information.""" + + layout: RegisterLayout + trap_ids: tuple[int, ...] + + class BaseRegister(ABC): """The abstract class for a register.""" @abstractmethod - def __init__(self, qubits: Mapping[Any, ArrayLike]): + def __init__(self, qubits: Mapping[Any, ArrayLike], **kwargs: Any): """Initializes a custom Register.""" if not isinstance(qubits, dict): raise TypeError( @@ -49,7 +65,18 @@ def __init__(self, qubits: Mapping[Any, ArrayLike]): ) self._ids = list(qubits.keys()) self._coords = [np.array(v, dtype=float) for v in qubits.values()] - self._dim = 0 + self._dim = self._coords[0].size + self._layout_info: Optional[_LayoutInfo] = None + if kwargs: + if kwargs.keys() != {"layout", "trap_ids"}: + raise ValueError( + "If specifying 'kwargs', they must only be 'layout' and " + "'trap_ids'." + ) + layout: RegisterLayout = kwargs["layout"] + trap_ids: tuple[int, ...] = tuple(kwargs["trap_ids"]) + self._validate_layout(layout, trap_ids) + self._layout_info = _LayoutInfo(layout, trap_ids) @property def qubits(self) -> dict[QubitId, np.ndarray]: @@ -104,184 +131,37 @@ def from_coordinates( qubits = dict(cast(Iterable, enumerate(coords))) return cls(qubits) - @staticmethod - def _draw_2D( - ax: plt.axes._subplots.AxesSubplot, - pos: np.ndarray, - ids: list, - plane: tuple = (0, 1), - with_labels: bool = True, - blockade_radius: Optional[float] = None, - draw_graph: bool = True, - draw_half_radius: bool = False, - masked_qubits: set[QubitId] = set(), + def _validate_layout( + self, register_layout: RegisterLayout, trap_ids: tuple[int, ...] ) -> None: - ix, iy = plane - - ax.scatter(pos[:, ix], pos[:, iy], s=30, alpha=0.7, c="darkgreen") - - # Draw square halo around masked qubits - if masked_qubits: - mask_pos = [] - for i, c in zip(ids, pos): - if i in masked_qubits: - mask_pos.append(c) - mask_arr = np.array(mask_pos) - ax.scatter( - mask_arr[:, ix], - mask_arr[:, iy], - marker="s", - s=1200, - alpha=0.2, - c="black", + """Sets the RegisterLayout that originated this register.""" + trap_coords = register_layout.coords + if register_layout.dimensionality != self._dim: + raise ValueError( + "The RegisterLayout dimensionality is not the same as this " + "register's." ) + if len(set(trap_ids)) != len(trap_ids): + raise ValueError("Every 'trap_id' must be a unique integer.") - axes = "xyz" - - ax.set_xlabel(axes[ix] + " (µm)") - ax.set_ylabel(axes[iy] + " (µm)") - ax.axis("equal") - ax.spines["right"].set_color("none") - ax.spines["top"].set_color("none") - - if with_labels: - # Determine which labels would overlap and merge those - plot_pos = list(pos[:, (ix, iy)]) - plot_ids: list[Union[list, str]] = [[f"{i}"] for i in ids] - # Threshold distance between points - epsilon = 1.0e-2 * np.diff(ax.get_xlim())[0] - - i = 0 - bbs = {} - while i < len(plot_ids): - r = plot_pos[i] - j = i + 1 - overlap = False - # Put in a list all qubits that overlap at position plot_pos[i] - while j < len(plot_ids): - r2 = plot_pos[j] - if np.max(np.abs(r - r2)) < epsilon: - plot_ids[i] = plot_ids[i] + plot_ids.pop(j) - plot_pos.pop(j) - overlap = True - else: - j += 1 - # Sort qubits in plot_ids[i] according to masked status - plot_ids[i] = sorted( - plot_ids[i], - key=lambda s: s in [str(q) for q in masked_qubits], - ) - # Merge all masked qubits - has_masked = False - for j in range(len(plot_ids[i])): - if plot_ids[i][j] in [str(q) for q in masked_qubits]: - plot_ids[i][j:] = [", ".join(plot_ids[i][j:])] - has_masked = True - break - # Add a square bracket that encloses all masked qubits - if has_masked: - plot_ids[i][-1] = "[" + plot_ids[i][-1] + "]" - # Merge what remains - plot_ids[i] = ", ".join(plot_ids[i]) - bbs[plot_ids[i]] = overlap - i += 1 - - for q, coords in zip(plot_ids, plot_pos): - bb = ( - dict(boxstyle="square", fill=False, ec="gray", ls="--") - if bbs[q] - else None - ) - v_al = "center" if bbs[q] else "bottom" - txt = ax.text( - coords[0], - coords[1], - q, - ha="left", - va=v_al, - wrap=True, - bbox=bb, - ) - txt._get_wrap_line_width = lambda: 50.0 - - if draw_half_radius and blockade_radius is not None: - for p in pos: - circle = plt.Circle( - tuple(p[[ix, iy]]), - blockade_radius / 2, - alpha=0.1, - color="darkgreen", - ) - ax.add_patch(circle) - ax.autoscale() - if draw_graph and blockade_radius is not None: - epsilon = 1e-9 # Accounts for rounding errors - edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) - bonds = pos[(tuple(edges),)] - if len(bonds) > 0: - lines = bonds[:, :, (ix, iy)] - else: - lines = [] - lc = mc.LineCollection(lines, linewidths=0.6, colors="grey") - ax.add_collection(lc) - - else: - # Only draw central axis lines when not drawing the graph - ax.axvline(0, c="grey", alpha=0.5, linestyle=":") - ax.axhline(0, c="grey", alpha=0.5, linestyle=":") - - @staticmethod - def _register_dims( - pos: np.ndarray, - blockade_radius: Optional[float] = None, - draw_half_radius: bool = False, - ) -> np.ndarray: - """Returns the dimensions of the register to be drawn.""" - diffs = np.ptp(pos, axis=0) - diffs[diffs < 9] *= 1.5 - diffs[diffs < 9] += 2 - if blockade_radius and draw_half_radius: - diffs[diffs < blockade_radius] = blockade_radius - - return np.array(diffs) - - def _draw_checks( - self, - blockade_radius: Optional[float] = None, - draw_graph: bool = True, - draw_half_radius: bool = False, - ) -> None: - """Checks common in all register drawings. - - Keyword Args: - blockade_radius(float, default=None): The distance (in μm) between - atoms below the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw the - half the blockade radius surrounding each atoms. If `True`, - requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the - interaction between atoms as edges in a graph. Will only draw - if the `blockade_radius` is defined. - """ - # Check spacing - if blockade_radius is not None and blockade_radius <= 0.0: + if len(trap_ids) != len(self._ids): raise ValueError( - "Blockade radius (`blockade_radius` =" - f" {blockade_radius})" - " must be greater than 0." + "The amount of 'trap_ids' must be equal to the number of atoms" + " in the register." ) - if draw_half_radius: - if blockade_radius is None: - raise ValueError("Define 'blockade_radius' to draw.") - if len(self._ids) == 1: - raise NotImplementedError( - "Needs more than one atom to draw " "the blockade radius." + for reg_coord, trap_id in zip(self._coords, trap_ids): + if np.any(reg_coord != trap_coords[trap_id]): + raise ValueError( + "The chosen traps from the RegisterLayout don't match this" + " register's coordinates." ) @abstractmethod def _to_dict(self) -> dict[str, Any]: qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) + if self._layout_info is not None: + return obj_to_dict(self, qs, **(self._layout_info._asdict())) return obj_to_dict(self, qs) def __eq__(self, other: Any) -> bool: @@ -290,7 +170,7 @@ def __eq__(self, other: Any) -> bool: return set(self._ids) == set(other._ids) and all( ( - np.array_equal( + np.allclose( # Accounts for rounding errors self._coords[i], other._coords[other._ids.index(id)], ) diff --git a/pulser/register/register.py b/pulser/register/register.py index 14addc018..fe99544d3 100644 --- a/pulser/register/register.py +++ b/pulser/register/register.py @@ -23,10 +23,12 @@ from numpy.typing import ArrayLike import pulser +import pulser.register._patterns as patterns +from pulser.register._reg_drawer import RegDrawer from pulser.register.base_register import BaseRegister -class Register(BaseRegister): +class Register(BaseRegister, RegDrawer): """A 2D quantum register containing a set of qubits. Args: @@ -35,10 +37,9 @@ class Register(BaseRegister): (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). """ - def __init__(self, qubits: Mapping[Any, ArrayLike]): + def __init__(self, qubits: Mapping[Any, ArrayLike], **kwargs: Any): """Initializes a custom Register.""" - super().__init__(qubits) - self._dim = self._coords[0].size + super().__init__(qubits, **kwargs) if any(c.shape != (self._dim,) for c in self._coords) or ( self._dim != 2 ): @@ -117,13 +118,7 @@ def rectangle( " must be greater than 0." ) - coords = ( - np.array( - [(x, y) for y in range(rows) for x in range(columns)], - dtype=float, - ) - * spacing - ) + coords = patterns.square_rect(rows, columns) * spacing return cls.from_coordinates(coords, center=True, prefix=prefix) @@ -176,99 +171,10 @@ def triangular_lattice( " must be greater than 0." ) - coords = np.array( - [(x, y) for y in range(rows) for x in range(atoms_per_row)], - dtype=float, - ) - coords[:, 0] += 0.5 * np.mod(coords[:, 1], 2) - coords[:, 1] *= np.sqrt(3) / 2 - coords *= spacing + coords = patterns.triangular_rect(rows, atoms_per_row) * spacing return cls.from_coordinates(coords, center=True, prefix=prefix) - @classmethod - def _hexagon_helper( - cls, - layers: int, - atoms_left: int, - spacing: float, - prefix: Optional[str] = None, - ) -> Register: - """Helper function for building hexagonal arrays. - - Args: - layers (int): Number of full layers around a central atom. - atoms_left (int): Number of atoms on the external layer. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit - id starts with the prefix, followed by an int from 0 to N-1 - (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - - Returns: - Register: A register with qubits placed in a hexagonal layout - with extra atoms on the outermost layer if needed. - """ - # y coordinates of the top vertex of a triangle - crest_y = np.sqrt(3) / 2.0 - - # Coordinates of vertices - start_x = [-1.0, -0.5, 0.5, 1.0, 0.5, -0.5] - start_y = [0.0, crest_y, crest_y, 0, -crest_y, -crest_y] - - # Steps to place atoms, starting from a vertex - delta_x = [0.5, 1.0, 0.5, -0.5, -1.0, -0.5] - delta_y = [crest_y, 0.0, -crest_y, -crest_y, 0.0, crest_y] - - coords = np.array( - [ - ( - start_x[side] * layer + atom * delta_x[side], - start_y[side] * layer + atom * delta_y[side], - ) - for layer in range(1, layers + 1) - for side in range(6) - for atom in range(1, layer + 1) - ], - dtype=float, - ) - - if atoms_left > 0: - layer = layers + 1 - min_atoms_per_side = atoms_left // 6 - # Extra atoms after balancing all sides - atoms_left %= 6 - - # Order for placing left atoms - # Top-Left, Top-Right, Bottom (C3 symmetry)... - # ...Top, Bottom-Right, Bottom-Left (C6 symmetry) - sides_order = [0, 3, 1, 4, 2, 5] - - coords2 = np.array( - [ - ( - start_x[side] * layer + atom * delta_x[side], - start_y[side] * layer + atom * delta_y[side], - ) - for side in range(6) - for atom in range( - 1, - min_atoms_per_side + 2 - if atoms_left > sides_order[side] - else min_atoms_per_side + 1, - ) - ], - dtype=float, - ) - - coords = np.concatenate((coords, coords2)) - - coords *= spacing - coords = np.concatenate((np.zeros((1, 2)), coords)) - - return cls.from_coordinates(coords, center=False, prefix=prefix) - @classmethod def hexagon( cls, layers: int, spacing: float = 4.0, prefix: Optional[str] = None @@ -301,7 +207,10 @@ def hexagon( " must be greater than 0." ) - return cls._hexagon_helper(layers, 0, spacing, prefix) + n_atoms = 1 + 3 * (layers**2 + layers) + coords = patterns.triangular_hex(n_atoms) * spacing + + return cls.from_coordinates(coords, center=False, prefix=prefix) @classmethod def max_connectivity( @@ -366,26 +275,9 @@ def max_connectivity( f" ({device.min_atom_distance})." ) - if n_qubits < 7: - crest_y = np.sqrt(3) / 2.0 - hex_coords = np.array( - [ - (0.0, 0.0), - (-0.5, crest_y), - (0.5, crest_y), - (1.0, 0.0), - (0.5, -crest_y), - (-0.5, -crest_y), - ] - ) - return cls.from_coordinates( - spacing * hex_coords[:n_qubits], prefix=prefix, center=False - ) - - full_layers = int((-3.0 + np.sqrt(9 + 12 * (n_qubits - 1))) / 6.0) - atoms_left = n_qubits - 1 - (full_layers**2 + full_layers) * 3 + coords = patterns.triangular_hex(n_qubits) * spacing - return cls._hexagon_helper(full_layers, atoms_left, spacing, prefix) + return cls.from_coordinates(coords, center=False, prefix=prefix) def rotate(self, degrees: float) -> None: """Rotates the array around the origin by the given angle. @@ -393,33 +285,16 @@ def rotate(self, degrees: float) -> None: Args: degrees (float): The angle of rotation in degrees. """ + if self._layout_info is not None: + raise TypeError( + "A register defined from a RegisterLayout cannot be rotated." + ) theta = np.deg2rad(degrees) rot = np.array( [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]] ) self._coords = [rot @ v for v in self._coords] - def _initialize_fig_axes( - self, - pos: np.ndarray, - blockade_radius: Optional[float] = None, - draw_half_radius: bool = False, - ) -> tuple[plt.figure.Figure, plt.axes.Axes]: - """Creates the Figure and Axes for drawing the register.""" - diffs = super()._register_dims( - pos, - blockade_radius=blockade_radius, - draw_half_radius=draw_half_radius, - ) - big_side = max(diffs) - proportions = diffs / big_side - Ls = proportions * min( - big_side / 4, 10 - ) # Figsize is, at most, (10,10) - fig, axes = plt.subplots(figsize=Ls) - - return (fig, axes) - def draw( self, with_labels: bool = True, @@ -455,6 +330,7 @@ def draw( radius because it helps in seeing the interactions between atoms. """ super()._draw_checks( + len(self._ids), blockade_radius=blockade_radius, draw_graph=draw_graph, draw_half_radius=draw_half_radius, diff --git a/pulser/register/register3d.py b/pulser/register/register3d.py index cbc5d7763..746cb3b41 100644 --- a/pulser/register/register3d.py +++ b/pulser/register/register3d.py @@ -16,19 +16,18 @@ from __future__ import annotations from collections.abc import Mapping -from itertools import combinations from typing import Any, Optional import matplotlib.pyplot as plt import numpy as np from numpy.typing import ArrayLike -from scipy.spatial import KDTree +from pulser.register._reg_drawer import RegDrawer from pulser.register.base_register import BaseRegister from pulser.register.register import Register -class Register3D(BaseRegister): +class Register3D(BaseRegister, RegDrawer): """A 3D quantum register containing a set of qubits. Args: @@ -37,16 +36,15 @@ class Register3D(BaseRegister): (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). """ - def __init__(self, qubits: Mapping[Any, ArrayLike]): + def __init__(self, qubits: Mapping[Any, ArrayLike], **kwargs: Any): """Initializes a custom Register.""" - super().__init__(qubits) - coords = [np.array(v, dtype=float) for v in qubits.values()] - self._dim = coords[0].size - if any(c.shape != (self._dim,) for c in coords) or (self._dim != 3): + super().