diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 00000000..94eae645 --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. 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b/_sources/algorithms.ipynb.txt @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Qlasskit implements high level representation of quantum algorithms that relies on black boxes functions and oracles.\n", + "\n", + "- Grover search\n", + "- Simon periodicity\n", + "- Deutsch Jozsa\n", + "- Bernstein Vazirani" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Abstraction tools\n", + "\n", + "All algorithms share the same base class `QAlgorithm` which inherits from `QCircuitWrapper`. The `QCircuitWrapper` is also father of `qlassf` functions, and offers useful methods to handle high level types.\n", + "\n", + "One of those tools is `decode_counts`, that translates the result dictionary from a quantum sampling in its binary form, to the high level representation of data types used in the qlasskit function; `decode_output` instead, translate a single bitstring." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from typing import Tuple\n", + "from qlasskit import qlassf, Qint, Qint4\n", + "\n", + "\n", + "@qlassf\n", + "def test_tools(a: Qint[4]) -> Qint[4]:\n", + " return a + 1\n", + "\n", + "\n", + "test_tools.decode_output(\"0100\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another method called `encode_input` is able to do the reverse job, transforming high level types to binary form." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0100'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_tools.encode_input(Qint4(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grover search\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "\n", + "@qlassf\n", + "def g_orac(a: Tuple[bool, bool]) -> bool:\n", + " return a[0] and not a[1]\n", + "\n", + "\n", + "q_algo = Grover(g_orac, True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Further examples can be found on the following notebooks:\n", + "\n", + "- [example_grover.ipynb]()\n", + "- [example_grover_subset.ipynb]()\n", + "- [example_grover_hash.ipynb]()\n", + "- [example_grover_sudoku.ipynb]()\n", + "- [example_grover_factors.ipynb]()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simon periodicity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Simon\n", + "\n", + "\n", + "@qlassf\n", + "def f(a: Qint[4]) -> Qint[4]:\n", + " return (a >> 3) + 1\n", + "\n", + "\n", + "q_algo = Simon(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Further examples can be found on the following notebooks:\n", + "\n", + "- [example_simon.ipynb]()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deutsch Jozsa" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import DeutschJozsa\n", + "\n", + "\n", + "@qlassf\n", + "def f(b: Qint[4]) -> bool:\n", + " return b < 16\n", + "\n", + "\n", + "q_algo = DeutschJozsa(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Further examples can be found on the following notebooks:\n", + "\n", + "- [example_deutsch_jozsa.ipynb]()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bernstein Vazirani" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import BernsteinVazirani, secret_oracle\n", + "\n", + "oracle = secret_oracle(4, 14)\n", + "\n", + "q_algo = BernsteinVazirani(oracle)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further examples can be found on the following notebooks:\n", + "\n", + "- [example_bernstein_vazirani.ipynb]()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/api.rst.txt b/_sources/api.rst.txt new file mode 100644 index 00000000..02673f0d --- /dev/null +++ b/_sources/api.rst.txt @@ -0,0 +1,22 @@ +API +=== + +.. autosummary:: + :toctree: generated + :recursive: + + qlasskit.qlassfun.qlassf + qlasskit.qlassfun.qlassfa + qlasskit.qlassfun.QlassF + qlasskit.algorithms.qalgorithm + qlasskit.algorithms.grover.Grover + qlasskit.algorithms.simon.Simon + qlasskit.algorithms.deutschjozsa.DeutschJozsa + qlasskit.qcircuit.qcircuit.QCircuit + qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper + qlasskit.qcircuit.gates + qlasskit.bqm.decode_samples + qlasskit.decompiler.Decompiler + qlasskit.types + + .. qlasskit.decompiler.circuit_boolean_optimizer \ No newline at end of file diff --git a/_sources/bqm.ipynb.txt b/_sources/bqm.ipynb.txt new file mode 100644 index 00000000..92893061 --- /dev/null +++ b/_sources/bqm.ipynb.txt @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Binary Quadratic Model, Qubo & Ising " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any qlassf function can be transformed to a binary quadratic model using the `to_bqm` function. In this example we create a generic function that factorize `num`, and we bind `num` with 15." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vars: 14 \n", + "Interactions: 41\n" + ] + } + ], + "source": [ + "from qlasskit import qlassf, Qint, Parameter\n", + "\n", + "\n", + "@qlassf\n", + "def test_factor_generic(num: Parameter[Qint[4]], a: Qint[3], b: Qint[3]) -> Qint[4]:\n", + " return num - (a * b)\n", + "\n", + "\n", + "test_factor = test_factor_generic.bind(num=15)\n", + "bqm = test_factor.to_bqm()\n", + "\n", + "print(\"Vars:\", bqm.num_variables, \"\\nInteractions:\", bqm.num_interactions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A qlassf function can also be exported as a QUBO model using `to_bqm('qubo')` or as an Ising model using `to_bqm('ising')`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Running on simulated sampler annealer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run a simulated sampler annealer to minimize this function; qlasskit offer a `decode_samples` helper function that translates sample result to the high level types of qlasskit." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 3, 'b': 5}\n" + ] + } + ], + "source": [ + "import neal\n", + "from qlasskit.bqm import decode_samples\n", + "\n", + "sa = neal.SimulatedAnnealingSampler()\n", + "sampleset = sa.sample(bqm, num_reads=10)\n", + "decoded_samples = decode_samples(test_factor, sampleset)\n", + "best_sample = min(decoded_samples, key=lambda x: x.energy)\n", + "print(best_sample.sample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is 5 and 3 as expected (since 5 times 3 is equal to 15)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running on DWave annealer\n", + "\n", + "If we have a valid DWave account, we can run our problem on a real quantum annealer as follow:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "SolverFailureError", + "evalue": "Problem not accepted because user has insufficient remaining solver access time in project DEV.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSolverFailureError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m sampler \u001b[38;5;241m=\u001b[39m EmbeddingComposite(DWaveSampler())\n\u001b[1;32m 4\u001b[0m sampleset \u001b[38;5;241m=\u001b[39m sampler\u001b[38;5;241m.\u001b[39msample(bqm, num_reads\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m decoded_samples \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_factor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msampleset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m best_sample \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(decoded_samples, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39menergy)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(best_sample\u001b[38;5;241m.\u001b[39msample)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/bqm.py:142\u001b[0m, in \u001b[0;36mdecode_samples\u001b[0;34m(qf, sampleset)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Get dimod sampleset and return an high level decoded solution\"\"\"\u001b[39;00m\n\u001b[1;32m 141\u001b[0m model \u001b[38;5;241m=\u001b[39m qf\u001b[38;5;241m.\u001b[39mto_bqm(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpq_model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 142\u001b[0m decoded \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_sampleset\u001b[49m\u001b[43m(\u001b[49m\u001b[43msampleset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 144\u001b[0m new_dec \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m el \u001b[38;5;129;01min\u001b[39;00m decoded:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1121\u001b[0m, in \u001b[0;36mSampleSet.record\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1105\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrecord\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\":obj:`numpy.recarray` containing the samples, energies, number of occurences, and other sample data.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \n\u001b[1;32m 1109\u001b[0m \u001b[38;5;124;03m Examples:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1119\u001b[0m \n\u001b[1;32m 1120\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_record\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485\u001b[0m, in \u001b[0;36mSampleSet.resolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1483\u001b[0m \u001b[38;5;66;03m# if it doesn't have the attribute then it is already resolved\u001b[39;00m\n\u001b[1;32m 1484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_future\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m-> 1485\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_result_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_future\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1486\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(samples\u001b[38;5;241m.\u001b[39mrecord, samples\u001b[38;5;241m.\u001b[39mvariables, samples\u001b[38;5;241m.\u001b[39minfo, samples\u001b[38;5;241m.\u001b[39mvartype)\n\u001b[1;32m 1487\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_future\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/composites/embedding.py:284\u001b[0m, in \u001b[0;36mEmbeddingComposite.sample..async_unembed\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21masync_unembed\u001b[39m(response):\n\u001b[1;32m 280\u001b[0m \u001b[38;5;66;03m# unembed the sampleset aysnchronously.\u001b[39;00m\n\u001b[1;32m 282\u001b[0m warninghandler\u001b[38;5;241m.\u001b[39mchain_break(response, embedding)\n\u001b[0;32m--> 284\u001b[0m sampleset \u001b[38;5;241m=\u001b[39m \u001b[43munembed_sampleset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_bqm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbqm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 285\u001b[0m \u001b[43m \u001b[49m\u001b[43mchain_break_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchain_break_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 286\u001b[0m \u001b[43m \u001b[49m\u001b[43mchain_break_fraction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchain_break_fraction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 287\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_embedding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_embedding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_embedding:\n\u001b[1;32m 290\u001b[0m sampleset\u001b[38;5;241m.\u001b[39minfo[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124membedding_context\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[1;32m 291\u001b[0m embedding_parameters\u001b[38;5;241m=\u001b[39membedding_parameters,\n\u001b[1;32m 292\u001b[0m chain_strength\u001b[38;5;241m=\u001b[39membedding\u001b[38;5;241m.\u001b[39mchain_strength)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/embedding/transforms.py:606\u001b[0m, in \u001b[0;36munembed_sampleset\u001b[0;34m(target_sampleset, embedding, source_bqm, chain_break_method, chain_break_fraction, return_embedding)\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 604\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgiven bqm does not match the embedding\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 606\u001b[0m record \u001b[38;5;241m=\u001b[39m \u001b[43mtarget_sampleset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecord\u001b[49m\n\u001b[1;32m 608\u001b[0m unembedded, idxs \u001b[38;5;241m=\u001b[39m chain_break_method(target_sampleset, chains)\n\u001b[1;32m 610\u001b[0m reserved \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124menergy\u001b[39m\u001b[38;5;124m'\u001b[39m}\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1121\u001b[0m, in \u001b[0;36mSampleSet.record\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1105\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrecord\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\":obj:`numpy.recarray` containing the samples, energies, number of occurences, and other sample data.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \n\u001b[1;32m 1109\u001b[0m \u001b[38;5;124;03m Examples:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1119\u001b[0m \n\u001b[1;32m 1120\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_record\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485\u001b[0m, in \u001b[0;36mSampleSet.resolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1483\u001b[0m \u001b[38;5;66;03m# if it doesn't have the attribute then it is already resolved\u001b[39;00m\n\u001b[1;32m 1484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_future\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m-> 1485\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_result_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_future\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1486\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(samples\u001b[38;5;241m.\u001b[39mrecord, samples\u001b[38;5;241m.\u001b[39mvariables, samples\u001b[38;5;241m.\u001b[39minfo, samples\u001b[38;5;241m.\u001b[39mvartype)\n\u001b[1;32m 1487\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_future\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:452\u001b[0m, in \u001b[0;36mDWaveSampler.sample.._hook\u001b[0;34m(computation)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SolverError, InvalidAPIResponseError) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 451\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfailover:\n\u001b[0;32m--> 452\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exc, SolverAuthenticationError):\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:439\u001b[0m, in \u001b[0;36mDWaveSampler.sample.._hook\u001b[0;34m(computation)\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sampleset\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcomputation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProblemUploadError, RequestTimeout, PollingTimeout) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfailover:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:429\u001b[0m, in \u001b[0;36mDWaveSampler.sample.._hook..resolve\u001b[0;34m(computation)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresolve\u001b[39m(computation):\n\u001b[1;32m 428\u001b[0m sampleset \u001b[38;5;241m=\u001b[39m computation\u001b[38;5;241m.\u001b[39msampleset\n\u001b[0;32m--> 429\u001b[0m \u001b[43msampleset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m warninghandler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 432\u001b[0m warninghandler\u001b[38;5;241m.\u001b[39mtoo_few_samples(sampleset)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485\u001b[0m, in \u001b[0;36mSampleSet.resolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1483\u001b[0m \u001b[38;5;66;03m# if it doesn't have the attribute then it is already resolved\u001b[39;00m\n\u001b[1;32m 1484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_future\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m-> 1485\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_result_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_future\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1486\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(samples\u001b[38;5;241m.\u001b[39mrecord, samples\u001b[38;5;241m.\u001b[39mvariables, samples\u001b[38;5;241m.\u001b[39minfo, samples\u001b[38;5;241m.\u001b[39mvartype)\n\u001b[1;32m 1487\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_future\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:823\u001b[0m, in \u001b[0;36mFuture.sampleset..\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m 818\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt construct SampleSet without dimod. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 820\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-install the library with \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbqm\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m support.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 822\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampleset \u001b[38;5;241m=\u001b[39m sampleset \u001b[38;5;241m=\u001b[39m dimod\u001b[38;5;241m.\u001b[39mSampleSet\u001b[38;5;241m.\u001b[39mfrom_future(\n\u001b[0;32m--> 823\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;28;01mlambda\u001b[39;00m f: \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_sampleset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 825\u001b[0m \u001b[38;5;66;03m# propagate id to sampleset as well\u001b[39;00m\n\u001b[1;32m 826\u001b[0m \u001b[38;5;66;03m# note: this requires dimod>=0.8.21 (before that version SampleSet\u001b[39;00m\n\u001b[1;32m 827\u001b[0m \u001b[38;5;66;03m# had slots set which prevented dynamic addition of attributes).\u001b[39;00m\n\u001b[1;32m 828\u001b[0m sampleset\u001b[38;5;241m.\u001b[39mwait_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwait_id\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:755\u001b[0m, in \u001b[0;36mFuture.wait_sampleset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 752\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking sampleset getter.\"\"\"\u001b[39;00m\n\u001b[1;32m 754\u001b[0m \u001b[38;5;66;03m# blocking result get\u001b[39;00m\n\u001b[0;32m--> 755\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 757\u001b[0m \u001b[38;5;66;03m# common problem info: id/label\u001b[39;00m\n\u001b[1;32m 758\u001b[0m problem_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(problem_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:893\u001b[0m, in \u001b[0;36mFuture._load_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[38;5;66;03m# Check for other error conditions\u001b[39;00m\n\u001b[1;32m 892\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 893\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 895\u001b[0m \u001b[38;5;66;03m# If someone else took care of this while we were waiting\u001b[39;00m\n\u001b[1;32m 896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/client/base.py:1309\u001b[0m, in \u001b[0;36mClient._handle_problem_status\u001b[0;34m(self, message, future)\u001b[0m\n\u001b[1;32m 1307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror_code\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m message \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror_msg\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m message:\n\u001b[1;32m 1308\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError response received: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, message)\n\u001b[0;32m-> 1309\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SolverFailureError(message[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror_msg\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 1311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m message:\n\u001b[1;32m 1312\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidAPIResponseError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m missing in problem description response\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mSolverFailureError\u001b[0m: Problem not accepted because user has insufficient remaining solver access time in project DEV." + ] + } + ], + "source": [ + "from dwave.system import DWaveSampler, EmbeddingComposite\n", + "\n", + "sampler = EmbeddingComposite(DWaveSampler())\n", + "sampleset = sampler.sample(bqm, num_reads=10)\n", + "decoded_samples = decode_samples(test_factor, sampleset)\n", + "best_sample = min(decoded_samples, key=lambda x: x.energy)\n", + "print(best_sample.sample)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/cli_tools.rst.txt b/_sources/cli_tools.rst.txt new file mode 100644 index 00000000..5996a012 --- /dev/null +++ b/_sources/cli_tools.rst.txt @@ -0,0 +1,62 @@ +CLI Tools +========= + +Qlasskit also offer two cli tools: +- py2bexp: translate python code to boolean expressions +- py2qasm: translate python code to quantum circuits in qasm format + + +py2bexp +------- + +Receives a python script (file or stdin) in input and outputs bool expressions. The python script should +contain at least one qlassf function. + +.. code-block:: + + usage: py2bexp [-h] [-i INPUT_FILE] [-e ENTRYPOINT] [-o OUTPUT] [-f {anf,cnf,dnf,nnf}] [-t {sympy,dimacs}] [-v] + + Convert qlassf functions in a Python script to boolean expressions. + + options: + -h, --help show this help message and exit + -i INPUT_FILE, --input-file INPUT_FILE + Input file (default: stdin) + -e ENTRYPOINT, --entrypoint ENTRYPOINT + Entrypoint function name + -o OUTPUT, --output OUTPUT + Output file (default: stdout) + -f {anf,cnf,dnf,nnf}, --form {anf,cnf,dnf,nnf} + Expression form (default: sympy) + -t {sympy,dimacs}, --format {sympy,dimacs} + Output format (default: sympy) + -v, --version show program's version number and exit + + + +py2qasm +------- + +Receives a python script (file or stdin) in input and outputs qasm code. The python script should +contain at least one qlassf function. + +.. code-block:: + + usage: py2qasm [-h] [-i INPUT_FILE] [-e ENTRYPOINT] [-o OUTPUT] [-c {internal,tweedledum,recompiler}] [-q {2.0,3.0}] + [-v] + + Convert qlassf functions in a Python script to qasm code expressions. + + options: + -h, --help show this help message and exit + -i INPUT_FILE, --input-file INPUT_FILE + Input file (default: stdin) + -e ENTRYPOINT, --entrypoint ENTRYPOINT + Entrypoint function name + -o OUTPUT, --output OUTPUT + Output file (default: stdout) + -c {internal,tweedledum,recompiler}, --compiler {internal,tweedledum,recompiler} + QASM compiler (default: internal) + -q {2.0,3.0}, --qasm-version {2.0,3.0} + QASM version (default: 3.0) + -v, --version show program's version number and exit \ No newline at end of file diff --git a/_sources/decompiler_and_optimizer.ipynb.txt b/_sources/decompiler_and_optimizer.ipynb.txt new file mode 100644 index 00000000..3fd0566a --- /dev/null +++ b/_sources/decompiler_and_optimizer.ipynb.txt @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Decompiler and Circuit Optimizer\n", + "\n", + "Qlasskit offers two useful tool for circuit analysis and optimization.\n", + "\n", + "- Decompiler: given a quantum circuit is able to detect section that can be represented as boolean expressions\n", + "- circuit_boolean_optimizer: a pipeline that given a quantum circuit, decompose it in boolean expressions form and optimize it using boolean algebra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first write a qlasskit function that perform an And between the elements of a Qlist; we use the `fastOptimizer` in order to obtain an unoptimized circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "QCircuit(7 gates, 6 qubits)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qlasskit import qlassfa, qlassf, boolopt, Qlist\n", + "from qlasskit.decompiler import Decompiler, circuit_boolean_optimizer\n", + "\n", + "\n", + "@qlassfa(bool_optimizer=boolopt.fastOptimizer)\n", + "def qf(a: Qlist[bool, 2]) -> bool:\n", + " s = True\n", + " for i in a:\n", + " s = s and i\n", + " return s\n", + "\n", + "\n", + "qf.circuit()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the circuit, this is not the best solution." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qf.export().draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the decompiler we are able to translate a quantum circuit to its boolean representation (if applicable):" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecompiledResults[\n", + "\t(\n", + "\t\t(0, 7)\n", + "\t\t(X, [2], None), (CCX, [0, 2, 3], None), (CCX, [1, 3, 4], None), (CX, [4, 5], None), (CCX, [1, 3, 4], None), (CCX, [0, 2, 3], None), (X, [2], None)\n", + "\t\t(q5, q4 ^ q5 ^ (q1 & (q3 ^ (q0 & ~q2))))\n", + "\t)\n", + "]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dc = Decompiler().decompile(qf.circuit())\n", + "dc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `circuit_boolean_optimizer` allows us to perform boolean optimizations in a quantum circuit; from the previous unoptimized example, we get the following optimized circuit:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = circuit_boolean_optimizer(qf.circuit(), preserve=[0, 1])\n", + "qc.export().draw(\"mpl\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_bernstein_vazirani.ipynb.txt b/_sources/example_bernstein_vazirani.ipynb.txt new file mode 100644 index 00000000..1cf434a9 --- /dev/null +++ b/_sources/example_bernstein_vazirani.ipynb.txt @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bernstein Vazirani algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first create an oracle embedding the secret number 14; we can do this in two equivalent ways:\n", + "- using `secret_oracle()` function\n", + "- writing the oracle from scratch in python" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qlasskit import qlassf, Qint\n", + "\n", + "\n", + "@qlassf\n", + "def oracle(x: Qint[4]) -> bool:\n", + " s = Qint4(14)\n", + " return (x[0] & s[0]) ^ (x[1] & s[1]) ^ (x[2] & s[2]) ^ (x[3] & s[3])\n", + "\n", + "\n", + "oracle.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qlasskit.algorithms import secret_oracle\n", + "\n", + "oracle = secret_oracle(4, 14)\n", + "oracle.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use `BernsteinVazirani` to discover the secret embedded in the oracle." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qlasskit.algorithms import BernsteinVazirani\n", + "\n", + "q_algo = BernsteinVazirani(oracle)\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "result = simulator.run(qc).result()\n", + "counts = result.get_counts(qc)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_sources/example_big_circuit.ipynb.txt b/_sources/example_big_circuit.ipynb.txt new file mode 100644 index 00000000..637fc739 --- /dev/null +++ b/_sources/example_big_circuit.ipynb.txt @@ -0,0 +1,75 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with big circuits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qlasskit is capable of producing large circuit without any issue. The only thing that you have to do, is to use the `fastOptimizer`, since running CSE is too slow on large expressions lists.