__init__(qubits, **kwargs) + if any(c.shape != (self._dim,) for c in self._coords) or ( + self._dim != 3 + ): raise ValueError( "All coordinates must be specified as vectors of size 3." ) - self._coords = coords @classmethod def cubic( @@ -178,48 +176,6 @@ def to_2D(self, tol_width: float = 0.0) -> Register: ) return Register.from_coordinates(coords_2D, labels=self._ids) - def _initialize_fig_axes_projection( - self, - pos: np.ndarray, - blockade_radius: Optional[float] = None, - draw_half_radius: bool = False, - ) -> tuple[plt.figure.Figure, plt.axes.Axes]: - """Creates the Figure and Axes for drawing the register projections.""" - diffs = super()._register_dims( - pos, - blockade_radius=blockade_radius, - draw_half_radius=draw_half_radius, - ) - - proportions = [] - for (ix, iy) in combinations(np.arange(3), 2): - big_side = max(diffs[[ix, iy]]) - Ls = diffs[[ix, iy]] / big_side - Ls *= max( - min(big_side / 4, 10), 4 - ) # Figsize is, at most, (10,10), and, at least (4,*) or (*,4) - proportions.append(Ls) - - fig_height = np.max([Ls[1] for Ls in proportions]) - - max_width = 0 - for i, (width, height) in enumerate(proportions): - proportions[i] = (width * fig_height / height, fig_height) - max_width = max(max_width, proportions[i][0]) - widths = [max(Ls[0], max_width / 5) for Ls in proportions] - fig_width = min(np.sum(widths), fig_height * 4) - - rescaling = 20 / max(max(fig_width, fig_height), 20) - figsize = (rescaling * fig_width, rescaling * fig_height) - - fig, axes = plt.subplots( - ncols=3, - figsize=figsize, - gridspec_kw=dict(width_ratios=widths), - ) - - return (fig, axes) - def draw( self, with_labels: bool = False, @@ -258,6 +214,7 @@ def draw( radius because it helps in seeing the interactions between atoms. """ super()._draw_checks( + len(self._ids), blockade_radius=blockade_radius, draw_graph=draw_graph, draw_half_radius=draw_half_radius, @@ -265,102 +222,15 @@ def draw( pos = np.array(self._coords) - if draw_graph and blockade_radius is not None: - epsilon = 1e-9 # Accounts for rounding errors - edges = KDTree(pos).query_pairs(blockade_radius * (1 + epsilon)) - - if projection: - labels = "xyz" - fig, axes = self._initialize_fig_axes_projection( - pos, - blockade_radius=blockade_radius, - draw_half_radius=draw_half_radius, - ) - fig.tight_layout(w_pad=6.5) - - for ax, (ix, iy) in zip(axes, combinations(np.arange(3), 2)): - super()._draw_2D( - ax, - pos, - self._ids, - plane=( - ix, - iy, - ), - with_labels=with_labels, - blockade_radius=blockade_radius, - draw_graph=draw_graph, - draw_half_radius=draw_half_radius, - ) - ax.set_title( - "Projection onto\n the " - + labels[ix] - + labels[iy] - + "-plane" - ) - - else: - fig = plt.figure(figsize=2 * plt.figaspect(0.5)) - - if draw_graph and blockade_radius is not None: - bonds = {} - for i, j in edges: - xi, yi, zi = pos[i] - xj, yj, zj = pos[j] - bonds[(i, j)] = [[xi, xj], [yi, yj], [zi, zj]] - - for i in range(1, 3): - ax = fig.add_subplot( - 1, 2, i, projection="3d", azim=-60 * (-1) ** i, elev=15 - ) - - ax.scatter( - pos[:, 0], - pos[:, 1], - pos[:, 2], - s=30, - alpha=0.7, - c="darkgreen", - ) - - if with_labels: - for q, coords in zip(self._ids, self._coords): - ax.text( - coords[0], - coords[1], - coords[2], - q, - fontsize=12, - ha="left", - va="bottom", - ) - - if draw_half_radius and blockade_radius is not None: - mesh_num = 20 if len(self._ids) > 10 else 40 - for r in pos: - x0, y0, z0 = r - radius = blockade_radius / 2 - - # Strange behavior pf mypy using "imaginary slice step" - # u, v = np.pi * np.mgrid[0:2:50j, 0:1:50j] - - v, u = np.meshgrid( - np.arccos(np.linspace(-1, 1, num=mesh_num)), - np.linspace(0, 2 * np.pi, num=mesh_num), - ) - x = radius * np.cos(u) * np.sin(v) + x0 - y = radius * np.sin(u) * np.sin(v) + y0 - z = radius * np.cos(v) + z0 - # alpha controls opacity - ax.plot_surface(x, y, z, color="darkgreen", alpha=0.1) - - if draw_graph and blockade_radius is not None: - for x, y, z in bonds.values(): - ax.plot(x, y, z, linewidth=1.5, color="grey") - - ax.set_xlabel("x (µm)") - ax.set_ylabel("y (µm)") - ax.set_zlabel("z (µm)") + self._draw_3D( + pos, + self._ids, + projection=projection, + with_labels=with_labels, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) if fig_name is not None: plt.savefig(fig_name, **kwargs_savefig) diff --git a/pulser/register/register_layout.py b/pulser/register/register_layout.py new file mode 100644 index 000000000..4310e1be4 --- /dev/null +++ b/pulser/register/register_layout.py @@ -0,0 +1,269 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Defines a generic Register layout, from which a Register can be created.""" + +from __future__ import annotations + +from collections.abc import Sequence as abcSequence +from dataclasses import dataclass +from hashlib import sha256 +from sys import version_info +from typing import Any, Optional, cast + +import matplotlib.pyplot as plt +import numpy as np +from numpy.typing import ArrayLike + +from pulser.json.utils import obj_to_dict +from pulser.register._reg_drawer import RegDrawer +from pulser.register.base_register import BaseRegister, QubitId +from pulser.register.register import Register +from pulser.register.register3d import Register3D + +if version_info[:2] >= (3, 8): # pragma: no cover + from functools import cached_property +else: # pragma: no cover + try: + from backports.cached_property import cached_property # type: ignore + except ImportError: + raise ImportError( + "Using pulser with Python version 3.7 requires the" + " `backports.cached-property` module. Install it by running" + " `pip install backports.cached-property`." + ) + +COORD_PRECISION = 6 + + +@dataclass(repr=False, eq=False, frozen=True) +class RegisterLayout(RegDrawer): + """A layout of traps out of which registers can be defined. + + The traps are always sorted under the same convention: ascending order + along x, then along y, then along z (if applicable). Respecting this order, + the traps are then numbered starting from 0. + + Args: + trap_coordinates(ArrayLike): The trap coordinates defining the layout. + """ + + trap_coordinates: ArrayLike + + def __post_init__(self) -> None: + shape = np.array(self.trap_coordinates).shape + if len(shape) != 2: + raise ValueError( + "'trap_coordinates' must be an array or list of coordinates." + ) + if shape[1] not in (2, 3): + raise ValueError( + f"Each coordinate must be of size 2 or 3, not {shape[1]}." + ) + + @property + def traps_dict(self) -> dict: + """Mapping between trap IDs and coordinates.""" + return dict(enumerate(self.coords)) + + @cached_property # Acts as an attribute in a frozen dataclass + def _coords(self) -> np.ndarray: + coords = np.array(self.trap_coordinates, dtype=float) + # Sorting the coordinates 1st left to right, 2nd bottom to top + rounded_coords = np.round(coords, decimals=COORD_PRECISION) + dims = rounded_coords.shape[1] + sorter = [rounded_coords[:, i] for i in range(dims - 1, -1, -1)] + sorting = np.lexsort(tuple(sorter)) + return cast(np.ndarray, rounded_coords[sorting]) + + @cached_property # Acts as an attribute in a frozen dataclass + def _coords_to_traps(self) -> dict[tuple[float, ...], int]: + return {tuple(coord): id for id, coord in self.traps_dict.items()} + + @property + def coords(self) -> np.ndarray: + """The sorted trap coordinates.""" + # Copies to prevent direct access to self._coords + return self._coords.copy() + + @property + def number_of_traps(self) -> int: + """The number of traps in the layout.""" + return len(self._coords) + + @property + def max_atom_num(self) -> int: + """Maximum number of atoms that can be trapped to form a Register.""" + return self.number_of_traps // 2 + + @property + def dimensionality(self) -> int: + """The dimensionality of the layout (2 or 3).""" + return self._coords.shape[1] + + def get_traps_from_coordinates(self, *coordinates: ArrayLike) -> list[int]: + """Finds the trap ID for a given set of trap coordinates. + + Args: + *coordinates (ArrayLike): The coordinates to return the trap IDs. + + Returns + list[int]: The list of trap IDs corresponding to the coordinates. + """ + traps = [] + rounded_coords = np.round( + cast(ArrayLike, coordinates), decimals=COORD_PRECISION + ) + for coord, rounded in zip(coordinates, rounded_coords): + key = tuple(rounded) + if key not in self._coords_to_traps: + raise ValueError( + f"The coordinate '{coord!s}' is not a part of the " + "RegisterLayout." + ) + traps.append(self._coords_to_traps[key]) + return traps + + def define_register( + self, *trap_ids: int, qubit_ids: Optional[abcSequence[QubitId]] = None + ) -> BaseRegister: + """Defines a register from selected traps. + + Args: + *trap_ids (int): The trap IDs selected to form the Register. + qubit_ids (Optional[abcSequence[QubitId]] = None): A sequence of + unique qubit IDs to associated to the selected traps. Must be + of the same length as the selected traps. + + Returns: + BaseRegister: The respective register instance. + """ + trap_ids_set = set(trap_ids) + + if len(trap_ids_set) != len(trap_ids): + raise ValueError("Every 'trap_id' must be a unique integer.") + + if not trap_ids_set.issubset(self.traps_dict): + raise ValueError( + "All 'trap_ids' must correspond to the ID of a trap." + ) + + if qubit_ids: + if len(set(qubit_ids)) != len(qubit_ids): + raise ValueError( + "'qubit_ids' must be a sequence of unique IDs." + ) + if len(qubit_ids) != len(trap_ids): + raise ValueError( + "'qubit_ids' must have the same size as the number of " + f"provided 'trap_ids' ({len(trap_ids)})." + ) + + if len(trap_ids) > self.max_atom_num: + raise ValueError( + "The number of required traps is greater than the maximum " + "number of qubits allowed for this layout " + f"({self.max_atom_num})." + ) + ids = ( + qubit_ids if qubit_ids else [f"q{i}" for i in range(len(trap_ids))] + ) + coords = self._coords[list(trap_ids)] + qubits = dict(zip(ids, coords)) + + reg_class = Register3D if self.dimensionality == 3 else Register + reg = reg_class(qubits, layout=self, trap_ids=trap_ids) + return reg + + def draw( + self, + blockade_radius: Optional[float] = None, + draw_graph: bool = False, + draw_half_radius: bool = False, + projection: bool = True, + ) -> None: + """Draws the entire register layout. + + Keyword Args: + blockade_radius(float, default=None): The distance (in μm) between + atoms below which the Rydberg blockade effect occurs. + draw_half_radius(bool, default=False): Whether or not to draw + half the blockade radius surrounding each trap. If `True`, + requires `blockade_radius` to be defined. + draw_graph(bool, default=True): Whether or not to draw the + interaction between atoms as edges in a graph. Will only draw + if the `blockade_radius` is defined. + projection(bool, default=True): If the layout is in 3D, draws it + as projections on different planes. + + Note: + When drawing half the blockade radius, we say there is a blockade + effect between atoms whenever their respective circles overlap. + This representation is preferred over drawing the full Rydberg + radius because it helps in seeing the interactions between atoms. + """ + coords = self.coords + self._draw_checks( + self.number_of_traps, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + ) + ids = list(range(self.number_of_traps)) + if self.dimensionality == 2: + fig, ax = self._initialize_fig_axes( + coords, + blockade_radius=blockade_radius, + draw_half_radius=draw_half_radius, + ) + self._draw_2D( + ax, + coords, + ids, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + are_traps=True, + ) + elif self.dimensionality == 3: + self._draw_3D( + coords, + ids, + projection=projection, + with_labels=True, + blockade_radius=blockade_radius, + draw_graph=draw_graph, + draw_half_radius=draw_half_radius, + are_traps=True, + ) + plt.show() + + def _safe_hash(self) -> bytes: + # Include dimensionality because the array is flattened with tobytes() + hash = sha256(bytes(self.dimensionality)) + hash.update(self.coords.tobytes()) + return hash.digest() + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, RegisterLayout): + return False + return self._safe_hash() == other._safe_hash() + + def __hash__(self) -> int: + return hash(self._safe_hash()) + + def __repr__(self) -> str: + return f"RegisterLayout_{self._safe_hash().hex()}" + + def _to_dict(self) -> dict[str, Any]: + return obj_to_dict(self, self.trap_coordinates) diff --git a/pulser/register/special_layouts.py b/pulser/register/special_layouts.py new file mode 100644 index 000000000..126d50e91 --- /dev/null +++ b/pulser/register/special_layouts.py @@ -0,0 +1,168 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Special register layouts defined for convenience.""" + +from __future__ import annotations + +from typing import cast + +import pulser.register._patterns as patterns +from pulser.register import Register +from pulser.register.register_layout import RegisterLayout + + +class SquareLatticeLayout(RegisterLayout): + """A RegisterLayout with a square lattice pattern in a rectangular shape. + + Args: + rows (int): The number of rows of traps. + columns (int): The number of columns of traps. + spacing (int): The distance between neighbouring traps (in µm). + """ + + def __init__(self, rows: int, columns: int, spacing: int): + """Initializes a SquareLatticeLayout.""" + self._rows = int(rows) + self._columns = int(columns) + self._spacing = int(spacing) + super().__init__( + patterns.square_rect(self._rows, self._columns) * self._spacing + ) + + def square_register(self, side: int, prefix: str = "q") -> Register: + """Defines a register with a square shape. + + Args: + side (int): The length of the square's side, in number of atoms. + prefix (str): The prefix for the qubit ids. Each qubit ID starts + with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: The register instance created from this layout. + """ + return self.rectangular_register(side, side, prefix=prefix) + + def rectangular_register( + self, + rows: int, + columns: int, + prefix: str = "q", + ) -> Register: + """Defines a register with a rectangular shape. + + Args: + rows (int): The number of rows in the register. + columns (int): The number of columns in the register. + prefix (str): The prefix for the qubit ids. Each qubit ID starts + with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: The register instance created from this layout. + """ + if rows * columns > self.max_atom_num: + raise ValueError( + f"A '{rows} x {columns}' array has more atoms than those " + f"available in this SquareLatticeLayout ({self.max_atom_num})." + ) + if rows > self._rows or columns > self._columns: + raise ValueError( + f"A '{rows} x {columns}' array doesn't fit a " + f"{self._rows}x{self._columns} SquareLatticeLayout." + ) + points = patterns.square_rect(rows, columns) * self._spacing + trap_ids = self.get_traps_from_coordinates(*points) + qubit_ids = [f"{prefix}{i}" for i in range(len(trap_ids))] + return cast( + Register, self.define_register(*trap_ids, qubit_ids=qubit_ids) + ) + + def __str__(self) -> str: + return ( + f"SquareLatticeLayout({self._rows}x{self._columns}, " + f"{self._spacing}µm)" + ) + + +class TriangularLatticeLayout(RegisterLayout): + """A RegisterLayout with a triangular lattice pattern in an hexagonal shape. + + Args: + n_traps (int): The number of traps in the layout. + spacing (int): The distance between neighbouring traps (in µm). + """ + + def __init__(self, n_traps: int, spacing: int): + """Initializes a TriangularLatticeLayout.""" + self._spacing = int(spacing) + super().__init__(patterns.triangular_hex(n_traps) * self._spacing) + + def hexagonal_register(self, n_atoms: int, prefix: str = "q") -> Register: + """Defines a register with an hexagonal shape. + + Args: + n_atoms (int): The number of atoms in the register. + prefix (str): The prefix for the qubit ids. Each qubit ID starts + with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: The register instance created from this layout. + """ + if n_atoms > self.max_atom_num: + raise ValueError( + f"This RegisterLayout can hold at most {self.max_atom_num} " + f"atoms, not '{n_atoms}'." + ) + points = patterns.triangular_hex(n_atoms) * self._spacing + trap_ids = self.get_traps_from_coordinates(*points) + qubit_ids = [f"{prefix}{i}" for i in range(len(trap_ids))] + return cast( + Register, self.define_register(*trap_ids, qubit_ids=qubit_ids) + ) + + def rectangular_register( + self, rows: int, atoms_per_row: int, prefix: str = "q" + ) -> Register: + """Defines a register with a rectangular shape. + + Args: + rows (int): The number of rows in the register. + atoms_per_row (int): The number of atoms in each row. + prefix (str): The prefix for the qubit ids. Each qubit ID starts + with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + + Returns: + Register: The register instance created from this layout. + """ + if rows * atoms_per_row > self.max_atom_num: + raise ValueError( + f"A '{rows} x {atoms_per_row}' rectangular subset of a " + "triangular lattice has more atoms than those available in " + f"this TriangularLatticeLayout ({self.max_atom_num})." + ) + points = patterns.triangular_rect(rows, atoms_per_row) * self._spacing + trap_ids = self.get_traps_from_coordinates(*points) + qubit_ids = [f"{prefix}{i}" for i in range(len(trap_ids))] + return cast( + Register, self.define_register(*trap_ids, qubit_ids=qubit_ids) + ) + + def __str__(self) -> str: + return ( + f"TriangularLatticeLayout({self.number_of_traps}, " + f"{self._spacing}µm)" + ) diff --git a/pulser/tests/test_devices.py b/pulser/tests/test_devices.py index fcec79c57..fb7c054e2 100644 --- a/pulser/tests/test_devices.py +++ b/pulser/tests/test_devices.py @@ -19,8 +19,10 @@ import pytest import pulser -from pulser.devices import Chadoq2 +from pulser.devices import Chadoq2, Device from pulser.register import Register, Register3D +from pulser.register.register_layout import RegisterLayout +from pulser.register.special_layouts import TriangularLatticeLayout def test_init(): @@ -110,4 +112,69 @@ def test_validate_register(): Register.triangular_lattice(3, 4, spacing=3.9) ) + with pytest.raises( + ValueError, match="associated with an incompatible register layout" + ): + tri_layout = TriangularLatticeLayout(201, 5) + Chadoq2.validate_register(tri_layout.hexagonal_register(10)) + Chadoq2.validate_register(Register.rectangle(5, 10, spacing=5)) + + +def test_validate_layout(): + with pytest.raises(ValueError, match="The number of traps"): + Chadoq2.validate_layout(RegisterLayout(Register.square(20)._coords)) + + coords = [(100, 0), (-100, 0)] + with pytest.raises(TypeError): + Chadoq2.validate_layout(Register.from_coordinates(coords)) + with pytest.raises(ValueError, match="at most 50 μm away from the center"): + Chadoq2.validate_layout(RegisterLayout(coords)) + + with pytest.raises(ValueError, match="at most 2 dimensions"): + coords = [(-10, 4, 0), (0, 0, 0)] + Chadoq2.validate_layout(RegisterLayout(coords)) + + with pytest.raises(ValueError, match="The minimal distance between traps"): + Chadoq2.validate_layout( + RegisterLayout( + Register.triangular_lattice(3, 4, spacing=3.9)._coords + ) + ) + + valid_layout = RegisterLayout( + Register.square(int(np.sqrt(Chadoq2.max_atom_num * 2)))._coords + ) + Chadoq2.validate_layout(valid_layout) + + +def test_calibrated_layouts(): + with pytest.raises(ValueError, match="The number of traps"): + Device( + name="TestDevice", + dimensions=2, + rydberg_level=70, + max_atom_num=100, + max_radial_distance=50, + min_atom_distance=4, + _channels=(), + pre_calibrated_layouts=(TriangularLatticeLayout(201, 5),), + ) + + TestDevice = Device( + name="TestDevice", + dimensions=2, + rydberg_level=70, + max_atom_num=100, + max_radial_distance=50, + min_atom_distance=4, + _channels=(), + pre_calibrated_layouts=( + TriangularLatticeLayout(100, 6.8), # Rounds down with int() + TriangularLatticeLayout(200, 5), + ), + ) + assert TestDevice.calibrated_register_layouts.keys() == { + "TriangularLatticeLayout(100, 6µm)", + "TriangularLatticeLayout(200, 5µm)", + } diff --git a/pulser/tests/test_json.py b/pulser/tests/test_json.py index 9fb61a02a..ae3e2b243 100644 --- a/pulser/tests/test_json.py +++ b/pulser/tests/test_json.py @@ -21,6 +21,7 @@ from pulser.devices import Chadoq2, MockDevice from pulser.json.coders import PulserDecoder, PulserEncoder from pulser.parametrized.decorators import parametrize +from pulser.register.register_layout import RegisterLayout from pulser.waveforms import BlackmanWaveform @@ -55,6 +56,17 @@ def test_register_3d(): assert reg == encode_decode(seq).register +def test_register_from_layout(): + layout = RegisterLayout([[0, 0], [1, 1], [1, 0], [0, 1]]) + reg = layout.define_register(1, 0) + assert reg == Register({"q0": [0, 1], "q1": [0, 0]}) + seq = Sequence(reg, device=MockDevice) + new_reg = encode_decode(seq).register + assert reg == new_reg + assert new_reg._layout_info.layout == layout + assert new_reg._layout_info.trap_ids == (1, 0) + + def test_rare_cases(): reg = Register.square(4) seq = Sequence(reg, Chadoq2) diff --git a/pulser/tests/test_register.py b/pulser/tests/test_register.py index ec153cc65..252aa41e0 100644 --- a/pulser/tests/test_register.py +++ b/pulser/tests/test_register.py @@ -79,6 +79,11 @@ def test_creation(): ) assert np.all(np.array(reg6._coords) == coords_) + with pytest.raises( + ValueError, match="must only be 'layout' and 'trap_ids'" + ): + Register(qubits, spacing=10, layout="square", trap_ids=(0, 1, 3)) + def test_rectangle(): # Check rows diff --git a/pulser/tests/test_register_layout.py b/pulser/tests/test_register_layout.py new file mode 100644 index 000000000..ce6058810 --- /dev/null +++ b/pulser/tests/test_register_layout.py @@ -0,0 +1,185 @@ +# Copyright 2020 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from hashlib import sha256 +from unittest.mock import patch + +import numpy as np +import pytest + +from pulser.register import Register, Register3D +from pulser.register.register_layout import RegisterLayout +from pulser.register.special_layouts import ( + SquareLatticeLayout, + TriangularLatticeLayout, +) + +layout = RegisterLayout([[0, 0], [1, 1], [1, 0], [0, 1]]) +layout3d = RegisterLayout([[0, 0, 0], [1, 1, 1], [0, 1, 0], [1, 0, 1]]) + + +def test_creation(): + with pytest.raises( + ValueError, match="must be an array or list of coordinates" + ): + RegisterLayout([[0, 0, 0], [1, 1], [1, 0], [0, 1]]) + + with pytest.raises(ValueError, match="size 2 or 3"): + RegisterLayout([[0], [1], [2]]) + + assert np.all(layout.coords == [[0, 0], [0, 1], [1, 0], [1, 1]]) + assert np.all( + layout3d.coords == [[0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 1, 1]] + ) + assert layout.number_of_traps == 4 + assert layout.max_atom_num == 2 + assert layout.dimensionality == 2 + for i, coord in enumerate(layout.coords): + assert np.all(layout.traps_dict[i] == coord) + + +def test_register_definition(): + with pytest.raises(ValueError, match="must be a unique integer"): + layout.define_register(0, 1, 1) + + with pytest.raises(ValueError, match="correspond to the ID of a trap"): + layout.define_register(0, 4, 3) + + with pytest.raises(ValueError, match="must be a sequence of unique IDs"): + layout.define_register(0, 1, qubit_ids=["a", "b", "b"]) + + with pytest.raises(ValueError, match="must have the same size"): + layout.define_register(0, 1, qubit_ids=["a", "b", "c"]) + + with pytest.raises( + ValueError, match="greater than the maximum number of qubits" + ): + layout.define_register(0, 1, 3) + + assert layout.define_register(0, 1) == Register.from_coordinates( + [[0, 0], [0, 1]], prefix="q", center=False + ) + + assert layout3d.define_register(0, 1) == Register3D( + {"q0": [0, 0, 0], "q1": [0, 1, 0]} + ) + + reg2d = layout.define_register(0, 2) + assert reg2d._layout_info == (layout, (0, 2)) + with pytest.raises(ValueError, match="dimensionality is not the same"): + reg2d._validate_layout(layout3d, (0, 2)) + with pytest.raises( + ValueError, match="Every 'trap_id' must be a unique integer" + ): + reg2d._validate_layout(layout, (0, 2, 2)) + with pytest.raises( + ValueError, match="must be equal to the number of atoms" + ): + reg2d._validate_layout(layout, (0,)) + with pytest.raises( + ValueError, match="don't match this register's coordinates" + ): + reg2d._validate_layout(layout, (0, 1)) + + with pytest.raises(TypeError, match="cannot be rotated"): + reg2d.rotate(30) + + +def test_draw(): + with patch("matplotlib.pyplot.show"): + layout.draw() + + with patch("matplotlib.pyplot.show"): + layout3d.draw() + + with patch("matplotlib.pyplot.show"): + layout3d.draw(projection=False) + + +def test_repr(): + hash_ = sha256(bytes(2)) + hash_.update(layout.coords.tobytes()) + assert repr(layout) == f"RegisterLayout_{hash_.hexdigest()}" + + +def test_eq(): + assert RegisterLayout([[0, 0], [1, 0]]) != Register.from_coordinates( + [[0, 0], [1, 0]] + ) + assert layout != layout3d + layout1 = RegisterLayout([[0, 0], [1, 0]]) + layout2 = RegisterLayout([[1, 0], [0, 0]]) + assert layout1 == layout2 + assert hash(layout1) == hash(layout2) + + +def test_traps_from_coordinates(): + assert layout._coords_to_traps == { + (0, 0): 0, + (0, 1): 1, + (1, 0): 2, + (1, 1): 3, + } + assert layout.get_traps_from_coordinates( + (0.9999995, 0.0000004), (0, 1), (1, 1) + ) == [2, 1, 3] + with pytest.raises(ValueError, match="not a part of the RegisterLayout"): + layout.get_traps_from_coordinates((0.9999994, 1)) + + +def test_square_lattice_layout(): + square = SquareLatticeLayout(9, 7, 5) + assert str(square) == "SquareLatticeLayout(9x7, 5µm)" + assert square.square_register(3) == Register.square( + 3, spacing=5, prefix="q" + ) + # An even number of atoms on the side won't align the center with an atom + assert square.square_register(4) != Register.square( + 4, spacing=5, prefix="q" + ) + with pytest.raises( + ValueError, match="'6 x 6' array has more atoms than those available" + ): + square.square_register(6) + + assert square.rectangular_register(3, 7, prefix="r") == Register.rectangle( + 3, 7, spacing=5, prefix="r" + ) + with pytest.raises(ValueError, match="'10 x 3' array doesn't fit"): + square.rectangular_register(10, 3) + + +def test_triangular_lattice_layout(): + tri = TriangularLatticeLayout(50, 5) + assert str(tri) == "TriangularLatticeLayout(50, 5µm)" + + assert tri.hexagonal_register(19) == Register.hexagon( + 2, spacing=5, prefix="q" + ) + with pytest.raises(ValueError, match="hold at most 25 atoms, not '26'"): + tri.hexagonal_register(26) + + with pytest.raises( + ValueError, match="has more atoms than those available" + ): + tri.rectangular_register(7, 4) + + # Case where the register doesn't fit + with pytest.raises(ValueError, match="not a part of the RegisterLayout"): + tri.rectangular_register(8, 3) + + # But this fits fine, though off-centered with the Register default + tri.rectangular_register(5, 5) != Register.triangular_lattice( + 5, 5, spacing=5, prefix="q" + ) From d76fb60e7479ae5a8a08f29912699848ff5ca638 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Tue, 22 Feb 2022 17:30:46 +0100 Subject: [PATCH 46/51] Mappable Register (#334) * Creating the SuspendedRegister class * Interfacing the SuspendedRegister with the Sequence * Completing unit tests * Import sorting * Adding check in Simulation * Addressing PR comments (pt.1) + Mypy * Replacing SuspendedRegister with MappableRegister --- pulser/_seq_drawer.py | 3 +- pulser/register/_reg_drawer.py | 16 ++-- pulser/register/base_register.py | 7 +- pulser/register/mappable_reg.py | 75 +++++++++++++++ pulser/register/register_layout.py | 24 +++++ pulser/sequence.py | 133 +++++++++++++++++++++++---- pulser/simulation/simulation.py | 14 +++ pulser/tests/test_json.py | 8 ++ pulser/tests/test_paramseq.py | 2 + pulser/tests/test_register.py | 6 +- pulser/tests/test_register_layout.py | 19 ++++ pulser/tests/test_sequence.py | 47 ++++++++++ pulser/tests/test_simulation.py | 16 ++++ 13 files changed, 338 insertions(+), 32 deletions(-) create mode 100644 pulser/register/mappable_reg.py diff --git a/pulser/_seq_drawer.py b/pulser/_seq_drawer.py index e6e603b97..b87d26eaf 100644 --- a/pulser/_seq_drawer.py +++ b/pulser/_seq_drawer.py @@ -161,10 +161,9 @@ def phase_str(phi: float) -> str: area_ph_box = dict(boxstyle="round", facecolor="ghostwhite", alpha=0.7) slm_box = dict(boxstyle="round", alpha=0.4, facecolor="grey", hatch="//") - pos = np.array(seq.register._coords) - # Draw masked register if draw_register: + pos = np.array(seq.register._coords) if isinstance(seq.register, Register3D): labels = "xyz" fig_reg, axes_reg = seq.register._initialize_fig_axes_projection( diff --git a/pulser/register/_reg_drawer.py b/pulser/register/_reg_drawer.py index e36b38bbc..e8aeaa67c 100644 --- a/pulser/register/_reg_drawer.py +++ b/pulser/register/_reg_drawer.py @@ -14,8 +14,9 @@ from __future__ import annotations +from collections.abc import Sequence as abcSequence from itertools import combinations -from typing import Optional, Union +from typing import Optional import matplotlib.pyplot as plt import numpy as np @@ -32,7 +33,7 @@ class RegDrawer: def _draw_2D( ax: plt.axes._subplots.AxesSubplot, pos: np.ndarray, - ids: list, + ids: abcSequence[QubitId], plane: tuple = (0, 1), with_labels: bool = True, blockade_radius: Optional[float] = None, @@ -77,12 +78,13 @@ def _draw_2D( if with_labels: # Determine which labels would overlap and merge those plot_pos = list(pos[:, (ix, iy)]) - plot_ids: list[Union[str, list[str]]] = [[f"{i}"] for i in ids] + plot_ids: list[list[str]] = [[f"{i}"] for i in ids] # Threshold distance between points epsilon = 1.0e-2 * np.diff(ax.get_xlim())[0] i = 0 bbs = {} + final_plot_ids: list[str] = [] while i < len(plot_ids): r = plot_pos[i] j = i + 1 @@ -112,11 +114,11 @@ def _draw_2D( if has_masked: plot_ids[i][-1] = "[" + plot_ids[i][-1] + "]" # Merge what remains - plot_ids[i] = ", ".join(plot_ids[i]) - bbs[plot_ids[i]] = overlap + final_plot_ids.append(", ".join(plot_ids[i])) + bbs[final_plot_ids[i]] = overlap i += 1 - for q, coords in zip(plot_ids, plot_pos): + for q, coords in zip(final_plot_ids, plot_pos): bb = ( dict(boxstyle="square", fill=False, ec="gray", ls="--") if bbs[q] @@ -165,7 +167,7 @@ def _draw_2D( @staticmethod def _draw_3D( pos: np.ndarray, - ids: list, + ids: abcSequence[QubitId], projection: bool = False, with_labels: bool = True, blockade_radius: Optional[float] = None, diff --git a/pulser/register/base_register.py b/pulser/register/base_register.py index bc505489a..b486d3503 100644 --- a/pulser/register/base_register.py +++ b/pulser/register/base_register.py @@ -63,7 +63,7 @@ def __init__(self, qubits: Mapping[Any, ArrayLike], **kwargs: Any): raise ValueError( "Cannot create a Register with an empty qubit " "dictionary." ) - self._ids = list(qubits.keys()) + self._ids: tuple[QubitId, ...] = tuple(qubits.keys()) self._coords = [np.array(v, dtype=float) for v in qubits.values()] self._dim = self._coords[0].size self._layout_info: Optional[_LayoutInfo] = None @@ -83,6 +83,11 @@ def qubits(self) -> dict[QubitId, np.ndarray]: """Dictionary of the qubit names and their position coordinates.""" return dict(zip(self._ids, self._coords)) + @property + def qubit_ids(self) -> tuple[QubitId, ...]: + """The qubit IDs of this register.""" + return self._ids + @classmethod def from_coordinates( cls: Type[T], diff --git a/pulser/register/mappable_reg.py b/pulser/register/mappable_reg.py new file mode 100644 index 000000000..58ef9e052 --- /dev/null +++ b/pulser/register/mappable_reg.py @@ -0,0 +1,75 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Allows for a temporary register to exist, when associated with a layout.""" +from __future__ import annotations + +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any + +from pulser.json.utils import obj_to_dict + +if TYPE_CHECKING: # pragma: no cover + from pulser.register.base_register import BaseRegister, QubitId + from pulser.register.register_layout import RegisterLayout + + +class MappableRegister: + """A register with the traps of each qubit still to be defined. + + Args: + register_layout (RegisterLayout): The register layout on which this + register will be defined. + qubit_ids (QubitId): The Ids for the qubits to pre-declare on this + register. + """ + + def __init__(self, register_layout: RegisterLayout, *qubit_ids: QubitId): + """Initializes the mappable register.""" + self._layout = register_layout + if len(qubit_ids) > self._layout.max_atom_num: + raise ValueError( + "The number of required traps is greater than the maximum " + "number of qubits allowed for this layout " + f"({self._layout.max_atom_num})." + ) + self._qubit_ids = qubit_ids + + @property + def qubit_ids(self) -> tuple[QubitId, ...]: + """The qubit IDs of this mappable register.""" + return self._qubit_ids + + def build_register(self, qubits: Mapping[QubitId, int]) -> BaseRegister: + """Builds an actual register. + + Args: + qubits (Mapping[QubitId, int]): A map between the qubit IDs to use + and the layout traps where the qubits will be placed. Qubit IDs + declared in the MappableRegister but not defined here will + simply be left out of the final register. + + Returns: + BaseRegister: The resulting register. + """ + chosen_ids = tuple(qubits.keys()) + if not set(chosen_ids) <= set(self._qubit_ids): + raise ValueError( + "All qubits must be labeled with pre-declared qubit IDs." + ) + return self._layout.define_register( + *tuple(qubits.values()), qubit_ids=chosen_ids + ) + + def _to_dict(self) -> dict[str, Any]: + return obj_to_dict(self, self._layout, *self._qubit_ids) diff --git a/pulser/register/register_layout.py b/pulser/register/register_layout.py index 4310e1be4..090ea46f7 100644 --- a/pulser/register/register_layout.py +++ b/pulser/register/register_layout.py @@ -28,6 +28,7 @@ from pulser.json.utils import obj_to_dict from pulser.register._reg_drawer import RegDrawer from pulser.register.base_register import BaseRegister, QubitId +from pulser.register.mappable_reg import MappableRegister from pulser.register.register import Register from pulser.register.register3d import Register3D @@ -248,6 +249,29 @@ def draw( ) plt.show() + def make_mappable_register( + self, n_qubits: int, prefix: str = "q" + ) -> MappableRegister: + """Creates a mappable register associated with this layout. + + A mappable register is a register whose atoms' positions have not yet + been defined. It can be used to create a sequence whose register is + only defined when it is built. Note that not all the qubits 'reserved' + in a MappableRegister need to be in the final Register, as qubits not + associated with trap IDs won't be included. If you intend on defining + registers of different sizes from the same mappable register, reserve + as many qubits as you need for your largest