\n", + "\n", + "In the next example we are going to create a quantum circuit with 64 `Qint8` in input, and one `Qint8` in output, resulting on a circuit of ~21984 qubits and ~1044 gates in around 5 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import Qint8, Qlist, boolopt, qlassfa\n", + "\n", + "\n", + "@qlassfa(bool_optimizer=boolopt.fastOptimizer)\n", + "def test(a_list: Qlist[Qint8, 64]) -> Qint8:\n", + " h_val = Qint8(0)\n", + " for c in a_list:\n", + " h_val = h_val + c\n", + " return h_val" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QCircuit(21831 gates, 1036 qubits)\n" + ] + } + ], + "source": [ + "print(test.circuit())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_bqm_polynomial.ipynb.txt b/_sources/example_bqm_polynomial.ipynb.txt new file mode 100644 index 00000000..7c417fd0 --- /dev/null +++ b/_sources/example_bqm_polynomial.ipynb.txt @@ -0,0 +1,124 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BQM Polynomial solver" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(x0, ~(x.3 ^ x.4 ^ (x.4 & x.5))),\n", + " (x1, x0 & y.4),\n", + " (x2, x.3 & x.4),\n", + " (x3, x.4 & x.5 & (x.3 ^ x.4)),\n", + " (x4, ~(x.0 ^ x.3 ^ x2 ^ x3)),\n", + " (x5, ~(x.4 ^ x.5)),\n", + " (x6, x5 & y.5 & (x0 ^ y.4)),\n", + " (x7, x1 ^ x4 ^ x6 ^ y.3),\n", + " (x8, ~x7),\n", + " (x9, x4 & y.3),\n", + " (x10, x.0 & x.3),\n", + " (x11, (x.0 ^ x.3) & (x2 ^ x3)),\n", + " (x12, ~(x.0 ^ x.1 ^ x10 ^ x11)),\n", + " (x13, (x1 ^ x6) & (x4 ^ y.3)),\n", + " (x14, ~(x12 ^ x13 ^ x9 ^ y.0)),\n", + " (_ret.0, x7),\n", + " (_ret.1, x14 ^ x8),\n", + " (_ret.2,\n", + " (x14 & x8) ^ ~(y.1 ^ (x12 & y.0) ^ ((x12 ^ y.0) & (x13 ^ x9)) ^ ~(x.1 ^ x.2 ^ (x.0 & x.1) ^ ((x.0 ^ x.1) & (x10 ^ x11))))),\n", + " (_ret.3, ~(x0 ^ y.4 ^ (x5 & y.5))),\n", + " (_ret.4, ~(x5 ^ y.5)),\n", + " (_ret.5, x.5)]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qlasskit import qlassf, Qfixed\n", + "\n", + "\n", + "@qlassf\n", + "def poly(x: Qfixed[3, 3], y: Qfixed[3, 3]) -> Qfixed[3, 3]:\n", + " return x * 3 - y * 2 + 1.0\n", + "\n", + "\n", + "poly.expressions" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vars: 127 \n", + "Interactions: 1951\n" + ] + } + ], + "source": [ + "bqm = poly.to_bqm()\n", + "print(\"Vars:\", bqm.num_variables, \"\\nInteractions:\", bqm.num_interactions)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'x': 7.875, 'y': 7.375}\n" + ] + } + ], + "source": [ + "import neal\n", + "from qlasskit.bqm import decode_samples\n", + "\n", + "sa = neal.SimulatedAnnealingSampler()\n", + "sampleset = sa.sample(bqm, num_reads=10)\n", + "decoded_samples = decode_samples(poly, sampleset)\n", + "best_sample = min(decoded_samples, key=lambda x: x.energy)\n", + "print(best_sample.sample)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_bqm_tsp.ipynb.txt b/_sources/example_bqm_tsp.ipynb.txt new file mode 100644 index 00000000..b7288e48 --- /dev/null +++ b/_sources/example_bqm_tsp.ipynb.txt @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TSP" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Encode the TSP using qlasskit and parameters\n", + "\n", + "from qlasskit import qlassf, Parameter, Qlist, Qmatrix, Qint\n", + "\n", + "\n", + "@qlassf\n", + "def tsp(\n", + " dst_matrix: Parameter[Qmatrix[Qint[4], 3, 3]], order: Qlist[Qint[2], 3]\n", + ") -> Qint[6]:\n", + " dst_sum = Qint4(0)\n", + " assertion = False\n", + " \n", + " if sum(order) != 3:\n", + " assertion = True\n", + "\n", + " for i in range(2):\n", + " oim = order[i]\n", + " oi = order[i + 1]\n", + " dst_sum += dst_matrix[oim][oi] if not assertion else 0xF\n", + " return dst_sum\n", + "\n", + "\n", + "dst_matrix=[\n", + " [0xF, 1, 4], \n", + " [1, 0xF, 1], \n", + " [1, 1, 0xF]\n", + "]\n", + "tsp_f = tsp.bind(dst_matrix=dst_matrix)\n", + "# tsp_f.expressions" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vars: 18 \n", + "Interactions: 81\n" + ] + } + ], + "source": [ + "bqm = tsp_f.to_bqm()\n", + "print(\"Vars:\", bqm.num_variables, \"\\nInteractions:\", bqm.num_interactions)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'order': (0, 1, 2)}\n" + ] + } + ], + "source": [ + "import neal\n", + "from qlasskit.bqm import decode_samples\n", + "\n", + "sa = neal.SimulatedAnnealingSampler()\n", + "sampleset = sa.sample(bqm, num_reads=10)\n", + "decoded_samples = decode_samples(tsp_f, sampleset)\n", + "best_sample = min(decoded_samples, key=lambda x: x.energy)\n", + "print(best_sample.sample)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_deutsch_jozsa.ipynb.txt b/_sources/example_deutsch_jozsa.ipynb.txt new file mode 100644 index 00000000..b7f4c059 --- /dev/null +++ b/_sources/example_deutsch_jozsa.ipynb.txt @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deutsch Jozsa algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint\n", + "\n", + "\n", + "@qlassf\n", + "def f(a: Qint[4]) -> bool:\n", + " return a > 7" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import DeutschJozsa\n", + "\n", + "q_algo = DeutschJozsa(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = q_algo.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover.ipynb.txt b/_sources/example_grover.ipynb.txt new file mode 100644 index 00000000..91e1160b --- /dev/null +++ b/_sources/example_grover.ipynb.txt @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search\n", + "\n", + "Qlasskit offer a class to easily perform a `Grover` search over a qlasskit oracle. \n", + "First, we define a function named `and_all` that returns `True` iff all the element of an input list `a_list` are `True`. We want to use a Grover search to find the input value that led to a `True` result of the function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qlist\n", + "\n", + "\n", + "@qlassf\n", + "def and_all(a_list: Qlist[bool, 4]) -> bool:\n", + " r = True\n", + " for i in a_list:\n", + " r = r and i\n", + " return r" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The qlasskit compiler will produce an optimized quantum circuit performing the given function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "and_all.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now can use our quantum function as an oracle for a Grover search. For instance, we want to find the input value that yeld to a `True` value of the function:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "q_algo = Grover(and_all, True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qlasskit prepares the quantum circuit for the Grover search:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = q_algo.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "\n", + "The `Grover` class, along with all circuit wrappers in qlasskit, provides utilities to encode inputs and decode outputs from a quantum circuit using the high level type definitions. In the output histogram, it's now evident that the input leading to a `True` result in the `and_all` function is a list where all elements are set to `True`, aligning with our expectations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_factors.ipynb.txt b/_sources/example_grover_factors.ipynb.txt new file mode 100644 index 00000000..84040984 --- /dev/null +++ b/_sources/example_grover_factors.ipynb.txt @@ -0,0 +1,105 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover: factorize number" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Tuple\n", + "from qlasskit import qlassf, Qint\n", + "from qlasskit.algorithms import Grover\n", + "\n", + "\n", + "@qlassf\n", + "def factorize(a: Tuple[Qint[2], Qint[2]]) -> bool:\n", + " return a[0] * a[1] == 9\n", + "\n", + "\n", + "q_algo = Grover(factorize)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1m0aNGiEkJAT79u3D5cuX1eXNmjVDUFAQduzYgRs3bqjLH3vsMfj5+SE6OtpqQCMiIuDu7o6NGzdataFbt264desWYmJi1GVOTk7o3r07rly5gr1796rLvby80KFDByQnJyMhIUFd7uvrizZt2iAxMRF//fWXupx9Yp/YJ/aJfWKfKkufgO6wRWn3yTIhVphyd07ekCFD8MUXX8Dd3R1JSUkICAhQ1x0/fhyPPPII6tati6SkJKxatQovvfQS3n33XUybNi3Pvt59913MmDED69atQ+/evXHx4kXUrFkTYWFh2LVrV57tt27disjISAwfPhwfffRRvu3LbyYvMDAQV65cUUMiPymxT+wT+8Q+sU/sU8Xt01uf2jaT9/nw0u1TWloafHx8Cj0nr9zN5Flm2Vq2bGkV8ADgwQcfRL169ZCUlITr16+r2xY082b5KtWyXXG3z4+rqytcXV3zLHd2doazs/VBodfr8/1auKAfkxS0/O79lmS5TqeDTpf3FMyClhfUdvaJfSrucvaJfQLYp4LaWNzl7JPj+lQcjupTnnpF2qoMNWrUCABQtWrVfNdblt+6davQc+juPmfPw8MD/v7+OHPmjFWSLmh7IiIiooqq3IW8iIgIAMCJEyfyrMvJyUFSUhI8PDzg6+uL4OBgBAQEYPfu3TAYDFbbGgwG7N69G3Xr1lUvnAwA7dq1U9fdzXJ9vLsvrUJERERU0ZS7kFe/fn1ERkYiKSkJS5YssVo3a9YsXL9+Hb1794aTkxMURcGgQYOQmZmJqKgoq22joqKQmZmJwYMHWy0fMmQIAGDixInIzs5Wl2/atAmxsbGIjIxEUFBQKfWOiIiIqGyUux9eAMCpU6fQpk0bXLp0Cd27d0dISAgOHz6M7du3IygoCPHx8ahRowaAOzN2YWFhOHLkCCIjI9GiRQscOnQI0dHRCA0NRVxcHNzd3a32P3jwYPW2Zt27d0dKSgq+/fZbeHp6Yu/evWjYsGGR28qLIRMREWkLL4ZciurXr48DBw5gwIABOHjwID7++GMkJiZi6NCh2LdvnxrwgDvn2cXFxWHEiBE4ceIEPvzwQ/z5558YPXo0fv311zwBDwA+//xz9dezH330ETZu3IjevXtj3759xQp4REREROVVuZzJq0g4k0dERKQtnMkjIiIionKLIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSoxCFvx44dOH/+/D23SU5Oxo4dO0pagoiIiIhKqMQhLyIiAl999dU9t1mxYgUiIiJKWoKIiIiISqjEIU9ECt3GbDZDUZSSliAiIiKiEirVc/ISExPh7e1dmiWIiIiIKB9Oxdn41Vdftfr3Dz/8gLNnz+bZzmQyqefjde3a1aYGEhEREVHxFSvk5T4HT1EUJCQkICEhId9tFUVBaGgo5s+fb0v7iIiIiKgEihXyzpw5A+DO+Xj16tXDiBEj8O9//zvPdnq9HtWqVYOHh4d9WklERERExVKskBcUFKT+/7Jly9C8eXOrZURERERUPhQr5OXWv39/e7aDiIiIiOyoxCHPYt++fdi/fz+uX78Ok8mUZ72iKJg4caKtZYiIiIioGEoc8tLS0tCrVy/s3r37ntfMY8gjIiIiKnslDnmjRo3Crl270L59e/Tv3x+1atWCk5PNE4NEREREZAclTmW//PILWrVqhV9//ZV3tSAiIiIqZ0p8x4tbt24hPDycAY+IiIioHCpxyGvWrFm+d7sgIiIiIscrccibNGkSfvrpJ8THx9uzPURERERkByU+Jy81NRXdu3dHu3bt8NJLL6FFixaoUqVKvtv269evxA0kIiIiouJT5F7XP7kHnU4HRVGsLp9y9/l5IgJFUfK9fp5WZGRkwNvbG+np6QWGXCIiIqo4Bi+w7fFfjLBHKwpW1OxR4pm8ZcuWlfShRERERFTKeFszIiIiIg0q8Q8vytLs2bOhKAoURcn3hx4ZGRkYNWoUgoKC4Orqijp16mDMmDHIzMzMd39msxkLFy7EQw89BHd3d/j6+qJPnz44ffp0aXeFiIiIqEyUeCbv/PnzRd62du3aJS2D48ePY9KkSfDw8IDBYMiz3mAwoF27dkhISEBkZCT69OmDw4cPY+7cuYiLi8OOHTvg5uZm9ZjXX38dS5YsQdOmTTF8+HBcvHgR3333HaKjoxEfH4/g4OASt5eIiIioPChxyKtTp06RLoSsKAqMRmOJauTk5KB///5o1qwZgoODsXLlyjzbzJkzBwkJCRg7dixmzZqlLh83bhxmz56N+fPnY/z48erymJgYLFmyBOHh4di6dStcXFwAAH379kW3bt0wbNgwbNmypUTtJSIiIiovShzy+vXrl2/IS09Px5EjR3DmzBm0a9cOderUKXHjpk+fjt9//x2HDh3CnDlz8qwXESxZsgSenp6YOHGi1bqJEyfi008/xZIlS6xC3hdffAEAiIqKUgMeAHTt2hXt27dHdHQ0zp8/b9PsIxEREZGjlTjkffXVVwWuExF8+OGHmDNnDpYuXVqi/R86dAjTp0/H1KlT0aRJk3y3SUxMxMWLF9G5c2d4eHhYrfPw8EBYWBi2bNmC5ORkBAYGAgBiY2PVdXfr3LkzYmNjERcXh1deeaVE7SYiIiIqD0oc8u5FURT85z//wYYNGzBmzBisXbu2WI/PyspCv3790KxZM7zzzjsFbpeYmAgABZ5DFxwcjC1btiAxMRGBgYEwGAxISUnBgw8+CL1en+/2ufdbUNuysrLUf2dkZAC489VyTk4OgDvXENTr9TCZTDCbzeq2luVGo9Hq+oJ6vR46na7A5Zb9Wjg53Rm2u78GL2i5s7MzzGaz1fUKFUWBk5NTgcsLajv7xD6xT+wT+8Q+ab9PzrBFWfSpKEol5Fm0bNkSS5YsKfbj3n//fSQmJuLgwYP5hjGL9PR0AIC3t3e+6y0XCLRsV9zt8zNz5kxMmTIlz/Lo6Gjcd999AO780KR58+Y4evSo1Q9UGjVqhJCQEOzbtw+XL19Wlzdr1gxBQUHYsWMHbty4oS5/7LHH4Ofnh+joaKsBjYiIgLu7OzZu3GjVhm7duuHWrVuIiYlRlzk5OaF79+64cuUK9u7dqy738vJChw4dkJycjISEBHW5r68v2rRpg8TERPz111/qcvaJfWKf2Cf2iX2qLH0CusMWpd2ngwcPFqkdJb7jRVE8/fTT2Lp1q9WTWJi9e/eibdu2mDx5stV5dgMGDMDy5cuxd+9ePProowCAVatW4aWXXsK7776LadOm5dnXu+++ixkzZmDdunXo3bs3Ll68iJo1ayIsLAy7du3Ks/3WrVsRGRmJ4cOH46OPPsq3ffnN5AUGBuLKlStqSOQnJfaJfWKf2Cf2iX2quH1661PbZvI+H166fUpLS4OPj0/p3fGiIGazGX///Te++uor/Pjjj+jYsWORH2s0GtG/f388/PDDGDduXKHbW2bkCpp5s3yVatmuuNvnx9XVFa6urnmWOzs7w9nZ+qDQ6/X5zkRaDvaiLr97vyVZrtPpoNPlvSxiQcsLajv7xD4Vdzn7xD4B7FNBbSzucvbJcX0qDkf1Kc92RdoqH5Z71xZERFCtWjV8+OGHRd5nZmamej5c7l++5vbYY48BANavX6/+IKOgc+juPmfPw8MD/v7+OHPmDEwmU54nrrBz/IiIiIgqihKHvPDw8HxDnk6nQ7Vq1RAaGoqBAwfCz8+vyPt0dXXFa6+9lu+6HTt2IDExET169ICvry/q1KmD4OBgBAQEYPfu3TAYDFa/sDUYDNi9ezfq1q2r/rIWANq1a4dvvvkGu3fvRnh4uFUNy/Xx7l5OREREVNGUOOTFxsbasRl3uLu7F/hDjQEDBiAxMRHjx49Xz8kDgEGDBmHq1KmIioqyuhhyVFQUMjMzMWHCBKv9DBkyBN988w0mTpxodTHkTZs2ITY2FpGRkQgKCrJ734iIiIjKUqn+urYsvPPOO/jxxx8xe/ZsHD58GC1atMChQ4cQHR2N0NBQjBgxwmr7iIgIDBo0CEuWLEGLFi3QvXt3pKSk4Ntvv0X16tWxcOFCx3SEiIiIyI7sEvJ2796NhIQEZGRkoEqVKmjWrFm+FxsuDR4eHoiLi8PkyZOxdu1axMTEwN/fH6NHj8akSZPg7u6e5zGff/45HnroISxevBgfffQRPD090bt3b0yfPh3169cvk3YTERERlSabLqGyZ88eDBw4EElJSQDu/NjCcp5ecHAwli1bpv5QQqsyMjLg7e1d6M+YiYiIqGIYvMC2x38xwh6tKFhRs0eJZ/J+//13REZG4ubNm+jUqRMiIiLg7++P1NRUxMTEIDo6Gp07d0Z8fHyBtyUjIiIiotJR4pA3depUZGdnY+PGjejSpYvVurFjx2Lz5s3o0aMHpk6dim+++cbmhhIRERFR0eW9Il8RxcbG4tlnn80T8Cy6dOmCZ5991uoWJkRERERUNkoc8tLT01G3bt17blO3bt173geWiIiIiEpHiUNeQEAA4uPj77nNb7/9hoCAgJKWICIiIqISKnHI69GjB2JjYzFx4kTcvn3bat3t27cxadIkxMTEoGfPnjY3koiIiIiKp8SXULl69Spat26NM2fOwMfHB61atcIDDzyAf/75B/v378fly5dRr1497Nu3D9WrV7d3u8sNXkKFiIhIWyr9JVR8fHwQHx+Pd955B9988w02btyornNzc8PAgQMxe/ZsTQc8IiIiovLKpjte3H///fjyyy/x+eef488//1TveBESEgJnZ2d7tZGIiIiIiqnYIW/69OkwGAyYMmWKGuScnZ3x0EMPqdtkZ2fj3XffhZeXF8aNG2e/1hIRERFRkRTrhxfbtm3D+++/Dx8fn3vO1Lm4uMDHxwfvvvsur5NHRERE5ADFCnkrVqxAtWrVMGzYsEK3HTp0KKpXr45ly5aVuHFEREREVDLFCnl79uzBE088AVdX10K3dXV1xRNPPIHdu3eXuHFEREREVDLFCnkXL15EvXr1irx93bp1kZKSUuxGEREREZFtihXydDodcnJyirx9Tk4OdLoSX2+ZiIiIiEqoWAksICAAx48fL/L2x48fR82aNYvdKCIiIiKyTbFC3uOPP47t27fj7NmzhW579uxZbN++HeHh4SVtGxERERGVULFC3tChQ5GTk4Nnn30WV65cKXC7q1ev4rnnnoPRaMSbb75pcyOJiIiIqHiKdTHkFi1aYMSIEViwYAGaNGmCN954AxEREahVqxYA4O+//8avv/6KxYsX4/Llyxg1ahRatGhRKg0nIiIiooIV+44XH374Idzc3PDBBx9g+vTpmD59utV6EYFer8f48eMxbdo0uzWUiIiIiIqu2CFPURTMmDEDr732GpYtW4Y9e/YgNTUVAFCjRg2EhYVhwIABqF+/vt0bS0RERERFU+yQZ1G/fn3O1BERERGVU7yIHREREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGMeQRERERaRBDHhEREZEGlbuQ9/fff2PBggWIjIxE7dq14eLigho1auCZZ57Bb7/9lu9jMjIyMGrUKAQFBcHV1RV16tTBmDFjkJmZme/2ZrMZCxcuxEMPPQR3d3f4+vqiT58+OH36dGl2jYiIiKjMlLuQt3DhQowcORKnT59GZGQkRo8ejbZt2+LHH39EmzZt8O2331ptbzAY0K5dO8yfPx8hISEYOXIkGjVqhLlz56JDhw64fft2nhqvv/46hg8fDhHB8OHD0aVLF6xbtw6hoaFITEwsq64SERERlRonRzfgbq1atUJsbCzatWtntXznzp3o2LEj3nzzTfTq1Quurq4AgDlz5iAhIQFjx47FrFmz1O3HjRuH2bNnY/78+Rg/fry6PCYmBkuWLEF4eDi2bt0KFxcXAEDfvn3RrVs3DBs2DFu2bCmDnhIRERGVHkVExNGNKKrOnTsjOjoa+/fvR8uWLSEiqFWrFjIyMpCamgoPDw91W4PBgBo1asDPzw+nTp1Sl/ft2xerV69GXFwcwsPDrfYfERGB2NhYnDt3DrVr1y5SmzIyMuDt7Y309HRUqVLFPh0lIiIihxm8wLbHfzHCHq0oWFGzR7n7uvZenJ2dAQBOTncmIBMTE3Hx4kWEhYVZBTwA8PDwQFhYGE6fPo3k5GR1eWxsrLrubp07dwYAxMXFlVYXiIiIiMpEhQl558+fx7Zt2+Dv74+HHnoIANTz54KDg/N9jGW5ZTuDwYCUlBTUrVsXer2+0O2JiIiIKqpyd05efnJycvDKK68gKysLs2fPVgNaeno6AMDb2zvfx1mmMC3bFXf7/GRlZSErK0v9d0ZGhtrGnJwcAIBOp4Ner4fJZILZbFa3tSw3Go3I/S25Xq+HTqcrcLllvxaWmUyj0Vik5c7OzjCbzTCZTOoyRVHg5ORU4PKC2s4+sU/sE/vEPrFP2u+TM2xRFn0qinIf8sxmMwYMGIAdO3Zg8ODBeOWVVxzanpkzZ2LKlCl5lkdHR+O+++4DANSuXRvNmzfH0aNHcf78eXWbRo0aISQkBPv27cPly5fV5c2aNUNQUBB27NiBGzduqMsfe+wx+Pn5ITo62mpAIyIi4O7ujo0bN1q1oVu3brh16xZiYmLUZU5OTujevTuuXLmCvXv3qsu9vLzQoUMHJCcnIyEhQV3u6+uLNm3aIDExEX/99Ze6nH1in9gn9ol9Yp8qS5+A7rBFaffp4MGDRWpHuf7hhdlsxquvvorly5fj5ZdfxvLly6HT/e8b5g0bNuDJJ5/EsGHDsHDhwjyPf/vtt/HJJ5/g119/RYcOHWAwGODp6YkHH3wQx44dy7P92rVr8eyzz2LixImYOnVqvm3KbyYvMDAQV65cUWcC+UmJfWKf2Cf2iX1inypun9761LaZvM+Hl26f0tLS4OPjU+gPL8rtTJ7ZbMbAgQOxYsUK9OnTB1999ZVVwAMKP4fu7nP2PDw84O/vjzNnzsBkMuU5L6+wc/wAwNXVVb18S27Ozs7qD0Ms9Hp9vuf+WQ72oi6/e78lWa7T6fI8f/daXlDb2Sf2qbjL2Sf2CWCfCmpjcZezT47rU3E4qk956hVpqzKWO+C98MIL+Prrr/PtZHBwMAICArB7924YDAardQaDAbt370bdunURGBioLm/Xrp267m6W6+PdfWkVIiIiooqm3IU8y1e0K1aswHPPPYeVK1fmG/CAO9OcgwYNQmZmJqKioqzWRUVFITMzE4MHD7ZaPmTIEADAxIkTkZ2drS7ftGkTYmNjERkZiaCgIDv3ioiIiKhslbtz8iZPnowpU6bA09MT//73v/OdkuzVqxeaNWsG4M6MXVhYGI4cOYLIyEi0aNEChw4dQnR0NEJDQxEXFwd3d3erxw8ePBhLlixB06ZN0b17d6SkpODbb7+Fp6cn9u7di4YNGxa5vbwYMhERkbZo5WLI5e6cvLNnzwIAMjMzMX369Hy3qVOnjhryPDw8EBcXh8mTJ2Pt2rWIiYmBv78/Ro8ejUmTJuUJeADw+eef46GHHsLixYvx0UcfwdPTE71798b06dNRv3790uoaERERUZkpdzN5FQ1n8oiIiLRFKzN55e6cPCIiIiKyHUMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeERERkQYx5BERERFpEEMeEd3TzJkzERoaCi8vL/j5+aFXr17466+/rLZJTU3FK6+8gho1asDDwwMtWrTA2rVrHdRiIqroCnvfOXv2LBRFyfe/77//3oEtL18Y8ojonuLi4jB06FDEx8dj69atyMnJQWRkJAwGg7pNv3798Ndff+Gnn37CsWPH8PTTT+P555/H4cOHHdhyIqqoCnvfCQwMREpKitV/U6ZMgaenJ7p27erg1pcfioiIoxtRkWVkZMDb2xvp6emoUqWKo5tDVOouX74MPz8/xMXFITw8HADg6emJzz77DK+88oq6nY+PD2bPno1BgwY5qqlEpBH5ve/crXnz5mjRogWWLl1qc73BC2x7/BcjbG7CPRU1e3Amj4qksKnztLQ0vP3222jUqBHc3d1Ru3ZtDB8+HOnp6Q5stTaUt+fest/q1aury9q0aYNvv/0WaWlpMJvN+Oabb3D79m20b9++VNpAVBaKcqrC66+/jvr168Pd3R2+vr7o2bMn/vzzTwe12L7KU//ze9/J7eDBg0hISMBrr71m99oVGUMeFUlhU+cXL17ExYsXMXfuXBw/fhxfffUVNm/ebLcXXFHebBYvXoz27dujSpUqUBQF169ft0ttR3P0c5+b2WzGiBEjEBYWhgcffFBd/t133yEnJwc+Pj5wdXXF66+/jvXr16NBgwZ2bwNRWSnKqQr/+te/sGzZMpw4cQJbtmyBiCAyMhImk8nm+o5+33N0/y0Ket/JbenSpWjcuDHatGljt7qaIGST9PR0ASDp6emlWicuLk6efPJJ8ff3FwCyfv16q/WpqanSv39/8ff3F3d3d+ncubOcPHmy1Npz6dIlASBxcXEFbvPdd9+Ji4uL5OTk2Fyvc+fOsmzZMjl+/LgkJCRIt27dpHbt2pKZmaluM3/+fJk5c6bMnDlTAMi1a9dsrmtR2PN/48YNGTp0qNSsWVPc3NykcePG8tlnn9mtfm5l/dzn9sYbb0hQUJAkJydbLR82bJi0atVKtm3bJgkJCTJ58mTx9vaWo0eP2rU+kSMV5bV35MgRASBJSUk213P0+97dyrr/FgW971jcvHlTvL29Ze7cuXarOWi+bf+VtqJmD87kVRAGgwGPPPIIPv300zzrRAS9evXC6dOn8eOPP+Lw4cMICgrCE088YfWJy54Kmzq3bFOlShU4OTnZXG/z5s0YMGAAmjZtikceeQRfffUVzp8/j4MHD6rbjBgxAuPGjcOjjz5qc7273ev5B4BRo0Zh8+bNWLlyJU6cOIERI0Zg2LBh+Omnn+zelrJ+7i2GDRuGX375BTExMahVq5a6/NSpU/jkk0/w5ZdfomPHjnjkkUcwadIktGzZssDnqyLZsWMHnnrqKQQEBEBRFPzwww9W6wv6hd8HH3xQJvUB4MSJE+jRowe8vb3h4eGB0NBQnD9/vkzql3b/y5PCXnsGgwHLli1D3bp1ERgYaHM9R7/v3a2s+w8U/L6T25o1a3Dz5k3069fPLjW1hCGvgujatSumTZuG3r1751mXmJiI+Ph4fPbZZwgNDUWjRo3w2Wef4datW1i9erXd21KUqfMrV64gKioKQ4YMsXt9oGhBx57u9fwDwJ49e9C/f3+0b98ederUwZAhQ/DII49g3759dm2HI557EcGwYcOwfv16bN++HXXr1rVaf/PmTQCATmf9dqLX62E2m+3SBkcqLODf/Qu/L7/8Eoqi4JlnnimT+qdOnULbtm0REhKC2NhYHD16FBMnToSbm1uZ1C/t/pcX93rt/d///R88PT3h6emJTZs2YevWrXBxcbF7G8r6fS+3su5/Ye87uS1duhQ9evSAr6+vTTU1qfQnFbWtrL6uzQ13fV149OjRfKfHa9WqJf3797d7/cKmztPT06VVq1bSpUsXyc7Otnt9k8kk3bt3l7CwsHzXx8TElOrXFnc//yIigwcPlpYtW8qFCxfEbDbL9u3bxdPT855fa5SEI577N998U7y9vSU2NlZSUlLU/27evCkiItnZ2dKgQQN5/PHH5bfffpOkpCSZO3euKIoiGzZssEsbyov8xv5uPXv2lA4dOpRZ/RdeeEFefvnlUqlXlPp3s3f/CztVYtKkSdKoUSO57777pGrVqtKxY0eJj4+3W32Le732rl+/LidPnpS4uDh56qmnpEWLFnLr1i271nf0+15Z97+w9x2LxMREURRFNm3aZFO9u/HrWio3QkJCULt2bYwfPx7Xrl1DdnY2Zs+ejQsXLiAlJcWutQqbOr9x4wa6dOkCLy8vrF+/Hs7OznatDwBDhw7F8ePH8c0339h93yW1cOFCNGnSBLVq1YKLiwu6dOmCTz/9tMCf+peEo577zz77DOnp6Wjfvj38/f3V/7799lsAgLOzMzZu3AhfX1889dRTePjhh7FixQosX74c3bp1s0sbKop//vkHGzZsKLNf+JnNZmzYsAENGzZE586d4efnh9atW+f7lW5ZKI3+FzaT2LBhQ3zyySc4duwYdu3ahTp16iAyMhKXL1+2WxsKe+15e3sjODgY4eHhWLNmDf7880+sX7/ebvUBx77vOaL/hb3vWHz55ZeoVasWIiMjbaqnVfY7YYccxtnZGevWrcNrr72G6tWrQ6/X44knnkDXrl0hdroMoojg7bffxvr16xEbG5vv1HlGRgY6d+4MV1dX/PTTT3b7uig3y5vNjh07Cjw/wxEWLlyI+Ph4/PTTTwgKCsKOHTswdOhQBAQE4IknnrBp345+7otyDAUHB5fpHS5u3LiBiRMnYv369bh06RKaN2+Ojz76CKGhoWXWhvwsX74cXl5eePrpp8uk3qVLl5CZmYlZs2Zh2rRpmD17NjZv3oynn34aMTExaNeuXZm0w6I0+t+1a9d7Xty2b9++Vv+eN28eli5diqNHj6Jjx4421S7Kay+/x4gIsrKybKqdm6Pe9xzZ/6L+7ZoxYwZmzJhhUy0tY8jTiH/9619ISEhAeno6srOz4evri9atW6Nly5Z22f/QoUOxatUq/Pjjj/Dy8kJqaiqAO5/g3N3dkZGRgcjISNy8eRMrV65ERkYGMjIyAAC+vr7Q6/U21S/Jm01ZuXXrFiZMmID169eje/fuAICHH34YCQkJmDt3rs0hz9HPfXk0aNAgHD9+HF9//TUCAgKwcuVKPPHEE/jjjz9Qs2ZNh7Xryy+/xEsvvVQqH3DyYznnsWfPnhg5ciQAoFmzZtizZw8WLVpU5iGvrPt/t+zsbCxevBje3t545JFHbN5fYa+906dP49tvv0VkZCR8fX1x4cIFzJo1C+7u7naZxXb0+56j+0+2Y8jTGG9vbwB3foxx4MABREVF2WW/n332GQDkubjtsmXLMGDAABw6dAi//fYbAOS5NtqZM2dQp04dm+oX9mYD3Ll/ampqKpKSkgAAx44dg5eXF2rXrl2qJyrn5OQgJyen1H544Ojnvry5desW1q5dix9//FH9Onzy5Mn4+eef8dlnn2HatGkOadfOnTvx119/5fk6qTTdf//9cHJyQpMmTayWN27cGLt27SqzdgCO6b/FL7/8ghdffBE3b96Ev78/tm7divvvv9/m/Rb22nNzc8POnTuxYMECXLt2DQ888ADCw8OxZ88e+Pn52Vzf0e97ju4/2Y4hr4LIzMxUX8TAnT/eCQkJqF69OmrXro3vv/8evr6+qF27No4dO4Z///vf6NWrl93OUyhs6rx9+/Z2+2o4P4W92QDAokWLMGXKFHWdJQDk3qakCnv+27VrhzFjxsDd3R1BQUGIi4vDihUrMG/ePJvqAo5/7svb7X2MRiNMJlOe2SJ3d/cyDza5LV26FP/617/sMoNUVC4uLggNDc1zgdyTJ08iKCiozNoBOKb/FhEREUhISMCVK1fwxRdf4Pnnn8dvv/1mc9Ao7HUVEBCAjRs32lTjXhz9vufo/pPtGPIqiAMHDiAiIkL996hRowAA/fv3x1dffYWUlBSMGjUK//zzD/z9/dGvXz9MnDjRUc21u6KEmMmTJ2Py5MmlUr+w5/+bb77B+PHj8dJLLyEtLQ1BQUGYPn063njjjVJpT2Xm5eWFxx57DFFRUWjcuDEeeOABrF69Gnv37i2VO2wUFvCBO+dEfv/99/jwww/LvP6YMWPwwgsvIDw8HBEREdi8eTN+/vlnxMbGlkl9oHT7XxQeHh5o0KABGjRogEcffRTBwcFYunQpxo8f75D22Iuj3/ccqbx9uKyoGPIqiMJma4YPH47hw4eXYYsql8Ke/xo1amDZsmVl2KLK7euvv8arr76KmjVrQq/Xo0WLFujTp4/VRWLtpbCADwDffPMNRAR9+vQp8/q9e/fGokWLMHPmTAwfPhyNGjXC2rVr0bZt2zKpD5Ru/0vCbDbb9YcPRBWVIqX5PU8lkJGRAW9vb/UOA1rFT1WO4+jn3tH178VgMCAjIwP+/v544YUXkJmZiQ0bNpReQSpzuWcSmzdvjnnz5iEiIgLVq1eHj48Ppk+fjh49esDf3x9XrlzBp59+ilWrVuHgwYNo2rSpTbXL87Ff2hzd98pevzBFzR6cyaNyr7y/2MhxPDw84OHhgWvXrmHLli2YM2eOo5tEdnavmcRFixbhzz//xPLly3HlyhX4+PggNDQUO3futDnglQd87yNbMeRVEHyxOw6f+/Jny5YtEBE0atQISUlJGDNmDEJCQjBw4EBHN43srLBTJdatW1eGrSGqWBjyiKjCSU9Px/jx43HhwgVUr14dzzzzDKZPn14qd1ixJeRX9K+MHF2fiGzDkEdEFc7zzz+P559/3tHNoFLGkElkm0p779r9+/ejW7duqFq1Kjw8PPDoo4/iu+++c3SziIiIiOyiUs7kxcTEoHPnznBzc8OLL74ILy8vrF27Fi+88AKSk5MxevRoRzeRiP4/zuYQEZVMpZvJMxqNGDx4MHQ6HXbs2IHFixfjww8/xJEjR9CwYUNMmDAB586dc3QziYiIiGxS6ULe9u3bcerUKfTt2xfNmjVTl3t7e2PChAnIzs7G8uXLHddAIiIiIjuodCHPcquf/O7p2rlzZwBAXFxcWTaJiIiIyO4q3Tl5iYmJAIDg4OA862rUqAFPT091m/xkZWVZ3S4nPT0dAJCWloacnBwAgE6ng16vh8lkgtlsVre1LDcajVbXfdLr9dDpdAUuz8nJQfZt2y4Ncf26GSaTSf23oihwcnKC2Zz/8rvbbmv9q1fvPDe5+5Sbk9OdQ9FoNOZZnn1bsal2Wpox3z4VdZzs1XeLgvrq7Oyc73hk37btZXr1ag6PPZTs2LtTv+THH489HnuAY449AMjIAI+9CnrsFTZOaWlpAAq/v3Glu61ZZGQktm7disTExHxvZl6zZk1kZmaq4e1ukydPxpQpU0q7mURERET3lJycjFq1ahW4vtLN5Nlq/Pjx6m11gDs3wk5LS4OPjw8UxbZPXSWVkZGBwMBAJCcnO+T+uY6sX5n7zvo89lif9XnsV776wJ0ZvBs3biAgIOCe21W6kOft7Q0ABc7UZWRkoFq1agU+3tXVFa6urlbLqlatarf22aJKlSoOO+AcXb8y9531eeyxPutXttqs/788cy+V7ocXlnPx8jvvLjU1FZmZmfmer0dERERUkVS6kNeuXTsAQHR0dJ51W7ZssdqGiIiIqKKqdCGvY8eOqFevHlatWoWEhAR1eXp6OmbMmAEXFxf069fPcQ0sAVdXV0yaNCnP18iVoX5l7jvr89hjfdbnsV/56hdHpft1LVDwbc3OnTuHuXPn8rZmREREVOFVypAHAPv27cOkSZOwZ88e5OTk4KGHHsKoUaPwwgsvOLppRERERDartCGPiIiISMsq3Tl5RERERJUBQx4RERGRBjHkEREREWkQQ55GWE6tdNQplpYbKIuIQ9rA+o6pn7ueI/pd2esDjj32HN1/R9Z3dN8Bjn1lrl9U/OEFERERkQZVunvXasnNmzehKAoOHToERVFw69YtXLt2DU2bNkXNmjXh7OwMFxcX6PX6Uql//fp13Lp1C7t374aLiwsuXbqErKwsNG/eHF5eXqhatSruv/9+uLu7s77G6v/zzz9ITU3Fjh074OzsjAsXLgAAHn74YXh4eCAgIAB169Yttfs6V/b6jj72HN1/R9Z3dN859pW7frEJVVg9e/YUT09P8fT0FEVRrP7z9/eX559/Xj799FM5efKk+hiTyWS3+hEREaIoitx333156lerVk3Cw8NlzJgx8uuvv8r169dZX0P1H3vsMVEURapWrSrOzs6i0+nU2m5ubtKwYUN5/vnnZfny5XLmzBm1ttlsZn07cPSx5+j+O7K+o/vOsa/c9YuLX9dWUDdu3MCQIUPwxBNPICcnB25ubvD29kZCQgKSkpJw6tQpnDp1ClevXkX9+vXx6quvYsSIEXb7dHfz5k1MmzYN7dq1w82bN1G1alW4u7vj8OHD+Pvvv3Hy5EkcPXoUSUlJuP/++/Hss89i/PjxqFmzJutX8PpZWVlYsWIFWrZsiZs3byIgIABZWVn4/fffkZaWhpMnT2Lv3r04cOAAdDodOnfujAkTJiA0NNQOPWd9Rx97ju6/I+s7uu8c+8pdv0QcEi2p1J07d042b94sY8eOlcaNG4uiKNKkSRNZs2ZNmdS/fPmyJCQkyGeffSaRkZHqJ5+PP/5Ybt68KSJSqp9sWN9x9W/evCmpqamyadMmefXVV6VatWqiKIqMGzdOLl++LCL2nVlgfWuOPvYc3X9H1nd03zn2lbt+fhjyKijLgWI2m63+32g05tnu0KFDMmzYMFEURWrVqiU///yzXetb3jRMJlOeA9hsNktSUpLMmjVLfHx8xM3NTRYtWqSp+pb/ryz1Lc937j8Wuccht0uXLsnKlSulWbNmoiiKjB8/3qba5a1+fsdhadd39LFXnp7/sq7Psa+8Y393fUeMf0kw5FVwuV/cRqPR6iC8+8A7dOiQhIWFiYuLiyxbtqwsmykid2YXn3nmGVEURSZNmmSXfRbnU2lp1y+sLaVRP/f45+TkWLXl7vaURv2iunr1qgwbNkz0er3069dPbt26ZfM+c/e9sOe+NOrn5oj6FWXsRbQ9/hz7e9Py2JeH+oVhyKuA7j6obt++nWeb3C+C3J/0Dhw4IO3btxc3NzfZuHFj6TY0n/rnzp2Tl156SRRFkZUrV1bI+ne/wV67ds1h9UVEPbm6rOoXR+4/OpmZmfLee++JoiiyYMGCEu8vt9x/4Mq6/tWrVyUlJaVM61eksRfR1vhz7ItHS2N/d31HjH9J8RIqFZCiKDCbzVi1ahUSEhJw9epVZGdno2XLlnjyyScRHBwMne7Oda7NZjMURYGiKACAf/3rX9i6dSt69+6NHTt2oEuXLuq6khCRQh9vaQsA1K5dGytXroSHhwcSEhLQp08fq/XF8euvv+Lw4cOoWrUqgoOD0a5duzKpr9PpkJWVhdGjR+Ps2bNwd3dHjx498Morr+TZ1mQyQVEUtYa96mdnZ2PBggU4fvw4zp8/D6PRiMcffxwRERFo1qwZ/Pz81Po6nU4dI3s9//L/LwRa2GNzHxseHh6IioqCq6srzp07B5PJVOzL+1j29/XXX+PYsWNwcXFBSEgInnrqKXh7e5dJ/evXr+Ott95CUlISRAQ9e/bE6NGj8/yoyXKhWstzZI/65WHsgco5/hz7Oyrj2Fv258jxL7EyjZRkE8ungrNnz8rAgQPVn217eXlZ/Yw+NDRUFi9enO8Mn+WcvcTERImPjy9ROwwGg3oSb+62FTZtbTQaJTs7W0REUlNTJSEhoVh1Lfu/cuWKTJ061eqn6wEBAfLBBx+o+8+PyWRSP/3ZUv/MmTPSt29fURRFfH19xd3dXe6//35Zt26dum1SUpJkZWXl2Yfl+belfmJiovTq1Us9qbp69epW4x8SEiKTJ0+WS5cu2a2+pfb169clIyMjz7qijL3luTcYDFaX9SlO/YsXL8rIkSPVvjo5OUmVKlVk9OjRYjAYCnx87rG3pX5SUpI89dRToiiKNG3aVLy9vcXd3V0WL14sIndmFw4fPpzvLItlVsWW+o4Y+9z1K+P4c+wr79jnru+o8bcVQ14FYjlQ3nzzTXFxcZERI0bI7t275dKlS7Jnzx6JioqS9u3bi7OzsyiKIsHBwbJ8+fJCp7WLyvJGMX36dHnjjTdk/fr1cvr06Twv8tL69Zal/5Zp7yeeeEI+/PBDmTJlitSpU0cURZEvv/yy1NpgqT9y5Ehxc3OTd999V37//Xf58ccfxcfHR3r06CHnzp2Tp556Svz8/KRGjRoybNgwu72oLfUHDx4sHh4eMmnSJDl9+rSIiBw5ckS++OILGTBggNSrV08URRF3d3eZNWtWoV/rFIVl7MeOHSudOnWSTz75RBISEvJ8hVRa14Oy1HnnnXdEURR57rnn5Ouvv5YVK1ZIWFiYKIoikydPFpHSHfu3335bqlSpIjNnzpSrV6/K0aNHpVGjRtKmTRv5448/5NFHHxVFUUSv18uzzz4rhw4dsmt9R4y9SOUef4595R373PUdNf62YsirYEwmk3h5ecmgQYPynbUyGo0SExMjr7zyiiiKIn5+fuplU4pzsuq96ls+SVWvXl06dOggUVFRsm3bNklNTc2zrYjIP//8IxMmTJCvvvqqRDXv3mfVqlXlySeflCtXrojInb7Ex8dLUFCQ1KpVK0+oMhgMsmnTphLPXN5d38fHR/r27Ws1U/fCCy9IzZo1pVWrVtKwYUOJiIhQw/Yrr7yittMe9atUqVLg+JtMJjl48KBMmDBBneFduHChus7W2nq9Xn0ja9iwobz22muycuVK9Y9O7m1F7nz6fvXVV+WDDz6wqbZln97e3tK7d291RsFoNMqJEyckNDRUPDw85ODBgyLyv+f6xo0b8vXXX9vtF+XVqlWTl19+2erk6cGDB0uVKlWkWbNm0rp1a+nTp4/Ur19fFEWRdu3a3XOWobj1HTX2ln1U1vHn2FfesbfUd+T424Ihr4KJiYkRNzc3mT9/voj871NWfj+j37NnjwQEBMgDDzwgx48ft6mu5YWzbds2URRFOnXqJK+88or4+/urX5c+/fTTsnDhQomPj5f09HT1sT///LO4urrKmDFj1LaWtP6aNWvEzc1N/vvf/4qIWF0y5qOPPhJFUeTdd98Vkf+dmLt7926pV6+ezJw502pfJan//fffi6urq1rfss4SqmfMmKGG3WvXrkmXLl1EURT5/vvvi10zP7t27RIPDw+ZMWOGiNx7/M+cOSMtW7YUV1dX2bFjR4lrWvq+fft2URRFevbsKRMmTJCGDRuKoiji4uIiLVu2lP/85z/y888/W31dtGHDBtHpdPLWW2+p7Sxp/XXr1om7u3u+Y79mzRpRFEWGDBkiIv8b+z179kjt2rXlvffes9pXSeqvWbNG3N3d5euvv1bXmUwmGTx4sPpH1fIH4Pbt2+opFfb6Jbsjxl6kco8/x77yjn3uxzh6/G3BkFfBHDlyRNzd3eWdd94pcBuj0ai+oFasWCGKosgXX3xhU13LwW4JUsuXLxeROwFq3rx50rlzZ6lSpYrodDr1U96KFSvkjz/+UK/Rd+zYMRGx7cU+cuRIqVu3rhw4cMBquYhIVlaWPP7441KjRg1JS0tT1y1YsEAURZEjR47YXP/NN9+Uxo0bW03Fnzp1Stq0aSOtWrVSl1neaLZs2SJ6vV7mzp1b7Jr5OXXqlFSvXl1efvnlfNsnYv3Gv3HjRtHr9TJnzhyba//f//2fKIqizsieO3dO1q1bJ/3795cHHnhAFEURb29v6dixo0yfPl127dolQ4cOFUVR1A8Ztjz3Y8eOlcDAQHVG9u437RdffFFcXFzk/Pnz6jJ7jv3w4cOlQYMG6rEncmc8WrduLaGhoeq2lj9ABw4cEDc3N3n//feLXTM/jhx7kco5/hz7Oyrj2Oeu4+jxtwVDXgVz8+ZNefjhh8Xb21tWr16d748rRP53QJ85c0aqVq0qw4YNs7m20WiU6dOni06nkxMnTqjLs7Oz5cKFC7Jt2zZ57733pFWrVuLi4iJubm7yyCOPiJeXlzRu3FhEbPvKMicnR9544w3x8vLK86MGS6havny5OqMmInL+/Hnp0qWLBAcH21z/9u3b0qtXLwkODrZ63teuXSuKosjSpUtFxDpkHzt2TJydnWX06NElrpubyWSSdu3aibu7uyxcuDDfrwNyX6gzOTlZ/P39ZcCAATbXXbhwobi5ueU51+T27duSmJgoX3zxhXTv3l08PDzU2V1nZ2dp0qSJ2q6SMhqNMnLkSHFxcclznSlLX7ds2SKKosjIkSNF5E7f7TX2WVlZMmDAAKlfv75V/U2bNomrq6v6iT33tSpPnDghXl5e6kyGrRw19pbalXX8OfaVd+xFysf424IhrwLaunWr6HQ68fT0lMmTJ8vx48cLDHubNm0SLy+vPF/vloTZbJZt27bJW2+9JefOnct3m5s3b0pSUpKsXbtWRowYIYGBgaIoisybN09ECr+20b2YTCaJioqSxo0bF3iNovT0dGnZsqUEBARITk6OxMbGioeHh0ybNs3m+tnZ2bJw4UL1HDvLc5mcnCyjR4/Oc1FSkf9N83/++edWj7HFsWPHJCAgQBRFkYEDB8qWLVvkn3/+yfeNbMOGDeLp6anOJNpS/8CBAzJ9+nS5cOFCgdtkZmbKgQMHZN68eRISEmK3sRcRWbhwoTRs2LDAH7Lcvn1bunTpIq6urnLt2jWJi4sTDw8PmT59us31jUaj/Pe//1X/iFj+uKSmpsqcOXOsnte7v2L69NNP1X3YylFjL1J5x59jX3nHXqT8jH9JMeRVMJaD6JdffpFmzZqJTqeTkJAQGTt2rERHR8uJEyfUAHTy5Enp1KmTeHp6qudK2OvXR0WZ+r58+bL07NlTFEWxW/2TJ0/KmjVr1PsA5mfhwoWiKIrMnz9foqKiRFEU+eeff+xSP7fC9pWTkyNjxoyxa33L8753717p0KGD6PV68ff3l5deekkWL14sMTExcurUKTGZTLJz505p06aNVK1a1a7jX5R93L59W/r06WPXsU9LS5M9e/bI1atXC9zGMqv67rvvyqRJk0pt7AuTnZ0t//nPfzQ39kXdT2Uef4595R17kdIZf1sw5FVghw4dktGjR6snwVqmx1u1aqV+Term5iZTp04VEfv9yqowlk8tCQkJEhAQIOHh4XarXxR///23NG3aVGrWrCmhoaHquXK21s99zkVu+V1KQEQkPj5eQkJCpFu3bnapf7fU1FRZuHChdOjQQby8vESv18sDDzwgfn5+UqtWLdHr9VKtWjX107Q9fl1b1G0OHDggNWrUKPOxz8jIkMcff1w8PT3lkUcekdatW9utfnHGfs+ePdKwYUN58skn7VY/t7Ie+6LuQ6vjz7GvvGMvUr7Gv7gY8iq4rKws2b9/vyxYsED69+8v4eHhUqtWLfHx8ZGePXvKhg0b1AO0rD9NrF69WpycnNSTde1xvb7C+mB5QU2fPl291MuqVatEpOynzDt16iR+fn7q7eNKo77RaJTExERZu3atvP/++/L8889LSEiIPPzwwzJkyBDZuXOnum1Zjv8PP/wg9913n3q+ir2u1Xgvlv5ZzstUFEVWr14tImU/9m3atBF/f3/ZsmVLqdUvr2MvUrnHn2NfecdepGzGvzgY8iqwu1+8N27ckJSUFLl9+3a+Vz0va3/++ad88sknDvkkc/78eWnVqpV4e3uX+YvMcs2qsLAwmTx5cpm9yWZlZanX0CrsfrqlLSUlRX7++WeHfE1x+fJl6dy5s/j4+JT52JvNZjlx4oQ8+eSTMmfOnEo59iKVc/w59ndUxrEXcdz4F0YRESmbG6hRaTGbzfneR1CKcF9ZLYuNjcWNGzfw1FNPle29AgEYDAZcvnwZPj4+8PLyKtWxKGjflXn8ExMTceXKFTz22GMFvj5Ki9FoxO3bt+Hs7AxXV9dSrcWxz5+jxp9j73iV5bVfVAx5VGoq+5tNZcaxr9w4/pUXx758YcgjzeKbDRERVWZlN49JVMYY8IiIqDJjyCMiIiLSIIY8IiIiIg1iyNMQs9nM+pW4PhERUW4MeRpi+am4o8IG6zu2voXJZKqUtVmf9Xnss35lrV8QhrwKLicnBwCwefNmxMTEAECZXheI9R1b32g0AgB++OEHLF26FFlZWWV2PUBH1mZ91uexz/qVtX6xlPHFl6kEcl+5u6CraFtu5fLcc8/Z/W4XrO/Y+kVRvXp1URRFHn/8cTl27Filqa31+iaTqdAr57N+6dR3dN+NRiPrV+L69sKQpwE5OTny3nvvScOGDUVRFBk4cKCkpaWxfiWpbzQaZcGCBdK+fXtRFEW6du0qKSkpmq+t1frFuQ0g69u3vqP7XhysX7nrFxVDXjl19epV2bx5s/znP/+RiRMnyooVK2Tbtm2SmJgot2/fzvcxJpNJ5syZI9WrV5dx48axfgWuX9Kben/33XdSq1Yt6d+/f4WszfoiBoNBfvvtN/n8889l3bp1cuXKFREp/EbzrG97fUf3PSkpSaZNmyZdu3aV2bNnyz///GO1vqB2sL426pcGhrxyxPIp8tdff5U2bdqoXwFa/vP29pa2bdvK5MmTJT4+Xv0aMTs7W71B9a1bt+Szzz6Tbdu2sX4Fq5/fG4jJZCr0Rttms9kqmGzYsEHi4+MrTG3W/9+xd+TIEXn++eetjrvu3bvL77//XmB9y7HH+iWrX176vnnzZmncuLFV/WeeeUatYTnODAaD+m/Wr/j1SxtDXjljNBqladOm4ufnJ3PmzJGdO3fKqlWrZP78+dK/f3+pX7++6HQ6qVevnsydO1d9XGGfNFm/4tR/7rnnZPXq1eqbSe51xfk6qSLVZv07f2w6deokzs7OEhkZKZMnT5annnpKdDqdhIeHl/pXQZW5vqP7bjQapXXr1vLAAw/ImjVrZP/+/fLyyy+Loiiyd+9e2bp1q/To0UOaNWsmQ4cOlYMHD7K+huqXJoa8csISEr7++mtxcXGRJUuW5Nnm6tWrsn//fvnggw+kefPm6nkAly9fttpPSQIH65eP+t988436KTIwMFDeeustiYuLy7O90WhUP0Xu27dP1q1bJwaDocLVZv3/1f/vf/8riqLI2LFj1XXXr1+XN954QxRFkQ8++CDPY86fP6/ONBU268j65at27n2tXLlS3N3d5eOPP1bXJScni6urq/Tt21eqVq0qNWrUkAYNGoizs7O4urrKzz//XKKarF9+6pcFhrxywnKw9evXTwIDA+Wvv/4SkTtfBd79xyMnJ0f2798vvXr1EkVR5J133mF9jdR/4YUXRFEUadu2reh0OjV0PPzwwzJlyhQ5fvx4nscOHDhQFEVR21yRarP+/74u6tSpk7Ru3VpOnDghIv8LDmlpaRIWFibVqlWz+kAhIjJ37lzR6XRy+vRp1q9gtXPXj4yMlJYtW8qff/6prjty5Ij4+flJvXr1ZNGiRWI2m+Xo0aMyadIkURRFIiIi5ObNmyWuzfqOr18WGPLKEaPRKCNGjJAqVaqofzTudRK40WiU9u3bS82aNSUzM5P1K3h9g8Eg7du3l1q1aomIyI0bN+TTTz+V1q1bW50nEhERIZ9++qmkp6fLpUuXpGXLltKkSRMRKfnXxo6szfp3Zo0aNGggAwYMsDrmLH+E1qxZI4qiyOzZs9V1KSkp0qVLFwkODmZ9G+o7uu/Xrl2TOnXqyBtvvGFVf/Xq1aLT6WThwoV5HvPSSy+Jl5eXXLx4scR1Wb981C9tvBhyOSEi0Ov1aNu2LW7cuIFZs2YBAJycnNT1ue+kYLn4Ytu2bZGZmYnDhw+zfgWuDwCpqalITU1F1apVAQCenp546623EB8fj5MnT+K9995D3bp1ERsbi2HDhqF27dro2bMnDh48iIEDBwIo+VXXHVmb9YHk5GRkZ2fDy8sLTk5O6rFmubB2jx490KJFC3z88cdISUkBABw9ehQ7d+7Eq6++yvo21Hd03//8809kZWXB29tbfb/JysrC/v37ISIYNGgQgDvvQdnZ2QAAX19fODk54ezZsyWuy/rlo35pY8grJxRFgdlsRpcuXfDiiy/iq6++QmRkJLZs2YJbt25BURT1TcdoNMLV1RUmkwk5OTnIzs5G06ZNWb8C1wcAHx8f9OvXD0OHDlX/0BiNRogIGjRogKlTp+LUqVPYtWsXBg8eDEVREB8fDwBq0CjpVdcdWZv1AQ8PD1y8eBFZWVn5rnd2dsa4ceNw8eJFbNiwAWazGTt37sTNmzfx2muvsb4N9R3d96pVq6J169bo1KkTgDu3RTQajWjQoAFmzJgBNzc3mEwmKIoCFxcX5OTk4Nq1a9Dr9WjSpEmJ67J++ahf6sp24pAKkvuXe3/88Yd07dpVFEURDw8Pee655+Tzzz+Xo0ePWj1m06ZNUrNmTenUqVOefbB+xayfmZkp169fz3f93V8d79u3T7y8vOSJJ56wqb4ja7P+/x4bGxsr3333XYFf/RkMBmnUqJG0atVKDh06JK1bt5awsDDWt6G+o/tuqff333/n++MNyzU5c/+g68SJE9KsWTOJiIhg/Qpevyww5JVjK1askFatWqnnA9WsWVNCQ0Olb9++0qlTJ3F3d5fg4GCJiYkRkZL/wov1y2f9glh+2fnxxx+Loijy3//+t8zqO7I264ssWrRIvX6XXq+X5cuXs34Z1Xd03y0h44MPPhA3NzdZvXo161ei+iXFkFcOfPDBB+ofD5PJZPXJ4PLly7JhwwYZNmyYhISEiKIo4uzsLIGBgdKpUyc5dOgQ62usflHcuHFDIiIiRFEUmz5JOrI261vXL8ofi9TUVAkKChIXFxfx9PRkfTsd+47ue1H3deLECWnSpIm0bt3a5mtzsr5j65cVhjwHi4mJEScnJ1m7du09t7McUMnJybJr1y65dOmS+hWSLW82rF8x6t/NZDLJr7/+KosWLRKRkt2Ky5G1Wb/k9WfPnq3OKImUfCahMteviH3Pzs6WwYMHi6urqzqLVNL3HtZ3bP2yxJDnQCaTSbKzs6Vfv37i6el5zxklkfx/pm/LpwnWL9/1S/OToiNrs75t9XNycuTrr7/Oc0031i//tW2tLyJy+vRpuXXrloiU7P2H9R1bv6wx5JUDFy5ckPbt24urq6uMHTtW0tPTrdbnPvBK46Bi/fJdP/dJv1qqzfqsz2O/6PXt3RbWd2z9ssKQV04YjUb1FjoNGzaURYsWyalTp/Jsl/urIXue8Mn6Fa9+Sb8mLE+1WZ/1eeyzfmWtXxYY8soBS1i4cOGCvPPOO+Lk5CROTk7SoUMHmTt3rmzbtk0uXLiQ72MtP/FmfdavaLVZn/V57LN+Za1fVhQREUdfq4+sHTt2DJ988gl++OEHXL58Gb6+vggMDMT999+PJk2awN/fH5cuXUJWVhYMBgM6d+6M559/nvVZv0LXZn3W57HP+pW1fmlhyCtH5M7MKnQ6HW7cuIEjR45g//792LlzJ/bu3YsrV67A1dUVRqMR9913H/z8/NC0aVN8+OGHqFOnDuuzfoWszfqsz2Of9Str/dLGkFfOWW6t5OzsjJMnTyItLQ0NGzZEWloa6tata9PtdFif9ctrbdZnfR77rF9Z69sTQ14FJiJQFIX1Wb9S1WZ91uexz/qVtX5xMeQRERERaZDO0Q0gIiIiIvtjyCMiIiLSIIY8IiIiIg1iyCMiIiLSIIY8IiIiIg1iyCMiIiLSIIY8IiIiIg1iyCMiIiLSIIY8IiIiIg1iyCMiIiLSoP8HosjueTjnaKYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts, discard_lower=5)\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc.draw(\"mpl\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_hash.ipynb.txt b/_sources/example_grover_hash.ipynb.txt new file mode 100644 index 00000000..41d5f9de --- /dev/null +++ b/_sources/example_grover_hash.ipynb.txt @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search: hash function preimage attack\n", + "\n", + "In the ever-evolving landscape of cybersecurity, the advent of quantum computing presents both an extraordinary opportunity and an unprecedented challenge. In this notebook we exploit a Grover Search to perform a preimage attack on a toy hash function.