register. + + Args: + n_qubits(int): The number of qubits to reserve in the mappable + register. + prefix (str): The prefix for the qubit ids. Each qubit ID starts + with the prefix, followed by an int from 0 to N-1 + (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). + """ + qubit_ids = [f"{prefix}{i}" for i in range(n_qubits)] + return MappableRegister(self, *qubit_ids) + def _safe_hash(self) -> bytes: # Include dimensionality because the array is flattened with tobytes() hash = sha256(bytes(self.dimensionality)) diff --git a/pulser/sequence.py b/pulser/sequence.py index 41b697353..0353104b8 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -20,7 +20,7 @@ import os import warnings from collections import namedtuple -from collections.abc import Callable, Generator, Iterable +from collections.abc import Callable, Generator, Iterable, Mapping from functools import wraps from itertools import chain from sys import version_info @@ -50,6 +50,7 @@ from pulser.parametrized.variable import VariableItem from pulser.pulse import Pulse from pulser.register.base_register import BaseRegister +from pulser.register.mappable_reg import MappableRegister if version_info[:2] >= (3, 8): # pragma: no cover from typing import Literal, get_args @@ -160,7 +161,10 @@ class Sequence: generated from a single "parametrized" ``Sequence``. Args: - register(BaseRegister): The atom register on which to apply the pulses. + register(Union[BaseRegister, MappableRegister]): The atom register on + which to apply the pulses. If given as a MappableRegister + instance, the traps corrresponding to each qubit ID must be given + when building the sequence. device(Device): A valid device in which to execute the Sequence (import it from ``pulser.devices``). @@ -169,7 +173,9 @@ class Sequence: they are the same for all Sequences built from a parametrized Sequence. """ - def __init__(self, register: BaseRegister, device: Device): + def __init__( + self, register: Union[BaseRegister, MappableRegister], device: Device + ): """Initializes a new pulse sequence.""" if not isinstance(device, Device): raise TypeError( @@ -190,9 +196,12 @@ def __init__(self, register: BaseRegister, device: Device): warnings.warn(warns_msg, stacklevel=2) # Checks if register is compatible with the device - device.validate_register(register) + if isinstance(register, MappableRegister): + device.validate_layout(register._layout) + else: + device.validate_register(register) - self._register: BaseRegister = register + self._register: Union[BaseRegister, MappableRegister] = register self._device: Device = device self._in_xy: bool = False self._mag_field: Optional[tuple[float, float, float]] = None @@ -204,7 +213,7 @@ def __init__(self, register: BaseRegister, device: Device): # Stores the names and dict ids of declared channels self._taken_channels: dict[str, str] = {} # IDs of all qubits in device - self._qids: set[QubitId] = set(self.qubit_info.keys()) + self._qids: set[QubitId] = set(self._register.qubit_ids) # Last time each qubit was used, by basis self._last_used: dict[str, dict[QubitId, int]] = {} # Last time a target happened, by channel @@ -224,12 +233,22 @@ def __init__(self, register: BaseRegister, device: Device): @property def qubit_info(self) -> dict[QubitId, np.ndarray]: """Dictionary with the qubit's IDs and positions.""" - return self._register.qubits + if self.is_register_mappable(): + raise RuntimeError( + "Can't access the qubit information when the register is " + "mappable." + ) + return cast(BaseRegister, self._register).qubits @property def register(self) -> BaseRegister: """Register with the qubit's IDs and positions.""" - return self._register + if self.is_register_mappable(): + raise RuntimeError( + "Can't access the sequence's register because the register " + "is mappable." + ) + return cast(BaseRegister, self._register) @property def declared_channels(self) -> dict[str, Channel]: @@ -291,6 +310,18 @@ def is_parametrized(self) -> bool: """ return not self._building + def is_register_mappable(self) -> bool: + """States whether the sequence's register is mappable. + + A sequence with a mappable register will require its qubit Id's to be + mapped to trap Ids of its associated RegisterLayout through the + `Sequence.build()` call. + + Returns: + bool: Whether the register is a MappableRegister. + """ + return isinstance(self._register, MappableRegister) + @_screen def get_duration( self, channel: Optional[str] = None, include_fall_time: bool = False @@ -356,7 +387,7 @@ def current_phase_ref( if qubit not in self._qids: raise ValueError( "'qubit' must be the id of a qubit declared in " - "this sequence's device." + "this sequence's register." ) if basis not in self._phase_ref: @@ -597,6 +628,13 @@ def declare_variable( To avoid confusion, it is recommended to store the returned Variable instance in a Python variable with the same name. """ + if name == "qubits": + # Necessary because 'qubits' is a keyword arg in self.build() + raise ValueError( + "'qubits' is a protected name. Please choose a different name " + "for the variable." + ) + if name in self._variables: raise ValueError("Name for variable is already being used.") @@ -922,10 +960,19 @@ def align(self, *channels: Union[str, Parametrized]) -> None: ) self._delay(delta, id) - def build(self, **vars: Union[ArrayLike, float, int, str]) -> Sequence: + def build( + self, + *, + qubits: Optional[Mapping[QubitId, int]] = None, + **vars: Union[ArrayLike, float, int, str], + ) -> Sequence: """Builds a sequence from the programmed instructions. Keyword Args: + qubits (Optional[Mapping[QubitId, int]]): A mapping between qubit + IDs and trap IDs used to define the register. Must only be + provided when the sequence is initialized with a + MappableRegister. vars: The values for all the variables declared in this Sequence instance, indexed by the name given upon declaration. Check ``Sequence.declared_variables`` to see all the variables. @@ -943,13 +990,33 @@ def build(self, **vars: Union[ArrayLike, float, int, str]) -> Sequence: # Build a sequence with specific values for both variables >>> seq1 = seq.build(x=0.5, y=[1, 2, 3]) """ - if not self.is_parametrized(): - warnings.warn( - "Building a non-parametrized sequence simply returns" - " a copy of itself.", - stacklevel=2, + # Shallow copy with stored parametrized objects (if any) + seq = copy.copy(self) + + if self.is_register_mappable(): + if qubits is None: + raise ValueError( + "'qubits' must be specified when the sequence is created " + "with a MappableRegister." + ) + reg = cast(MappableRegister, self._register).build_register(qubits) + self._set_register(seq, reg) + + elif qubits is not None: + raise ValueError( + "'qubits' must not be specified when the sequence already has " + "a concrete register." ) - return copy.copy(self) + + if not self.is_parametrized(): + if not self.is_register_mappable(): + warnings.warn( + "Building a non-parametrized sequence simply returns" + " a copy of itself.", + stacklevel=2, + ) + return seq + all_keys, given_keys = self._variables.keys(), vars.keys() if given_keys != all_keys: invalid_vars = given_keys - all_keys @@ -970,8 +1037,6 @@ def build(self, **vars: Union[ArrayLike, float, int, str]) -> Sequence: for name, value in vars.items(): self._variables[name]._assign(value) - # Shallow copy with stored parametrized objects - seq = copy.copy(self) # Eliminates the source of recursiveness errors seq._reset_parametrized() # Deepcopy the base sequence (what remains) @@ -1064,7 +1129,8 @@ def draw( information should be added to the plot, defaults to False. draw_register (bool): Whether to draw the register before the pulse sequence, with a visual indication (square halo) around the - qubits masked by the SLM, defaults to False. + qubits masked by the SLM, defaults to False. Can't be set to + True if the sequence is defined with a mappable register. fig_name(str, default=None): The name on which to save the figure. If `draw_register` is True, both pulses and register will be saved as figures, with a suffix ``_pulses`` and @@ -1099,6 +1165,11 @@ def draw( stacklevel=2, ) draw_interp_pts = False + if draw_register and self.is_register_mappable(): + raise ValueError( + "Can't draw the register for a sequence without a defined " + "register." + ) fig_reg, fig = draw_sequence( self, draw_phase_area=draw_phase_area, @@ -1327,6 +1398,30 @@ def _reset_parametrized(self) -> None: self._variables = {} self._to_build_calls = [] + def _set_register(self, seq: Sequence, reg: BaseRegister) -> None: + """Sets the register on a sequence who had a mappable register.""" + self._device.validate_register(reg) + seq._register = reg + seq._qids = set(seq.register.qubit_ids) + used_qubits = set() + for ch, ch_obj in self._channels.items(): + # Correct the targets of global channels + if ch_obj.addressing == "Global": + for i, slot in enumerate(self._schedule[ch]): + stored_values = slot._asdict() + stored_values["targets"] = seq._qids + seq._schedule[ch][i] = _TimeSlot(**stored_values) + else: + # Make sure all explicit targets are in the register + for slot in self._schedule[ch]: + used_qubits.update(slot.targets) + + if not used_qubits <= seq._qids: + raise ValueError( + f"Qubits {used_qubits - seq._qids} are being targeted but" + " have not been assigned a trap." + ) + class _PhaseTracker: """Tracks a phase reference over time.""" diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index a480eb3a0..be1fc3ea2 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -82,6 +82,11 @@ def __init__( "The provided sequence has to be a valid " "pulser.Sequence instance." ) + if sequence.is_parametrized() or sequence.is_register_mappable(): + raise ValueError( + "The provided sequence needs to be built to be simulated. Call" + " `Sequence.build()` with the necessary parameters." + ) if not sequence._schedule: raise ValueError("The provided sequence has no declared channels.") if all(sequence._schedule[x][-1].tf == 0 for x in sequence._channels): @@ -93,6 +98,15 @@ def __init__( self._qdict = self._seq.qubit_info self._size = len(self._qdict) self._tot_duration = self._seq.get_duration() + + # Type hints for attributes defined outside of __init__ + self.basis_name: str + self._config: SimConfig + self.op_matrix: dict[str, qutip.Qobj] + self.basis: dict[str, qutip.Qobj] + self.dim: int + self._eval_times_array: np.ndarray + if not (0 < sampling_rate <= 1.0): raise ValueError( "The sampling rate (`sampling_rate` = " diff --git a/pulser/tests/test_json.py b/pulser/tests/test_json.py index ae3e2b243..034008da5 100644 --- a/pulser/tests/test_json.py +++ b/pulser/tests/test_json.py @@ -67,6 +67,14 @@ def test_register_from_layout(): assert new_reg._layout_info.trap_ids == (1, 0) +def test_mappable_register(): + layout = RegisterLayout([[0, 0], [1, 1], [1, 0], [0, 1]]) + mapp_reg = layout.make_mappable_register(2) + new_mapp_reg = encode_decode(mapp_reg) + assert new_mapp_reg._layout == layout + assert new_mapp_reg.qubit_ids == ("q0", "q1") + + def test_rare_cases(): reg = Register.square(4) seq = Sequence(reg, Chadoq2) diff --git a/pulser/tests/test_paramseq.py b/pulser/tests/test_paramseq.py index 364b915e5..50a311e19 100644 --- a/pulser/tests/test_paramseq.py +++ b/pulser/tests/test_paramseq.py @@ -43,6 +43,8 @@ def test_var_declarations(): var3 = sb.declare_variable("var3") assert sb.declared_variables["var3"] == var3.var assert isinstance(var3, VariableItem) + with pytest.raises(ValueError, match="'qubits' is a protected name"): + sb.declare_variable("qubits", size=10, dtype=str) def test_stored_calls(): diff --git a/pulser/tests/test_register.py b/pulser/tests/test_register.py index 252aa41e0..ba58eff8b 100644 --- a/pulser/tests/test_register.py +++ b/pulser/tests/test_register.py @@ -27,7 +27,7 @@ def test_creation(): Register(empty_dict) coords = [(0, 0), (1, 0)] - ids = ["q0", "q1"] + ids = ("q0", "q1") qubits = dict(zip(ids, coords)) with pytest.raises(TypeError): Register(coords) @@ -50,14 +50,14 @@ def test_creation(): assert reg1._ids == reg2._ids reg2b = Register.from_coordinates(coords, center=False, labels=["a", "b"]) - assert reg2b._ids == ["a", "b"] + assert reg2b._ids == ("a", "b") with pytest.raises(ValueError, match="Label length"): Register.from_coordinates(coords, center=False, labels=["a", "b", "c"]) reg3 = Register.from_coordinates(np.array(coords), prefix="foo") coords_ = np.array([(-0.5, 0), (0.5, 0)]) - assert reg3._ids == ["foo0", "foo1"] + assert reg3._ids == ("foo0", "foo1") assert np.all(reg3._coords == coords_) assert not np.all(coords_ == coords) diff --git a/pulser/tests/test_register_layout.py b/pulser/tests/test_register_layout.py index ce6058810..bcc605e85 100644 --- a/pulser/tests/test_register_layout.py +++ b/pulser/tests/test_register_layout.py @@ -183,3 +183,22 @@ def test_triangular_lattice_layout(): tri.rectangular_register(5, 5) != Register.triangular_lattice( 5, 5, spacing=5, prefix="q" ) + + +def test_mappable_register_creation(): + tri = TriangularLatticeLayout(50, 5) + with pytest.raises(ValueError, match="greater than the maximum"): + tri.make_mappable_register(26) + + mapp_reg = tri.make_mappable_register(5) + assert mapp_reg.qubit_ids == ("q0", "q1", "q2", "q3", "q4") + + with pytest.raises( + ValueError, match="labeled with pre-declared qubit IDs" + ): + mapp_reg.build_register({"q0": 0, "q5": 2}) + + reg = mapp_reg.build_register({"q0": 10, "q1": 49}) + assert reg == Register( + {"q0": tri.traps_dict[10], "q1": tri.traps_dict[49]} + ) diff --git a/pulser/tests/test_sequence.py b/pulser/tests/test_sequence.py index 0fac5dfbc..009856a9f 100644 --- a/pulser/tests/test_sequence.py +++ b/pulser/tests/test_sequence.py @@ -22,6 +22,7 @@ from pulser.channels import Raman, Rydberg from pulser.devices import Chadoq2, MockDevice from pulser.devices._device_datacls import Device +from pulser.register.special_layouts import TriangularLatticeLayout from pulser.sequence import _TimeSlot from pulser.waveforms import ( BlackmanWaveform, @@ -658,3 +659,49 @@ def test_hardware_constraints(): ): seq.draw(mode="output") seq.draw(mode="input+output") + + +def test_mappable_register(): + layout = TriangularLatticeLayout(100, 5) + mapp_reg = layout.make_mappable_register(10) + seq = Sequence(mapp_reg, Chadoq2) + assert seq.is_register_mappable() + reserved_qids = tuple([f"q{i}" for i in range(10)]) + assert seq._qids == set(reserved_qids) + with pytest.raises(RuntimeError, match="Can't access the qubit info"): + seq.qubit_info + with pytest.raises( + RuntimeError, match="Can't access the sequence's register" + ): + seq.register + + seq.declare_channel("ryd", "rydberg_global") + seq.declare_channel("ram", "raman_local", initial_target="q2") + seq.add(Pulse.ConstantPulse(100, 1, 0, 0), "ryd") + seq.add(Pulse.ConstantPulse(200, 1, 0, 0), "ram") + assert seq._last("ryd").targets == set(reserved_qids) + assert seq._last("ram").targets == {"q2"} + + with pytest.raises(ValueError, match="Can't draw the register"): + seq.draw(draw_register=True) + + # Can draw if 'draw_register=False' + with patch("matplotlib.pyplot.show"): + seq.draw() + + with pytest.raises(ValueError, match="'qubits' must be specified"): + seq.build() + + with pytest.raises( + ValueError, match="targeted but have not been assigned" + ): + seq.build(qubits={"q0": 1, "q1": 10}) + + seq_ = seq.build(qubits={"q2": 20, "q0": 10}) + seq_._last("ryd").targets == {"q2", "q0"} + assert not seq_.is_register_mappable() + assert seq_.register == Register( + {"q0": layout.traps_dict[10], "q2": layout.traps_dict[20]} + ) + with pytest.raises(ValueError, match="already has a concrete register"): + seq_.build(qubits={"q2": 20, "q0": 10}) diff --git a/pulser/tests/test_simulation.py b/pulser/tests/test_simulation.py index 9db3e9f08..9841c8d9f 100644 --- a/pulser/tests/test_simulation.py +++ b/pulser/tests/test_simulation.py @@ -21,6 +21,7 @@ from pulser import Pulse, Register, Sequence from pulser.devices import Chadoq2, MockDevice +from pulser.register.register_layout import RegisterLayout from pulser.simulation import SimConfig, Simulation from pulser.waveforms import BlackmanWaveform, ConstantWaveform, RampWaveform @@ -103,6 +104,21 @@ def test_initialization_and_construction_of_hamiltonian(): for sh in qobjevo.qobj.shape: assert sh == sim.dim**sim._size + assert not seq.is_parametrized() + seq_copy = seq.build() # Take a copy of the sequence + x = seq_copy.declare_variable("x") + seq_copy.add(Pulse.ConstantPulse(x, 1, 0, 0), "ryd") + assert seq_copy.is_parametrized() + with pytest.raises(ValueError, match="needs to be built"): + Simulation(seq_copy) + + layout = RegisterLayout([[0, 0], [10, 10]]) + mapp_reg = layout.make_mappable_register(1) + seq_ = Sequence(mapp_reg, Chadoq2) + assert seq_.is_register_mappable() and not seq_.is_parametrized() + with pytest.raises(ValueError, match="needs to be built"): + Simulation(seq_) + def test_extraction_of_sequences(): sim = Simulation(seq) From acc4c7cfac517433cb6f143f7039a820851d14d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Thu, 24 Feb 2022 14:41:26 +0100 Subject: [PATCH 47/51] Protected deserialization (#247) * List supported modules and classes * Adds validations to coders * Forbid deserialization of non-Sequence objects in Sequence.deserialize() * Complete unit tests * Adding Register3D to supported serialization * Mypy fix * Removing 'getattr' from supported functions * Fixing broken tests * Sorting imports with `isort` * Bugfix from the merge * Updating the supported classes * Refining serialization of layouts * Addressing review comments --- pulser/devices/__init__.py | 8 ++- pulser/json/coders.py | 3 + pulser/json/supported.py | 97 ++++++++++++++++++++++++++++++ pulser/json/utils.py | 3 + pulser/parametrized/paramobj.py | 10 ++- pulser/register/register_layout.py | 8 ++- pulser/register/special_layouts.py | 11 +++- pulser/sequence.py | 5 +- pulser/simulation/simulation.py | 1 + pulser/tests/test_json.py | 74 +++++++++++++++++++++-- pulser/tests/test_paramseq.py | 2 +- 11 files changed, 206 insertions(+), 16 deletions(-) create mode 100644 pulser/json/supported.py diff --git a/pulser/devices/__init__.py b/pulser/devices/__init__.py index bf6d17fca..72b97ca30 100644 --- a/pulser/devices/__init__.py +++ b/pulser/devices/__init__.py @@ -13,10 +13,14 @@ # limitations under the License. """Valid devices for Pulser Sequence execution.""" +from __future__ import annotations + +from typing import TYPE_CHECKING + from pulser.devices._device_datacls import Device from pulser.devices._devices import Chadoq2 from pulser.devices._mock_device import MockDevice # Registers which devices can be used to avoid definition of custom devices -_mock_devices = (MockDevice,) -_valid_devices = (Chadoq2,) +_mock_devices: tuple[Device, ...] = (MockDevice,) +_valid_devices: tuple[Device, ...] = (Chadoq2,) diff --git a/pulser/json/coders.py b/pulser/json/coders.py index 011140d01..53cdf5a49 100644 --- a/pulser/json/coders.py +++ b/pulser/json/coders.py @@ -22,6 +22,7 @@ import numpy as np +from pulser.json.supported import validate_serialization from pulser.json.utils import obj_to_dict from pulser.parametrized import Variable @@ -61,6 +62,8 @@ def object_hook(self, obj: dict[str, Any]) -> Any: except KeyError: return obj + validate_serialization(obj) + if ( obj_name == "Variable" and module_str == "pulser.parametrized.variable" diff --git a/pulser/json/supported.py b/pulser/json/supported.py new file mode 100644 index 000000000..f8793a892 --- /dev/null +++ b/pulser/json/supported.py @@ -0,0 +1,97 @@ +# Copyright 2020 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Supported modules and objects for JSON deserialization.""" + +from __future__ import annotations + +from typing import Any, Mapping + +import pulser + +SUPPORTED_BUILTINS = ("float", "int", "str", "set") + +SUPPORTED_OPERATORS = ( + "neg", + "abs", + "getitem", + "add", + "sub", + "mul", + "truediv", + "floordiv", + "pow", + "mod", +) + +SUPPORTS_SUBMODULE = ("Pulse", "BlackmanWaveform", "KaiserWaveform") + +SUPPORTED_MODULES = { + "builtins": SUPPORTED_BUILTINS, + "_operator": SUPPORTED_OPERATORS, + "operator": SUPPORTED_OPERATORS, + "numpy": ("array",), + "pulser.register.register": ("Register",), + "pulser.register.register3d": ("Register3D",), + "pulser.register.register_layout": ("RegisterLayout",), + "pulser.register.special_layouts": ( + "SquareLatticeLayout", + "TriangularLatticeLayout", + ), + "pulser.register.mappable_reg": ("MappableRegister",), + "pulser.devices": tuple( + [dev.name for dev in pulser.devices._valid_devices] + ["MockDevice"] + ), + "pulser.pulse": ("Pulse",), + "pulser.waveforms": ( + "CompositeWaveform", + "CustomWaveform", + "ConstantWaveform", + "RampWaveform", + "BlackmanWaveform", + "InterpolatedWaveform", + "KaiserWaveform", + ), + "pulser.sequence": ("Sequence",), + "pulser.parametrized.variable": ("Variable",), + "pulser.parametrized.paramobj": ("ParamObj",), +} + + +def validate_serialization(obj_dict: Mapping[str, Any]) -> None: + """Checks if 'obj_dict' can be serialized.""" + try: + obj_dict["_build"] + obj_str = obj_dict["__name__"] + module_str = obj_dict["__module__"] + except KeyError: + raise TypeError("Invalid 'obj_dict'.") + + if module_str not in SUPPORTED_MODULES: + raise SystemError( + f"No serialization support for module '{module_str}'." + ) + + if "__submodule__" in obj_dict: + submodule_str = obj_dict["__submodule__"] + if submodule_str not in SUPPORTS_SUBMODULE: + raise SystemError( + "No serialization support for attributes of " + f"'{module_str}.{submodule_str}'." + ) + obj_str = submodule_str + + if obj_str not in SUPPORTED_MODULES[module_str]: + raise SystemError( + f"No serialization support for '{module_str}.{obj_str}'." + ) diff --git a/pulser/json/utils.py b/pulser/json/utils.py index 1dbd43742..dfd4e395a 100644 --- a/pulser/json/utils.py +++ b/pulser/json/utils.py @@ -17,6 +17,8 @@ from typing import Any, Optional +import pulser + def obj_to_dict( obj: object, @@ -56,4 +58,5 @@ def obj_to_dict( if _submodule: d["__submodule__"] = _submodule + pulser.json.supported.validate_serialization(d) return d diff --git a/pulser/parametrized/paramobj.py b/pulser/parametrized/paramobj.py index f5fc6849e..eda835f1e 100644 --- a/pulser/parametrized/paramobj.py +++ b/pulser/parametrized/paramobj.py @@ -141,7 +141,10 @@ def class_to_dict(cls: Callable) -> dict[str, Any]: args = list(self.args) if isinstance(self.cls, Parametrized): - cls_dict = self.cls._to_dict() + raise ValueError( + "Serialization of calls to parametrized objects is not " + "supported." + ) elif hasattr(args[0], self.cls.__name__) and inspect.isfunction( self.cls ): @@ -181,6 +184,11 @@ def __call__(self, *args: Any, **kwargs: Any) -> ParamObj: def __getattr__(self, name: str) -> ParamObj: if hasattr(self.cls, name): + warnings.warn( + "Serialization of 'getattr' calls to parametrized " + "objects is not supported, so this object can't be serialied.", + stacklevel=2, + ) return ParamObj(getattr, self, name) else: raise AttributeError(f"No attribute named '{name}' in {self}.") diff --git a/pulser/register/register_layout.py b/pulser/register/register_layout.py index 090ea46f7..f51b8b5d8 100644 --- a/pulser/register/register_layout.py +++ b/pulser/register/register_layout.py @@ -290,4 +290,10 @@ def __repr__(self) -> str: return f"RegisterLayout_{self._safe_hash().hex()}" def _to_dict(self) -> dict[str, Any]: - return obj_to_dict(self, self.trap_coordinates) + # Allows for serialization of subclasses without a special _to_dict() + return obj_to_dict( + self, + self.trap_coordinates, + _module=__name__, + _name="RegisterLayout", + ) diff --git a/pulser/register/special_layouts.py b/pulser/register/special_layouts.py index 126d50e91..27f60ee45 100644 --- a/pulser/register/special_layouts.py +++ b/pulser/register/special_layouts.py @@ -15,9 +15,10 @@ from __future__ import annotations -from typing import cast +from typing import Any, cast import pulser.register._patterns as patterns +from pulser.json.utils import obj_to_dict from pulser.register import Register from pulser.register.register_layout import RegisterLayout @@ -95,6 +96,9 @@ def __str__(self) -> str: f"{self._spacing}µm)" ) + def _to_dict(self) -> dict[str, Any]: + return obj_to_dict(self, self._rows, self._columns, self._spacing) + class TriangularLatticeLayout(RegisterLayout): """A RegisterLayout with a triangular lattice pattern in an hexagonal shape. @@ -107,7 +111,7 @@ class TriangularLatticeLayout(RegisterLayout): def __init__(self, n_traps: int, spacing: int): """Initializes a TriangularLatticeLayout.""" self._spacing = int(spacing) - super().__init__(patterns.triangular_hex(n_traps) * self._spacing) + super().__init__(patterns.triangular_hex(int(n_traps)) * self._spacing) def hexagonal_register(self, n_atoms: int, prefix: str = "q") -> Register: """Defines a register with an hexagonal shape. @@ -166,3 +170,6 @@ def __str__(self) -> str: f"TriangularLatticeLayout({self.number_of_traps}, " f"{self._spacing}µm)" ) + + def _to_dict(self) -> dict[str, Any]: + return obj_to_dict(self, self.number_of_traps, self._spacing) diff --git a/pulser/sequence.py b/pulser/sequence.py index 0353104b8..429263265 100644 --- a/pulser/sequence.py +++ b/pulser/sequence.py @@ -1092,9 +1092,8 @@ def deserialize(obj: str, **kwargs: Any) -> Sequence: formatted string. """ if "Sequence" not in obj: - warnings.warn( - "The given JSON formatted string does not encode a Sequence.", - stacklevel=2, + raise ValueError( + "The given JSON formatted string does not encode a Sequence." ) return cast(Sequence, json.loads(obj, cls=PulserDecoder, **kwargs)) diff --git a/pulser/simulation/simulation.py b/pulser/simulation/simulation.py index be1fc3ea2..ba611c016 100644 --- a/pulser/simulation/simulation.py +++ b/pulser/simulation/simulation.py @@ -888,6 +888,7 @@ def run( def _run_solver() -> CoherentResults: """Returns CoherentResults: Object containing evolution results.""" # Decide if progress bar will be fed to QuTiP solver + p_bar: Optional[bool] if progress_bar is True: p_bar = True elif (progress_bar is False) or (progress_bar is None): diff --git a/pulser/tests/test_json.py b/pulser/tests/test_json.py index 034008da5..35a8cead7 100644 --- a/pulser/tests/test_json.py +++ b/pulser/tests/test_json.py @@ -20,8 +20,13 @@ from pulser import Register, Register3D, Sequence from pulser.devices import Chadoq2, MockDevice from pulser.json.coders import PulserDecoder, PulserEncoder +from pulser.json.supported import validate_serialization from pulser.parametrized.decorators import parametrize from pulser.register.register_layout import RegisterLayout +from pulser.register.special_layouts import ( + SquareLatticeLayout, + TriangularLatticeLayout, +) from pulser.waveforms import BlackmanWaveform @@ -56,6 +61,23 @@ def test_register_3d(): assert reg == encode_decode(seq).register +def test_layout(): + custom_layout = RegisterLayout([[0, 0], [1, 1], [1, 0], [0, 1]]) + new_custom_layout = encode_decode(custom_layout) + assert new_custom_layout == custom_layout + assert type(new_custom_layout) is RegisterLayout + + tri_layout = TriangularLatticeLayout(100, 10) + new_tri_layout = encode_decode(tri_layout) + assert new_tri_layout == tri_layout + assert type(new_tri_layout) is TriangularLatticeLayout + + square_layout = SquareLatticeLayout(8, 10, 6) + new_square_layout = encode_decode(square_layout) + assert new_square_layout == square_layout + assert type(new_square_layout) is SquareLatticeLayout + + def test_register_from_layout(): layout = RegisterLayout([[0, 0], [1, 1], [1, 0], [0, 1]]) reg = layout.define_register(1, 0) @@ -80,24 +102,64 @@ def test_rare_cases(): seq = Sequence(reg, Chadoq2) var = seq.declare_variable("var") - wf = BlackmanWaveform(100, var) - with pytest.warns( - UserWarning, match="Calls to methods of parametrized " "objects" + wf = BlackmanWaveform(var * 100 // 10, var) + with pytest.raises( + ValueError, match="Serialization of calls to parametrized objects" ): s = encode(wf.draw()) + s = encode(wf) - with pytest.warns(UserWarning, match="not encode a Sequence"): + with pytest.raises(ValueError, match="not encode a Sequence"): wf_ = Sequence.deserialize(s) + wf_ = decode(s) seq._variables["var"]._assign(-10) with pytest.raises(ValueError, match="No value assigned"): wf_.build() var_ = wf_._variables["var"] - var_._assign(-10) + var_._assign(10) + assert wf_.build() == BlackmanWaveform(100, 10) + with pytest.warns(UserWarning, match="Serialization of 'getattr'"): + draw_func = wf_.draw with patch("matplotlib.pyplot.show"): - wf_.build() + with pytest.warns( + UserWarning, match="Calls to methods of parametrized objects" + ): + draw_func().build() rotated_reg = parametrize(Register.rotate)(reg, var) with pytest.raises(NotImplementedError): encode(rotated_reg) + + +def test_support(): + seq = Sequence(Register.square(2), Chadoq2) + var = seq.declare_variable("var") + + obj_dict = BlackmanWaveform.from_max_val(1, var)._to_dict() + del obj_dict["__module__"] + with pytest.raises(TypeError, match="Invalid 'obj_dict'."): + validate_serialization(obj_dict) + + obj_dict["__module__"] = "pulser.fake" + with pytest.raises( + SystemError, match="No serialization support for module 'pulser.fake'." + ): + validate_serialization(obj_dict) + + wf_obj_dict = obj_dict["__args__"][0] + wf_obj_dict["__submodule__"] = "RampWaveform" + with pytest.raises( + SystemError, + match="No serialization support for attributes of " + "'pulser.waveforms.RampWaveform'", + ): + validate_serialization(wf_obj_dict) + + del wf_obj_dict["__submodule__"] + with pytest.raises( + SystemError, + match="No serialization support for 'pulser.waveforms.from_max_val'", + ): + validate_serialization(wf_obj_dict) diff --git a/pulser/tests/test_paramseq.py b/pulser/tests/test_paramseq.py index 50a311e19..f81717745 100644 --- a/pulser/tests/test_paramseq.py +++ b/pulser/tests/test_paramseq.py @@ -148,7 +148,7 @@ def test_build(): sb.delay(var * 50, "ch1") sb.align("ch2", "ch1") sb.phase_shift(var, targ_var[0]) - pls2 = Pulse.ConstantPulse(wf.duration, var, var, 0) + pls2 = Pulse.ConstantPulse(var * 100, var, var, 0) sb.add(pls2, "ch2") sb.measure() with pytest.warns(UserWarning, match="No declared variables"): From 304fde3ff0e3ac385baa38accc15d507697d3aa1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Thu, 24 Feb 2022 16:33:34 +0100 Subject: [PATCH 48/51] Accounting for rounding errors in Register and RegisterLayout validation (#336) --- pulser/devices/_device_datacls.py | 7 +++++-- pulser/tests/test_devices.py | 9 ++++++--- pulser/tests/test_register.py | 4 ++++ 3 files changed, 15 insertions(+), 5 deletions(-) diff --git a/pulser/devices/_device_datacls.py b/pulser/devices/_device_datacls.py index 6b21ff826..382c575db 100644 --- a/pulser/devices/_device_datacls.py +++ b/pulser/devices/_device_datacls.py @@ -25,7 +25,7 @@ from pulser.devices.interaction_coefficients import c6_dict from pulser.json.utils import obj_to_dict from pulser.register.base_register import BaseRegister, QubitId -from pulser.register.register_layout import RegisterLayout +from pulser.register.register_layout import COORD_PRECISION, RegisterLayout @dataclass(frozen=True, repr=False) @@ -268,7 +268,10 @@ def _validate_coords( if len(coords) > 1: distances = pdist(coords) # Pairwise distance between atoms - if np.any(distances < self.min_atom_distance): + if np.any( + distances - self.min_atom_distance + < -(10 ** (-COORD_PRECISION)) + ): sq_dists = squareform(distances) mask = np.triu(np.ones(len(coords), dtype=bool), k=1) bad_pairs = np.argwhere( diff --git a/pulser/tests/test_devices.py b/pulser/tests/test_devices.py index fb7c054e2..89c851bac 100644 --- a/pulser/tests/test_devices.py +++ b/pulser/tests/test_devices.py @@ -137,9 +137,7 @@ def test_validate_layout(): with pytest.raises(ValueError, match="The minimal distance between traps"): Chadoq2.validate_layout( - RegisterLayout( - Register.triangular_lattice(3, 4, spacing=3.9)._coords - ) + TriangularLatticeLayout(12, Chadoq2.min_atom_distance - 1e-6) ) valid_layout = RegisterLayout( @@ -147,6 +145,11 @@ def test_validate_layout(): ) Chadoq2.validate_layout(valid_layout) + valid_tri_layout = TriangularLatticeLayout( + Chadoq2.max_atom_num * 2, Chadoq2.min_atom_distance + ) + Chadoq2.validate_layout(valid_tri_layout) + def test_calibrated_layouts(): with pytest.raises(ValueError, match="The number of traps"): diff --git a/pulser/tests/test_register.py b/pulser/tests/test_register.py index ba58eff8b..e7d560bed 100644 --- a/pulser/tests/test_register.py +++ b/pulser/tests/test_register.py @@ -204,6 +204,7 @@ def test_max_connectivity(): ] ) reg = Register.max_connectivity(i, device) + device.validate_register(reg) reg2 = Register.from_coordinates( spacing * hex_coords[:i], center=False ) @@ -215,6 +216,7 @@ def