\n", + "\n", + "A preimage attack on a cryptographic hash function `h(m)` tries to find a message `m` that has a specific hash value. Using qlasskit it is easy to write an hash function like the following `hash_simp`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint, Qint8, Qlist\n", + "\n", + "\n", + "@qlassf\n", + "def hash_simp(m: Qlist[Qint[4], 2]) -> Qint[8]:\n", + " hv = 0\n", + " for i in m:\n", + " hv = ((hv << 4) ^ (hv >> 1) ^ i) & 0xFF\n", + "\n", + " return hv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thanks to the fact that qlasskit function are standard python functions, we can call the `original_f` to perform some kind of analysis on the hash function. Since the input space is tiny (it is a toy hash function), we can detect if the hash function is uniform (if it maps equally to the output space)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hash function output space: 256\n" + ] + } + ], + "source": [ + "from collections import Counter\n", + "\n", + "d = Counter(hex(hash_simp.original_f((x, y))) for x in range(2**4) for y in range(2**4))\n", + "\n", + "print(\"Hash function output space:\", len(d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got that `hash_simp` is following an uniform distribution. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hash_simp.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now we use our quantum function as an oracle for a Grover search, in order to find which input maps to the value `0xca`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "q_algo = Grover(hash_simp, Qint8(0xCA))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts, discard_lower=5)\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `QlassF.original_f` we can double check the result without invoking a quantum simulator; calling it with the tuple `(12,12)` must result in the hash value `0xca`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0xca\n" + ] + } + ], + "source": [ + "print(hex(hash_simp.original_f((12, 12))))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_subset.ipynb.txt b/_sources/example_grover_subset.ipynb.txt new file mode 100644 index 00000000..325e5259 --- /dev/null +++ b/_sources/example_grover_subset.ipynb.txt @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search: subset problem\n", + "\n", + "We define a function named `subset_sum(i,j)` that returns the sum of the elements `i` and `j` of a list `set_`. We want to use a Grover search to find which `i` `j` combination led to a given value." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint, Qint3, Qlist, Parameter\n", + "from typing import Tuple\n", + "\n", + "\n", + "@qlassf\n", + "def subset_sum(\n", + " ii: Tuple[Qint[2], Qint[2]], set_: Parameter[Qlist[Qint3, 4]]\n", + ") -> Qint[3]:\n", + " return set_[ii[0]] + set_[ii[1]] if ii[0] != ii[1] else 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our quantum function `subset_sum` will be used as an oracle for a Grover search. For instance, here we want to find the input value that produce the value `7`. Since we know that there are at least two result (`(i,j)` and `(j,i)`), we set `n_matching=2`. We also bind the parameter `set_` with the set of numbers where we want to search the solution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "q_algo = Grover(subset_sum.bind(set_=[0, 5, 2, 3]), Qint3(7), n_matching=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "\n", + "In the output histogram, it's now evident that the input leading to a value of `7` are the tuples `(1,2)` and `(2,1)` (5+2 and 2+5), aligning with our expectations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_sudoku.ipynb.txt b/_sources/example_grover_sudoku.ipynb.txt new file mode 100644 index 00000000..bae66351 --- /dev/null +++ b/_sources/example_grover_sudoku.ipynb.txt @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search: sudoku solver\n", + "\n", + "In this example we are going to solve a sudoku puzzle. Since we have few qubits, we cannot solve a real 9x9 sudoku puzzle; our toy examples uses a 2x2 matrix where a valid solution is when in every row and every column there are no repeated values (`0` or `1`). We encode these xor-ing the values for each row and column. \n", + "Since we want a specific solution, we add a constraint `constr`: we want the `[0][0]` element to be `True`.\n", + "\n", + "`sudoku_check` is already an oracle so we can instantiate the `Grover` algorithm without value." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qmatrix\n", + "from qlasskit.algorithms import Grover\n", + "\n", + "\n", + "@qlassf\n", + "def sudoku_check(m: Qmatrix[bool, 2, 2]) -> bool:\n", + " constr = m[0][0]\n", + " sub0 = m[0][0] ^ m[0][1]\n", + " sub1 = m[1][0] ^ m[1][1]\n", + " sub2 = m[0][0] ^ m[1][0]\n", + " sub3 = m[0][1] ^ m[1][1]\n", + " return sub0 and sub1 and sub2 and sub3 and constr\n", + "\n", + "\n", + "q_algo = Grover(sudoku_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "\n", + "We obtain that the solution for this puzzle is the matrix `[[True, False], [False, True]]`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts, discard_lower=20)\n", + "\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can exploit *matplotlib* for drawing the result sudoku matrix as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def draw_matrix(matrix):\n", + " fig, ax = plt.subplots()\n", + " ax.matshow(matrix, cmap=\"viridis\")\n", + "\n", + " for i in range(len(matrix)):\n", + " for j in range(len(matrix[0])):\n", + " ax.text(\n", + " j,\n", + " i,\n", + " str(matrix[i][j]),\n", + " va=\"center\",\n", + " ha=\"center\",\n", + " fontsize=12,\n", + " color=\"black\",\n", + " )\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "m_res = list(filter(lambda x: x[1] > 500, counts_readable.items()))[0][0]\n", + "draw_matrix(m_res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create a more realistic sudoku game using numbers instead of booleans, but the resources required will scale exponentially. In the following code snippets, we recreate `sudoku_check` using `Qint[2]` and a 4x4 matrix. The sum of each column and row must be equal to 6 (3+2+1+0). As we can see, the resulting circuit of the checker requires more than 100 qubits, way above our simulation capabilities." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QCircuit(1309 gates, 178 qubits)\n" + ] + } + ], + "source": [ + "from qlasskit import Qint, Qint4\n", + "\n", + "\n", + "@qlassf\n", + "def sudoku_check(m: Qmatrix[Qint[2], 4, 4]) -> bool:\n", + " res = True\n", + "\n", + " # Constraints\n", + " res = (m[0][2] == 3) and (m[0][0] == 1)\n", + "\n", + " # Check every row and column\n", + " for i in range(4):\n", + " c = (Qint4(0) + m[i][0] + m[i][1] + m[i][2] + m[i][3]) == 6\n", + " r = (Qint4(0) + m[0][i] + m[1][i] + m[2][i] + m[3][i]) == 6\n", + " res = res and c and r\n", + "\n", + " return res\n", + "\n", + "\n", + "print(sudoku_check.circuit())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_simon.ipynb.txt b/_sources/example_simon.ipynb.txt new file mode 100644 index 00000000..96c7b26b --- /dev/null +++ b/_sources/example_simon.ipynb.txt @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simon function periodicity" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint\n", + "\n", + "\n", + "@qlassf\n", + "def f(a: Qint[4]) -> Qint[4]:\n", + " return (a >> 3) + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Simon\n", + "\n", + "q_algo = Simon(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = q_algo.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADORyyEtKStK5c+fu2uf8+fNKSkpy9SMAAADgIpdDXlRUlNavX3/XPhs3blRUVJSrHwEAAAAXuRzyCgsL79mnoKBAFovF1Y8AAACAi9x6TV5qaqqCg4Pd+REAAAAohU9FOj///PNOr7du3aozZ86U6Jefn++4Hm/w4MGVKhAAAAAVV6GQ98Nr8CwWi1JSUpSSklJqX4vFoi5duuidd96pTH0AAABwQYVC3unTpyUVXY/XokULzZ49Wy+88EKJfjabTXXr1lVAQEDVVAkAAIAKqVDIa9q0qeO/4+Pj1bFjR6c2AAAAeIcKhbwfGj9+fFXWAQAAgCrkcsgrdvDgQR06dEjXrl1Tfn5+ie0Wi0Xz588v9/5u3bqlV155Rf/zP/+jU6dO6cqVKwoJCVHLli01adIkjRkzRr6+vk7vyc7O1oIFC7Rlyxalp6erYcOGeuqppxQbG6s6deqU+IyCggK99957Wr16tU6dOqU6deqof//+WrRokVq0aFHxHwIAAICXsRSWZ8G7Uly5ckW//OUvlZycfNc18ywWS6nhrywZGRmKiIjQ448/rtatWys8PFxXr17Vjh07dPbsWUVHR2vHjh2yWotWf8nNzVXPnj2VkpKi6OhodezYUV999ZUSEhLUpUsXJSUlqXbt2k6fMXnyZK1Zs0aPPvqohg4dqrS0NG3evFl16tTRgQMH1KpVq3LXm52dreDgYGVlZSkoKKjc76uoye+6bddAhb0/29MVAM74joQ3cfd3ZHmzh8szeXPmzNEXX3yhPn36aPz48WrSpIl8fCo9Mah69eopKytLtWrVcmrPy8vTgAEDlJCQoB07dmjo0KGSpLffflspKSmaO3eu3nrrLUf/l19+WXFxcXrnnXc0b948R/uePXu0Zs0a9erVS3/7298cn/Pss89qyJAhmjFjhnbu3FnpcQAAAHiSy6ls27Ztevzxx7Vr164qfaqF1WotEfAkycfHR8OHD1diYqJOnTolqegu3zVr1qhOnTolTgnPnz9f7733ntasWeMU8t5//31J0htvvOH0OYMHD1afPn2UkJCgc+fO6aGHHqqyMQEAAFQ3l594cfPmTfXq1avaHltWUFCgzz77TJLUtm1bSUVP1EhLS1OPHj1KLNcSEBCgHj166LvvvtP58+cd7YmJiY5tPzZw4EBJ0t69e901DAAAgGrh8kxehw4dSn3aRVW5c+eOFi9erMLCQmVmZmrXrl06ceKEJkyYoH79+kkqCnmSyryGrlWrVtq5c6dSU1MVERGh3NxcXbx4UW3btpXNZiu1/w/3W5rbt2/r9u3bjtfZ2dmSJLvdLrvdLqloNtJmsyk/P18FBQWOvsXteXl5Ttcx2mw2Wa3WMtuL9ut8swngScW/68WKL9XIy8tzavf19VVBQYHTdbkWi0U+Pj5ltpd13FTt8XTv2hlTzRqTxHPS4T2q43gqD5dDXmxsrIYNG6YDBw6oa9euru6mTHfu3NHChQsdry0Wi1588UUtWbLE0ZaVlSVJZT4ft/hixOJ+Fe1fmiVLljjVVSwhIUEPPPCAJOmhhx5Sx44ddfToUZ07d87R5+GHH1ZkZKQOHjyoy5cvO9o7dOigpk2bKikpSTk5OY72bt26qX79+kpISJA0tMyagOq2fft2p9dDhgzRzZs3tWfPHkebj4+Phg4dqoyMDO3fv9/RHhgYqL59++r8+fNOT8wJDw9X9+7dlZqaqpMnTzra3XE8/fALMioqSv7+/oypho9Jct+Nb0BFuft4Onz4cLnqcPnu2o0bN+qTTz7Rtm3b9Nxzz6lTp05l3uExbtw4Vz5CUlEaTktL03//93/rlVde0aOPPqrt27crKChIH374oZ577jm9+uqrevPNN0u899VXX9XixYv1X//1Xxo+fLjS0tLUuHFj9ejRQ1988UWJ/n/7298UHR2tWbNmafny5aXWU9pMXkREhDIyMhzjd8dftNPeYyYP3uMP05nJY0zeNaYpy5nJg/dYNcu9x9OVK1cUGhrqvrtrY2JiZLFYVFhYqPXr12v9+vUlrs8rLCyUxWKpVMizWq1q0qSJpk6dqrCwMI0aNUqLFi1SXFycY0aurJm34lOpxf0q2r80fn5+8vPzK9Hu6+tbYv0+m81W6mnhsu5CLqv9x/sFPK2s38nS2q1Wq2PJo/K0l3XcuPt4Ykw1e0yAN/HU8VSiX7l6lSI+Pt7Vt7osOjpaUtHNE9K9r6H78TV7AQEBatiwoU6fPq38/PwSP7h7XeMHAABQU9Sox5qlpaVJ+udfcq1atVKjRo2UnJys3Nxcpztsc3NzlZycrObNmysiIsLR3rt3b3300UdKTk5Wr169nPZfvD7ej9sBAABqGpeXUHGX//3f/9WNGzdKtN+4cUNz5syRVHShrVR0LnvSpEm6fv263njjDaf+b7zxhq5fv67Jkyc7tU+ZMkVS0Tp6d+7ccbTv2LFDiYmJio6OVtOmTat0TAAAANXN5Zm8H97tcS8VWVh48+bN+t3vfqeePXuqWbNmCgoK0vfff68dO3YoMzNTTzzxhH71q185+r/00kv65JNPFBcXp6+++kqdOnXSl19+6Xis2ezZs532HxUVpUmTJmnNmjXq1KmThg4dqosXL2rTpk2qV6+eVqxYUe5aAQAAvJXLIa9Zs2blWgjZYrGUez0XSfrZz36mtLQ07du3T/v379f169cVHBys9u3b6+mnn9bzzz/vdMFhQECA9u7dqwULFmjLli3as2ePGjZsqF//+teKjY2Vv79/ic9YtWqV2rVrp9WrV2v58uWqU6eOhg8frkWLFqlly5blrhUAAMBbubyESvHdtT+WlZWlI0eO6PTp0+rdu7eaNWvmkZs0qkt5HxJcWTx8G97E3Q/fBiqK70h4E3d/R5Y3e7g8k7d+/foytxUWFmrZsmV6++23tXbtWlc/AgAAAC5yy40XxU+nePTRR/Xv//7v7vgIAAAA3IVb767t3Lmzdu/e7c6PAAAAQCncGvK+/fbbCt10AQAAgKrh8jV5ZSkoKND333+v9evX65NPPlG/fv2q+iMAAABwDy6HPKvVetclVAoLC1W3bl0tW7bM1Y8AAACAi1wOeb169So15FmtVtWtW1ddunTRhAkTVL9+/UoVCAAAgIpzOeQlJiZWYRkAAACoSl737FoAAABUXpXceJGcnKyUlBRlZ2crKChIHTp0UI8ePapi1wAAAHBBpULevn37NGHCBJ06dUpS0c0WxdfptWrVSvHx8erWrVvlqwQAAECFuBzyjh8/rujoaN24cUMDBgxQVFSUGjZsqPT0dO3Zs0cJCQkaOHCgDhw4oEceeaQqawYAAMA9uBzyfvOb3+jOnTvavn27Bg0a5LRt7ty5+uyzzzRs2DD95je/0UcffVTpQgEAAFB+Lt94kZiYqJEjR5YIeMUGDRqkkSNHas+ePS4XBwAAANe4HPKysrLUvHnzu/Zp3ry5srKyXP0IAAAAuMjlkNeoUSMdOHDgrn3+/ve/q1GjRq5+BAAAAFzkcsgbNmyYEhMTNX/+fN26dctp261btxQbG6s9e/boF7/4RaWLBAAAQMW4fOPF/PnztW3bNi1evFirVq3S448/rgcffFCXLl3SoUOHdPnyZbVo0ULz58+vynoBAABQDi6HvNDQUB04cEAvvfSSPvroI23fvt2xrXbt2powYYLi4uJUr169KikUAAAA5VepxZDDwsK0bt06rVq1SidOnHA88SIyMlK+vr5VVSMAAAAqqMIhb9GiRcrNzdXChQsdQc7X11ft2rVz9Llz545effVVBQYG6uWXX666agEAAFAuFbrx4vPPP9frr7+u0NDQu87U1apVS6GhoXr11VdZJw8AAMADKhTyNm7cqLp162rGjBn37Dt9+nTVq1dP8fHxLhcHAAAA11Qo5O3bt0/9+/eXn5/fPfv6+fmpf//+Sk5Odrk4AAAAuKZCIS8tLU0tWrQod//mzZvr4sWLFS4KAAAAlVOhkGe1WmW328vd3263y2p1eb1lAAAAuKhCCaxRo0b6+uuvy93/66+/VuPGjStcFAAAACqnQiHviSee0O7du3XmzJl79j1z5ox2796tXr16uVobAAAAXFShkDd9+nTZ7XaNHDlSGRkZZfbLzMzUU089pby8PE2dOrXSRQIAAKBiKrQYcqdOnTR79my9++67euSRR/Rv//ZvioqKUpMmTSRJ33//vXbt2qXVq1fr8uXLmjNnjjp16uSWwgEAAFC2Cj/xYtmyZapdu7aWLl2qRYsWadGiRU7bCwsLZbPZNG/ePL355ptVVigAAADKr8Ihz2KxaPHixZo4caLi4+O1b98+paenS5IaNGigHj16KCYmRi1btqzyYgEAAFA+FQ55xVq2bMlMHQAAgJdiETsAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADOSVIe+DDz7Qv/7rv6pz587y8/OTxWLR+vXrS+27YMECWSyWMv+dOXOm1Pft3LlTvXv3VmBgoIKCghQVFaVdu3a5b1AAAADVyMfTBZTmtdde09mzZxUWFqaGDRvq7Nmz93zP+PHj1axZsxLtISEhJdo++OADjR07VuHh4YqJiZEkbdq0SQMGDNDmzZs1cuTISo4AAADAs7wy5K1Zs0atWrVS06ZN9dZbb2nevHn3fE9MTIz69Olzz35Xr17VzJkzFRYWpi+//FJNmjSRJM2dO1cdO3bU1KlTNXDgQAUGBlZ2GAAAAB7jladr+/fvr6ZNm7pl3x9//LGuXbummTNnOgKeJDVp0kQzZsxQRkaG/vKXv7jlswEAAKqLV4Y8VyQlJSkuLk5Lly7V1q1bdf369VL7JSYmSpKio6NLbBs4cKAkae/evW6rEwAAoDp45elaV8TGxjq9DgkJ0fLlyzVu3Din9tTUVElSq1atSuyjuK24T2lu376t27dvO15nZ2dLkux2u+x2uyTJarXKZrMpPz9fBQUFjr7F7Xl5eSosLHS022w2Wa3WMtuL9ut71/ED1an4d72Yj0/RV0leXp5Tu6+vrwoKCpSfn+9os1gs8vHxKbO9rOOmao+ne9fOmGrWmCSLAG9RHcdTedT4kPeTn/xE69atU58+fdSwYUOlp6dr27Ztev311xUTE6OQkBANGzbM0T8rK0uSFBwcXGJfQUFBTn1Ks2TJEi1cuLBEe0JCgh544AFJ0kMPPaSOHTvq6NGjOnfunKPPww8/rMjISB08eFCXL192tHfo0EFNmzZVUlKScnJyHO3dunVT/fr1lZCQIGloOX8igPtt377d6fWQIUN08+ZN7dmzx9Hm4+OjoUOHKiMjQ/v373e0BwYGqm/fvjp//rxSUlIc7eHh4erevbtSU1N18uRJR7s7jqcffkFGRUXJ39+fMdXwMUlBAryFu4+nw4cPl6sOS+EP/4TyQsU3XsTHxzvuhC2PXbt2acCAAWrbtq2OHj3qaG/durVSU1Nlt9t/8BdgEbvdrlq1aql9+/Y6cuRIqfstbSYvIiJCGRkZjpDojr9op73HTB68xx+mM5PHmLxrTFOWM5MH77FqlnuPpytXrig0NFRZWVmO7FGaGj+TV5Z+/fqpZcuWOnbsmLKzsx0/hOIZvKysLIWGhjq9p/jUa2mzfMX8/Pzk5+dXot3X11e+vs5BzGazyWazlej743B5r/Yf7xfwtLJ+J0trt1qtslpLXv5bVntZx427jyfGVLPHBHgTTx1PJT6vXL1qqLCwMEnSjRs3HG13u+7ubtfrAQAA1CTGhrzc3FwdP35cAQEBjrAnSb1795ak/3+dm7OdO3c69QEAAKipanTIy8nJ0TfffFOi/ebNm5o8ebJycnI0atQop2nNUaNGKTg4WCtWrNCFCxcc7RcuXNDKlSsVFham4cOHV0v9AAAA7uKV1+StWbNGX3zxhSTp2LFjjrbiNe569uypSZMmKTMzU5GRkerSpYvatGmjBg0a6NKlS/r888914cIFtWvXTkuXLnXad926dbVy5UqNHTtWnTp10ujRoyUVPdYsMzNTmzZt4mkXAACgxvPKkPfFF19ow4YNTm3JyclKTk52vJ40aZLq1aunadOm6eDBg9q+fbuuXr0qf39/tWnTRrNmzdKMGTPk7+9fYv9jxoxRWFiYFi9erPj4eFksFj322GN67bXX1L9/f7ePDwAAwN28fgkVb5edna3g4OB73sZcWZPfdduugQp7f7anKwCc8R0Jb+Lu78jyZo8afU0eAAAASkfIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMBAhDwAAwECEPAAAAAMR8gAAAAxEyAMAADAQIQ8AAMBAhDwAAAADEfIAAAAMRMgDAAAwECEPAADAQIQ8AAAAAxHyAAAADETIAwAAMNB9HfIOHTqkIUOGKCQkRAEBAeratas2b97s6bIAAAAqzcfTBXjKnj17NHDgQNWuXVtPP/20AgMDtWXLFo0ePVrnz5/Xr3/9a0+XCAAA4LL7ciYvLy9PkydPltVqVVJSklavXq1ly5bpyJEjat26tV555RWdPXvW02UCAAC47L4Mebt379a3336rZ599Vh06dHC0BwcH65VXXtGdO3e0YcMGzxUIAABQSfdlyEtMTJQkRUdHl9g2cOBASdLevXursyQAAIAqdV9ek5eamipJatWqVYltDRo0UJ06dRx9fuz27du6ffu243VWVpYk6cqVK7Lb7ZIkq9Uqm82m/Px8FRQUOPoWt+fl5amwsNDRbrPZZLVay2y32+26c8u3EiMGqlZmpt3ptY9P0VdJXl6eU7uvr68KCgqUn5/vaLNYLPLx8SmzvazjpiqPp/LUzphq1pju3LII8BbXrrn3eLpy5YokOR07pbkvQ15xMAsODi51e1BQkKPPjy1ZskQLFy4s0d68efOqKxDwchvneboCAPBe1fUdmZOTU2aWke7TkFcZ8+bN05w5cxyvCwoKdOXKFYWGhspi4S9Jb5adna2IiAidP39eQUFBni4HALwK35E1R2FhoXJyctSoUaO79rsvQ15x6i1rti47O1t169YtdZufn5/8/Pyc2kJCQqq0PrhXUFAQX2AAUAa+I2uGu83gFbsvb7wovhavtOvu0tPTdf369VKv1wMAAKgp7suQ17t3b0lSQkJCiW07d+506gMAAFAT3Zchr1+/fmrRooU+/PBDpaSkONqzsrK0ePFi1apVS+PGjfNcgXALPz8/xcbGljjdDgDgO9JElsJ73X9rqLIea3b27Fn99re/5bFmAACgRrtvQ54kHTx4ULGxsdq3b5/sdrvatWunOXPmaPTo0Z4uDQAAoFLu65AHAABgqvvymjwAAADTEfIAAAAMRMgDAAAwECEPAADAQIQ8GKn4fiLuKwIA3K8IeTCSxWJRYWGhLBaL0tPTVVBQ4OmSAMAr5OXlKSUlRevXr9ff//53/hg2mI+nCwCqWl5enpKTk7Vu3Tp98803slqt8vf3V4cOHTRixAh1797d0yUCgEecO3dOS5cu1XvvvSdJaty4sZYsWaIxY8Z4uDK4A+vkwTi//e1v9cYbbygnJ0ctW7aUj4+PTp486dgeGRmpKVOm6Omnn1aDBg08WCkAVK8pU6boT3/6k/r06aOOHTtq48aNCgsL07Zt25Sfn6/du3fr/PnzGjx4sLp06eLpclFJhDwY5fTp02rXrp06deqkDRs2qFatWnrwwQeVnp6uTz/9VH/+85+VmJgoSerbt6/efvttderUybNFA0A1+O6779S2bVuNHj1af/zjH+Xn56fk5GT9/Oc/19SpU7Vjxw6n57l3795dv//97/mOrMG4Jg9GiY+PV0BAgGJjY9W8eXM1btxYNptNTZo00bRp07R7924dOXJEY8eOVXJysp599lkdPnzY02UDgNt9+OGHqlu3rsaMGSM/Pz9JUps2bRQUFKSNGzeqfv36+utf/6p33nlHnTt31r59+xQXF6ebN296uHK4ipAHoxw/flx16tRRq1atJBVdn1d8E0Z+fr4kqV27dtqwYYPeeustffPNN1q5cqUnSwaAanHo0CEFBwcrMjLS0XbixAnl5uaqW7du+vOf/6zBgwfrhRdeUHJysn72s5/p448/1rlz5zxYNSqDkAejdOzYUadPn9b169clST4+RfcWWSwW2Ww2Sf9cVuWFF17QiBEjtHv3bn333XeeKRgAqoHdblfjxo31/fffq169eo72zz77TJmZmZo1a5bq1Kmj/Px83bx5U76+vurbt69sNhtnO2owQh6MEhUVJUl67rnntHv3bt25c6fUfsWzeg8//LAyMjIcoRAATFNYWChfX1+1b99eOTk5GjFihOLj4xUXF6c333xTwcHB+pd/+RdJks1mk7+/v6SiMyEFBQWKiIjwZPmoBEIejNK1a1fNmTNHR44c0YwZM/T+++/r0qVLTn2KZ/WuXr2qCxcuKCAgQO3bt/dQxQDgXhaLRZIUHR2t/v37a+fOnZo4caLmzZunKVOmqHPnzo4b0oplZGToq6++UnBwsJ544gkPVI2qwN21MNKqVau0dOlSfffdd2rUqJGGDx+uwYMHKyIiQjabTSEhIVqxYoXeffddTZs2TcuWLfN0yQDgdvn5+fr000917NgxtW7dWk8++aSGDh2q3Nxc/f73v1ezZs0UGhqq3/3ud5o7d66mT5+ud99919Nlw0WEPBipsLBQp06d0vvvv6+PPvpIFy5ckCTVr19fvr6+unjxogoKCvTMM88oLi5OTZo08XDFAOAZW7Zs0VNPPaWIiAh17NhRJ06cUGpqqtq3b68tW7aoRYsWni4RLiLkwXi5ubk6ePCgPv30U6Wlpekf//iHgoKCNGrUKD355JOqXbu2p0sEAI+Kj4/XihUrdP78eeXk5KhHjx566623WBC5hiPk4b5it9vl6+vr6TIAwOtkZWXp9OnTCgwMVGhoqEJCQhzPAEfNRMgDAAAwEHfXAgAAGIiQBwAAYCBCHgAAgIEIeQAAAAYi5AEAABiIkAcAAGAgQh4AAICBCHkAAAAGIuQBAAAYiJAHAABgoP8H3Jj8oK9BCPMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "qc.measure_all()\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_unitary_of_f.ipynb.txt b/_sources/example_unitary_of_f.ipynb.txt new file mode 100644 index 00000000..3675f37b --- /dev/null +++ b/_sources/example_unitary_of_f.ipynb.txt @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unitary of qlasskit function\n", + "\n", + "In qlasskit, we can exploit external low-level frameworks to perform operations on the resulting quantum circuit. In this example, we use qiskit in order to obtain the unitary matrix of our `QlassF` function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QlassF(a:bool, b:bool) -> bool:\n", + "\t_ret = a ^ ~b\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit_aer import AerSimulator\n", + "from qiskit.visualization import array_to_latex\n", + "from qlasskit import qlassf\n", + "\n", + "\n", + "@qlassf\n", + "def f(a: bool, b: bool) -> bool:\n", + " return a ^ (not b)\n", + "\n", + "\n", + "print(f\"\\n{f}\\n\")\n", + "\n", + "qc = QuantumCircuit(f.num_qubits, f.num_qubits)\n", + "qc.append(f.gate(), f.qubits)\n", + "\n", + "qc.save_state()\n", + "qc.decompose().draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$$\n", + "\n", + "\\begin{bmatrix}\n", + "0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\\\\n", + " 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n", + " 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\\\\n", + " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\\\\n", + " 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n", + " 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\\\\n", + " 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\\\\n", + " 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\\\\n", + " \\end{bmatrix}\n", + "$$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simulator = AerSimulator(method=\"unitary\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "array_to_latex(result.get_unitary(circ, 3), max_size=16)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/exporter.ipynb.txt b/_sources/exporter.ipynb.txt new file mode 100644 index 00000000..310a9390 --- /dev/null +++ b/_sources/exporter.ipynb.txt @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exporting to other frameworks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qlasskit implements circuit / gate exporters for Qiskit, Cirq, Qasm, Sympy and Pennylane. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import Qint, qlassf\n", + "\n", + "\n", + "@qlassf\n", + "def hello_world(a: bool, b: Qint[2]) -> Qint[2]:\n", + " return b + (1 if a else 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qiskit" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FRAynIBcRMZyCXETEcApyERHDKchFRAynIBcRMZyCXETEcApyERHDKchFRAynIBcRMZyCXETEcApyERHDKchFRAynIBcRMZyCXETEcApyERHDKchFRAynIBcRMZyCXETEcBMiyD0eD6WlpWRlZREbG0taWhobN26ks7OTdevW4XA42LFjR6TLlBDxeqH+BDzzNvz7f8HP/wQvvgcn2yJdmUhwxES6gFCrqalh5cqVuN1upk2bxoIFCzh+/Djbt2+nvr6elpYWABYtWhTZQiUkao/Db6rBfWbosoqDMM8J9xbBrITw1yYSLLbeI/d4PKxevRq3282mTZtobm6muroat9vN1q1bKS8vp7KyEofDQX5+fqTLlSB7twGe/qPvED/vkBv+9+/geGu4qhIJPlsH+YYNG3C5XKxfv55t27aRkHBxt6u0tJSCggL6+vpIT08nMTExgpVKsB3zwH/ugwHvyOt29sBTf4Su3pCXJRIStg3y2tpaysrKmDlzJlu2bPG5zuLFiwEoKCi48Nxzzz3HPffcw9y5c4mLi+Paa6/lu9/9Lh0dHWGpW4Kjohb6B0a//umzUHk0dPWIhJJtg3zXrl0MDAywZs0a4uPjfa4zdepUYHCQb9u2jejoaH74wx/yyiuv8I1vfIOf/vSn3H777QwM+JEMEjFnzsL+Rv/bvVFnHRgVMY1tD3ZWVFQAUFJSMuw6LpcLGBzkL730ErNmzbrw/7fccguzZs1izZo1vPHGG9x8880hqliC5ZB7dEMqn3ayDVo6Idn3332Rccu2QX7s2DEA5s6d63N5X18fe/fuBQYH+aUhfl5hYSEATU1NAdVSWFiI2+0OqG2wfe47lcRdkUKzu5nU1CWRLickrln2Na67638E1Pbmks9wpvlgkCsaPybC6z+Sn6yrJCkhhebm8fVv4HQ6qaqqCqitbYO8s7MTgK6uLp/Ly8rK8Hg8JCQkkJGRcdltvf766wDMnz8/oFrcbnfAfwSCrb+//8LjeKkp2K44Efjv1dR4hDNjaD/eTYTXfyR2/DewbZA7nU5aW1uprq6muLh40LLm5mY2b94MQH5+Pg6HY9jtNDU18b3vfY/bb7894LnmTqczoHahEB0dfeFx9uzZEa4mNLwd1rcxr9d72df207rbPyFhSj/xNv13gYnx+o9kvP4bjCUnbBvkK1asoLa2lq1bt3LbbbeRk5MDQGVlJWvXrsXj8QCXPxGoo6ODu+66i8mTJ/Pzn/884FoC/boUCo++AGe6IMWZcuEYgR1t/z0c+WT0IQ6w+vpZ/OyYvaeuTJTX/3L2/Ax6OiAlxT7/BradtVJaWkpycjKNjY3k5uaSl5dHdnY2RUVFZGZmsnz5cmDw+Pilurq6WL16NUePHuX3v/89KSkp4SxfxuiWa/1bPyYairNCU4tIqNk2yFNTU9mzZw+rVq0iNjaWhoYGkpKS2LlzJ+Xl5dTV1QG+g/zcuXPce++9VFVV8corr7BgwYJwly9jVDAHSkZ5SMMBrF0GSZqtIoay7dAKWAcnd+/ePeT5jo4OGhoaiIqKYuHChYOWnZ97/tprr/Hyyy9TVFQUrnIlyO68DqZOht/tH/7koGlT4IFiyB0/Q6UifrN1kA/nwIEDeL1ecnJyiIuLG7Tsm9/8Js8++yzf/va3iYuL46233rqw7JprrvE5PVHGJ4cDPrMQlmXBO0egqgGaW8ELREfBl6+39twnRUe6UpGxse3QyuXs378f8D2s8sorrwDwox/9iOLi4kE/5eXlYa1TgiM+FpYvgNLPQeLUvzw3BQozFOJiDxNyj/xyQd7Q0BDmakRExkZ75CIihpuQe+Tnr8MiImIHE3KPXETEThTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgF+QTSPwBeb6SrkEjxei++/nof2EtMpAuQ0PB64chJ+LAZGlusn86ei8vbuuBnFZCWBNemQOaV4HBErl4Jvr5++KDJeh80tkBTK/T2WcvauuGffg1pydZ7IC8NrkqMaLkyBgpym+ntg7frYe9H4D4z/HperJD/sBn+cACcV8AN2bD0Gpisd4XRzpyFN+pgXz10dA+/Xkun9fP+x7C7BnKccGMO5KXqj7pp9JG1kfqTsGsfeDr8b+s+A89XwX99CF8uhmuuDH59ElpeL7xVD79+F3r6/G9f57Z+cpxw/1JIig9+jRIaGiO3gYEB+E017PhDYCF+KU+HtZ3fVFvbFTN09sDO16Hs7cBC/FJ1bthaDlVHg1ObhJ72yA3XPwC/fBPeOxa8bXqB12vh9Fl4cBlE68/9uNbeBf/2GjRfZijNXz191vuqowduvTZ425XQ0EfUYF4v/L+3ghvil3rvmLV9zXAYv7p64aevBzfEL/Xrd2Hf4dBsW4JHQW6wt+qhMsRffyuPWgdPZXx6oQqOt4a2j+cqQ9+HjI2GVgzV2mntLfnrH26HxKnW9MOf/HZ0bX5dDfNSYMY0//uT0PnA5f8f8kBe//4B+M+34Fuf1TDbeDUhXhaPx0NpaSlZWVnExsaSlpbGxo0b6ezsZN26dTgcDnbs2BHpMv3yQlVgB7USp8L0OOtxtLrPwa8C+KMhodPXD8++43+7QF5/AFcL7Dnkf38SHrbfI6+pqWHlypW43W6mTZvGggULOH78ONu3b6e+vp6WlhYAFi1aFNlC/XCqw9obC6f9jVa/yZqSNi7UfAxnusLb5546uHkeRE2I3T+z2Pol8Xg8rF69GrfbzaZNm2hubqa6uhq3283WrVspLy+nsrISh8NBfn5+pMsdtb0fWTNLwskLvPlRmDuVYb1RF/4+T3VAbXP4+5WR2TrIN2zYgMvlYv369Wzbto2EhIQLy0pLSykoKKCvr4/09HQSE804P9nrjdz83sqjmsEyHnjaocETmb4rj0SmX7k82wZ5bW0tZWVlzJw5ky1btvhcZ/HixQAUFBRceG7Pnj2sWLGClJQUpkyZQmpqKl/60peora0NS90jOdNlHaiKhLau8H+dl6GOnYpc340tketbhmfbMfJdu3YxMDDAmjVriI/3PbA7dap1xOfSIG9tbSUvL4+HH36YK6+8EpfLxZYtWyguLuaDDz4gNTU1LPUPpzGCH+Lz/U+Pi2wNE10k3wOnOqyzSKdNiVwNMpRtg7yiogKAkpKSYddxuawjhpcG+Z133smdd945aL0lS5Ywb948nn/+eTZu3BiCakfvchfCClf/eWmRrWGiGw/vAV2LZ3yxbZAfO2ad7jh37lyfy/v6+ti7dy8wOMh9SU5OBiAmJrB/rsLCQtxud0BtP23hZx/h2uV/53PZ+TnCl5MYe/HxsbuHX2+4ecb//MSTfO13W0dZ7fjzue9UEndFCs3uZlJTl0S6nIDc+vXnmZmx1Oeykd4Do339Yfj3wJe+vBb3oddHWe3485N1lSQlpNDcPL7eA06nk6qqqoDa2jbIOzs7Aejq8j2oW1ZWhsfjISEhgYyMjCHL+/v7GRgY4NixY/zjP/4jTqeTL37xiwHV4na7aWpqCqjtp81pbxt22fk5wqMRFRXYEElb25mg/S6R0N/ff+HR1N+jp6dn2GWjfQ8E+voDfPLJSWP/7cAe74FPs22QO51OWltbqa6upri4eNCy5uZmNm/eDEB+fj4OHxdfvuWWWy7ssWdlZVFRUcGsWbMCriVYpk4e/vj0aA6CJsZaH+KBAevmAv5uKy42htmzZ4/c0TgVHR194dHU3yPa0T/sspHeA6N9/S+3rRmJ0+g39N8Oxu97YCw54fB67TmhbMOGDTz55JOkpaXx6quvkpOTA0BlZSVr167lyJEjnDt3jm9+85s+z+o8dOgQp0+f5ujRo/z4xz/m5MmT7N27lzlz5oT7VxnkAxf8+38F3v6xu609sdNn4bFf+d/+b26BhZE93jsmj75gzby5Yir84AuRriYwv6m2rk4ZiLG+/gD/616zD3bu+Rn0dMCUeLjp65GuJjhsO/2wtLSU5ORkGhsbyc3NJS8vj+zsbIqKisjMzGT58uXA8OPj8+bNY+nSpdx///289tprtLe38/jjj4fzV/ApNWli9y+RfQ2S480OcbuybZCnpqayZ88eVq1aRWxsLA0NDSQlJbFz507Ky8upq7NOjRvpQCfA9OnTycrK4vDhyF/P84qp1s9E61sumpscub7nRLBvGZ5tx8gB5s+fz+7du4c839HRQUNDA1FRUSxcuHDE7Zw8eZJDhw6xdKnvmQLh5HBAYQa8djD8fS/J1L0cx4OZCZAxC45+Ev6+lwydFyDjgK2DfDgHDhzA6/WSk5NDXNzgQ/cPPvggWVlZLFq0iOnTp/PRRx/xxBNPEBMTw7e+9a0IVTzYsmyoOBje6604HLAsK4wdymXdmB3+IE+Oh2uvDm+fMjq2HVq5nP379wO+h1Wuv/56Xn75Zb72ta+xcuVKfvzjH3PTTTdRU1NDVtb4SLLkeMgP80k5+am6Ge94UjAn/GfY3jwPovSNbFyakHvklwvy9evXs379+nCX5Le7C+GQ27pWeKjFTrL6k/EjJhq+WARP/TE8/c1JhhtzwtOX+E975IaaHgd3L/a/XVuXNfXMnwtv3b1Y11cZjxbMhqJM/9oE8vpHR8EDxbo70Hg2IffIz1+HxXRFmXDkE//uqTna23udt/Qa/8NCwucLhXD8tHUHn9Hw9/UHa8/feYX/7SR89DfWYA6H9SFbnB6a7S9Ohy8VaabKeBY7Cb5eAlfPCM327ym0/pjL+KYgN1x0FKxZBn+9IHiB63BY21uzTLf1MkF8LKxfAblBPNs8dhJ85Qa4aV7wtimhMyGHVuwmygGrr7NOnd/1Fpwc/rpaI7oyEb58vTVPWcwRN9m6fELlUetG2V29gW/r2hS4/3odFzGJgtxGMmbBf19p3QrujTpr7HS0rp5hzU0uzIDJelcYyeGwjmfMT7Hu67rv8Ojv6OTAmiN+Y7Z1EFXDaWbRR9ZmJsdYJwwVZ1n3dTzUbN2ey9VizVTwYn1oE6da1+xIS4J5KZA+Ux9eu0iYCrfnw20L4WCTdUDc1QKu1ot76lEO63yEtCRIS4a8VOuMUTGTgtymHA5rD/3SIRKvFwa81odYoW1/0VHW3ZwuvaPTgNd6H2gqob0oyCcQhwOiFeATWpQD6yuZ2Ir+LouIGE5BLiJiOAW5iIjhFOQiIoZTkIuIGE5BLiJiOAW5iIjhFOQiIoZTkIuIGE5BLiJiOAW5iIjhFOQiIoZTkIuIGE5BLiJiOAW5iIjhFOQiIoZTkIuIGE5BLiJiOAW5iIjhFOQiIoZTkIuIGE5BLiJiOAW5iIjhFOQiIoZTkIuIGE5BLiJiOAW5iIjhFOQiIoZTkIuIGE5BLiJiuJhIFyASSmd7wNUKjafgZDuc7bWe7+qFN+ogNQmung6T9UmwJa8Xejqg7QS0u6G7Dc51W8v6eqD5ACRcBdOSwGHwbq3D6/V6I12ESDANDEBtsxXUHx6Hkd7gk6JhcTrckANpSeGoUEKtvxfcH0JjDXScHHn9yXEwO9/6iU0MeXlBpyAXWznUDM++A56OwNpnXwVfXAqzEoJbl4SH1wuuGqh/w9rj9psDZudB9i0QMyXY1YWOglxsofscvFgNbx4e+7YmRcMdi+CmeRDlGPv2JDzOnoba30Fr49i3NSUBFnwGkjPGvq1wUJCL8dq74WcV0NQa3O0WpsOXiyHa4LHTieJMM9Q8f3H8O1jmLYe0vwruNkNBh3jEaJ098K+vgvtM8Ldd1QD9Xli7DKIU5uNW2wmoftYaFw+2QxXW43gPc709xVgDXvjFntCE+HnvHYPf7g/d9mVses9ae+KhCPHzDlXAqaOh234waI9cjLW3Dj464V+bf7gdEqdCWxf85Leja/PqAViYCnOS/a9RQuvDV60w90fRgzB5GvR2wju/HF2bg7+H4q+O3wOg2iMXI53qgJfe879d4lSYHmc9jtaAF3btg75+//uT0DlRByfr/G83eRrEJliPo9XTDnV/9L+vcLF9kHs8HkpLS8nKyiI2Npa0tDQ2btxIZ2cn69atw+FwsGPHjkiXKX56/SD0hjFYm8/An4MwG0KCw+uFo2+Gt8/jH1gnFI1Hth5aqampYeXKlbjdbqZNm8aCBQs4fvw427dvp76+npaWFgAWLVoU2ULFL93noDICY5Z7P4K/Sg9/vzLUmSbo8IS5Uy+4/gxZN4a531Gw7R65x+Nh9erVuN1uNm3aRHNzM9XV1bjdbrZu3Up5eTmVlZU4HA7y8/MjXa74oboBevrC32/9ydAeWJXRc70fmX6P/9k6c3i8sW2Qb9iwAZfLxfr169m2bRsJCRdP1SstLaWgoIC+vj7S09NJTDTwnNwJzN8DnMF0OIJ9y0XBOOknEL1n4eypyPR9ObYM8traWsrKypg5cyZbtmzxuc7ixYsBKCgoGHY7K1euxOFw8Nhjj4WiTAmQq2Vi9i2Wnk7rQliR0jYO/5jbMsh37drFwMAAa9asIT4+3uc6U6da0xaGC/JnnnmGmpqaUJUoAeo+B5+0R67/RgV5xLVHOEgj3b8vtjzYWVFhnY5VUlIy7DoulwvwHeRtbW38/d//Pdu2bePBBx8ccz2FhYW43e4xb0cgbkYan/v2vmGXn58nPpzE2IuPj909/HrDzTP/6OhxUlOLRlmthMKy+ffwt5/9F5/Lzs8Rv5wp0y4+3vjw8OsNN8/8mV2/ZudD60dZ7eg5nU6qqqoCamvLID927BgAc+fO9bm8r6+PvXv3Ar6D/Lvf/S45OTmsWbMmKEHudrtpamoa83YEpvfHXXb5+XniI4mKGt16Qzii9VpGWPvVw4+rnJ8jPhqOqNGve6lzvX3j7j1gyyDv7OwEoKury+fysrIyPB4PCQkJZGQMvrxZVVUVTz/9NO+++27Q6nE6nUHb1kQXN33GZZe3+X7JL0iMtUJ8YADaLnOBpeG24x04x+zZs0eoUkIpPnH4Xe7ezpHbT5lmhbh3wBpv93dbMZOiQvIeGEtO2DLInU4nra2tVFdXU1xcPGhZc3MzmzdvBiA/Px+H4+J1Svv7+3n44YdZv349ubm5Qasn0K9LMlRfPzzyDPQPMwVspNPuH7vb2hNv64bHfuV//3k5qTz9l2E5iYzWRni3zPey0Zxyf+PD1p54Tye8sdP//h/46hf4/v/5gv8NQ8iWBztXrFgBwNatW6mru3gOb2VlJSUlJXg81pkEnz4RaMeOHZw4cUKzVMaxmGhImR65/tN0vZWIS7gqsv0nRrh/X2wZ5KWlpSQnJ9PY2Ehubi55eXlkZ2dTVFREZmYmy5cvBwaPj3s8Hr73ve/x/e9/n76+Pk6fPs3p06cB6O7u5vTp0wyMxzMBJqBI3o5Nt4KLvJjJEBfB1yHSf0h8sWWQp6amsmfPHlatWkVsbCwNDQ0kJSWxc+dOysvLL+ylXxrkLpeL9vZ2Hn74YWbMmHHhB6w9+xkzZvDxxx9H5PeRwXIjNEQdHQXzUiLTtww2KzMy/cbPHJ/39LTlGDnA/Pnz2b1795DnOzo6aGhoICoqioULF154Pisri9dff33I+iUlJTz00EN89atf1UHLcWLB1TAjDlr9vHzpWBWkQUJsePsU32YvgmMROPSUuggc4/D2f7YN8uEcOHAAr9dLTk4OcXEX55/Fx8dz6623+myTnp4+7DIJv6goWJYN5WG+3sYNOeHtT4YXNx2S0+FUQ/j6jJ4EzgXh688fthxauZz9+63bvVzu1HwZ/26aBzP8uJ70WOWnQeas8PUnI8u62ZpGGC6ZN1jj8+PRhNsj9zfIdW/q8Sl2Enz5evi310LfV9xkuG/J+PxKPZElXAkZ18ORMFyX/IrZMGcc37dTe+RirBwn3HKtf23auuD02ZFPHLrUF5dCgh93FJLwSV8KV/h5ALq3E7rbR3fyEFi3d8u9Pbx7//5yeLXLKQYbGID/2AfvNoRm+/cUWsM4Mn71dkF1WWhuNBE9Ca67D6ZfHfxtB5OCXIw3MADPVsK+w8HbZpQD7iuC4qzgbVNCp7cL3n8BzjQHb5uTpsKiL/i/xx8JCnKxjZpjVqB39oxtO1dPhweKIVUn/xhloB8a3oajb1nXURmLWdlw7YqLV0oc7xTkYivt3VBeYw21nPPz5szxsXBzDixfYF0KQMzUfhIO/ymwqYnTkiGjGK6aZ9bBbQW52FJnD7xzxLq/5/HTw19ka0oMzJ0JS6+xTvhRgNvH2VZw1YDniPXfw5kcBzPSYHaB9WhSgJ+nIBfb6+u3wvxkm7WX7sCavpgyA2YlWOPhYm99PdYt2rrbrCGYqCiYFGdNYZwSb2Z4X0pBLiJiuHE8M1JEREZDQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4RTkIiKGU5CLiBhOQS4iYjgFuYiI4f4/rsFC1rA5DD8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = hello_world.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## QASM" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OPENQASM 3.0;\n", + "\n", + "gate hello_world a b.0 b.1 _ret.0 _ret.1 {\n", + "\tcx a _ret.0\n", + "\tcx b.0 _ret.0\n", + "\tcx b.1 _ret.1\n", + "\tccx a b.0 _ret.1\n", + "}\n", + "\n", + "hello_world q[0],q[1],q[2],q[3],q[4];\n", + "\n" + ] + } + ], + "source": [ + "qc = hello_world.export(\"qasm\")\n", + "print(qc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cirq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