test_max_connectivity(): # Check full layers on a small hexagon (1 layer) reg = Register.max_connectivity(7, device) + device.validate_register(reg) assert len(reg.qubits) == 7 atoms = list(reg.qubits.values()) assert np.all(np.isclose(atoms[0], [0.0, 0.0])) @@ -227,6 +229,7 @@ def test_max_connectivity(): # Check full layers for a bigger hexagon (2 layers) reg = Register.max_connectivity(19, device) + device.validate_register(reg) assert len(reg.qubits) == 19 atoms = list(reg.qubits.values()) assert np.all(np.isclose(atoms[7], [-1.5 * spacing, crest_y * spacing])) @@ -241,6 +244,7 @@ def test_max_connectivity(): # Check extra atoms (2 full layers + 7 extra atoms) # for C3 symmetry, C6 symmetry and offset for next atoms reg = Register.max_connectivity(26, device) + device.validate_register(reg) assert len(reg.qubits) == 26 atoms = list(reg.qubits.values()) assert np.all(np.isclose(atoms[19], [-2.5 * spacing, crest_y * spacing])) From 2b04db79ffbc86ba25353509d9e8573ac3ec3013 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Fri, 25 Feb 2022 09:55:10 +0100 Subject: [PATCH 49/51] Adding the IroiseMVP device (#329) * Adding the IroiseMVP device * Preliminary IroiseMVP specs * Removing the modulation bandwith the channel * Changing back the `min_atom_distance` --- pulser/devices/__init__.py | 4 ++-- pulser/devices/_devices.py | 19 +++++++++++++++++++ 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/pulser/devices/__init__.py b/pulser/devices/__init__.py index 72b97ca30..066250083 100644 --- a/pulser/devices/__init__.py +++ b/pulser/devices/__init__.py @@ -18,9 +18,9 @@ from typing import TYPE_CHECKING from pulser.devices._device_datacls import Device -from pulser.devices._devices import Chadoq2 +from pulser.devices._devices import Chadoq2, IroiseMVP from pulser.devices._mock_device import MockDevice # Registers which devices can be used to avoid definition of custom devices _mock_devices: tuple[Device, ...] = (MockDevice,) -_valid_devices: tuple[Device, ...] = (Chadoq2,) +_valid_devices: tuple[Device, ...] = (Chadoq2, IroiseMVP) diff --git a/pulser/devices/_devices.py b/pulser/devices/_devices.py index 9a757ec91..0c9dd81c6 100644 --- a/pulser/devices/_devices.py +++ b/pulser/devices/_devices.py @@ -30,3 +30,22 @@ ("raman_local", Raman.Local(2 * np.pi * 20, 2 * np.pi * 10)), ), ) + +IroiseMVP = Device( + name="IroiseMVP", + dimensions=2, + rydberg_level=60, + max_atom_num=100, + max_radial_distance=60, + min_atom_distance=5, + _channels=( + ( + "rydberg_global", + Rydberg.Global( + max_abs_detuning=2 * np.pi * 4, + max_amp=2 * np.pi * 3, + phase_jump_time=500, + ), + ), + ), +) From 7365949a2aa22d33024c188343d09d6f8b34bb82 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Fri, 25 Feb 2022 13:49:22 +0100 Subject: [PATCH 50/51] Update tutorials --- .../Quantum Evolution Kernel.ipynb | 1553 ----------------- 1 file changed, 1553 deletions(-) delete mode 100644 tutorials/applications/Quantum Evolution Kernel.ipynb diff --git a/tutorials/applications/Quantum Evolution Kernel.ipynb b/tutorials/applications/Quantum Evolution Kernel.ipynb deleted file mode 100644 index bc89f817e..000000000 --- a/tutorials/applications/Quantum Evolution Kernel.ipynb +++ /dev/null @@ -1,1553 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f4015010", - "metadata": {}, - "source": [ - "# Quantum Evolution Kernel with Rydberg atoms" - ] - }, - { - "cell_type": "markdown", - "id": "19d2d2bc", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The following notebook illustrates how to use Pulser to evaluate the Quantum Evolution Kernel of graphs, and use it in a classification problem on a benchmark dataset.\n", - "\n", - "The idea is to imprint properties of a graph onto a driven quantum system, and then to characterize the graph through measurements of this system after an optimized time-evolution.\n", - "\n", - "The properties of a graph $\\mathcal{G}=(\\mathcal{V},\\mathcal{E})$ are encoded in the graph Hamiltonian $\\hat{\\mathcal{H}}_\\mathcal{G} = \\sum_{(i,j)\\in\\mathcal{E}} \\hat{h}_{ij}$ of a system, on which a pulse Hamiltonian $\\hat{\\mathcal{H}}_1 = \\sum_{i\\in\\mathcal{V}} \\hat{h}'_i$, independent of the graph, can be applied.\n", - "\n", - "Starting with the system in the empty state $\\left|\\psi_0\\right\\rangle=\\bigotimes_{i\\in\\mathcal{V}} \\left|0\\right\\rangle$, it is first brought to a superposition of computational basis states via the action of $\\hat{\\mathcal{H}}_1$ with parameter (or time) $\\vartheta_0$. It is then alternatively left to evolve with the graph Hamiltonian $\\hat{\\mathcal{H}}_\\mathcal{G}$ for a duration $\\tau_i$, and driven with the pulse Hamiltonian $\\hat{\\mathcal{H}}_1$ with parameter $\\vartheta_i$. The final state is then measure after $p$ such alternations (layers) :\n", - "\n", - "$$\n", - "\\left|\\psi_f(\\vartheta)\\right\\rangle = \\prod_{i=1}^p\\left(\\mathbf{\\text{e}}^{-{\\rm i} \\vartheta_i \\hat{\\mathcal{H}}_1}\n", - " \\mathbf{\\text{e}}^{-{\\rm i} \\tau_i\\hat{\\mathcal{H}}_\\mathcal{G}}\\right)\n", - " \\mathbf{\\text{e}}^{-{\\rm i} \\vartheta_0 \\hat{\\mathcal{H}}_1}\\left|\\psi_0\\right\\rangle.\n", - "$$\n", - "\n", - "An observable $\\hat{\\mathcal{O}}$ is then measured in the final state, and is used to build a probability distribution that will serve as a vector representation of the graph.\n", - "\n", - "The distance between two representative vectors is then computed using standard methods (here the Jensen-Shannon divergence) and can be used in an classification task, for example with a Support Vector Machin (SVM).\n", - "\n", - "This is based upon [arxiv.org/2107.03247](https://arxiv.org/abs/2107.03247)." - ] - }, - { - "attachments": { - "featuremap.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "e7e0a884", - "metadata": {}, - "source": [ - "
\n", - "\"Feature\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "380a14cd", - "metadata": {}, - "source": [ - "### Jensen-Shannon divergence\n", - "A distance between two probability distributions $\\mathcal{P}=\\{p_k\\}_k$ and $\\mathcal{P}'=\\{p'_k\\}_k$ can be constructed from the Shannon entropy $H(\\mathcal{P})=-\\sum_kp_k\\log p_k$. It is the Jensen-Shannon divergence, defined as\n", - "\n", - "$$\n", - "JS(\\mathcal{P}, \\mathcal{P}') = H\\left(\\frac{\\mathcal{P}+\\mathcal{P}'}{2}\\right) -\\frac{H(\\mathcal{P})+H(\\mathcal{P}')}{2}.\n", - "$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0b40c9f3", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "def JSdiv(p1, p2):\n", - " \"\"\"Compute the Jensen-Shannon divergence between two distributions.\"\"\"\n", - " q1 = np.array(p1) / np.sum(p1)\n", - " q2 = np.array(p2) / np.sum(p2)\n", - " # Alowing for distributions to have different sizes\n", - " delta = len(q1) - len(q2)\n", - " if delta < 0:\n", - " q1 = np.concatenate((q1, np.array([0 for i in range(-delta)])))\n", - " elif delta > 0:\n", - " q2 = np.concatenate((q2, np.array([0 for i in range(delta)])))\n", - " pq = (q1 + q2) / 2\n", - "\n", - " def entropy(pl_unscaled):\n", - " # Making sure the probability distributions are similarly normalized\n", - " pl = np.array(pl_unscaled) / np.sum(pl_unscaled)\n", - " res = 0\n", - " for p in pl:\n", - " if p > 0:\n", - " res += p * np.log(p)\n", - " return -res\n", - "\n", - " out = entropy(pq) - (entropy(q1) + entropy(q2)) / 2\n", - " return out" - ] - }, - { - "cell_type": "markdown", - "id": "e63351e8", - "metadata": {}, - "source": [ - "## First example\n", - "\n", - "As an example, let us first implement the kernel with a scheme that allows for the computation of closed formulas. Readers interested only in the implementation of this kernel using Pulser can skip to [Application on a benchmark dataset](#Application-on-a-benchmark-dataset)." - ] - }, - { - "cell_type": "markdown", - "id": "75883ac1", - "metadata": {}, - "source": [ - "### Scheme\n", - "The graph Hamiltonian is here $\\hat{\\mathcal{H}}_\\mathcal{G} = \\sum_{(i,j)\\in\\mathcal{E}} \\hat{n}_i\\hat{n}_j$, and the pulse Hamiltonian is $\\hat{\\mathcal{H}}_1 = \\sum_{i\\in\\mathcal{V}} \\hat{\\sigma}^y_i$.\n", - "\n", - "The scheme is here limited to $p=1$ layer, and the two pulses are set to be Ramsey pulses of opposite parameters $\\vartheta$ and $-\\vartheta$, so that the final state is \n", - "\n", - "$$\n", - "\\left|\\psi_f(\\vartheta)\\right\\rangle = \\mathbf{\\text{e}}^{{\\rm i} \\vartheta \\hat{\\mathcal{H}}_1}\n", - " \\mathbf{\\text{e}}^{-{\\rm i} t\\hat{\\mathcal{H}}_\\mathcal{G}}\n", - " \\mathbf{\\text{e}}^{-{\\rm i} \\vartheta \\hat{\\mathcal{H}}_1}\\left|\\psi_0\\right\\rangle.\n", - "$$\n", - "\n", - "The total occupation $\\sum_{i\\in\\mathcal{V}}\\hat{n}_i$ is then measured in the final state and its Fourier transform $\\{p_k\\}_{k\\in\\mathbb{N}}$ is the probability distribution extracted." - ] - }, - { - "cell_type": "markdown", - "id": "6d180391", - "metadata": {}, - "source": [ - "### Total occupation and Fourier transform\n", - "In that case, for a graph $\\mathcal{G}$ containing $m_\\mathcal{G}(\\kappa)$ nodes of degree $\\kappa$, the total occupation can be expressed explicitely as\n", - "\n", - "$$\n", - "n(t)=2\\,{\\cos^2\\vartheta\\sin^2\\vartheta}\\sum_{\\kappa\\geq0} m_\\mathcal{G}(\\kappa) w_\\kappa(t),\n", - "\\hspace{.4cm}\n", - "\\text{with } w_\\kappa(t)={\\Re\\left\\{1-\\left(\\cos^2\\vartheta+\\mathbf{\\text{e}}^{-{\\rm i} t}\\sin^2\\vartheta\\right)^{\\kappa}\\right\\}}.\n", - "$$\n", - "\n", - "With $c_\\vartheta = \\cos^2\\vartheta$, the Fourier transform of $n(t)$ (over $t\\in\\mathbb{R}$) can be expressed as \n", - "\n", - "$$\n", - "p_0^{(\\infty)}=2\\,{c_\\vartheta(1-c_\\vartheta)}\\sum_{\\kappa\\geq0} m_\\mathcal{G}(\\kappa)\\,(1-c_\\vartheta^{\\kappa}),\\hspace{.4cm}\tp_{k>0}^{(\\infty)}={(1-c_\\vartheta)^{1+k}}\\sum_{\\kappa\\geq k} \\binom{\\kappa}{k}m_\\mathcal{G}(\\kappa)\\,c_\\vartheta^{\\kappa+1-k}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "dcb340a8", - "metadata": {}, - "source": [ - "### Illustration on random graphs\n", - "\n", - "Let us illustrate this formula on a few Erdős–Rényi graphs of $N=100$ nodes, with edge probability $\\rho$ ranging from 0.2 to 0.8." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6ea03462", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "from IPython.display import Latex\n", - "import scipy.special\n", - "\n", - "# Load graph package\n", - "import networkx as nx\n", - "\n", - "\n", - "def pk(G, theta=np.pi / 4):\n", - " cnt = nx.degree_histogram(G)\n", - " kappamax = len(cnt)\n", - "\n", - " c = np.cos(theta) ** 2\n", - " s = 1 - c\n", - " t = np.tan(theta) ** 2\n", - " sp = 2 * c * s\n", - "\n", - " res0 = 0\n", - " for kappa, m in enumerate(cnt):\n", - " res0 += m * (1 - c**kappa)\n", - " res = [(sp * res0)]\n", - " for k in range(1, kappamax):\n", - " res0 = 0\n", - " for kappa in range(k, kappamax):\n", - " m_kappa = cnt[kappa]\n", - " binom = scipy.special.comb(kappa, k, exact=True)\n", - " res0 += m_kappa * binom * (c ** (kappa + 1 - k))\n", - " res.append(((s ** (1 + k)) * res0))\n", - " return res" - ] - }, - { - "cell_type": "markdown", - "id": "c5861861", - "metadata": {}, - "source": [ - "We now build an artificial set of graphs of two different Erdős–Rényi classes $\\rho=0.35$ and $\\rho=0.65$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "10fa3104", - "metadata": {}, - "outputs": [], - "source": [ - "# Largest allowed graph\n", - "N_max = 100\n", - "# Size of the dataset\n", - "n_graphs = 100\n", - "\n", - "\n", - "def create_random_graphs(N_max=100, n_graphs=100, rho_low=0.35, rho_high=0.65):\n", - " # Dataset with graphs of two different Erdős–Rényi classes\n", - " graphs = []\n", - " # Classes of these graphs\n", - " classes = []\n", - " # Probability distributions of these graphs as described above\n", - " probability_distributions = []\n", - " for _ in range(n_graphs):\n", - " # Number of nodes in the graph in [N_max/2,N_max]\n", - " N = np.random.randint(N_max // 2, N_max + 1)\n", - " if np.random.rand() < 0.5:\n", - " rho = rho_low\n", - " classes.append(0)\n", - " else:\n", - " rho = rho_high\n", - " classes.append(1)\n", - " G = nx.erdos_renyi_graph(N, rho)\n", - " graphs.append(G)\n", - " pdist = pk(G)\n", - " probability_distributions.append(pdist / np.sum(pdist))\n", - "\n", - " return graphs, classes, probability_distributions" - ] - }, - { - "cell_type": "markdown", - "id": "5612c6f5", - "metadata": {}, - "source": [ - "From two graphs $\\mathcal{G}$ and $\\mathcal{G}'$, and their respective probability distributions $\\mathcal{P}=\\{p_k\\}_k$ constructed from the time evolution described above, the kernel can then be expressed as\n", - "\n", - "$$\n", - "K(\\mathcal{G},\\mathcal{G}') = \\exp\\left(-\\mu JS(\\mathcal{P}, \\mathcal{P}')\\right).\n", - "$$\n", - "\n", - "We now build the kernel matrix containing the graph kernels between graphs in a random data set (we set $\\mu=1$ in the entire tutorial)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3b126a2f", - "metadata": {}, - "outputs": [], - "source": [ - "def kernel_matrix(pdist1, pdist2, mu=1):\n", - " Kmat = np.array(\n", - " [[np.exp(-mu * JSdiv(p1, p2)) for p1 in pdist1] for p2 in pdist2]\n", - " )\n", - " return Kmat\n", - "\n", - "\n", - "graphs, classes, proba_dists = create_random_graphs()\n", - "\n", - "Kmat = kernel_matrix(proba_dists, proba_dists)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "90740f18", - "metadata": {}, - "outputs": [ - { - 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def plot_kernel_matrix(Kmat):\n", - " fig, ax = plt.subplots(figsize=(8, 8))\n", - " im = ax.imshow(Kmat, cmap=\"OrRd\")\n", - " ax.set_xlabel(\"Graph #\", fontsize=18)\n", - " ax.set_ylabel(\"Graph #\", fontsize=18)\n", - " cbar = plt.colorbar(im, extend=\"max\")\n", - "\n", - "\n", - "plot_kernel_matrix(Kmat)" - ] - }, - { - "cell_type": "markdown", - "id": "c581e23b", - "metadata": {}, - "source": [ - "### Classification : Support Vector Machine\n", - "From this kernel matrix one can build a support vector machine and use it as a prediction tool for the class of any new graph.\n", - "We use here the `sklearn` package handling the classification. More details can be found here : https://scikit-learn.org/stable/modules/svm.html#svm-mathematical-formulation.\n", - "\n", - "One first trains the classifier, using the Kernel matrix computed above and the known classes of the corresponding graphs :" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a8757521", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import svm\n", - "from sklearn.metrics import (\n", - " f1_score,\n", - " accuracy_score,\n", - " recall_score,\n", - " precision_score,\n", - ")\n", - "\n", - "\n", - "scores_types = [\"Accuracy \", \"f1 \", \"Precision\", \"Recall \"]\n", - "\n", - "\n", - "def trained_classifier_from_Kmat(Kmat, classes_train):\n", - " \"\"\"\n", - " Create and train a classifier from the Kernel matrix `Kmat`\n", - " obtained from graphs of classes `classes_train`\n", - " \"\"\"\n", - " classifier = svm.SVC(kernel=\"precomputed\")\n", - " classifier.fit(Kmat, classes_train)\n", - "\n", - " return classifier\n", - "\n", - "\n", - "def trained_classifier_pdist(p_dist_train, classes_train):\n", - " \"\"\"\n", - " Create and train a classifier from the probability\n", - " distributions `p_dist_train` and the corresponding classes\n", - " `classes_train`\n", - " \"\"\"\n", - " Kmat = kernel_matrix(p_dist_train, p_dist_train)\n", - "\n", - " return trained_classifier_from_Kmat(Kmat, classes_train)\n", - "\n", - "\n", - "def test_classifier(\n", - " classifier, p_dist_train, p_dist_test, classes_test, verbose=False\n", - "):\n", - " \"\"\"\n", - " Test a trained classifier `classifier` from the probability\n", - " distributions of the train and test data sets `p_dist_train`\n", - " and `p_dist_test` respectively, and from the classes of the\n", - " test set `classes_test`\n", - " \"\"\"\n", - " X = kernel_matrix(p_dist_train, p_dist_test)\n", - "\n", - " predicted_classes = classifier.predict(X)\n", - "\n", - " scores = [\n", - " accuracy_score(classes_test, predicted_classes),\n", - " f1_score(classes_test, predicted_classes, average=\"weighted\"),\n", - " precision_score(\n", - " classes_test,\n", - " predicted_classes,\n", - " average=\"weighted\",\n", - " zero_division=0,\n", - " ),\n", - " recall_score(classes_test, predicted_classes, average=\"weighted\"),\n", - " ]\n", - "\n", - " if verbose:\n", - " for st, s in zip(scores_types, scores):\n", - " print(f\"{st} : {s:6.3}\")\n", - "\n", - " return scores\n", - "\n", - "\n", - "def train_and_test_classifier(\n", - " p_dist_train, classes_train, p_dist_test, classes_test, verbose=False\n", - "):\n", - " \"\"\"\n", - " Train and test a classifier from test and\n", - " train probability distributions and classes\n", - " \"\"\"\n", - " classifier = trained_classifier_pdist(p_dist_train, classes_train)\n", - "\n", - " return classifier, test_classifier(\n", - " classifier, p_dist_train, p_dist_test, classes_test, verbose=verbose\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "d759d39c", - "metadata": {}, - "source": [ - "Given a new dataset, one first computes the kernel matrix between the new graphs and the old ones :" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6afe8d74", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy : 0.92\n", - "f1 : 0.919\n", - "Precision : 0.931\n", - "Recall : 0.92\n" - ] - } - ], - "source": [ - "# Create a random training set\n", - "graphs_train, classes_train, p_dist_train = create_random_graphs()\n", - "# Create a random test set\n", - "graphs_test, classes_test, p_dist_test = create_random_graphs(n_graphs=50)\n", - "\n", - "# Compute the score of the classifier\n", - "classifier, scores = train_and_test_classifier(\n", - " p_dist_train, classes_train, p_dist_test, classes_test, verbose=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5c30755d", - "metadata": {}, - "source": [ - "## Application on a benchmark dataset\n", - "\n", - "### Load the dataset\n", - "We now load a known benchmark dataset and apply our method to it, using Pulser and its emulator to study it on a realistic device.