0: ───hello_world───\n",
+       "      │\n",
+       "1: ───hello_world───\n",
+       "      │\n",
+       "2: ───hello_world───\n",
+       "      │\n",
+       "3: ───hello_world───\n",
+       "      │\n",
+       "4: ───hello_world───
" + ], + "text/plain": [ + "0: ───hello_world───\n", + " │\n", + "1: ───hello_world───\n", + " │\n", + "2: ───hello_world───\n", + " │\n", + "3: ───hello_world───\n", + " │\n", + "4: ───hello_world───" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cirq\n", + "\n", + "qc = hello_world.export(\"cirq\")\n", + "qc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pennylane" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pennylane as qml\n", + "\n", + "tape = hello_world.export(\"pennylane\")\n", + "tape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sympy" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle C_{0,1}{\\left(X_{4}\\right)} \\text{CNOT}_{2,4} \\text{CNOT}_{1,3} \\text{CNOT}_{0,3} {\\left|00000\\right\\rangle }$" + ], + "text/plain": [ + "C((0,1),X(4))*CNOT(2,4)*CNOT(1,3)*CNOT(0,3)*|00000>" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = hello_world.export(\"sympy\")\n", + "qc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qutip" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Disabled on docs for a depencency problem\n", + "# qc = hello_world.export(\"qutip\")\n", + "# qc.gates" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/generated/qlasskit.algorithms.deutschjozsa.DeutschJozsa.rst.txt b/_sources/generated/qlasskit.algorithms.deutschjozsa.DeutschJozsa.rst.txt new file mode 100644 index 00000000..7276e224 --- /dev/null +++ b/_sources/generated/qlasskit.algorithms.deutschjozsa.DeutschJozsa.rst.txt @@ -0,0 +1,41 @@ +qlasskit.algorithms.deutschjozsa.DeutschJozsa +============================================= + +.. currentmodule:: qlasskit.algorithms.deutschjozsa + +.. autoclass:: DeutschJozsa + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~DeutschJozsa.__init__ + ~DeutschJozsa.circuit + ~DeutschJozsa.decode_counts + ~DeutschJozsa.decode_output + ~DeutschJozsa.draw + ~DeutschJozsa.encode_input + ~DeutschJozsa.export + ~DeutschJozsa.gate + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~DeutschJozsa.input_qubits + ~DeutschJozsa.input_size + ~DeutschJozsa.num_gates + ~DeutschJozsa.num_qubits + ~DeutschJozsa.output_qubits + ~DeutschJozsa.output_size + ~DeutschJozsa.qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.algorithms.grover.Grover.rst.txt b/_sources/generated/qlasskit.algorithms.grover.Grover.rst.txt new file mode 100644 index 00000000..b1c92670 --- /dev/null +++ b/_sources/generated/qlasskit.algorithms.grover.Grover.rst.txt @@ -0,0 +1,41 @@ +qlasskit.algorithms.grover.Grover +================================= + +.. currentmodule:: qlasskit.algorithms.grover + +.. autoclass:: Grover + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Grover.__init__ + ~Grover.circuit + ~Grover.decode_counts + ~Grover.decode_output + ~Grover.draw + ~Grover.encode_input + ~Grover.export + ~Grover.gate + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~Grover.input_qubits + ~Grover.input_size + ~Grover.num_gates + ~Grover.num_qubits + ~Grover.output_qubits + ~Grover.output_size + ~Grover.qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.algorithms.qalgorithm.rst.txt b/_sources/generated/qlasskit.algorithms.qalgorithm.rst.txt new file mode 100644 index 00000000..d819a261 --- /dev/null +++ b/_sources/generated/qlasskit.algorithms.qalgorithm.rst.txt @@ -0,0 +1,41 @@ +qlasskit.algorithms.qalgorithm +============================== + +.. automodule:: qlasskit.algorithms.qalgorithm + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + oraclize + + + + + + .. rubric:: Classes + + .. autosummary:: + + QAlgorithm + + + + + + .. rubric:: Exceptions + + .. autosummary:: + + ConstantOracleException + + + + + diff --git a/_sources/generated/qlasskit.algorithms.simon.Simon.rst.txt b/_sources/generated/qlasskit.algorithms.simon.Simon.rst.txt new file mode 100644 index 00000000..4c79da06 --- /dev/null +++ b/_sources/generated/qlasskit.algorithms.simon.Simon.rst.txt @@ -0,0 +1,41 @@ +qlasskit.algorithms.simon.Simon +=============================== + +.. currentmodule:: qlasskit.algorithms.simon + +.. autoclass:: Simon + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Simon.__init__ + ~Simon.circuit + ~Simon.decode_counts + ~Simon.decode_output + ~Simon.draw + ~Simon.encode_input + ~Simon.export + ~Simon.gate + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~Simon.input_qubits + ~Simon.input_size + ~Simon.num_gates + ~Simon.num_qubits + ~Simon.output_qubits + ~Simon.output_size + ~Simon.qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.bqm.decode_samples.rst.txt b/_sources/generated/qlasskit.bqm.decode_samples.rst.txt new file mode 100644 index 00000000..5ba0e806 --- /dev/null +++ b/_sources/generated/qlasskit.bqm.decode_samples.rst.txt @@ -0,0 +1,6 @@ +qlasskit.bqm.decode\_samples +============================ + +.. currentmodule:: qlasskit.bqm + +.. autofunction:: decode_samples \ No newline at end of file diff --git a/_sources/generated/qlasskit.decompiler.Decompiler.rst.txt b/_sources/generated/qlasskit.decompiler.Decompiler.rst.txt new file mode 100644 index 00000000..4da16935 --- /dev/null +++ b/_sources/generated/qlasskit.decompiler.Decompiler.rst.txt @@ -0,0 +1,23 @@ +qlasskit.decompiler.Decompiler +============================== + +.. currentmodule:: qlasskit.decompiler + +.. autoclass:: Decompiler + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Decompiler.__init__ + ~Decompiler.decompile + + + + + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.qcircuit.gates.rst.txt b/_sources/generated/qlasskit.qcircuit.gates.rst.txt new file mode 100644 index 00000000..f95b2ca8 --- /dev/null +++ b/_sources/generated/qlasskit.qcircuit.gates.rst.txt @@ -0,0 +1,53 @@ +qlasskit.qcircuit.gates +======================= + +.. automodule:: qlasskit.qcircuit.gates + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + apply + + + + + + .. rubric:: Classes + + .. autosummary:: + + Barrier + CCX + CP + CX + H + I + MCX + MCtrl + NopGate + P + QControlledGate + QGate + S + Swap + T + Toffoli + X + Y + Z + + + + + + + + + diff --git a/_sources/generated/qlasskit.qcircuit.qcircuit.QCircuit.rst.txt b/_sources/generated/qlasskit.qcircuit.qcircuit.QCircuit.rst.txt new file mode 100644 index 00000000..9dd024df --- /dev/null +++ b/_sources/generated/qlasskit.qcircuit.qcircuit.QCircuit.rst.txt @@ -0,0 +1,53 @@ +qlasskit.qcircuit.qcircuit.QCircuit +=================================== + +.. currentmodule:: qlasskit.qcircuit.qcircuit + +.. autoclass:: QCircuit + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~QCircuit.__init__ + ~QCircuit.add_qubit + ~QCircuit.append + ~QCircuit.append_circuit + ~QCircuit.barrier + ~QCircuit.ccx + ~QCircuit.copy + ~QCircuit.cp + ~QCircuit.cx + ~QCircuit.draw + ~QCircuit.export + ~QCircuit.get_key_by_index + ~QCircuit.h + ~QCircuit.iqft + ~QCircuit.mctrl + ~QCircuit.mcx + ~QCircuit.qft + ~QCircuit.random + ~QCircuit.repeat + ~QCircuit.s + ~QCircuit.swap + ~QCircuit.t + ~QCircuit.x + ~QCircuit.y + ~QCircuit.z + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~QCircuit.num_gates + ~QCircuit.used_qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.rst.txt b/_sources/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.rst.txt new file mode 100644 index 00000000..e1307587 --- /dev/null +++ b/_sources/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.rst.txt @@ -0,0 +1,41 @@ +qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper +================================================= + +.. currentmodule:: qlasskit.qcircuit.qcircuitwrapper + +.. autoclass:: QCircuitWrapper + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~QCircuitWrapper.__init__ + ~QCircuitWrapper.circuit + ~QCircuitWrapper.decode_counts + ~QCircuitWrapper.decode_output + ~QCircuitWrapper.draw + ~QCircuitWrapper.encode_input + ~QCircuitWrapper.export + ~QCircuitWrapper.gate + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~QCircuitWrapper.input_qubits + ~QCircuitWrapper.input_size + ~QCircuitWrapper.num_gates + ~QCircuitWrapper.num_qubits + ~QCircuitWrapper.output_qubits + ~QCircuitWrapper.output_size + ~QCircuitWrapper.qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.qlassfun.QlassF.rst.txt b/_sources/generated/qlasskit.qlassfun.QlassF.rst.txt new file mode 100644 index 00000000..82b07c08 --- /dev/null +++ b/_sources/generated/qlasskit.qlassfun.QlassF.rst.txt @@ -0,0 +1,54 @@ +qlasskit.qlassfun.QlassF +======================== + +.. currentmodule:: qlasskit.qlassfun + +.. autoclass:: QlassF + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~QlassF.__init__ + ~QlassF.bind + ~QlassF.circuit + ~QlassF.compile + ~QlassF.decode_counts + ~QlassF.decode_output + ~QlassF.draw + ~QlassF.encode_input + ~QlassF.export + ~QlassF.f + ~QlassF.from_function + ~QlassF.gate + ~QlassF.to_bqm + ~QlassF.to_logicfun + ~QlassF.truth_table + ~QlassF.truth_table_header + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~QlassF.input_qubits + ~QlassF.input_size + ~QlassF.num_gates + ~QlassF.num_qubits + ~QlassF.output_qubits + ~QlassF.output_size + ~QlassF.qubits + ~QlassF.name + ~QlassF.original_f + ~QlassF.args + ~QlassF.returns + ~QlassF.expressions + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.qlassfun.qlassf.rst.txt b/_sources/generated/qlasskit.qlassfun.qlassf.rst.txt new file mode 100644 index 00000000..51d99df6 --- /dev/null +++ b/_sources/generated/qlasskit.qlassfun.qlassf.rst.txt @@ -0,0 +1,6 @@ +qlasskit.qlassfun.qlassf +======================== + +.. currentmodule:: qlasskit.qlassfun + +.. autofunction:: qlassf \ No newline at end of file diff --git a/_sources/generated/qlasskit.qlassfun.qlassfa.rst.txt b/_sources/generated/qlasskit.qlassfun.qlassfa.rst.txt new file mode 100644 index 00000000..cb63d945 --- /dev/null +++ b/_sources/generated/qlasskit.qlassfun.qlassfa.rst.txt @@ -0,0 +1,6 @@ +qlasskit.qlassfun.qlassfa +========================= + +.. currentmodule:: qlasskit.qlassfun + +.. autofunction:: qlassfa \ No newline at end of file diff --git a/_sources/generated/qlasskit.types.parameter.rst.txt b/_sources/generated/qlasskit.types.parameter.rst.txt new file mode 100644 index 00000000..47b5904d --- /dev/null +++ b/_sources/generated/qlasskit.types.parameter.rst.txt @@ -0,0 +1,30 @@ +qlasskit.types.parameter +======================== + +.. automodule:: qlasskit.types.parameter + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Parameter + ParameterMeta + + + + + + + + + diff --git a/_sources/generated/qlasskit.types.qbool.rst.txt b/_sources/generated/qlasskit.types.qbool.rst.txt new file mode 100644 index 00000000..0262cb65 --- /dev/null +++ b/_sources/generated/qlasskit.types.qbool.rst.txt @@ -0,0 +1,29 @@ +qlasskit.types.qbool +==================== + +.. automodule:: qlasskit.types.qbool + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Qbool + + + + + + + + + diff --git a/_sources/generated/qlasskit.types.qchar.rst.txt b/_sources/generated/qlasskit.types.qchar.rst.txt new file mode 100644 index 00000000..e6132755 --- /dev/null +++ b/_sources/generated/qlasskit.types.qchar.rst.txt @@ -0,0 +1,29 @@ +qlasskit.types.qchar +==================== + +.. automodule:: qlasskit.types.qchar + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Qchar + + + + + + + + + diff --git a/_sources/generated/qlasskit.types.qfixed.rst.txt b/_sources/generated/qlasskit.types.qfixed.rst.txt new file mode 100644 index 00000000..d77f9470 --- /dev/null +++ b/_sources/generated/qlasskit.types.qfixed.rst.txt @@ -0,0 +1,44 @@ +qlasskit.types.qfixed +===================== + +.. automodule:: qlasskit.types.qfixed + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Qfixed + Qfixed1_2 + Qfixed1_3 + Qfixed1_4 + Qfixed1_6 + Qfixed2_2 + Qfixed2_3 + Qfixed2_4 + Qfixed2_6 + Qfixed3_3 + Qfixed3_4 + Qfixed3_6 + Qfixed4_4 + Qfixed4_6 + QfixedImp + QfixedMeta + + + + + + + + + diff --git a/_sources/generated/qlasskit.types.qint.rst.txt b/_sources/generated/qlasskit.types.qint.rst.txt new file mode 100644 index 00000000..f7ed60bc --- /dev/null +++ b/_sources/generated/qlasskit.types.qint.rst.txt @@ -0,0 +1,40 @@ +qlasskit.types.qint +=================== + +.. automodule:: qlasskit.types.qint + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Qint + Qint12 + Qint16 + Qint2 + Qint3 + Qint4 + Qint5 + Qint6 + Qint7 + Qint8 + QintImp + QintMeta + + + + + + + + + diff --git a/_sources/generated/qlasskit.types.qlist.rst.txt b/_sources/generated/qlasskit.types.qlist.rst.txt new file mode 100644 index 00000000..363bebaa --- /dev/null +++ b/_sources/generated/qlasskit.types.qlist.rst.txt @@ -0,0 +1,30 @@ +qlasskit.types.qlist +==================== + +.. automodule:: qlasskit.types.qlist + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Qlist + QlistMeta + + + + + + + + + diff --git a/_sources/generated/qlasskit.types.qmatrix.rst.txt b/_sources/generated/qlasskit.types.qmatrix.rst.txt new file mode 100644 index 00000000..24bf940c --- /dev/null +++ b/_sources/generated/qlasskit.types.qmatrix.rst.txt @@ -0,0 +1,30 @@ +qlasskit.types.qmatrix +====================== + +.. automodule:: qlasskit.types.qmatrix + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Qmatrix + QmatrixMeta + + + + + + + + + diff --git a/_sources/generated/qlasskit.types.qtype.rst.txt b/_sources/generated/qlasskit.types.qtype.rst.txt new file mode 100644 index 00000000..682e0556 --- /dev/null +++ b/_sources/generated/qlasskit.types.qtype.rst.txt @@ -0,0 +1,42 @@ +qlasskit.types.qtype +==================== + +.. automodule:: qlasskit.types.qtype + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + bin_to_bool_list + bool_list_to_bin + + + + + + .. rubric:: Classes + + .. autosummary:: + + Qtype + + + + + + .. rubric:: Exceptions + + .. autosummary:: + + TypeErrorException + + + + + diff --git a/_sources/generated/qlasskit.types.rst.txt b/_sources/generated/qlasskit.types.rst.txt new file mode 100644 index 00000000..38f42f94 --- /dev/null +++ b/_sources/generated/qlasskit.types.rst.txt @@ -0,0 +1,47 @@ +qlasskit.types +============== + +.. automodule:: qlasskit.types + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + const_to_qtype + format_outcome + interpret_as_qtype + type_repr + + + + + + + + + + + + + +.. rubric:: Modules + +.. autosummary:: + :toctree: + :recursive: + + qlasskit.types.parameter + qlasskit.types.qbool + qlasskit.types.qchar + qlasskit.types.qfixed + qlasskit.types.qint + qlasskit.types.qlist + qlasskit.types.qmatrix + qlasskit.types.qtype + diff --git a/_sources/how_it_works.ipynb.txt b/_sources/how_it_works.ipynb.txt new file mode 100644 index 00000000..f904bef9 --- /dev/null +++ b/_sources/how_it_works.ipynb.txt @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How it works\n", + "\n", + "To convert Python code into a quantum circuit, qlasskit implements a series of transformations:\n", + "\n", + "1. It begins with the Python *AST* (Abstract Syntax Tree), converting it into a more streamlined form using the `ast2ast` module. \n", + "2. Next, the streamlined AST is translated into *boolean expressions* as an intermediate step by the `ast2logic` module. \n", + "During this phase, boolean expressions are refined and optimized in preparation for the final transformation.\n", + "3. Finally, the `compiler` module takes these optimized boolean expressions and compiles them into a \n", + "*quantum circuit*.\n", + "\n", + "Unlike other libraries that translate individual operations into quantum circuits before combining them, \n", + "qlasskit constructs a single boolean expression for each output qubit of the entire function. \n", + "This unique approach facilitates advanced optimization leveraging boolean algebraic properties.\n", + "\n", + "![Transformations](_images/trasformations/qlasskit_transformations.png)\n", + "\n", + "\n", + "For instance, let assume we have the following function:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint\n", + "from qiskit import QuantumCircuit\n", + "\n", + "\n", + "@qlassf\n", + "def f_comp(b: bool, n: Qint[2]) -> Qint[2]:\n", + " for i in range(3):\n", + " n += 1 if b else 2\n", + " return n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we decompose the algorithm in 3 separate additions and we compile them separately, we obtain the following circuit:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Operations: OrderedDict([('cx', 12), ('barrier', 3), ('x', 3), ('ccx', 3)])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@qlassf\n", + "def f1(b: bool, n: Qint[2]) -> Qint[2]:\n", + " return n + (1 if b else 2)\n", + "\n", + "\n", + "qc = QuantumCircuit(f_comp.num_qubits * 2 - 1)\n", + "\n", + "for i in range(3):\n", + " qc.barrier(label=f\"it_{i}\")\n", + " qc.append(f1.gate(), [0] + list(range(1 + i * 2, 5 + i * 2)))\n", + "\n", + "print(\"Operations:\", qc.decompose().count_ops())\n", + "qc.decompose().draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While if we compile the whole function to a quantum circuit using qlasskit, we obtain the following quantum circuit:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Operations: OrderedDict([('cx', 7), ('x', 1), ('ccx', 1)])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = QuantumCircuit(f_comp.num_qubits)\n", + "qc.append(f_comp.gate(), f_comp.qubits)\n", + "\n", + "print(\"Operations:\", qc.decompose().count_ops())\n", + "qc.decompose().draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the circuit drawings, qlasskit approach needs half the number of qubits and half the number of gates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AST Traslator\n", + "Given a python function, the `qlasskit.ast2logic` module walks its syntax tree translating all the statements / \n", + "expressions to boolean expressions.\n", + "\n", + "\n", + "For instance, the following function:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "@qlassf\n", + "def f(n: Qint[4]) -> bool:\n", + " return n == 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is translated to this boolean expression:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(_ret, n.0 & n.1 & ~n.2 & ~n.3)]\n" + ] + } + ], + "source": [ + "print(f.expressions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Compiler\n", + "\n", + "The boolean expressions are then being fed to the `qlasskit.compiler`` which compiles boolean expressions\n", + "to invertible circuits, introducing auxiliary qubits. In this step, the compiler will automatically uncompute \n", + "auxiliary qubits in order to reduce the number of qubits needed and the circuit footprint. \n", + "\n", + "For the compilation, two backends are supported:\n", + "\n", + "- InternalCompiler\n", + "- Tweedledum.xag_synth\n", + "\n", + "\n", + "## Result \n", + "\n", + "The result of the compiler is a quantum circuit represented with qlasskit `QCircuit`. This circuit\n", + "can now be exported to one of the supported framework as a gate or as a standalone circuit.\n", + "\n", + "\n", + "The previous example function `f`, is translated to the following quantum circuit: the \n", + "result is available at qubit `q6`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.export().draw(\"mpl\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..9f7bc3a3 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,86 @@ +Qlasskit +==================================== + +.. image:: https://img.shields.io/badge/GitHub-Repository-blue?logo=github + :alt: GitHub Repository + :target: https://github.com/dakk/qlasskit +.. image:: https://img.shields.io/badge/supported_by-Unitary_Fund-ffff00.svg + :alt: Unitary Fund + :target: https://unitary.fund +.. image:: https://github.com/dakk/qlasskit/actions/workflows/ci.yaml/badge.svg + :alt: CI Status + :target: https://github.com/dakk/qlasskit/actions/workflows/ci.yaml +.. image:: https://img.shields.io/pypi/v/qlasskit + :alt: PyPI - Version + :target: https://pypi.org/project/qlasskit/ +.. image:: https://img.shields.io/badge/license-Apache_2.0-blue + :alt: License: Apache 2.0 + :target: https://opensource.org/licenses/Apache-2.0 +.. image:: https://img.shields.io/badge/qlasskit-Discord-yellow?logo=discord&logoColor=f5f5f5 + :alt: Discord + :target: https://discord.com/channels/764231928676089909/1210279373865754624 +.. image:: https://app.codacy.com/project/badge/Grade/05acc06af76848028183a66448217d91 + :alt: Codacy Badge + :target: https://app.codacy.com/gh/dakk/qlasskit/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade +.. image:: https://static.pepy.tech/badge/qlasskit + :alt: Downloads + :target: https://pepy.tech/project/qlasskit + +Qlasskit is a Python library that allows quantum developers to write classical algorithms in pure +Python and translate them into unitary operators (gates) for use in quantum circuits supporting a wide +range of quantum frameworks. + +Qlasskit also support exporting to Binary Quadratic Models (bqm, ising and qubo) ready to be used in +quantum annealers, ising machines, simulators, etc. + +.. toctree:: + :maxdepth: 2 + :caption: Qlasskit + + quickstart.ipynb + how_it_works.ipynb + supported + parameters.ipynb + algorithms.ipynb + exporter.ipynb + bqm.ipynb + decompiler_and_optimizer.ipynb + api + cli_tools + +.. toctree:: + :maxdepth: 2 + :caption: Examples + + example_grover.ipynb + example_grover_subset.ipynb + example_grover_hash.ipynb + example_grover_sudoku.ipynb + example_grover_factors.ipynb + example_simon.ipynb + example_deutsch_jozsa.ipynb + example_bernstein_vazirani.ipynb + example_unitary_of_f.ipynb + example_big_circuit.ipynb + example_bqm_tsp.ipynb + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + + +Cite +====== + +.. code-block:: latex + + @software{qlasskit2023, + author = {Davide Gessa}, + title = {qlasskit: a python-to-quantum circuit compiler}, + url = {https://github.com/dakk/qlasskit}, + year = {2023}, + } \ No newline at end of file diff --git a/_sources/parameters.ipynb.txt b/_sources/parameters.ipynb.txt new file mode 100644 index 00000000..0e3767b6 --- /dev/null +++ b/_sources/parameters.ipynb.txt @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameters\n", + "\n", + "Qlasskit allow to create `qlassf` function with parameters. Those parameters are not free variables of the final circuit, and need to be bound before compilation.\n", + "\n", + "In the next example we define `test` with a parameter `a`; after binding the parameter `a` to `True`, the final function expression will be `b`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(_ret, b)]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qlasskit import qlassf, Parameter\n", + "\n", + "\n", + "@qlassf\n", + "def test(a: Parameter[bool], b: bool) -> bool:\n", + " return a and b\n", + "\n", + "\n", + "qf = test.bind(a=True)\n", + "qf.expressions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parameterizable qlassf also supports custom types, and complex types like `Qlist` and `Qtuple`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(_ret.0, b.0),\n", + " (_ret.1, ~b.1),\n", + " (_ret.2, ~(b.1 ^ b.2)),\n", + " (_ret.3, b.2 ^ b.3 ^ (b.1 & ~b.2))]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qlasskit import Qlist, Qint, Qint4\n", + "\n", + "\n", + "@qlassf\n", + "def test(a: Parameter[Qlist[Qint[2], 4]], b: Qint[4]) -> Qint[4]:\n", + " s = Qint4(0)\n", + " for n in a:\n", + " s += n\n", + " return s + b\n", + "\n", + "\n", + "qf = test.bind(a=[2, 1, 0, 3])\n", + "qf.expressions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qf.circuit().export().draw(\"mpl\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/quickstart.ipynb.txt b/_sources/quickstart.ipynb.txt new file mode 100644 index 00000000..1aea4736 --- /dev/null +++ b/_sources/quickstart.ipynb.txt @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quickstart\n", + "\n", + "First install qlasskit using pip.\n", + "\n", + "```pip install qlasskit```\n", + "\n", + "We now define a qlassf function that sums two numbers:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint, Qint2\n", + "\n", + "\n", + "@qlassf\n", + "def sum_two_numbers(a: Qint[2], b: Qint[2]) -> Qint[2]:\n", + " return a + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now export the resulting quantum circuit to any supported framework:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuit = sum_two_numbers.export(\"qiskit\")\n", + "circuit.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The qlassf function can be also exported as a gate, if the destination framwork supports it. We can use `encode_input` and `decode_output` in order to convert from/to high level types of qlasskit without worrying about the binary representation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", + "\n", + "qc = QuantumCircuit(sum_two_numbers.num_qubits, len(sum_two_numbers.output_qubits))\n", + "\n", + "qc.initialize(\n", + " sum_two_numbers.encode_input(Qint2(1), Qint2(2)), sum_two_numbers.input_qubits\n", + ")\n", + "qc.append(sum_two_numbers.gate(\"qiskit\"), sum_two_numbers.qubits)\n", + "qc.measure(sum_two_numbers.output_qubits, range(len(sum_two_numbers.output_qubits)))\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_aer import AerSimulator\n", + "\n", + "simulator = AerSimulator()\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = sum_two_numbers.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/supported.rst.txt b/_sources/supported.rst.txt new file mode 100644 index 00000000..0acae255 --- /dev/null +++ b/_sources/supported.rst.txt @@ -0,0 +1,286 @@ +Supported python subset +==================================== + +Qlasskit supports a subset of python. This subset will be expanded, but it is +limited by the linearity of quantum circuits and by the number of qubits. + +The structure of a qlasskit function has the following pattern: + +.. code-block:: python + + @qlasskit + def f(param: type, [...param: type]) -> type: + statement + ... + statement + + +Types +----- + +All types has a static size. + +bool +^^^^ + +Boolean type. + + +Qint +^^^^ + +Unsigned integers; `Qint[2]` has 2 bits, and there other sizes are supported. +Single bit of the Qint are accessible by the subscript operator `[]`. +All the supported sizes have a constructor `Qintn()` defined in `qlasskit.types`. + +Qfixed +^^^^^^ + +Fixed point rational number; `Qfixed[2,3]` has 2 bits for the integer part and 3 bits for the fractional. +All the supported sizes have a constructor `Qfixedn_m()` defined in `qlasskit.types`. + +Qchar +^^^^^ + +A character. + +Tuple +^^^^^ + +Container type holding different types. + + +List +^^^^ + +Qlist[T, size] denotes a fixed-size list in qlasskit. +For example, the list `[1,2,3]` is typed as `Qlist[Qint[2],3]`. + + +Matrix +^^^^^^ + +Qmatrix[T, m, n] denotes a fixed-size list in qlasskit. +For example, the matrix `[[1,2],[3,4]]` is typed as `Qmatrix[Qint[2],2,2]`. + + + + + +Expressions +----------- + +Constants +^^^^^^^^^^^^^ + +.. code-block:: python + + True + +.. code-block:: python + + 42 + +.. code-block:: python + + 3.14 + +.. code-block:: python + + 'a' + + +Tuple +^^^^^ + +.. code-block:: python + + (a, b) + +List (fixed size) +^^^^^^^^^^^^^^^^^ + +.. code-block:: python + + [a, b] + + +2D Matrix (fixed size) +^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: python + + [[a, b], [c,d]] + + +Subscript +^^^^^^^^^ + +.. code-block:: python + + a[0] + +Boolean operators +^^^^^^^^^^^^^^^^^ + +.. code-block:: python + + not a + +.. code-block:: python + + a and b + +.. code-block:: python + + a or b + + + +If expressions +^^^^^^^^^^^^^^ + +.. code-block:: python + + a if b else c + +Comparators +^^^^^^^^^^^ + +.. code-block:: python + + a > b or b <= c and c == d or c != a + + +Unary Op +^^^^^^^^^ + +.. code-block:: python + + ~a + + + +Bin Op +^^^^^^^^^ + +.. code-block:: python + + a << 1 + +.. code-block:: python + + a >> 2 + +.. code-block:: python + + a + b + +.. code-block:: python + + a - b + +.. code-block:: python + + a * b + +.. code-block:: python + + a ** b + +.. code-block:: python + + a % 2 + +.. note:: + Modulo operator only works with 2^n values. + + +Function call +^^^^^^^^^^^^^ + +Bultin functions: + +* `print()`: debug function, ignore by conversion +* `len(Tuple)`, `len(Qlist)``: returns the length of a tuple +* `max(a, b, ...)`, `max(Tuple)`, `max(Qlist)`: returns the max of a tuple +* `min(a, b, ...)`, `min(Tuple)`, `min(Qlist)`: returns the min of a tuple +* `sum(Tuple)`, `sum(Qlist)`: returns the sum of the elemnts of a tuple / list +* `all(Tuple)`, `all(Qlist)`: returns True if all of the elemnts are True +* `any(Tuple)`, `any(Qlist)`: returns True if any of the elemnts are True +* `ord(Qchar)`: returns the integer value of the given Qchar +* `chr(Qint)`: returns the char given its ascii code +* `int(Qfixed | Qint)`: returns the integer part of a Qfixed +* `float(Qint | Qfixed)`: returns a Qfixed representing the Qint + + +Statements +---------- + +Assign +^^^^^^ + +.. code-block:: python + + c = not a + +Return +^^^^^^ + +.. code-block:: python + + return b+1 + + +For loop +^^^^^^^^ + +.. code-block:: python + + for i in range(4): + a += i + + +.. note:: + Please note that in qlasskit, for loops are unrolled during compilation. Therefore, + it is essential that the number of iterations for each for loop is known at the + time of compilation. + +Function def +^^^^^^^^^^^^ + +.. code-block:: python + + def f(t: Qlist[Qint[4],2]) -> Qint[4]: + return t[0] + t[1] + + +If then else +^^^^^^^^^^^^ + +.. code-block:: python + + c = 0 + if cond: + c += 12 + else: + c += 13 + +.. note:: + At present, the if-then-else statement in qlasskit is designed to support branch bodies + that exclusively contain assignment statements. + + + +Quantum Hybrid +--------------- + +In a qlassf function, you have the option to utilize quantum gates through the Q module. It's +important to keep in mind that incorporating quantum gates within a qlasskit function leads +to a Python function that exhibits distinct behaviors compared to its quantum counterpart. + +.. code-block:: python + + def bell(a: bool, b: bool) -> bool: + return Q.CX(Q.H(a), b) + diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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.rst-content .wy-alert-warning.hint, +html[data-theme="dark"] .rst-content .wy-alert-warning.important, +html[data-theme="dark"] .rst-content .wy-alert-warning.note, +html[data-theme="dark"] .rst-content .wy-alert-warning.seealso, +html[data-theme="dark"] .rst-content .wy-alert-warning.tip, +html[data-theme="dark"] .wy-alert.wy-alert-warning { + background-color: #533500; +} + +html[data-theme="dark"] .rst-content .admonition-todo .admonition-title, +html[data-theme="dark"] .rst-content .admonition-todo .wy-alert-title, +html[data-theme="dark"] .rst-content .attention .admonition-title, +html[data-theme="dark"] .rst-content .attention .wy-alert-title, +html[data-theme="dark"] .rst-content .caution .admonition-title, +html[data-theme="dark"] .rst-content .caution .wy-alert-title, +html[data-theme="dark"] .rst-content .warning .admonition-title, +html[data-theme="dark"] .rst-content .warning .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.admonition + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.admonition + .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.danger .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.danger .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.error .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.error .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.hint .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.hint .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.important + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.important + .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.note .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.note .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.seealso + .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.seealso .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.tip .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.tip .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert.wy-alert-warning + .admonition-title, +html[data-theme="dark"] + .wy-alert.wy-alert-warning + .rst-content + .admonition-title, +html[data-theme="dark"] .wy-alert.wy-alert-warning .wy-alert-title { + background-color: #803b00; +} + +html[data-theme="dark"] .rst-content .danger, +html[data-theme="dark"] .rst-content .error, +html[data-theme="dark"] .rst-content .wy-alert-danger.admonition, +html[data-theme="dark"] .rst-content .wy-alert-danger.admonition-todo, +html[data-theme="dark"] .rst-content .wy-alert-danger.attention, +html[data-theme="dark"] .rst-content .wy-alert-danger.caution, +html[data-theme="dark"] .rst-content .wy-alert-danger.hint, +html[data-theme="dark"] .rst-content .wy-alert-danger.important, +html[data-theme="dark"] .rst-content .wy-alert-danger.note, +html[data-theme="dark"] .rst-content .wy-alert-danger.seealso, +html[data-theme="dark"] .rst-content .wy-alert-danger.tip, +html[data-theme="dark"] .rst-content .wy-alert-danger.warning, +html[data-theme="dark"] .wy-alert.wy-alert-danger { + background-color: #82231a; +} + +html[data-theme="dark"] .rst-content .danger .admonition-title, +html[data-theme="dark"] .rst-content .danger .wy-alert-title, +html[data-theme="dark"] .rst-content .error .admonition-title, +html[data-theme="dark"] .rst-content .error .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition-todo + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition-todo + .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition + .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.attention + .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.attention .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.caution .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.caution .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.hint .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.hint .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.important + .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.important .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.note .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.note .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.seealso .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.seealso .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.tip .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.tip .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.warning .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.warning .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert.wy-alert-danger + .admonition-title, +html[data-theme="dark"] + .wy-alert.wy-alert-danger + .rst-content + .admonition-title, +html[data-theme="dark"] .wy-alert.wy-alert-danger .wy-alert-title { + background-color: #b9372b; +} + +html[data-theme="dark"] .wy-nav-top { + background-color: #0b152d; +} + +html[data-theme="dark"] .rst-content table.docutils thead, +html[data-theme="dark"] .rst-content table.field-list thead, +html[data-theme="dark"] .wy-table thead { + color: var(--dark-text-color); +} + +html[data-theme="dark"] + .rst-content + table.docutils:not(.field-list) + tr:nth-child(2n-1) + td, +html[data-theme="dark"] .wy-table-backed, +html[data-theme="dark"] html[data-theme="dark"] .wy-table-odd td, +html[data-theme="dark"] .wy-table-striped tr:nth-child(2n-1) td { + background-color: #181818; +} + +html[data-theme="dark"] .rst-content table.docutils td, +html[data-theme="dark"] .wy-table-bordered-all td, +html[data-theme="dark"].writer-html5 .rst-content table.docutils th, +html[data-theme="dark"] .rst-content table.docutils, +html[data-theme="dark"] .wy-table-bordered-all { + border-color: #262626; +} + +html[data-theme="dark"] .rst-content table.docutils caption, +html[data-theme="dark"] .rst-content table.field-list caption, +html[data-theme="dark"] .wy-table caption { + color: var(--dark-text-color); +} + +html[data-theme="dark"] .wy-menu-vertical li.toctree-l3.current > a, +html[data-theme="dark"] + .wy-menu-vertical + li.toctree-l3.current + li.toctree-l4 + > a { + background-color: #18181a; +} + +html[data-theme="dark"] .guilabel { + background-color: #343434; + border-color: #4d4d4d; +} diff --git a/_static/dark_mode_css/general.css b/_static/dark_mode_css/general.css new file mode 100644 index 00000000..aa614f81 --- /dev/null +++ b/_static/dark_mode_css/general.css @@ -0,0 +1,68 @@ +input[type='color'], +input[type='date'], +input[type='datetime-local'], +input[type='datetime'], +input[type='email'], +input[type='month'], +input[type='number'], +input[type='password'], +input[type='search'], +input[type='tel'], +input[type='text'], +input[type='time'], +input[type='url'], +input[type='week'] { + box-shadow: none; +} + +.theme-switcher { + border-radius: 50%; + position: fixed; + right: 1.6em; + bottom: 1.4em; + z-index: 3; + border: none; + height: 2.2em; + width: 2.2em; + background-color: #fcfcfc; + font-size: 20px; + -webkit-box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + color: #404040; + transition: all 0.3s ease-in-out; +} + +body, +.wy-nav-content-wrap, +.wy-nav-content, +.section, +.highlight, +.rst-content div[class^='highlight'], +.wy-nav-content a, +.btn-neutral, +.btn, +footer, +.wy-nav-side, +.wy-menu-vertical li, +.wy-menu-vertical a, +.wy-side-nav-search .wy-dropdown, +.wy-side-nav-search a, +.wy-side-nav-search input, +html.writer-html4 .rst-content dl:not(.docutils) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + > dt, +.rst-content code, +.rst-content tt, +html.writer-html4 .rst-content dl:not(.docutils) dl:not(.field-list) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + dl:not(.field-list) + > dt, +code, +.rst-content code.xref, +.rst-content tt.xref { + transition: all 0.2s ease-in-out; +} diff --git a/_static/dark_mode_js/default_dark.js b/_static/dark_mode_js/default_dark.js new file mode 100644 index 00000000..ea63e072 --- /dev/null +++ b/_static/dark_mode_js/default_dark.js @@ -0,0 +1,13 @@ +const loadTheme = () => { + let theme = localStorage.getItem('theme'); + + if (theme !== null) { + if (theme === 'dark') + document.documentElement.setAttribute('data-theme', 'dark'); + } else { + localStorage.setItem('theme', 'dark'); + document.documentElement.setAttribute('data-theme', 'dark'); + } +}; + +loadTheme(); diff --git a/_static/dark_mode_js/default_light.js b/_static/dark_mode_js/default_light.js new file mode 100644 index 00000000..2b19f92e --- /dev/null +++ b/_static/dark_mode_js/default_light.js @@ -0,0 +1,13 @@ +const loadTheme = () => { + let theme = localStorage.getItem('theme'); + + if (theme !== null) { + if (theme === 'dark') + document.documentElement.setAttribute('data-theme', 'dark'); + } else { + localStorage.setItem('theme', 'light'); + document.documentElement.setAttribute('data-theme', 'light'); + } +}; + +loadTheme(); diff --git a/_static/dark_mode_js/theme_switcher.js b/_static/dark_mode_js/theme_switcher.js new file mode 100644 index 00000000..8e260552 --- /dev/null +++ b/_static/dark_mode_js/theme_switcher.js @@ -0,0 +1,39 @@ +const createThemeSwitcher = () => { + let btn = document.createElement('BUTTON'); + btn.className = 'theme-switcher'; + btn.id = 'themeSwitcher'; + btn.innerHTML = + ''; + document.body.appendChild(btn); + + if (localStorage.getItem('theme') === 'dark') $('#themeMoon').hide(0); + else $('#themeSun').hide(0); +}; + +$(document).ready(() => { + createThemeSwitcher(); + $('#themeSwitcher').click(switchTheme); + + $('footer').html( + $('footer').html() + + 'Dark theme provided by MrDogeBro.' + ); +}); + +const switchTheme = () => { + if (localStorage.getItem('theme') === 'dark') { + localStorage.setItem('theme', 'light'); + document.documentElement.setAttribute('data-theme', 'light'); + + $('#themeSun').fadeOut(200, () => { + $('#themeMoon').fadeIn(200); + }); + } else { + localStorage.setItem('theme', 'dark'); + document.documentElement.setAttribute('data-theme', 'dark'); + + $('#themeMoon').fadeOut(200, () => { + $('#themeSun').fadeIn(200); + }); + } +}; diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 00000000..4d67807d --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install 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+} + +/* Math align to the left */ +.cell_output .MathJax_Display { + text-align: left !important; +} + +/* Pandas tables. 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translated ANSI escape sequences +Color values are copied from Jupyter Notebook +https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21 +Background colors from +https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors +*/ +div.highlight .-Color-Bold { + font-weight: bold; +} + +div.highlight .-Color[class*=-Black] { + color: #3E424D +} + +div.highlight .-Color[class*=-Red] { + color: #E75C58 +} + +div.highlight .-Color[class*=-Green] { + color: #00A250 +} + +div.highlight .-Color[class*=-Yellow] { + color: #DDB62B +} + +div.highlight .-Color[class*=-Blue] { + color: #208FFB +} + +div.highlight .-Color[class*=-Magenta] { + color: #D160C4 +} + +div.highlight .-Color[class*=-Cyan] { + color: #60C6C8 +} + +div.highlight .-Color[class*=-White] { + color: #C5C1B4 +} + +div.highlight .-Color[class*=-BGBlack] { + background-color: #3E424D +} + +div.highlight .-Color[class*=-BGRed] { + background-color: #E75C58 +} + +div.highlight .-Color[class*=-BGGreen] { + background-color: #00A250 +} + +div.highlight .-Color[class*=-BGYellow] { + background-color: #DDB62B +} + +div.highlight .-Color[class*=-BGBlue] { + background-color: #208FFB +} + +div.highlight .-Color[class*=-BGMagenta] { + background-color: #D160C4 +} + +div.highlight .-Color[class*=-BGCyan] { + background-color: #60C6C8 +} + +div.highlight .-Color[class*=-BGWhite] { + background-color: #C5C1B4 +} + +/* Font colors for 8-bit ANSI */ + +div.highlight .-Color[class*=-C0] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC0] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C1] { + color: #800000 +} + +div.highlight .-Color[class*=-BGC1] { + background-color: #800000 +} + +div.highlight .-Color[class*=-C2] { + color: #008000 +} + +div.highlight .-Color[class*=-BGC2] { + background-color: #008000 +} + +div.highlight .-Color[class*=-C3] { + color: #808000 +} + +div.highlight .-Color[class*=-BGC3] { + background-color: #808000 +} + +div.highlight .-Color[class*=-C4] { + color: #000080 +} + +div.highlight .-Color[class*=-BGC4] { + background-color: #000080 +} + +div.highlight .-Color[class*=-C5] { + color: #800080 +} + +div.highlight .-Color[class*=-BGC5] { + background-color: #800080 +} + +div.highlight .-Color[class*=-C6] { + color: #008080 +} + +div.highlight .-Color[class*=-BGC6] { + background-color: #008080 +} + +div.highlight .-Color[class*=-C7] { + color: #C0C0C0 +} + +div.highlight .-Color[class*=-BGC7] { + background-color: #C0C0C0 +} + +div.highlight .-Color[class*=-C8] { + color: #808080 +} + +div.highlight .-Color[class*=-BGC8] { + background-color: #808080 +} + +div.highlight .-Color[class*=-C9] { + color: #FF0000 +} + +div.highlight .-Color[class*=-BGC9] { + background-color: #FF0000 +} + +div.highlight .-Color[class*=-C10] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC10] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C11] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC11] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C12] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC12] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C13] { + color: #FF00FF +} + +div.highlight .-Color[class*=-BGC13] { + background-color: #FF00FF +} + +div.highlight .-Color[class*=-C14] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC14] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C15] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC15] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C16] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC16] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C17] { + color: #00005F +} + +div.highlight .-Color[class*=-BGC17] { + background-color: #00005F +} + +div.highlight .-Color[class*=-C18] { + color: #000087 +} + +div.highlight .-Color[class*=-BGC18] { + background-color: #000087 +} + +div.highlight .-Color[class*=-C19] { + color: #0000AF +} + +div.highlight .-Color[class*=-BGC19] { + background-color: #0000AF +} + +div.highlight .-Color[class*=-C20] { + color: #0000D7 +} + +div.highlight .-Color[class*=-BGC20] { + background-color: #0000D7 +} + +div.highlight .-Color[class*=-C21] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC21] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C22] { + color: #005F00 +} + +div.highlight .-Color[class*=-BGC22] { + background-color: #005F00 +} + +div.highlight .-Color[class*=-C23] { + color: #005F5F +} + +div.highlight .-Color[class*=-BGC23] { + background-color: #005F5F +} + +div.highlight .-Color[class*=-C24] { + color: #005F87 +} + +div.highlight .-Color[class*=-BGC24] { + background-color: #005F87 +} + +div.highlight .-Color[class*=-C25] { + color: #005FAF +} + +div.highlight .-Color[class*=-BGC25] { + background-color: #005FAF +} + +div.highlight .-Color[class*=-C26] { + color: #005FD7 +} + +div.highlight .-Color[class*=-BGC26] { + background-color: #005FD7 +} + +div.highlight .-Color[class*=-C27] { + color: #005FFF +} + +div.highlight .-Color[class*=-BGC27] { + background-color: #005FFF +} + +div.highlight .-Color[class*=-C28] { + color: #008700 +} + +div.highlight .-Color[class*=-BGC28] { + background-color: #008700 +} + +div.highlight .-Color[class*=-C29] { + color: #00875F +} + +div.highlight .-Color[class*=-BGC29] { + background-color: #00875F +} + +div.highlight .-Color[class*=-C30] { + color: #008787 +} + +div.highlight .-Color[class*=-BGC30] { + background-color: #008787 +} + +div.highlight .-Color[class*=-C31] { + color: #0087AF +} + +div.highlight .-Color[class*=-BGC31] { + background-color: #0087AF +} + +div.highlight .-Color[class*=-C32] { + color: #0087D7 +} + +div.highlight .-Color[class*=-BGC32] { + background-color: #0087D7 +} + +div.highlight .-Color[class*=-C33] { + color: #0087FF +} + +div.highlight .-Color[class*=-BGC33] { + background-color: #0087FF +} + +div.highlight .-Color[class*=-C34] { + color: #00AF00 +} + +div.highlight .-Color[class*=-BGC34] { + background-color: #00AF00 +} + +div.highlight .-Color[class*=-C35] { + color: #00AF5F +} + +div.highlight .-Color[class*=-BGC35] { + background-color: #00AF5F +} + +div.highlight .-Color[class*=-C36] { + color: #00AF87 +} + +div.highlight .-Color[class*=-BGC36] { + background-color: #00AF87 +} + +div.highlight .-Color[class*=-C37] { + color: #00AFAF +} + +div.highlight .-Color[class*=-BGC37] { + background-color: #00AFAF +} + +div.highlight .-Color[class*=-C38] { + color: #00AFD7 +} + +div.highlight .-Color[class*=-BGC38] { + background-color: #00AFD7 +} + +div.highlight .-Color[class*=-C39] { + color: #00AFFF +} + +div.highlight .-Color[class*=-BGC39] { + background-color: #00AFFF +} + +div.highlight .-Color[class*=-C40] { + color: #00D700 +} + +div.highlight .-Color[class*=-BGC40] { + background-color: #00D700 +} + +div.highlight .-Color[class*=-C41] { + color: #00D75F +} + +div.highlight .-Color[class*=-BGC41] { + background-color: #00D75F +} + +div.highlight .-Color[class*=-C42] { + color: #00D787 +} + +div.highlight .-Color[class*=-BGC42] { + background-color: #00D787 +} + +div.highlight .-Color[class*=-C43] { + color: #00D7AF +} + +div.highlight .-Color[class*=-BGC43] { + background-color: #00D7AF +} + +div.highlight .-Color[class*=-C44] { + color: #00D7D7 +} + +div.highlight .-Color[class*=-BGC44] { + background-color: #00D7D7 +} + +div.highlight .-Color[class*=-C45] { + color: #00D7FF +} + +div.highlight .-Color[class*=-BGC45] { + background-color: #00D7FF +} + +div.highlight .-Color[class*=-C46] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC46] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C47] { + color: #00FF5F +} + +div.highlight .-Color[class*=-BGC47] { + background-color: #00FF5F +} + +div.highlight .-Color[class*=-C48] { + color: #00FF87 +} + +div.highlight .-Color[class*=-BGC48] { + background-color: #00FF87 +} + +div.highlight .-Color[class*=-C49] { + color: #00FFAF +} + +div.highlight .-Color[class*=-BGC49] { + background-color: #00FFAF +} + +div.highlight .-Color[class*=-C50] { + color: #00FFD7 +} + +div.highlight .-Color[class*=-BGC50] { + background-color: #00FFD7 +} + +div.highlight .-Color[class*=-C51] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC51] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C52] { + color: #5F0000 +} + +div.highlight .-Color[class*=-BGC52] { + background-color: #5F0000 +} + +div.highlight .-Color[class*=-C53] { + color: #5F005F +} + +div.highlight .-Color[class*=-BGC53] { + background-color: #5F005F +} + +div.highlight .-Color[class*=-C54] { + color: #5F0087 +} + +div.highlight .-Color[class*=-BGC54] { + background-color: #5F0087 +} + +div.highlight .-Color[class*=-C55] { + color: #5F00AF +} + +div.highlight .-Color[class*=-BGC55] { + background-color: #5F00AF +} + +div.highlight .-Color[class*=-C56] { + color: #5F00D7 +} + +div.highlight .-Color[class*=-BGC56] { + background-color: #5F00D7 +} + +div.highlight .-Color[class*=-C57] { + color: #5F00FF +} + +div.highlight .-Color[class*=-BGC57] { + background-color: #5F00FF +} + +div.highlight .-Color[class*=-C58] { + color: #5F5F00 +} + +div.highlight .-Color[class*=-BGC58] { + background-color: #5F5F00 +} + +div.highlight .-Color[class*=-C59] { + color: #5F5F5F +} + +div.highlight .-Color[class*=-BGC59] { + background-color: #5F5F5F +} + +div.highlight .-Color[class*=-C60] { + color: #5F5F87 +} + +div.highlight .-Color[class*=-BGC60] { + background-color: #5F5F87 +} + +div.highlight .-Color[class*=-C61] { + color: #5F5FAF +} + +div.highlight .-Color[class*=-BGC61] { + background-color: #5F5FAF +} + +div.highlight .-Color[class*=-C62] { + color: #5F5FD7 +} + +div.highlight .-Color[class*=-BGC62] { + background-color: #5F5FD7 +} + +div.highlight .-Color[class*=-C63] { + color: #5F5FFF +} + +div.highlight .-Color[class*=-BGC63] { + background-color: #5F5FFF +} + +div.highlight .-Color[class*=-C64] { + color: #5F8700 +} + +div.highlight .-Color[class*=-BGC64] { + background-color: #5F8700 +} + +div.highlight .-Color[class*=-C65] { + color: #5F875F +} + +div.highlight .-Color[class*=-BGC65] { + background-color: #5F875F +} + +div.highlight .-Color[class*=-C66] { + color: #5F8787 +} + +div.highlight .-Color[class*=-BGC66] { + background-color: #5F8787 +} + +div.highlight .-Color[class*=-C67] { + color: #5F87AF +} + +div.highlight .-Color[class*=-BGC67] { + background-color: #5F87AF +} + +div.highlight .-Color[class*=-C68] { + color: #5F87D7 +} + +div.highlight .-Color[class*=-BGC68] { + background-color: #5F87D7 +} + +div.highlight .-Color[class*=-C69] { + color: #5F87FF +} + +div.highlight .-Color[class*=-BGC69] { + background-color: #5F87FF +} + +div.highlight .-Color[class*=-C70] { + color: #5FAF00 +} + +div.highlight .-Color[class*=-BGC70] { + background-color: #5FAF00 +} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + 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color: #a5d6ff } /* Literal.Number.Integer.Long */ \ No newline at end of file diff --git a/_static/searchtools.js b/_static/searchtools.js new file mode 100644 index 00000000..92da3f8b --- /dev/null +++ b/_static/searchtools.js @@ -0,0 +1,619 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/algorithms.html b/algorithms.html new file mode 100644 index 00000000..276ad86e --- /dev/null +++ b/algorithms.html @@ -0,0 +1,280 @@ + + + + + + + Algorithms — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Algorithms