\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e543bacc", - "metadata": {}, - "outputs": [], - "source": [ - "prefix = \"./Fingerprint/Fingerprint_\"\n", - "\n", - "graphs = {}\n", - "node_to_graph = {}\n", - "class_count = {}\n", - "\n", - "label_file = prefix + \"graph_labels\" + \".txt\"\n", - "with open(label_file) as f:\n", - " lines = f.readlines()\n", - " for i, line in enumerate(lines):\n", - " labl = int(line)\n", - " graphs[i + 1] = nx.Graph(label=labl)\n", - " if labl in class_count.keys():\n", - " class_count[labl] += 1\n", - " else:\n", - " class_count[labl] = 1\n", - "\n", - "\n", - "node_to_graph_file = prefix + \"graph_indicator\" + \".txt\"\n", - "with open(node_to_graph_file) as f:\n", - " lines = f.readlines()\n", - " for i, line in enumerate(lines):\n", - " gi = int(line)\n", - " node_to_graph[i + 1] = gi\n", - " graphs[gi].add_node(i + 1)\n", - "\n", - "adjacency_file = prefix + \"A\" + \".txt\"\n", - "with open(adjacency_file) as f:\n", - " lines = f.readlines()\n", - " for line in lines:\n", - " Ind = line.split(\",\")\n", - " i = int(Ind[0])\n", - " j = int(Ind[1])\n", - " gi = node_to_graph[i]\n", - " graphs[gi].add_edge(i, j)\n", - "\n", - "coordinates_file = prefix + \"node_attributes\" + \".txt\"\n", - "with open(coordinates_file) as f:\n", - " lines = f.readlines()\n", - " for i, line in enumerate(lines):\n", - " Ind = line.split(\",\")\n", - " x = float(Ind[0])\n", - " y = float(Ind[1])\n", - " gi = node_to_graph[i + 1]\n", - " nx.set_node_attributes(graphs[gi], {i + 1: (x, y)}, \"coords\")" - ] - }, - { - "cell_type": "markdown", - "id": "f031fc1f", - "metadata": {}, - "source": [ - "### Preprocess dataset\n", - "The dataset is preprocessed in the following way :\n", - "\n", - "1) First, only graphs with at leat 5 nodes are kepts\n", - "\n", - "2) Secondly, only classes with enough representatives are kept. Therefore, any class that contains less than 10 times fewer representatives than the largest class are disregarded" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6a16c2da", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "After preprocessing, the dataset now contains 897 \n", - "graphs of at least 5 and at most 12 nodes, distributed \n", - "across the different classes in the following way {0: 345, 4: 176, 5: 335, 6: 41}\n", - "\n" - ] - } - ], - "source": [ - "# Minimum and maximum number of nodes in a graph\n", - "Nmin = 5\n", - "Nmax = 12\n", - "\n", - "# Number of classes in the dataset\n", - "number_of_classes = len(class_count.keys())\n", - "\n", - "# Tally the number of graphs in each class\n", - "count = {clas: 0 for clas in class_count.keys()}\n", - "for g in graphs.values():\n", - " if Nmin <= g.number_of_nodes() <= Nmax:\n", - " count[g.graph[\"label\"]] += 1\n", - "\n", - "# Number of graphs in the most represented class\n", - "size_of_largest_class = max(count.values())\n", - "# Include only classes with at least 10% of the size of the largest one\n", - "include_classes = {clas: False for clas in class_count.keys()}\n", - "for clas, prop in count.items():\n", - " if prop > 0.1 * size_of_largest_class:\n", - " include_classes[clas] = True\n", - "\n", - "\n", - "data_preprocessed = []\n", - "for g in graphs.values():\n", - " labl = g.graph[\"label\"]\n", - " if Nmin <= g.number_of_nodes() <= Nmax and include_classes[labl]:\n", - " mapping = {l: i for i, l in enumerate(g.nodes())}\n", - " g_shift = nx.relabel_nodes(g, mapping)\n", - " data_preprocessed.append(g_shift)\n", - "\n", - "# size of the dataset\n", - "n_graphs = len(data_preprocessed)\n", - "\n", - "\n", - "included_classes = {}\n", - "for clas, icount in count.items():\n", - " if include_classes[clas]:\n", - " included_classes[clas] = icount\n", - "\n", - "print(\n", - " f\"After preprocessing, the dataset now contains {len(data_preprocessed)} \\n\"\n", - " + f\"graphs of at least {Nmin} and at most {Nmax} nodes, distributed \\n\"\n", - " + f\"across the different classes in the following way {included_classes}\\n\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b3c46389", - "metadata": {}, - "source": [ - "In order to speed up the computations in this tutorial, we will artificialy reduce the number of classes to two, disregarding the others." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fc36d32b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "After preprocessing, the dataset now contains 400 graphs of at least 5 and at most 12 nodes, distributed across 2 different classes in the following way {0: 246, 4: 154}\n" - ] - } - ], - "source": [ - "# We here only sample 400 graphs\n", - "dataset_targetsize = 400\n", - "\n", - "kept_classes = {}\n", - "for cls in list(included_classes.keys())[0:2]:\n", - " kept_classes[cls] = 0\n", - "\n", - "\n", - "data_reduced = []\n", - "for g in data_preprocessed:\n", - " if len(data_reduced) < dataset_targetsize:\n", - " cls = g.graph[\"label\"]\n", - " if cls in kept_classes.keys():\n", - " kept_classes[cls] += 1\n", - " data_reduced.append(g)\n", - "\n", - "# size of the dataset\n", - "n_graphs = len(data_reduced)\n", - "\n", - "print(\n", - " f\"After preprocessing, the dataset now contains {len(data_reduced)} \"\n", - " + f\"graphs of at least {Nmin} and at most {Nmax} nodes, distributed \"\n", - " + f\"across {len(kept_classes)} different classes in the following way \"\n", - " + f\"{kept_classes}\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8c745d4b", - "metadata": {}, - "source": [ - "### Map graphs onto machine registers\n", - "For a given graph $\\mathcal{G}=(\\mathcal{V},\\mathcal{E})$, we first need to find a proper set of coordinates for the atoms, so that their interaction Hamiltonian encodes the topology of $\\mathcal{G}$. \n", - "\n", - "Graphs as provided in the Fingerprint library are not suited to be represented on the quantum hardware.\n", - "The hardware has constraints on the maximum extent of the graph and the minimum distance between two nodes.\n", - "Moreover, the connectivity between two nodes should be related to the distance between them.\n", - "For this reason the graphs are processed using the Fruchterman-Reingold algorithm, and then rescaled in such a way as to occupy as much space as possible on the device. \n", - "To this end, we need to find a proper register that satisfies the constraints of the device :\n", - "\n", - "1) Not too large (i.e. whose diameter is smaller that twice the maximal distance to the center)\n", - "\n", - "2) Not too dense (i.e. where no pair of atoms are closer than the minimal distance between two atoms)\n", - "\n", - "3) Well defined bonds (i.e. each bond of the graph correspond to atoms that are within each other's Rydberg blockade radius, for a value of the amplitude $\\Omega<\\Omega_{max}$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "101b61e5", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.optimize import minimize\n", - "from scipy.spatial.distance import pdist\n", - "from scipy.optimize import NonlinearConstraint\n", - "\n", - "\n", - "def correct_coordinates(g):\n", - " \"\"\"\n", - " Corrects the coordinates of the nodes so that the\n", - " atoms fit the hardware constraints.\n", - " \"\"\"\n", - " n = g.number_of_nodes()\n", - "\n", - " # Coordinates given in the dataset\n", - " r_list = np.array([g.nodes[node][\"coords\"] for node in g.nodes()])\n", - " r_list += -np.mean(r_list, axis=0)\n", - " scale = np.max([np.sqrt(r.dot(r)) for r in r_list])\n", - "\n", - " x0 = r_list.reshape(2 * n)\n", - "\n", - " # Ensures the atoms are within range of the device\n", - " xmax = device.max_radial_distance / np.sqrt(2)\n", - " bounds = [(-xmax, xmax)] * (2 * n)\n", - " x0 *= xmax / scale\n", - "\n", - " # Encode the constraint of a minimal distance bewteen atoms\n", - " def min_dist(params):\n", - " return np.min(pdist(params.reshape(n, 2)))\n", - "\n", - " dmin = 1.1 * device.min_atom_distance\n", - " nlc = NonlinearConstraint(min_dist, dmin, np.inf)\n", - "\n", - " def cost_function(params):\n", - " return 1\n", - "\n", - " res = minimize(\n", - " cost_function, x0=x0, bounds=bounds, constraints=nlc, method=\"SLSQP\"\n", - " )\n", - " x = res.x\n", - " rmax = device.max_radial_distance\n", - " scale_diameter = 0.95 * rmax / np.max(pdist(x.reshape(n, 2)))\n", - " x *= max(scale_diameter, 1.0)\n", - " r_list = x.reshape(n, 2)\n", - " r_list += -np.mean(r_list, axis=0)\n", - "\n", - " for node, r in zip(g.nodes(), r_list):\n", - " g.nodes[node][\"coords\"] = r\n", - "\n", - "\n", - "def max_edge_length(g):\n", - " \"\"\"\n", - " Computes the maximal distance between nodes connected by an edge\n", - " of the graph\n", - " \"\"\"\n", - " n = g.number_of_nodes()\n", - " edges = np.array(\n", - " [\n", - " 1 if (i, j) in g.edges() else 0\n", - " for i in range(n)\n", - " for j in range(i + 1, n)\n", - " ]\n", - " )\n", - " r_list = np.array([g.nodes[node][\"coords\"] for node in g.nodes()])\n", - "\n", - " distances = pdist(r_list)\n", - " max_length = np.max(edges * distances)\n", - "\n", - " return max_length" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "74880999", - "metadata": {}, - "outputs": [], - "source": [ - "from pulser import Register\n", - "from pulser.devices import Chadoq2\n", - "\n", - "device = Chadoq2\n", - "r_max = device.max_radial_distance\n", - "d_min = device.min_atom_distance\n", - "\n", - "omega_max = device.channels[\"rydberg_global\"].max_amp\n", - "min_bond_length = device.rabi_from_blockade(omega_max)\n", - "\n", - "\n", - "def reg_from_data(data_reduced):\n", - " # The list of registers for each graph\n", - " reg_list = []\n", - "\n", - " # The list of Rabi frequencies setting the Rydberg\n", - " # blockade radius to the maximal edge distance of each graph\n", - " rabi_list = []\n", - "\n", - " # List of list of edges\n", - " edges_list = []\n", - "\n", - " # List of class of each graph\n", - " label_list = []\n", - " for g in data_reduced:\n", - "\n", - " label_list.append(g.graph[\"label\"])\n", - "\n", - " correct_coordinates(g)\n", - " graph_dict = {i: g.nodes[i][\"coords\"] for i in g.nodes()}\n", - "\n", - " edges_list.append(g.edges)\n", - "\n", - " # Find the blockade radius and corresponding Rabi frequency\n", - " blockade_radius = max_edge_length(g)\n", - " rabi = min(Chadoq2.rabi_from_blockade(blockade_radius), omega_max)\n", - " rabi_list.append(rabi)\n", - "\n", - " # Create the register\n", - " reg = Register(graph_dict)\n", - " reg_list.append(reg)\n", - "\n", - " return reg_list, rabi_list, edges_list, label_list\n", - "\n", - "\n", - "reg_list, rabi_list, edges_list, label_list = reg_from_data(data_reduced)" - ] - }, - { - "attachments": { - "Ramsey.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "7e7bc165", - "metadata": {}, - "source": [ - "### Optimized preparation of the equal superposition of Ising states\n", - "In order to reduce the number of parameters to train in this tutorial, we first determine the optimal constant detuning pulse that prepares the system in an equal superposition of all Ising states $\\left|\\psi_s\\right\\rangle = \\bigotimes_{i\\in\\mathcal{V}} \\frac{1}{\\sqrt{2}}\\left(\\left|0\\right\\rangle+\\left|1\\right\\rangle\\right)$.\n", - "\n", - "In the absence of interactions, this is obtained from the empty state via a Ramsey pulse with Hamiltonian $\\hat{\\mathcal{H}}_1 = \\frac{\\Omega}{2}\\sum_{i\\in\\mathcal{V}} \\hat{\\sigma}^y_i$ for a duration $t= \\pi/(2\\Omega)$. \n", - "\n", - "
\n", - "\"Optimal\n", - "
\n", - "\n", - "As illustrated above, without interaction (red curve) the overlaps reaches a maximum of 1 at $t= \\pi/(2\\Omega)$. \n", - "In the presence of interactions (faint line), the maximal overlap is reached at the same time for all graphs, but the value of this overlap is slightly reduced, and the peak is narrower for most graphs. The solid line represents the average of the overlap over all sampled graphs." - ] - }, - { - "cell_type": "markdown", - "id": "0d4e0ca9", - "metadata": {}, - "source": [ - "### Single parameter Pulse\n", - "Let us now implement the Quantum Evolution Kernel on Pulser.\n", - "As an illustration we will here consider two layers, so that the final state is\n", - "\n", - "$$\n", - "\\left|\\psi_f(\\vartheta)\\right\\rangle = \\mathbf{\\text{e}}^{{\\rm i} \\hat{\\mathcal{H}}_1 t_2}\n", - " \\mathbf{\\text{e}}^{-{\\rm i} \\tau_1\\hat{\\mathcal{H}}_\\mathcal{G}}\\mathbf{\\text{e}}^{-{\\rm i} \\hat{\\mathcal{H}}_1 t_1}\n", - " \\mathbf{\\text{e}}^{-{\\rm i} \\tau_0\\hat{\\mathcal{H}}_\\mathcal{G}}\n", - " \\mathbf{\\text{e}}^{-{\\rm i} \\hat{\\mathcal{H}}_1 t_0}\\left|\\psi_0\\right\\rangle,\n", - "$$\n", - "\n", - "where $\\hat{\\mathcal{H}}_\\mathcal{G} = \\sum_{(i,j)\\in\\mathcal{E}} (C_6 /r_{ij}^{6})\\hat{n}_i\\hat{n}_j$ and $\\hat{\\mathcal{H}}_1(\\Omega) = \\frac{\\Omega}{2}\\sum_{i\\in\\mathcal{V}} \\hat{\\sigma}^y_i$.\n", - "\n", - "In practice, $\\hat{\\mathcal{H}}_\\mathcal{G}$ is never turned off, so that the *true* pulse Hamiltonian is $\\hat{\\mathcal{H}}_1(\\Omega)+\\hat{\\mathcal{H}}_\\mathcal{G}$.\n", - "Furthermore, in order to explicitely distinguish edges from other pairs of atoms, we include a finite amplitude $\\Omega_g$ during the graph Hamiltonian evolution, so that the effective graph Hamiltonian is $\\hat{\\mathcal{H}}_1(\\Omega_g)+\\hat{\\mathcal{H}}_\\mathcal{G}$\n", - "\n", - "At the end of the pulse the Ising energy $\\hat{\\mathcal{O}}=\\sum_{(i,j)\\in\\mathcal{E}}\\hat{n}_i\\hat{n}_j$ is measured.\n", - "\n", - "For the sake of brevity, we here set $t_0 = t_2 = t = \\pi/(2\\Omega)$, where $\\Omega$ is set to the highest possible value compatible with the device (in particular so that $t\\geq 16$ ns), $\\tau_0 = \\tau_1 = \\tau$ and set the total time $T = t_0+\\tau_0+t_1+\\tau_1+t_2$ to a constant (for instance $T = 512$ ns). This way, only $t_1$ needs to be optimized." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6ec15d07", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pulser import Pulse, Sequence, Simulation\n", - "\n", - "\n", - "def pulse_seqence(\n", - " reg,\n", - " t_1=100,\n", - " omega=omega_max, # amplitude of the initial and final pulses\n", - " omega_g=0, # amplitude in the \"free evolution\" parts\n", - " total_time=512,\n", - "): # total duration of the pulse\n", - " seq = Sequence(reg, device)\n", - " seq.declare_channel(\"Channel 0\", \"rydberg_global\")\n", - "\n", - " # making sure that the value of omega does not exceed the\n", - " # maximal value, and that it doesn't lead to a pulse\n", - " # duration that is too short\n", - " omega = min([omega, 1000 * np.pi / 2, omega_max])\n", - "\n", - " # Set the initial and final pulse times to the optimal value\n", - " # be careful about the units : Omega(rad/μs) -> t (ns)\n", - " t = 1000 * np.pi / (2 * omega)\n", - " # Set the total_time\n", - " tau = (total_time - 2 * t - t_1) / 2\n", - " # No detuning needed here\n", - " delta = 0\n", - " # We want the pulse to be along sigma_y\n", - " phi = np.pi / 2\n", - "\n", - " initial_pulse = Pulse.ConstantPulse(t, omega, delta, phase=phi)\n", - " if total_time > t_1 + 2 * t:\n", - " Hg_pulse = Pulse.ConstantPulse(tau, omega_g, delta, phase=phi)\n", - " if t_1 > 0:\n", - " middle_pulse = Pulse.ConstantPulse(t_1, omega, delta, phase=phi)\n", - " final_pulse = Pulse.ConstantPulse(t, omega, delta, phase=phi)\n", - "\n", - " seq.add(initial_pulse, \"Channel 0\")\n", - " if total_time > t_1 + 2 * t:\n", - " seq.add(Hg_pulse, \"Channel 0\")\n", - " if t_1 > 0:\n", - " seq.add(middle_pulse, \"Channel 0\")\n", - " if total_time > t_1 + 2 * t:\n", - " seq.add(Hg_pulse, \"Channel 0\")\n", - " seq.add(final_pulse, \"Channel 0\")\n", - "\n", - " seq.measure(basis=\"ground-rydberg\")\n", - "\n", - " return seq\n", - "\n", - "\n", - "# Illustrate the pulse on a register containing a single atom\n", - "reg = Register.from_coordinates([(0, 0)])\n", - "pulse_seqence(reg, t_1=160).draw()" - ] - }, - { - "cell_type": "markdown", - "id": "6e5be79a", - "metadata": {}, - "source": [ - "### Computing the probability distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "df676daf", - "metadata": {}, - "outputs": [], - "source": [ - "from tqdm.auto import tqdm\n", - "\n", - "\n", - "def proba_distributions(\n", - " t_1=100, # duration of the central pulse\n", - " omega=omega_max, # amplitude of the pulses\n", - " omega_g_factor=1, # set to 1 if the Amplitude is non\n", - " # zero during the \"free evolution\"\n", - " total_time=512, # total duration of the pulse\n", - " Nsamples=1000,\n", - " indices=list(range(n_graphs)),\n", - "): # graphs to be used\n", - " \"\"\"\n", - " Compute the probability distributions for a given pulse\n", - " for all graphs in `indices`\n", - " \"\"\"\n", - "\n", - " bins = np.linspace(0, Nmax * Nmax, Nmax * Nmax + 1)\n", - " histograms = []\n", - " for i in tqdm(indices):\n", - " reg, rabi, edges = reg_list[i], rabi_list[i], edges_list[i]\n", - " seq = pulse_seqence(\n", - " reg,\n", - " t_1=t_1,\n", - " omega=omega,\n", - " omega_g=omega_g_factor * rabi,\n", - " total_time=total_time,\n", - " )\n", - "\n", - " # Simulate and sample\n", - " simul = Simulation(seq, evaluation_times=0.5, sampling_rate=0.1)\n", - " results = simul.run()\n", - " sampling = results.sample_final_state(N_samples=Nsamples)\n", - "\n", - " # Create a list with the measurements of the ising energy\n", - " ie_meas = []\n", - " ie_weights = []\n", - " for bitstring, num in sampling.items():\n", - " ie_meas.append(compute_ising_energy(bitstring, edges))\n", - " ie_weights.append(num)\n", - "\n", - " # Create histogram of the measurements and append to list\n", - " ncount, b = np.histogram(\n", - " ie_meas, bins=bins, density=True, weights=ie_weights\n", - " )\n", - " histograms.append(ncount)\n", - " return histograms\n", - "\n", - "\n", - "def compute_ising_energy(outcome, edges):\n", - " \"\"\"\n", - " Computes the Ising energy (i.e. the observable\n", - " used by the kernel) from a measure bitstgring/state\n", - " \"\"\"\n", - " # split outcome string in a list\n", - " outcome_ls = [char for char in outcome]\n", - "\n", - " energy = 0\n", - "\n", - " for edge in edges:\n", - " i = int(edge[0])\n", - " j = int(edge[1])\n", - " if i < j:\n", - " energy += int(outcome_ls[i]) * int(outcome_ls[j])\n", - "\n", - " return energy" - ] - }, - { - "cell_type": "markdown", - "id": "2939040f", - "metadata": {}, - "source": [ - "Let us first ignore the middle pulse and set $t_1=0$. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4e860a36", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training in progress...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 150/150 [00:10<00:00, 14.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testing in progress...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 50/50 [00:02<00:00, 20.60it/s]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_graphs = len(data_reduced)\n", - "\n", - "# sample 150 graphs and train on 100 of them\n", - "n_train = 150\n", - "n_test = 50\n", - "\n", - "# randomize graph order\n", - "indices_all = list(range(n_graphs))\n", - "indices_train = indices_all[0:n_train]\n", - "indices_test = indices_all[n_train : n_train + n_test]\n", - "\n", - "# Labels of the sampled graphs\n", - "train_classes = np.array([label_list[i] for i in indices_train])\n", - "test_classes = np.array([label_list[i] for i in indices_test])\n", - "\n", - "\n", - "# Probability distributions obtained after the pulse\n", - "print(\"Training in progress...\")\n", - "probas_train = proba_distributions(t_1=0, indices=indices_train)\n", - "print(\"Testing in progress...\")\n", - "probas_test = proba_distributions(t_1=0, indices=indices_test)\n", - "\n", - "# Resulting kernel matrix\n", - "Kmat = kernel_matrix(probas_train, probas_train)\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 8))\n", - "im = ax.imshow(Kmat, cmap=\"OrRd\")\n", - "cbar = plt.colorbar(im, extend=\"max\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9091dfc3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy : 0.74\n", - "f1 : 0.661\n", - "Precision : 0.81\n", - "Recall : 0.74\n" - ] - } - ], - "source": [ - "classifier, scores = train_and_test_classifier(\n", - " probas_train, train_classes, probas_test, test_classes, verbose=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "20fad801", - "metadata": {}, - "source": [ - "### Optimization of the pulse sequence\n", - "We now proceed to the optimization of the pulse sequence. To this end, we evaluate the score of the classification (here, its accuracy) for various durations of the central pulse and choose the best one.\n", - "For a fixed duration $t_1$ of the central pulse, the procedure goes as follows:\n", - "\n", - "1) The data is divided randomly in N blocks.\n", - "\n", - "2) Use N-1 blocks to train the SVM, and the last block to test the predictions.\n", - "\n", - "3) Repeat the procedure M times and average the score.\n", - "\n", - "At this point, select the optimal duration of the middle pulse by performing a greedy search among the allowed values." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c6c76b5b", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import time\n", - "\n", - "N = 4\n", - "M = 1\n", - "\n", - "\n", - "def score_function(\n", - " t_1=100,\n", - " total_time=512,\n", - " repetitions=M,\n", - " nblocks=N,\n", - " label_list=label_list,\n", - " indices=list(range(n_graphs)),\n", - "): # list of graphs included\n", - " \"\"\"\n", - " Computes the accuracy, f1, precision and recall\n", - " \"\"\"\n", - "\n", - " accuracy = []\n", - " f1 = []\n", - " precision = []\n", - " recall = []\n", - "\n", - " n_g = len(indices)\n", - "\n", - " block_size = n_g // nblocks\n", - "\n", - " # Compute the probability distributions of all\n", - " # graphs in the data set\n", - " start_time = time.time()\n", - " probas_all = proba_distributions(\n", - " t_1=t_1, total_time=total_time, Nsamples=1000, indices=indices\n", - " )\n", - "\n", - " print(\n", - " f\" Probability lists were computed in {time.time() - start_time:4.1f} seconds\"\n", - " )\n", - "\n", - " classes = np.array([label_list[i] for i in indices])\n", - " start_time = time.time()\n", - "\n", - " for r in range(repetitions):\n", - " # divide data in training set and test set\n", - " indices_all = np.array(list(range(n_g)))\n", - " np.random.shuffle(indices_all)\n", - "\n", - " mean_scores = np.zeros((4,))\n", - " for iblock in range(nblocks):\n", - " indices_test = [\n", - " indices_all[(iblock * block_size + i) % n_g]\n", - " for i in range(block_size)\n", - " ]\n", - " indices_train = [\n", - " indices_all[((iblock + 1) * block_size + i) % n_g]\n", - " for i in range(n_g - block_size)\n", - " ]\n", - "\n", - " train_classes = np.array(\n", - " [label_list[indices[i]] for i in indices_train]\n", - " )\n", - " test_classes = np.array(\n", - " [label_list[indices[i]] for i in indices_test]\n", - " )\n", - "\n", - " # create probability histogram for train and test data\n", - " probas_train = np.array([probas_all[i] for i in indices_train])\n", - " probas_test = np.array([probas_all[i] for i in indices_test])\n", - "\n", - " classifier, scores = train_and_test_classifier(\n", - " probas_train,\n", - " train_classes,\n", - " probas_test,\n", - " test_classes,\n", - " verbose=False,\n", - " )\n", - " mean_scores += scores\n", - "\n", - " # calculate score metrics\n", - " accuracy.append(mean_scores[0] / nblocks)\n", - " f1.append(mean_scores[1] / nblocks)\n", - " precision.append(mean_scores[2] / nblocks)\n", - " recall.append(mean_scores[3] / nblocks)\n", - "\n", - " A = (np.mean(accuracy), np.std(accuracy))\n", - " B = (np.mean(f1), np.std(f1))\n", - " C = (np.mean(precision), np.std(precision))\n", - " D = (np.mean(recall), np.std(recall))\n", - "\n", - " print(\n", - " f\" Kernel scores computed in {time.time() - start_time:4.1f} seconds\"\n", - " )\n", - "\n", - " return A, B, C, D" - ] - }, - { - "cell_type": "markdown", - "id": "0dee506e", - "metadata": {}, - "source": [ - "We now look for the best pulse by varying the duration of the middle pulse. The total time is limited to a small value, and the data set is reduced to $n_g=100$ graphs for the sake of time in this tutorial.\n", - "\n", - "Furthermore, the score is evaluated only on $M=2$ random splits, with a $N=4$-fold cross validation.\n", - "\n", - "In this case, the computation takes a couple of minutes. For more accurate estimates, those numbers can be increased." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b69d8710", - "metadata": {}, - "outputs": [], - "source": [ - "def scan_scores(\n", - " M=2,\n", - " N=4,\n", - " indices=list(range(n_graphs)),\n", - " durations=[512],\n", - "):\n", - "\n", - " scores_dict = {}\n", - "\n", - " for s in scores_types:\n", - " scores_dict[s] = []\n", - "\n", - " print(\" ------------------------------------------------\")\n", - " print(f\"| Max. duration of the middle pulse: {durations[-1]:4d} ns |\")\n", - " print(f\"| Total duration of the pulse: {total_time:4d} ns |\")\n", - " print(\n", - " f\"| Using {N:2d} blocks of {len(indices)//N:4d} graphs each |\"\n", - " )\n", - " print(\" ------------------------------------------------\")\n", - "\n", - " for t_1 in durations:\n", - " print(f\" Duration of the middle pulse: {t_1:4d} ns\")\n", - " score_inst = score_function(\n", - " t_1=t_1,\n", - " total_time=total_time,\n", - " repetitions=M,\n", - " nblocks=N,\n", - " indices=indices_in,\n", - " )\n", - "\n", - " for sc, st in zip(score_inst, scores_types):\n", - " scores_dict[st].append(sc)\n", - " print(f\" > {st}: {sc[0]:6.3} +/- {sc[1]:6.3}\")\n", - " print()\n", - " return scores_dict\n", - "\n", - "\n", - "def plot_scores(scores_dict):\n", - " fig, ax = plt.subplots(figsize=(9, 5))\n", - " for k in scores_dict.keys():\n", - " ax.errorbar(\n", - " list(durations),\n", - " [s[0] for s in scores_dict[k]],\n", - " yerr=[s[1] for s in scores_dict[k]],\n", - " label=k,\n", - " )\n", - " ax.set_title(\"Score vs duration $t_1$ of the central pulse\", fontsize=16)\n", - " ax.set_ylabel(r\"Score\", fontsize=16)\n", - " ax.set_xlabel(r\"$t_1$ (ns)\", fontsize=16)\n", - " ax.legend()\n", - "\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a88a7a1b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ------------------------------------------------\n", - "| Max. duration of the middle pulse: 192 ns |\n", - "| Total duration of the pulse: 456 ns |\n", - "| Using 8 blocks of 25 graphs each |\n", - " ------------------------------------------------\n", - " Duration of the middle pulse: 0 ns\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:12<00:00, 15.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Probability lists were computed in 13.0 seconds\n", - " Kernel scores computed in 153.0 seconds\n", - " > Accuracy : 0.727 +/- 0.00901\n", - " > f1 : 0.73 +/- 0.00858\n", - " > Precision: 0.765 +/- 0.0068\n", - " > Recall : 0.727 +/- 0.00901\n", - "\n", - " Duration of the middle pulse: 32 ns\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:13<00:00, 14.77it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Probability lists were computed in 13.5 seconds\n", - " Kernel scores computed in 159.0 seconds\n", - " > Accuracy : 0.74 +/- 0.00612\n", - " > f1 : 0.742 +/- 0.00667\n", - " > Precision: 0.78 +/- 0.00522\n", - " > Recall : 0.74 +/- 0.00612\n", - "\n", - " Duration of the middle pulse: 64 ns\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:14<00:00, 13.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Probability lists were computed in 14.4 seconds\n", - " Kernel scores computed in 162.8 seconds\n", - " > Accuracy : 0.672 +/- 0.0025\n", - " > f1 : 0.673 +/- 0.00165\n", - " > Precision: 0.692 +/- 0.0097\n", - " > Recall : 0.672 +/- 0.0025\n", - "\n", - " Duration of the middle pulse: 96 ns\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:14<00:00, 13.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Probability lists were computed in 14.4 seconds\n", - " Kernel scores computed in 158.8 seconds\n", - " > Accuracy : 0.674 +/- 0.0074\n", - " > f1 : 0.669 +/- 0.00745\n", - " > Precision: 0.701 +/- 0.0187\n", - " > Recall : 0.674 +/- 0.0074\n", - "\n", - " Duration of the middle pulse: 128 ns\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:13<00:00, 15.03it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Probability lists were computed in 13.3 seconds\n", - " Kernel scores computed in 149.7 seconds\n", - " > Accuracy : 0.619 +/- 0.0114\n", - " > f1 : 0.517 +/- 0.0204\n", - " > Precision: 0.608 +/- 0.0751\n", - " > Recall : 0.619 +/- 0.0114\n", - "\n", - " Duration of the middle pulse: 160 ns\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:13<00:00, 15.18it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Probability lists were computed in 13.2 seconds\n", - " Kernel scores computed in 146.9 seconds\n", - " > Accuracy : 0.652 +/- 0.00559\n", - " > f1 : 0.616 +/- 0.00573\n", - " > Precision: 0.664 +/- 0.0146\n", - " > Recall : 0.652 +/- 0.00559\n", - "\n", - " Duration of the middle pulse: 192 ns\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:12<00:00, 15.39it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Probability lists were computed in 13.0 seconds\n", - " Kernel scores computed in 145.2 seconds\n", - " > Accuracy : 0.68 +/- 0.00866\n", - " > f1 : 0.67 +/- 0.00774\n", - " > Precision: 0.693 +/- 0.00482\n", - " > Recall : 0.68 +/- 0.00866\n", - "\n" - ] - } - ], - "source": [ - "# Duration of the initial and final pulses\n", - "t_1 = 4 * round(1000 * np.pi / (4 * 2 * omega_max))\n", - "\n", - "# Total duration of the pulse\n", - "total_time = 2 * t_1 + 256\n", - "\n", - "# duration of the middle pulse\n", - "durations = range(0, total_time - 2 * round(t_1) - 32, 32)\n", - "\n", - "\n", - "M = 4\n", - "N = 8\n", - "\n", - "n_g = 200\n", - "indices_all = list(range(n_graphs))\n", - "# Select a random subset of all graphs\n", - "np.random.shuffle(indices_all)\n", - "indices_in = indices_all[0:n_g]\n", - "\n", - "scores_2layers = scan_scores(M=M, N=N, indices=indices_in, durations=durations)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "6c012ba0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - } - }, - "cell_type": "markdown", - "id": "6a1340ab", - "metadata": {}, - "source": [ - "The following plot was obtained shows the same result, but using $n_g=400$ graphs and splitting them $M=5$ times into $N=10$ blocks. It took $\\sim 5$ h to generate the data.\n", - "\n", - "
\n", - "\"opti_long.png\"\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c137acae", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 10484bd21678e130afc3a4f82ac0a5aa01f88a7f Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Fri, 25 Feb 2022 13:58:33 +0100 Subject: [PATCH 51/51] Bump version to v0.5.0 --- pulser/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pulser/_version.py b/pulser/_version.py index b10f99188..efb8f89bb 100644 --- a/pulser/_version.py +++ b/pulser/_version.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.4.2.dev" +__version__ = "0.5.0"