+

Qlasskit implements high level representation of quantum algorithms that relies on black boxes functions and oracles.

+
    +

  • Grover search

    • +

  • Simon periodicity

    • +

  • Deutsch Jozsa

    • +

  • Bernstein Vazirani

    • +
+
+

Abstraction tools

+

All algorithms share the same base class QAlgorithm which inherits from QCircuitWrapper. The QCircuitWrapper is also father of qlassf functions, and offers useful methods to handle high level types.

+

One of those tools is decode_counts, that translates the result dictionary from a quantum sampling in its binary form, to the high level representation of data types used in the qlasskit function; decode_output instead, translate a single bitstring.

+
+
+
from typing import Tuple
+from qlasskit import qlassf, Qint, Qint4
+
+
+@qlassf
+def test_tools(a: Qint[4]) -> Qint[4]:
+    return a + 1
+
+
+test_tools.decode_output("0100")
+
+
+
+
+
4
+
+
+
+
+

Another method called encode_input is able to do the reverse job, transforming high level types to binary form.

+
+
+
test_tools.encode_input(Qint4(4))
+
+
+
+
+
'0100'
+
+
+
+
+
+ +
+

Simon periodicity

+
+
+
from qlasskit.algorithms import Simon
+
+
+@qlassf
+def f(a: Qint[4]) -> Qint[4]:
+    return (a >> 3) + 1
+
+
+q_algo = Simon(f)
+
+
+
+
+

Further examples can be found on the following notebooks:

+ +
+
+

Deutsch Jozsa

+
+
+
from qlasskit.algorithms import DeutschJozsa
+
+
+@qlassf
+def f(b: Qint[4]) -> bool:
+    return b < 16
+
+
+q_algo = DeutschJozsa(f)
+
+
+
+
+

Further examples can be found on the following notebooks:

+ +
+
+

Bernstein Vazirani

+
+
+
from qlasskit.algorithms import BernsteinVazirani, secret_oracle
+
+oracle = secret_oracle(4, 14)
+
+q_algo = BernsteinVazirani(oracle)
+
+
+
+
+

Further examples can be found on the following notebooks:

+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api.html b/api.html new file mode 100644 index 00000000..52a0c00f --- /dev/null +++ b/api.html @@ -0,0 +1,197 @@ + + + + + + + API — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

API

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

qlasskit.qlassfun.qlassf(f[, types, defs, ...])

Decorator / function creating a QlassF object

qlasskit.qlassfun.qlassfa([types, defs, ...])

Decorator with parameters for qlassf

qlasskit.qlassfun.QlassF(name, original_f, ...)

Class representing a qlassf function

qlasskit.algorithms.qalgorithm

qlasskit.algorithms.grover.Grover(oracle[, ...])

qlasskit.algorithms.simon.Simon(f)

qlasskit.algorithms.deutschjozsa.DeutschJozsa(f)

qlasskit.qcircuit.qcircuit.QCircuit([...])

qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper()

Wrapper interface for a class containing a qcircuit

qlasskit.qcircuit.gates

qlasskit.bqm.decode_samples(qf, sampleset)

Get dimod sampleset and return an high level decoded solution

qlasskit.decompiler.Decompiler()

qlasskit.types

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/bqm.html b/bqm.html new file mode 100644 index 00000000..85f58eba --- /dev/null +++ b/bqm.html @@ -0,0 +1,349 @@ + + + + + + + Binary Quadratic Model, Qubo & Ising — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Binary Quadratic Model, Qubo & Ising

+

Any qlassf function can be transformed to a binary quadratic model using the to_bqm function. In this example we create a generic function that factorize num, and we bind num with 15.

+
+
+
from qlasskit import qlassf, Qint, Parameter
+
+
+@qlassf
+def test_factor_generic(num: Parameter[Qint[4]], a: Qint[3], b: Qint[3]) -> Qint[4]:
+    return num - (a * b)
+
+
+test_factor = test_factor_generic.bind(num=15)
+bqm = test_factor.to_bqm()
+
+print("Vars:", bqm.num_variables, "\nInteractions:", bqm.num_interactions)
+
+
+
+
+
Vars: 14 
+Interactions: 41
+
+
+
+
+

A qlassf function can also be exported as a QUBO model using to_bqm('qubo') or as an Ising model using to_bqm('ising').

+
+

Running on simulated sampler annealer

+

Now we can run a simulated sampler annealer to minimize this function; qlasskit offer a decode_samples helper function that translates sample result to the high level types of qlasskit.

+
+
+
import neal
+from qlasskit.bqm import decode_samples
+
+sa = neal.SimulatedAnnealingSampler()
+sampleset = sa.sample(bqm, num_reads=10)
+decoded_samples = decode_samples(test_factor, sampleset)
+best_sample = min(decoded_samples, key=lambda x: x.energy)
+print(best_sample.sample)
+
+
+
+
+
{'a': 3, 'b': 5}
+
+
+
+
+

The result is 5 and 3 as expected (since 5 times 3 is equal to 15).

+
+
+

Running on DWave annealer

+

If we have a valid DWave account, we can run our problem on a real quantum annealer as follow:

+
+
+
from dwave.system import DWaveSampler, EmbeddingComposite
+
+sampler = EmbeddingComposite(DWaveSampler())
+sampleset = sampler.sample(bqm, num_reads=10)
+decoded_samples = decode_samples(test_factor, sampleset)
+best_sample = min(decoded_samples, key=lambda x: x.energy)
+print(best_sample.sample)
+
+
+
+
+
---------------------------------------------------------------------------
+SolverFailureError                        Traceback (most recent call last)
+Cell In[3], line 5
+      3 sampler = EmbeddingComposite(DWaveSampler())
+      4 sampleset = sampler.sample(bqm, num_reads=10)
+----> 5 decoded_samples = decode_samples(test_factor, sampleset)
+      6 best_sample = min(decoded_samples, key=lambda x: x.energy)
+      7 print(best_sample.sample)
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/qlasskit-0.1.18-py3.10.egg/qlasskit/bqm.py:142, in decode_samples(qf, sampleset)
+    140 """Get dimod sampleset and return an high level decoded solution"""
+    141 model = qf.to_bqm("pq_model")
+--> 142 decoded = model.decode_sampleset(sampleset)
+    144 new_dec = []
+    145 for el in decoded:
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1121, in SampleSet.record(self)
+   1105 @property
+   1106 def record(self):
+   1107     """:obj:`numpy.recarray` containing the samples, energies, number of occurences, and other sample data.
+   1108 
+   1109     Examples:
+   (...)
+   1119 
+   1120     """
+-> 1121     self.resolve()
+   1122     return self._record
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485, in SampleSet.resolve(self)
+   1483 # if it doesn't have the attribute then it is already resolved
+   1484 if hasattr(self, '_future'):
+-> 1485     samples = self._result_hook(self._future)
+   1486     self.__init__(samples.record, samples.variables, samples.info, samples.vartype)
+   1487     del self._future
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/composites/embedding.py:284, in EmbeddingComposite.sample.<locals>.async_unembed(response)
+    279 def async_unembed(response):
+    280     # unembed the sampleset aysnchronously.
+    282     warninghandler.chain_break(response, embedding)
+--> 284     sampleset = unembed_sampleset(response, embedding, source_bqm=bqm,
+    285                                   chain_break_method=chain_break_method,
+    286                                   chain_break_fraction=chain_break_fraction,
+    287                                   return_embedding=return_embedding)
+    289     if return_embedding:
+    290         sampleset.info['embedding_context'].update(
+    291             embedding_parameters=embedding_parameters,
+    292             chain_strength=embedding.chain_strength)
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/embedding/transforms.py:606, in unembed_sampleset(target_sampleset, embedding, source_bqm, chain_break_method, chain_break_fraction, return_embedding)
+    603 except KeyError:
+    604     raise ValueError("given bqm does not match the embedding")
+--> 606 record = target_sampleset.record
+    608 unembedded, idxs = chain_break_method(target_sampleset, chains)
+    610 reserved = {'sample', 'energy'}
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1121, in SampleSet.record(self)
+   1105 @property
+   1106 def record(self):
+   1107     """:obj:`numpy.recarray` containing the samples, energies, number of occurences, and other sample data.
+   1108 
+   1109     Examples:
+   (...)
+   1119 
+   1120     """
+-> 1121     self.resolve()
+   1122     return self._record
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485, in SampleSet.resolve(self)
+   1483 # if it doesn't have the attribute then it is already resolved
+   1484 if hasattr(self, '_future'):
+-> 1485     samples = self._result_hook(self._future)
+   1486     self.__init__(samples.record, samples.variables, samples.info, samples.vartype)
+   1487     del self._future
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:452, in DWaveSampler.sample.<locals>._hook(computation)
+    450 except (SolverError, InvalidAPIResponseError) as exc:
+    451     if not self.failover:
+--> 452         raise exc
+    453     if isinstance(exc, SolverAuthenticationError):
+    454         raise exc
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:439, in DWaveSampler.sample.<locals>._hook(computation)
+    436     return sampleset
+    438 try:
+--> 439     return resolve(computation)
+    441 except (ProblemUploadError, RequestTimeout, PollingTimeout) as exc:
+    442     if not self.failover:
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/system/samplers/dwave_sampler.py:429, in DWaveSampler.sample.<locals>._hook.<locals>.resolve(computation)
+    427 def resolve(computation):
+    428     sampleset = computation.sampleset
+--> 429     sampleset.resolve()
+    431     if warninghandler is not None:
+    432         warninghandler.too_few_samples(sampleset)
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dimod/sampleset.py:1485, in SampleSet.resolve(self)
+   1483 # if it doesn't have the attribute then it is already resolved
+   1484 if hasattr(self, '_future'):
+-> 1485     samples = self._result_hook(self._future)
+   1486     self.__init__(samples.record, samples.variables, samples.info, samples.vartype)
+   1487     del self._future
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:823, in Future.sampleset.<locals>.<lambda>(f)
+    818 except ImportError:
+    819     raise RuntimeError("Can't construct SampleSet without dimod. "
+    820                        "Re-install the library with 'bqm' support.")
+    822 self._sampleset = sampleset = dimod.SampleSet.from_future(
+--> 823     self, lambda f: f.wait_sampleset())
+    825 # propagate id to sampleset as well
+    826 # note: this requires dimod>=0.8.21 (before that version SampleSet
+    827 # had slots set which prevented dynamic addition of attributes).
+    828 sampleset.wait_id = self.wait_id
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:755, in Future.wait_sampleset(self)
+    752 """Blocking sampleset getter."""
+    754 # blocking result get
+--> 755 result = self._load_result()
+    757 # common problem info: id/label
+    758 problem_info = dict(problem_id=self.id)
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/computation.py:893, in Future._load_result(self)
+    891 # Check for other error conditions
+    892 if self._exception is not None:
+--> 893     raise self._exception
+    895 # If someone else took care of this while we were waiting
+    896 if self._result is not None:
+
+File ~/.pyenv/versions/3.10.13/envs/qlasskit_310-env/lib/python3.10/site-packages/dwave/cloud/client/base.py:1309, in Client._handle_problem_status(self, message, future)
+   1307 if 'error_code' in message and 'error_msg' in message:
+   1308     logger.debug("Error response received: %r", message)
+-> 1309     raise SolverFailureError(message['error_msg'])
+   1311 if 'status' not in message:
+   1312     raise InvalidAPIResponseError("'status' missing in problem description response")
+
+SolverFailureError: Problem not accepted because user has insufficient remaining solver access time in project DEV.
+
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/cli_tools.html b/cli_tools.html new file mode 100644 index 00000000..d1c7dadd --- /dev/null +++ b/cli_tools.html @@ -0,0 +1,195 @@ + + + + + + + CLI Tools — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

CLI Tools

+

Qlasskit also offer two cli tools: +- py2bexp: translate python code to boolean expressions +- py2qasm: translate python code to quantum circuits in qasm format

+
+

py2bexp

+

Receives a python script (file or stdin) in input and outputs bool expressions. The python script should +contain at least one qlassf function.

+
usage: py2bexp [-h] [-i INPUT_FILE] [-e ENTRYPOINT] [-o OUTPUT] [-f {anf,cnf,dnf,nnf}] [-t {sympy,dimacs}] [-v]
+
+Convert qlassf functions in a Python script to boolean expressions.
+
+options:
+-h, --help            show this help message and exit
+-i INPUT_FILE, --input-file INPUT_FILE
+                        Input file (default: stdin)
+-e ENTRYPOINT, --entrypoint ENTRYPOINT
+                        Entrypoint function name
+-o OUTPUT, --output OUTPUT
+                        Output file (default: stdout)
+-f {anf,cnf,dnf,nnf}, --form {anf,cnf,dnf,nnf}
+                        Expression form (default: sympy)
+-t {sympy,dimacs}, --format {sympy,dimacs}
+                        Output format (default: sympy)
+-v, --version         show program's version number and exit
+
+
+
+
+

py2qasm

+

Receives a python script (file or stdin) in input and outputs qasm code. The python script should +contain at least one qlassf function.

+
usage: py2qasm [-h] [-i INPUT_FILE] [-e ENTRYPOINT] [-o OUTPUT] [-c {internal,tweedledum,recompiler}] [-q {2.0,3.0}]
+            [-v]
+
+Convert qlassf functions in a Python script to qasm code expressions.
+
+options:
+-h, --help            show this help message and exit
+-i INPUT_FILE, --input-file INPUT_FILE
+                        Input file (default: stdin)
+-e ENTRYPOINT, --entrypoint ENTRYPOINT
+                        Entrypoint function name
+-o OUTPUT, --output OUTPUT
+                        Output file (default: stdout)
+-c {internal,tweedledum,recompiler}, --compiler {internal,tweedledum,recompiler}
+                        QASM compiler (default: internal)
+-q {2.0,3.0}, --qasm-version {2.0,3.0}
+                        QASM version (default: 3.0)
+-v, --version         show program's version number and exit
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/decompiler_and_optimizer.html b/decompiler_and_optimizer.html new file mode 100644 index 00000000..8d071d44 --- /dev/null +++ b/decompiler_and_optimizer.html @@ -0,0 +1,212 @@ + + + + + + + Decompiler and Circuit Optimizer — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Decompiler and Circuit Optimizer

+

Qlasskit offers two useful tool for circuit analysis and optimization.

+
    +

  • Decompiler: given a quantum circuit is able to detect section that can be represented as boolean expressions

    • +

  • circuit_boolean_optimizer: a pipeline that given a quantum circuit, decompose it in boolean expressions form and optimize it using boolean algebra

    • +
+

We first write a qlasskit function that perform an And between the elements of a Qlist; we use the fastOptimizer in order to obtain an unoptimized circuit.

+
+
+
from qlasskit import qlassfa, qlassf, boolopt, Qlist
+from qlasskit.decompiler import Decompiler, circuit_boolean_optimizer
+
+
+@qlassfa(bool_optimizer=boolopt.fastOptimizer)
+def qf(a: Qlist[bool, 2]) -> bool:
+    s = True
+    for i in a:
+        s = s and i
+    return s
+
+
+qf.circuit()
+
+
+
+
+
QCircuit<qf>(7 gates, 6 qubits)
+
+
+
+
+

As we can see from the circuit, this is not the best solution.

+
+
+
qf.export().draw("mpl")
+
+
+
+
+_images/68ebd5e59484021eb207a14eb3a4a3219a6ecd8b1971abfe71fe0b3484f576f0.png +
+
+

Using the decompiler we are able to translate a quantum circuit to its boolean representation (if applicable):

+
+
+
dc = Decompiler().decompile(qf.circuit())
+dc
+
+
+
+
+
DecompiledResults[
+	(
+		(0, 7)
+		(X, [2], None), (CCX, [0, 2, 3], None), (CCX, [1, 3, 4], None), (CX, [4, 5], None), (CCX, [1, 3, 4], None), (CCX, [0, 2, 3], None), (X, [2], None)
+		(q5, q4 ^ q5 ^ (q1 & (q3 ^ (q0 & ~q2))))
+	)
+]
+
+
+
+
+

The circuit_boolean_optimizer allows us to perform boolean optimizations in a quantum circuit; from the previous unoptimized example, we get the following optimized circuit:

+
+
+
qc = circuit_boolean_optimizer(qf.circuit(), preserve=[0, 1])
+qc.export().draw("mpl")
+
+
+
+
+_images/975f9c766c4ab546bee9118a765d044bdce43b9ce3694cef8b60e97c42ddb931.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_bernstein_vazirani.html b/example_bernstein_vazirani.html new file mode 100644 index 00000000..120ecd23 --- /dev/null +++ b/example_bernstein_vazirani.html @@ -0,0 +1,211 @@ + + + + + + + Bernstein Vazirani algorithm — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Bernstein Vazirani algorithm

+

We first create an oracle embedding the secret number 14; we can do this in two equivalent ways:

+
    +

  • using secret_oracle() function

    • +

  • writing the oracle from scratch in python

    • +
+
+
+
from qlasskit import qlassf, Qint
+
+
+@qlassf
+def oracle(x: Qint[4]) -> bool:
+    s = Qint4(14)
+    return (x[0] & s[0]) ^ (x[1] & s[1]) ^ (x[2] & s[2]) ^ (x[3] & s[3])
+
+
+oracle.export("qiskit").draw("mpl")
+
+
+
+
+_images/e017f3e106e1cd0f02b1307f42b531383710dab1353d524ef5e4ddbf761ef27a.png +
+
+
+
+
from qlasskit.algorithms import secret_oracle
+
+oracle = secret_oracle(4, 14)
+oracle.export("qiskit").draw("mpl")
+
+
+
+
+_images/e017f3e106e1cd0f02b1307f42b531383710dab1353d524ef5e4ddbf761ef27a.png +
+
+

Now we can use BernsteinVazirani to discover the secret embedded in the oracle.

+
+
+
from qlasskit.algorithms import BernsteinVazirani
+
+q_algo = BernsteinVazirani(oracle)
+qc = q_algo.export("qiskit")
+qc.draw("mpl")
+
+
+
+
+_images/a8917ace04cb0420613da4cfd474deb06536a17cf19a713c5f187839fee6a0e4.png +
+
+
+
+
from qiskit import QuantumCircuit
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+qc.measure_all()
+simulator = AerSimulator()
+result = simulator.run(qc).result()
+counts = result.get_counts(qc)
+
+counts_readable = q_algo.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/38de6c110ed81ea81303c7d605b59843cce73ba51cc6f4e85a6ff5669271c5a2.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_big_circuit.html b/example_big_circuit.html new file mode 100644 index 00000000..f409acf6 --- /dev/null +++ b/example_big_circuit.html @@ -0,0 +1,168 @@ + + + + + + + Working with big circuits — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Working with big circuits

+

Qlasskit is capable of producing large circuit without any issue. The only thing that you have to do, is to use the fastOptimizer, since running CSE is too slow on large expressions lists.

+

In the next example we are going to create a quantum circuit with 64 Qint8 in input, and one Qint8 in output, resulting on a circuit of ~21984 qubits and ~1044 gates in around 5 seconds.

+
+
+
from qlasskit import Qint8, Qlist, boolopt, qlassfa
+
+
+@qlassfa(bool_optimizer=boolopt.fastOptimizer)
+def test(a_list: Qlist[Qint8, 64]) -> Qint8:
+    h_val = Qint8(0)
+    for c in a_list:
+        h_val = h_val + c
+    return h_val
+
+
+
+
+
+
+
print(test.circuit())
+
+
+
+
+
QCircuit<test>(21831 gates, 1036 qubits)
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_bqm_polynomial.html b/example_bqm_polynomial.html new file mode 100644 index 00000000..ad2bd0c2 --- /dev/null +++ b/example_bqm_polynomial.html @@ -0,0 +1,208 @@ + + + + + + + BQM Polynomial solver — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

BQM Polynomial solver

+
+
+
from qlasskit import qlassf, Qfixed
+
+
+@qlassf
+def poly(x: Qfixed[3, 3], y: Qfixed[3, 3]) -> Qfixed[3, 3]:
+    return x * 3 - y * 2 + 1.0
+
+
+poly.expressions
+
+
+
+
+
[(x0, ~(x.3 ^ x.4 ^ (x.4 & x.5))),
+ (x1, x0 & y.4),
+ (x2, x.3 & x.4),
+ (x3, x.4 & x.5 & (x.3 ^ x.4)),
+ (x4, ~(x.0 ^ x.3 ^ x2 ^ x3)),
+ (x5, ~(x.4 ^ x.5)),
+ (x6, x5 & y.5 & (x0 ^ y.4)),
+ (x7, x1 ^ x4 ^ x6 ^ y.3),
+ (x8, ~x7),
+ (x9, x4 & y.3),
+ (x10, x.0 & x.3),
+ (x11, (x.0 ^ x.3) & (x2 ^ x3)),
+ (x12, ~(x.0 ^ x.1 ^ x10 ^ x11)),
+ (x13, (x1 ^ x6) & (x4 ^ y.3)),
+ (x14, ~(x12 ^ x13 ^ x9 ^ y.0)),
+ (_ret.0, x7),
+ (_ret.1, x14 ^ x8),
+ (_ret.2,
+  (x14 & x8) ^ ~(y.1 ^ (x12 & y.0) ^ ((x12 ^ y.0) & (x13 ^ x9)) ^ ~(x.1 ^ x.2 ^ (x.0 & x.1) ^ ((x.0 ^ x.1) & (x10 ^ x11))))),
+ (_ret.3, ~(x0 ^ y.4 ^ (x5 & y.5))),
+ (_ret.4, ~(x5 ^ y.5)),
+ (_ret.5, x.5)]
+
+
+
+
+
+
+
bqm = poly.to_bqm()
+print("Vars:", bqm.num_variables, "\nInteractions:", bqm.num_interactions)
+
+
+
+
+
Vars: 127 
+Interactions: 1951
+
+
+
+
+
+
+
import neal
+from qlasskit.bqm import decode_samples
+
+sa = neal.SimulatedAnnealingSampler()
+sampleset = sa.sample(bqm, num_reads=10)
+decoded_samples = decode_samples(poly, sampleset)
+best_sample = min(decoded_samples, key=lambda x: x.energy)
+print(best_sample.sample)
+
+
+
+
+
{'x': 7.875, 'y': 7.375}
+
+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023-2024, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/example_bqm_tsp.html b/example_bqm_tsp.html new file mode 100644 index 00000000..7a72c63e --- /dev/null +++ b/example_bqm_tsp.html @@ -0,0 +1,205 @@ + + + + + + + TSP — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

TSP

+
+
+
# Encode the TSP using qlasskit and parameters
+
+from qlasskit import qlassf, Parameter, Qlist, Qmatrix, Qint
+
+
+@qlassf
+def tsp(
+    dst_matrix: Parameter[Qmatrix[Qint[4], 3, 3]], order: Qlist[Qint[2], 3]
+) -> Qint[6]:
+    dst_sum = Qint4(0)
+    assertion = False
+    
+    if sum(order) != 3:
+        assertion = True
+
+    for i in range(2):
+        oim = order[i]
+        oi = order[i + 1]
+        dst_sum += dst_matrix[oim][oi] if not assertion else 0xF
+    return dst_sum
+
+
+dst_matrix=[
+    [0xF, 1, 4], 
+    [1, 0xF, 1], 
+    [1, 1, 0xF]
+]
+tsp_f = tsp.bind(dst_matrix=dst_matrix)
+# tsp_f.expressions
+
+
+
+
+
+
+
bqm = tsp_f.to_bqm()
+print("Vars:", bqm.num_variables, "\nInteractions:", bqm.num_interactions)
+
+
+
+
+
Vars: 18 
+Interactions: 81
+
+
+
+
+
+
+
import neal
+from qlasskit.bqm import decode_samples
+
+sa = neal.SimulatedAnnealingSampler()
+sampleset = sa.sample(bqm, num_reads=10)
+decoded_samples = decode_samples(tsp_f, sampleset)
+best_sample = min(decoded_samples, key=lambda x: x.energy)
+print(best_sample.sample)
+
+
+
+
+
{'order': (2, 1, 0)}
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_deutsch_jozsa.html b/example_deutsch_jozsa.html new file mode 100644 index 00000000..438c61e2 --- /dev/null +++ b/example_deutsch_jozsa.html @@ -0,0 +1,202 @@ + + + + + + + Deutsch Jozsa algorithm — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Deutsch Jozsa algorithm

+
+
+
from qlasskit import qlassf, Qint
+
+
+@qlassf
+def f(a: Qint[4]) -> bool:
+    return a > 7
+
+
+
+
+
+
+
f.export("qiskit").draw("mpl")
+
+
+
+
+_images/e701c8c7d288b7c8b31d67bfd74640291b3169f82970a1c5a96fbc03e0bfef59.png +
+
+
+
+
from qlasskit.algorithms import DeutschJozsa
+
+q_algo = DeutschJozsa(f)
+
+
+
+
+
+
+
qc = q_algo.export("qiskit")
+qc.draw("mpl")
+
+
+
+
+_images/317b4a8fac0c5bc2bcd5ab20dd179c2a9240473def08b3ba93eb3b0d553781ea.png +
+
+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+qc.measure_all()
+simulator = AerSimulator()
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/5c37e1641175e805bd069b05b0507bee333663522ca9e04c1584a329607d7490.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover.html b/example_grover.html new file mode 100644 index 00000000..7458cc48 --- /dev/null +++ b/example_grover.html @@ -0,0 +1,212 @@ + + + + + + + Grover search — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_factors.html b/example_grover_factors.html new file mode 100644 index 00000000..7d7dd7ea --- /dev/null +++ b/example_grover_factors.html @@ -0,0 +1,188 @@ + + + + + + + Grover: factorize number — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover: factorize number

+
+
+
from typing import Tuple
+from qlasskit import qlassf, Qint
+from qlasskit.algorithms import Grover
+
+
+@qlassf
+def factorize(a: Tuple[Qint[2], Qint[2]]) -> bool:
+    return a[0] * a[1] == 9
+
+
+q_algo = Grover(factorize)
+
+
+
+
+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = AerSimulator()
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts, discard_lower=5)
+plot_histogram(counts_readable)
+
+
+
+
+_images/a056b3021724ac94b5d8671bfd9097e73add05704cfb48363ea95e2e450eadb5.png +
+
+
+
+
qc.draw("mpl")
+
+
+
+
+_images/a1154c255c589e0e4d1936fc4b97b314aad8947cab4b38c090c94fd12164d179.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_hash.html b/example_grover_hash.html new file mode 100644 index 00000000..d85aab92 --- /dev/null +++ b/example_grover_hash.html @@ -0,0 +1,231 @@ + + + + + + + Grover search: hash function preimage attack — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover search: hash function preimage attack

+

In the ever-evolving landscape of cybersecurity, the advent of quantum computing presents both an extraordinary opportunity and an unprecedented challenge. In this notebook we exploit a Grover Search to perform a preimage attack on a toy hash function.

+

A preimage attack on a cryptographic hash function h(m) tries to find a message m that has a specific hash value. Using qlasskit it is easy to write an hash function like the following hash_simp:

+
+
+
from qlasskit import qlassf, Qint, Qint8, Qlist
+
+
+@qlassf
+def hash_simp(m: Qlist[Qint[4], 2]) -> Qint[8]:
+    hv = 0
+    for i in m:
+        hv = ((hv << 4) ^ (hv >> 1) ^ i) & 0xFF
+
+    return hv
+
+
+
+
+

Thanks to the fact that qlasskit function are standard python functions, we can call the original_f to perform some kind of analysis on the hash function. Since the input space is tiny (it is a toy hash function), we can detect if the hash function is uniform (if it maps equally to the output space).

+
+
+
from collections import Counter
+
+d = Counter(hex(hash_simp.original_f((x, y))) for x in range(2**4) for y in range(2**4))
+
+print("Hash function output space:", len(d))
+
+
+
+
+
Hash function output space: 256
+
+
+
+
+

We got that hash_simp is following an uniform distribution.

+
+
+
hash_simp.export("qiskit").draw("mpl")
+
+
+
+
+_images/e74cc1e022296f53ce2cd1099f078f7715eebe81e2ffa3bbedb069c9a35398dd.png +
+
+

Now we use our quantum function as an oracle for a Grover search, in order to find which input maps to the value 0xca.

+
+
+
from qlasskit.algorithms import Grover
+
+q_algo = Grover(hash_simp, Qint8(0xCA))
+
+
+
+
+

Then we use our prefered framework and simulator for sampling the result; this is an example using qiskit with aer_simulator.

+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = AerSimulator()
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts, discard_lower=5)
+plot_histogram(counts_readable)
+
+
+
+
+_images/f136ea25a6a1c5e568142875d165f98fbe43f5c4323eadeafad237a185d8ca93.png +
+
+

Using QlassF.original_f we can double check the result without invoking a quantum simulator; calling it with the tuple (12,12) must result in the hash value 0xca.

+
+
+
print(hex(hash_simp.original_f((12, 12))))
+
+
+
+
+
0xca
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_subset.html b/example_grover_subset.html new file mode 100644 index 00000000..84efd89b --- /dev/null +++ b/example_grover_subset.html @@ -0,0 +1,189 @@ + + + + + + + Grover search: subset problem — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover search: subset problem

+

We define a function named subset_sum(i,j) that returns the sum of the elements i and j of a list set_. We want to use a Grover search to find which i j combination led to a given value.

+
+
+
from qlasskit import qlassf, Qint, Qint3, Qlist, Parameter
+from typing import Tuple
+
+
+@qlassf
+def subset_sum(
+    ii: Tuple[Qint[2], Qint[2]], set_: Parameter[Qlist[Qint3, 4]]
+) -> Qint[3]:
+    return set_[ii[0]] + set_[ii[1]] if ii[0] != ii[1] else 0
+
+
+
+
+

Our quantum function subset_sum will be used as an oracle for a Grover search. For instance, here we want to find the input value that produce the value 7. Since we know that there are at least two result ((i,j) and (j,i)), we set n_matching=2. We also bind the parameter set_ with the set of numbers where we want to search the solution.

+
+
+
from qlasskit.algorithms import Grover
+
+q_algo = Grover(subset_sum.bind(set_=[0, 5, 2, 3]), Qint3(7), n_matching=2)
+
+
+
+
+

Then we use our prefered framework and simulator for sampling the result; this is an example using qiskit with aer_simulator.

+

In the output histogram, it’s now evident that the input leading to a value of 7 are the tuples (1,2) and (2,1) (5+2 and 2+5), aligning with our expectations.

+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = AerSimulator()
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/ad35bf91a8374681d3e8c22fb6a31f69e9e6518f1478a6054e776c29ee27b211.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_sudoku.html b/example_grover_sudoku.html new file mode 100644 index 00000000..6b20f9db --- /dev/null +++ b/example_grover_sudoku.html @@ -0,0 +1,254 @@ + + + + + + + Grover search: sudoku solver — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover search: sudoku solver

+

In this example we are going to solve a sudoku puzzle. Since we have few qubits, we cannot solve a real 9x9 sudoku puzzle; our toy examples uses a 2x2 matrix where a valid solution is when in every row and every column there are no repeated values (0 or 1). We encode these xor-ing the values for each row and column. +Since we want a specific solution, we add a constraint constr: we want the [0][0] element to be True.

+

sudoku_check is already an oracle so we can instantiate the Grover algorithm without value.

+
+
+
from qlasskit import qlassf, Qmatrix
+from qlasskit.algorithms import Grover
+
+
+@qlassf
+def sudoku_check(m: Qmatrix[bool, 2, 2]) -> bool:
+    constr = m[0][0]
+    sub0 = m[0][0] ^ m[0][1]
+    sub1 = m[1][0] ^ m[1][1]
+    sub2 = m[0][0] ^ m[1][0]
+    sub3 = m[0][1] ^ m[1][1]
+    return sub0 and sub1 and sub2 and sub3 and constr
+
+
+q_algo = Grover(sudoku_check)
+
+
+
+
+

Then we use our prefered framework and simulator for sampling the result; this is an example using qiskit with aer_simulator.

+

We obtain that the solution for this puzzle is the matrix [[True, False], [False, True]].

+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = AerSimulator()
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts, discard_lower=20)
+
+plot_histogram(counts_readable)
+
+
+
+
+_images/cf4aae3eb9d9826b4e5fe76c28059345d7d992a93d8d58ea268d206cb1d8f981.png +
+
+

We can exploit matplotlib for drawing the result sudoku matrix as follows:

+
+
+
import matplotlib.pyplot as plt
+
+
+def draw_matrix(matrix):
+    fig, ax = plt.subplots()
+    ax.matshow(matrix, cmap="viridis")
+
+    for i in range(len(matrix)):
+        for j in range(len(matrix[0])):
+            ax.text(
+                j,
+                i,
+                str(matrix[i][j]),
+                va="center",
+                ha="center",
+                fontsize=12,
+                color="black",
+            )
+
+    plt.show()
+
+
+m_res = list(filter(lambda x: x[1] > 500, counts_readable.items()))[0][0]
+draw_matrix(m_res)
+
+
+
+
+_images/23765ce2dceda23c652d4690e559278bbbcbf48bed62ae08c66b1d06767614c4.png +
+
+

We can create a more realistic sudoku game using numbers instead of booleans, but the resources required will scale exponentially. In the following code snippets, we recreate sudoku_check using Qint[2] and a 4x4 matrix. The sum of each column and row must be equal to 6 (3+2+1+0). As we can see, the resulting circuit of the checker requires more than 100 qubits, way above our simulation capabilities.

+
+
+
from qlasskit import Qint, Qint4
+
+
+@qlassf
+def sudoku_check(m: Qmatrix[Qint[2], 4, 4]) -> bool:
+    res = True
+
+    # Constraints
+    res = (m[0][2] == 3) and (m[0][0] == 1)
+
+    # Check every row and column
+    for i in range(4):
+        c = (Qint4(0) + m[i][0] + m[i][1] + m[i][2] + m[i][3]) == 6
+        r = (Qint4(0) + m[0][i] + m[1][i] + m[2][i] + m[3][i]) == 6
+        res = res and c and r
+
+    return res
+
+
+print(sudoku_check.circuit())
+
+
+
+
+
QCircuit<sudoku_check>(1309 gates, 178 qubits)
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_simon.html b/example_simon.html new file mode 100644 index 00000000..300ad690 --- /dev/null +++ b/example_simon.html @@ -0,0 +1,202 @@ + + + + + + + Simon function periodicity — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Simon function periodicity

+
+
+
from qlasskit import qlassf, Qint
+
+
+@qlassf
+def f(a: Qint[4]) -> Qint[4]:
+    return (a >> 3) + 1
+
+
+
+
+
+
+
f.export("qiskit").draw("mpl")
+
+
+
+
+_images/4502fb99b0b62a89285bf5856a32dbf7264a0322771aaf5ee33a084aaa9e9167.png +
+
+
+
+
from qlasskit.algorithms import Simon
+
+q_algo = Simon(f)
+
+
+
+
+
+
+
qc = q_algo.export("qiskit")
+qc.draw("mpl")
+
+
+
+
+_images/fb5ffd2c19f4003e4c45d4b0d46226c60255334a56fc579013eeac5c4c9a29a8.png +
+
+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+qc.measure_all()
+simulator = AerSimulator()
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/eea1152a92a8945a7719dd6b12beb892cf9e185f4f75aadc434c540509580588.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_unitary_of_f.html b/example_unitary_of_f.html new file mode 100644 index 00000000..f2d1f8fa --- /dev/null +++ b/example_unitary_of_f.html @@ -0,0 +1,196 @@ + + + + + + + Unitary of qlasskit function — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Unitary of qlasskit function

+

In qlasskit, we can exploit external low-level frameworks to perform operations on the resulting quantum circuit. In this example, we use qiskit in order to obtain the unitary matrix of our QlassF function.

+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit_aer import AerSimulator
+from qiskit.visualization import array_to_latex
+from qlasskit import qlassf
+
+
+@qlassf
+def f(a: bool, b: bool) -> bool:
+    return a ^ (not b)
+
+
+print(f"\n{f}\n")
+
+qc = QuantumCircuit(f.num_qubits, f.num_qubits)
+qc.append(f.gate(), f.qubits)
+
+qc.save_state()
+qc.decompose().draw("mpl")
+
+
+
+
+
QlassF<f>(a:bool, b:bool) -> bool:
+	_ret = a ^ ~b
+
+
+_images/c7d04b10d56938794e94628eaa66357bee167db80983c3bb53291d7d04661f70.png +
+
+
+
+
simulator = AerSimulator(method="unitary")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+array_to_latex(result.get_unitary(circ, 3), max_size=16)
+
+
+
+
+
+\[\begin{split}\begin{bmatrix} +0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ + 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ + 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ + 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ + \end{bmatrix} +\end{split}\]
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/exporter.html b/exporter.html new file mode 100644 index 00000000..4a95d4f4 --- /dev/null +++ b/exporter.html @@ -0,0 +1,268 @@ + + + + + + + Exporting to other frameworks — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Exporting to other frameworks

+

Qlasskit implements circuit / gate exporters for Qiskit, Cirq, Qasm, Sympy and Pennylane.

+
+
+
from qlasskit import Qint, qlassf
+
+
+@qlassf
+def hello_world(a: bool, b: Qint[2]) -> Qint[2]:
+    return b + (1 if a else 0)
+
+
+
+
+
+

Qiskit

+
+
+
qc = hello_world.export("qiskit")
+qc.draw("mpl")
+
+
+
+
+_images/da85ccdadfc65e79823ec5a7fc70b3b6b00b858a049aa91045f7704195a4891b.png +
+
+
+
+

QASM

+
+
+
qc = hello_world.export("qasm")
+print(qc)
+
+
+
+
+
OPENQASM 3.0;
+
+gate hello_world a b.0 b.1 _ret.0 _ret.1 {
+	cx a _ret.0
+	cx b.0 _ret.0
+	cx b.1 _ret.1
+	ccx a b.0 _ret.1
+}
+
+hello_world q[0],q[1],q[2],q[3],q[4];
+
+
+
+
+
+
+

Cirq

+
+
+
import cirq
+
+qc = hello_world.export("cirq")
+qc
+
+
+
+
+
0: ───hello_world───
+      │
+1: ───hello_world───
+      │
+2: ───hello_world───
+      │
+3: ───hello_world───
+      │
+4: ───hello_world───
+
+
+
+

Pennylane

+
+
+
import pennylane as qml
+
+tape = hello_world.export("pennylane")
+tape
+
+
+
+
+
<QuantumTape: wires=[0, 3, 1, 2, 4], params=0>
+
+
+
+
+
+
+

Sympy

+
+
+
qc = hello_world.export("sympy")
+qc
+
+
+
+
+
+\[\displaystyle C_{0,1}{\left(X_{4}\right)} \text{CNOT}_{2,4} \text{CNOT}_{1,3} \text{CNOT}_{0,3} {\left|00000\right\rangle }\]
+
+
+
+
+

Qutip

+
+
+
# Disabled on docs for a depencency problem
+# qc = hello_world.export("qutip")
+# qc.gates
+
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.algorithms.deutschjozsa.DeutschJozsa.html b/generated/qlasskit.algorithms.deutschjozsa.DeutschJozsa.html new file mode 100644 index 00000000..65a844b5 --- /dev/null +++ b/generated/qlasskit.algorithms.deutschjozsa.DeutschJozsa.html @@ -0,0 +1,236 @@ + + + + + + + qlasskit.algorithms.deutschjozsa.DeutschJozsa — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.algorithms.deutschjozsa.DeutschJozsa

+
+
+class qlasskit.algorithms.deutschjozsa.DeutschJozsa(f: QlassF)
+
+
+__init__(f: QlassF)
+
+
Parameters:
+

f (QlassF) – our f(x) -> bool

+
+
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__(f)

+
param f:
+

our f(x) -> bool

+
+
+

circuit()

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

gate([framework])

Returns the gate for a specific framework

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_gates

num_qubits

output_qubits

Returns the list of output qubits

output_size

qubits

Returns all the qubits of the circuit

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.algorithms.grover.Grover.html b/generated/qlasskit.algorithms.grover.Grover.html new file mode 100644 index 00000000..9f7af1ad --- /dev/null +++ b/generated/qlasskit.algorithms.grover.Grover.html @@ -0,0 +1,243 @@ + + + + + + + qlasskit.algorithms.grover.Grover — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.algorithms.grover.Grover

+
+
+class qlasskit.algorithms.grover.Grover(oracle: QlassF, element_to_search: Qtype | None = None, n_iterations: int | None = None, n_matching: int = 1)
+
+
+__init__(oracle: QlassF, element_to_search: Qtype | None = None, n_iterations: int | None = None, n_matching: int = 1)
+
+
Parameters:
+
    +

  • oracle (QlassF) – our f(x) -> bool that returns True if x satisfies the function or +a generic function f(x) = y that we want to compare with element_to_search

    • +

  • element_to_search (Qtype, optional) – the element we want to search

    • +

  • n_iterations (int, optional) – force a number of iterations +(otherwise, pi/4*sqrt(N/n_matching))

    • +

  • n_matching (int) – the number of expected matching values (default: 1)

    • +
+
+
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__(oracle[, element_to_search, ...])

+
param oracle:
+

our f(x) -> bool that returns True if x satisfies the function or

+
+
+

circuit()

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

gate([framework])

Returns the gate for a specific framework

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_gates

num_qubits

output_qubits

Returns the list of output qubits

output_size

qubits

Returns all the qubits of the circuit

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.algorithms.qalgorithm.html b/generated/qlasskit.algorithms.qalgorithm.html new file mode 100644 index 00000000..ce396476 --- /dev/null +++ b/generated/qlasskit.algorithms.qalgorithm.html @@ -0,0 +1,179 @@ + + + + + + + qlasskit.algorithms.qalgorithm — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.algorithms.qalgorithm

+

Functions

+ + + + + + +

oraclize(qf, element[, name])

Transform a QlassF qf and an element to an oracle {f(x) = x == element}

+

Classes

+ + + + + + +

QAlgorithm()

+

Exceptions

+ + + + + + +

ConstantOracleException

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.algorithms.simon.Simon.html b/generated/qlasskit.algorithms.simon.Simon.html new file mode 100644 index 00000000..0be8996d --- /dev/null +++ b/generated/qlasskit.algorithms.simon.Simon.html @@ -0,0 +1,236 @@ + + + + + + + qlasskit.algorithms.simon.Simon — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.algorithms.simon.Simon

+
+
+class qlasskit.algorithms.simon.Simon(f: QlassF)
+
+
+__init__(f: QlassF)
+
+
Parameters:
+

f (QlassF) – our f(x)

+
+
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__(f)

+
param f:
+

our f(x)

+
+
+

circuit()

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

gate([framework])

Returns the gate for a specific framework

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_gates

num_qubits

output_qubits

Returns the list of output qubits

output_size

qubits

Returns all the qubits of the circuit

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.bqm.decode_samples.html b/generated/qlasskit.bqm.decode_samples.html new file mode 100644 index 00000000..22d17fff --- /dev/null +++ b/generated/qlasskit.bqm.decode_samples.html @@ -0,0 +1,164 @@ + + + + + + + qlasskit.bqm.decode_samples — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.bqm.decode_samples

+
+
+qlasskit.bqm.decode_samples(qf, sampleset)
+

Get dimod sampleset and return an high level decoded solution

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.decompiler.Decompiler.html b/generated/qlasskit.decompiler.Decompiler.html new file mode 100644 index 00000000..a9af5452 --- /dev/null +++ b/generated/qlasskit.decompiler.Decompiler.html @@ -0,0 +1,182 @@ + + + + + + + qlasskit.decompiler.Decompiler — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.decompiler.Decompiler

+
+
+class qlasskit.decompiler.Decompiler
+
+
+__init__()
+
+ +

Methods

+ + + + + + + + + +

__init__()

decompile(qc)

Decompile a quantum circuit, searching for circuit sections that apply transformations on the z-basis on the same qubits, and return boolean expressions representing them

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qcircuit.gates.html b/generated/qlasskit.qcircuit.gates.html new file mode 100644 index 00000000..e20e1f09 --- /dev/null +++ b/generated/qlasskit.qcircuit.gates.html @@ -0,0 +1,225 @@ + + + + + + + qlasskit.qcircuit.gates — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qcircuit.gates

+

Functions

+ + + + + + +

apply(gate, qubits[, param])

+

Classes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Barrier()

CCX()

CP()

CX()

H()

I()

MCX(n_controls)

MCtrl(gate, n_controls)

NopGate([name])

P()

QControlledGate(gate, n_controls)

QGate(name[, n_qubits])

S()

Swap()

T()

Toffoli

alias of CCX

X()

Y()

Z()

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qcircuit.qcircuit.QCircuit.html b/generated/qlasskit.qcircuit.qcircuit.QCircuit.html new file mode 100644 index 00000000..0e616188 --- /dev/null +++ b/generated/qlasskit.qcircuit.qcircuit.QCircuit.html @@ -0,0 +1,268 @@ + + + + + + + qlasskit.qcircuit.qcircuit.QCircuit — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qcircuit.qcircuit.QCircuit

+
+
+class qlasskit.qcircuit.qcircuit.QCircuit(num_qubits=0, name='qc', native=None)
+
+
+__init__(num_qubits=0, name='qc', native=None)
+

Initialize a quantum circuit.

+
+
Parameters:
+

num_qubits (int, optional) – The number of qubits in the circuit. Defaults to 0.

+
+
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__([num_qubits, name, native])

Initialize a quantum circuit.

add_qubit([name])

Add a qubit to the circuit.

append(gate, qubits[, param])

Append a gate operation to the circuit.

append_circuit(other[, qubits])

Add a qcircuit, remapping to qubits

barrier([label])

Add a barrier to the circuit

ccx(w1, w2, w3)

CCX gate

copy([vanilla])

Create a copy of the quantum circuit; if vanilla is True, reset all mapping info

cp(phase, w1, w2)

CP gate

cx(w1, w2)

CX gate

draw()

Draw the circuit

export([mode, framework])

Exports the circuit to another framework.

get_key_by_index(i)

Return the qubit name given its index

h(w)

H gate

iqft(wl)

Apply the inverse quantum fourier transform

mctrl(g, wl, target[, param])

Multi controlled gate

mcx(wl, target)

Multi CX gate

qft(wl)

Apply the quantum fourier transform

random(qubits_n, depth[, gate_list])

repeat(n)

Return a copy of the QCircuit repeated n times

s(w)

S gate

swap(w1, w2)

t(w)

T gate

x(w)

X gate

y(w)

Y gate

z(w)

Z gate

+

Attributes

+ + + + + + + + + +

num_gates

used_qubits

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.html b/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.html new file mode 100644 index 00000000..cc2623bc --- /dev/null +++ b/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.html @@ -0,0 +1,227 @@ + + + + + + + qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper

+
+
+class qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper
+

Wrapper interface for a class containing a qcircuit

+
+
+__init__()
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__()

circuit()

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

gate([framework])

Returns the gate for a specific framework

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_gates

num_qubits

output_qubits

Returns the list of output qubits

output_size

qubits

Returns all the qubits of the circuit

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qlassfun.QlassF.html b/generated/qlasskit.qlassfun.QlassF.html new file mode 100644 index 00000000..ee016b0e --- /dev/null +++ b/generated/qlasskit.qlassfun.QlassF.html @@ -0,0 +1,266 @@ + + + + + + + qlasskit.qlassfun.QlassF — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qlassfun.QlassF

+
+
+class qlasskit.qlassfun.QlassF(name: str, original_f: Callable, args: List[Arg], returns: Arg, exps: List[Tuple[Symbol, Boolean]])
+

Class representing a qlassf function

+
+
+__init__(name: str, original_f: Callable, args: List[Arg], returns: Arg, exps: List[Tuple[Symbol, Boolean]])
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__(name, original_f, args, returns, exps)

bind(**kwargs)

Returns a new QlassF with defined params

circuit()

compile([compiler, uncompute])

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

f()

Returns the classical python function

from_function(f[, types, defs, to_compile, ...])

Create a QlassF from a function or a string containing a function

gate([framework])

Returns the gate for a specific framework

to_bqm([fmt])

to_logicfun()

truth_table([max])

Returns the truth table for the function using the sympy boolean for computing

truth_table_header()

Returns the list of string containing the truth table header

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_gates

num_qubits

output_qubits

Returns the list of output qubits

output_size

Return the size of the return type (in bits)

qubits

Returns all the qubits of the circuit

name

original_f

args

returns

expressions

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qlassfun.qlassf.html b/generated/qlasskit.qlassfun.qlassf.html new file mode 100644 index 00000000..05d474b2 --- /dev/null +++ b/generated/qlasskit.qlassfun.qlassf.html @@ -0,0 +1,179 @@ + + + + + + + qlasskit.qlassfun.qlassf — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qlassfun.qlassf

+
+
+qlasskit.qlassfun.qlassf(f: str | ~typing.Callable, types: ~typing.List[~qlasskit.types.qtype.Qtype] = [], defs: ~typing.List[~qlasskit.qlassfun.QlassF] = [], to_compile: bool = True, compiler: ~typing.Literal['internal', 'recompiler', 'tweedledum'] = 'internal', bool_optimizer: ~qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile = <qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile object>, uncompute: bool = True) → QlassF | UnboundQlassf
+

Decorator / function creating a QlassF object

+
+
Parameters:
+
    +

  • f (Union[str, Callable]) – the function to be parsed, as a str code or callable

    • +

  • types (List[Qtype]) – list of qtypes to inject

    • +

  • defs (List[Qlassf]) – list of qlassf to inject

    • +

  • to_compile (boolean, optional) – if True, compile to quantum circuit (default: True)

    • +

  • compiler (SupportedCompiler, optional) – override default compiler (default: internal)

    • +

  • bool_optimizer (BoolOptimizerProfile, optional) – override default optimizer +(default: defaultOptimizer)

    • +

  • uncompute (bool, optional) – whenever uncompute input qubits during compilation +(default: True)

    • +
+
+
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qlassfun.qlassfa.html b/generated/qlasskit.qlassfun.qlassfa.html new file mode 100644 index 00000000..413a122f --- /dev/null +++ b/generated/qlasskit.qlassfun.qlassfa.html @@ -0,0 +1,164 @@ + + + + + + + qlasskit.qlassfun.qlassfa — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qlassfun.qlassfa

+
+
+qlasskit.qlassfun.qlassfa(types: ~typing.List[~qlasskit.types.qtype.Qtype] = [], defs: ~typing.List[~qlasskit.qlassfun.QlassF] = [], to_compile: bool = True, compiler: ~typing.Literal['internal', 'recompiler', 'tweedledum'] = 'internal', bool_optimizer: ~qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile = <qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile object>, uncompute: bool = True)
+

Decorator with parameters for qlassf

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.html b/generated/qlasskit.types.html new file mode 100644 index 00000000..0182a1e8 --- /dev/null +++ b/generated/qlasskit.types.html @@ -0,0 +1,211 @@ + + + + + + + qlasskit.types — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types

+

Functions

+ + + + + + + + + + + + + + + +

const_to_qtype(value)

format_outcome(out[, out_len])

interpret_as_qtype(out, qtype[, out_len])

type_repr(typ)

+

Modules

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

qlasskit.types.parameter

qlasskit.types.qbool

qlasskit.types.qchar

qlasskit.types.qfixed

qlasskit.types.qint

qlasskit.types.qlist

qlasskit.types.qmatrix

qlasskit.types.qtype

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.parameter.html b/generated/qlasskit.types.parameter.html new file mode 100644 index 00000000..5e380553 --- /dev/null +++ b/generated/qlasskit.types.parameter.html @@ -0,0 +1,177 @@ + + + + + + + qlasskit.types.parameter — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.parameter

+

Classes

+ + + + + + + + + +

Parameter()

ParameterMeta

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.qbool.html b/generated/qlasskit.types.qbool.html new file mode 100644 index 00000000..fb984690 --- /dev/null +++ b/generated/qlasskit.types.qbool.html @@ -0,0 +1,174 @@ + + + + + + + qlasskit.types.qbool — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.qbool

+

Classes

+ + + + + + +

Qbool()

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.qchar.html b/generated/qlasskit.types.qchar.html new file mode 100644 index 00000000..157428f2 --- /dev/null +++ b/generated/qlasskit.types.qchar.html @@ -0,0 +1,174 @@ + + + + + + + qlasskit.types.qchar — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.qchar

+

Classes

+ + + + + + +

Qchar(value)

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.qfixed.html b/generated/qlasskit.types.qfixed.html new file mode 100644 index 00000000..620bea3c --- /dev/null +++ b/generated/qlasskit.types.qfixed.html @@ -0,0 +1,219 @@ + + + + + + + qlasskit.types.qfixed — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.qfixed

+

Classes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Qfixed()

Qfixed1_2(value)

Qfixed1_3(value)

Qfixed1_4(value)

Qfixed1_6(value)

Qfixed2_2(value)

Qfixed2_3(value)

Qfixed2_4(value)

Qfixed2_6(value)

Qfixed3_3(value)

Qfixed3_4(value)

Qfixed3_6(value)

Qfixed4_4(value)

Qfixed4_6(value)

QfixedImp(value)

Implementation of the Qfixed type A number i.f is encoded in a Qfixed2_4 as iiffff.

QfixedMeta

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.qint.html b/generated/qlasskit.types.qint.html new file mode 100644 index 00000000..08311e92 --- /dev/null +++ b/generated/qlasskit.types.qint.html @@ -0,0 +1,207 @@ + + + + + + + qlasskit.types.qint — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.qint

+

Classes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Qint()

Qint12(value)

Qint16(value)

Qint2(value)

Qint3(value)

Qint4(value)

Qint5(value)

Qint6(value)

Qint7(value)

Qint8(value)

QintImp(value)

QintMeta

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.qlist.html b/generated/qlasskit.types.qlist.html new file mode 100644 index 00000000..94f06be6 --- /dev/null +++ b/generated/qlasskit.types.qlist.html @@ -0,0 +1,177 @@ + + + + + + + qlasskit.types.qlist — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.qlist

+

Classes

+ + + + + + + + + +

Qlist()

QlistMeta

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.qmatrix.html b/generated/qlasskit.types.qmatrix.html new file mode 100644 index 00000000..35a8565c --- /dev/null +++ b/generated/qlasskit.types.qmatrix.html @@ -0,0 +1,177 @@ + + + + + + + qlasskit.types.qmatrix — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.qmatrix

+

Classes

+ + + + + + + + + +

Qmatrix()

QmatrixMeta

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.types.qtype.html b/generated/qlasskit.types.qtype.html new file mode 100644 index 00000000..35da81e5 --- /dev/null +++ b/generated/qlasskit.types.qtype.html @@ -0,0 +1,193 @@ + + + + + + + qlasskit.types.qtype — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.types.qtype

+

Functions

+ + + + + + + + + +

bin_to_bool_list(b[, bit_size])

bool_list_to_bin(b_lst)

+

Classes

+ + + + + + +

Qtype()

+

Exceptions

+ + + + + + +

TypeErrorException(got, excepted)

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..95a23ca6 --- /dev/null +++ b/genindex.html @@ -0,0 +1,321 @@ + + + + + + Index — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ _ + | D + | G + | M + | Q + | S + +
+

_

+ + +
+ +

D

+ + + +
+ +

G

+ + +
+ +

M

+ + +
+ +

Q

+ + + +
    +
  • + qlasskit.types.qbool + +
    • +
  • + qlasskit.types.qchar + +
    • +
  • + qlasskit.types.qfixed + +
    • +
  • + qlasskit.types.qint + +
    • +
  • + qlasskit.types.qlist + +
    • +
  • + qlasskit.types.qmatrix + +
    • +
  • + qlasskit.types.qtype + +
    • +
+ +

S

+ + +
+ + + +
+
+
+ +
+ +
+

© Copyright 2023-2024, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/how_it_works.html b/how_it_works.html new file mode 100644 index 00000000..ec97df9f --- /dev/null +++ b/how_it_works.html @@ -0,0 +1,273 @@ + + + + + + + How it works — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

How it works

+

To convert Python code into a quantum circuit, qlasskit implements a series of transformations:

+
    +

  1. It begins with the Python AST (Abstract Syntax Tree), converting it into a more streamlined form using the ast2ast module.

    2. +

  2. Next, the streamlined AST is translated into boolean expressions as an intermediate step by the ast2logic module. +During this phase, boolean expressions are refined and optimized in preparation for the final transformation.

    3. +

  3. Finally, the compiler module takes these optimized boolean expressions and compiles them into a +quantum circuit.

    4. +
+

Unlike other libraries that translate individual operations into quantum circuits before combining them, +qlasskit constructs a single boolean expression for each output qubit of the entire function. +This unique approach facilitates advanced optimization leveraging boolean algebraic properties.

+

Transformations

+

For instance, let assume we have the following function:

+
+
+
from qlasskit import qlassf, Qint
+from qiskit import QuantumCircuit
+
+
+@qlassf
+def f_comp(b: bool, n: Qint[2]) -> Qint[2]:
+    for i in range(3):
+        n += 1 if b else 2
+    return n
+
+
+
+
+

If we decompose the algorithm in 3 separate additions and we compile them separately, we obtain the following circuit:

+
+
+
@qlassf
+def f1(b: bool, n: Qint[2]) -> Qint[2]:
+    return n + (1 if b else 2)
+
+
+qc = QuantumCircuit(f_comp.num_qubits * 2 - 1)
+
+for i in range(3):
+    qc.barrier(label=f"it_{i}")
+    qc.append(f1.gate(), [0] + list(range(1 + i * 2, 5 + i * 2)))
+
+print("Operations:", qc.decompose().count_ops())
+qc.decompose().draw("mpl")
+
+
+
+
+
Operations: OrderedDict([('cx', 12), ('barrier', 3), ('x', 3), ('ccx', 3)])
+
+
+_images/5b50037dd2c0a981ffc361d45ac7e20ede9efca0e92c747457be1727c64ae36b.png +
+
+

While if we compile the whole function to a quantum circuit using qlasskit, we obtain the following quantum circuit:

+
+
+
qc = QuantumCircuit(f_comp.num_qubits)
+qc.append(f_comp.gate(), f_comp.qubits)
+
+print("Operations:", qc.decompose().count_ops())
+qc.decompose().draw("mpl")
+
+
+
+
+
Operations: OrderedDict([('cx', 4), ('x', 1), ('ccx', 1)])
+
+
+_images/f3e9cd6f4bfc037a2c61c72684f180e5ae89c4a01198673478cc56fb746c1be7.png +
+
+

As we can see from the circuit drawings, qlasskit approach needs half the number of qubits and half the number of gates.

+
+

AST Traslator

+

Given a python function, the qlasskit.ast2logic module walks its syntax tree translating all the statements / +expressions to boolean expressions.

+

For instance, the following function:

+
+
+
@qlassf
+def f(n: Qint[4]) -> bool:
+    return n == 3
+
+
+
+
+

Is translated to this boolean expression:

+
+
+
print(f.expressions)
+
+
+
+
+
[(_ret, n.0 & n.1 & ~n.2 & ~n.3)]
+
+
+
+
+
+
+

Compiler

+

The boolean expressions are then being fed to the `qlasskit.compiler`` which compiles boolean expressions +to invertible circuits, introducing auxiliary qubits. In this step, the compiler will automatically uncompute +auxiliary qubits in order to reduce the number of qubits needed and the circuit footprint.

+

For the compilation, two backends are supported:

+
    +

  • InternalCompiler

    • +

  • Tweedledum.xag_synth

    • +
+
+
+

Result

+

The result of the compiler is a quantum circuit represented with qlasskit QCircuit. This circuit +can now be exported to one of the supported framework as a gate or as a standalone circuit.

+

The previous example function f, is translated to the following quantum circuit: the +result is available at qubit q6.

+
+
+
f.export().draw("mpl")
+
+
+
+
+_images/816fddadcdd0ee15c32e424045ea5f4d76f6b0add32f18cce0949ecf40028a30.png +
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..936c15a6 --- /dev/null +++ b/index.html @@ -0,0 +1,250 @@ + + + + + + + Qlasskit — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Qlasskit

+GitHub Repository +Unitary Fund +CI Status +PyPI - Version +License: Apache 2.0 +Discord +Codacy Badge +Downloads +

Qlasskit is a Python library that allows quantum developers to write classical algorithms in pure +Python and translate them into unitary operators (gates) for use in quantum circuits supporting a wide +range of quantum frameworks.

+

Qlasskit also support exporting to Binary Quadratic Models (bqm, ising and qubo) ready to be used in +quantum annealers, ising machines, simulators, etc.

+
+

Qlasskit

+ +
+
+

Examples

+ +
+
+
+

Indices and tables

+ +
+
+

Cite

+
@software{qlasskit2023,
+  author = {Davide Gessa},
+  title = {qlasskit: a python-to-quantum circuit compiler},
+  url = {https://github.com/dakk/qlasskit},
+  year = {2023},
+}
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 00000000..1a537311 Binary files /dev/null and b/objects.inv differ diff --git a/parameters.html b/parameters.html new file mode 100644 index 00000000..f2d15a5e --- /dev/null +++ b/parameters.html @@ -0,0 +1,200 @@ + + + + + + + Parameters — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Parameters

+

Qlasskit allow to create qlassf function with parameters. Those parameters are not free variables of the final circuit, and need to be bound before compilation.

+

In the next example we define test with a parameter a; after binding the parameter a to True, the final function expression will be b.

+
+
+
from qlasskit import qlassf, Parameter
+
+
+@qlassf
+def test(a: Parameter[bool], b: bool) -> bool:
+    return a and b
+
+
+qf = test.bind(a=True)
+qf.expressions
+
+
+
+
+
[(_ret, b)]
+
+
+
+
+

Parameterizable qlassf also supports custom types, and complex types like Qlist and Qtuple.

+
+
+
from qlasskit import Qlist, Qint, Qint4
+
+
+@qlassf
+def test(a: Parameter[Qlist[Qint[2], 4]], b: Qint[4]) -> Qint[4]:
+    s = Qint4(0)
+    for n in a:
+        s += n
+    return s + b
+
+
+qf = test.bind(a=[2, 1, 0, 3])
+qf.expressions
+
+
+
+
+
[(_ret.0, b.0),
+ (_ret.1, ~b.1),
+ (_ret.2, ~(b.1 ^ b.2)),
+ (_ret.3, b.2 ^ b.3 ^ (b.1 & ~b.2))]
+
+
+
+
+
+
+
qf.circuit().export().draw("mpl")
+
+
+
+
+_images/d104916566a99e2da51900e9162fefb55e749ecadff8c23ba5799ed5707897f0.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 00000000..ec2b7db5 --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,205 @@ + + + + + + Python Module Index — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Python Module Index
    • +
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    • +
+
+
+
+
+ + +

Python Module Index

+ +
+ q +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ q
+ qlasskit +
    + qlasskit.algorithms.qalgorithm +
    + qlasskit.qcircuit.gates +
    + qlasskit.types +
    + qlasskit.types.parameter +
    + qlasskit.types.qbool +
    + qlasskit.types.qchar +
    + qlasskit.types.qfixed +
    + qlasskit.types.qint +
    + qlasskit.types.qlist +
    + qlasskit.types.qmatrix +
    + qlasskit.types.qtype +
+ + +
+
+
+ +
+ +
+

© Copyright 2023-2024, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/quickstart.html b/quickstart.html new file mode 100644 index 00000000..eaf1a396 --- /dev/null +++ b/quickstart.html @@ -0,0 +1,206 @@ + + + + + + + Quickstart — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Quickstart

+

First install qlasskit using pip.

+

pip install qlasskit

+

We now define a qlassf function that sums two numbers:

+
+
+
from qlasskit import qlassf, Qint, Qint2
+
+
+@qlassf
+def sum_two_numbers(a: Qint[2], b: Qint[2]) -> Qint[2]:
+    return a + b
+
+
+
+
+

We can now export the resulting quantum circuit to any supported framework:

+
+
+
circuit = sum_two_numbers.export("qiskit")
+circuit.draw("mpl")
+
+
+
+
+_images/134555be2b0ec8b6542fa4c9920ed78256557fd3823098fae0e5b794a7d039f7.png +
+
+

The qlassf function can be also exported as a gate, if the destination framwork supports it. We can use encode_input and decode_output in order to convert from/to high level types of qlasskit without worrying about the binary representation.

+
+
+
from qiskit import QuantumCircuit
+
+qc = QuantumCircuit(sum_two_numbers.num_qubits, len(sum_two_numbers.output_qubits))
+
+qc.initialize(
+    sum_two_numbers.encode_input(Qint2(1), Qint2(2)), sum_two_numbers.input_qubits
+)
+qc.append(sum_two_numbers.gate("qiskit"), sum_two_numbers.qubits)
+qc.measure(sum_two_numbers.output_qubits, range(len(sum_two_numbers.output_qubits)))
+qc.draw("mpl")
+
+
+
+
+_images/47b6cd81bccaa913335a53e27b39ebaa13a3e237adc57d32abf8f74664f5531b.png +
+
+
+
+
from qiskit import QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+from qiskit_aer import AerSimulator
+
+simulator = AerSimulator()
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = sum_two_numbers.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/e55818cf800a54a7925e4b1baed5679302c1ce0167ab0196e4a51a1b8b528332.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 00000000..f6195916 --- /dev/null +++ b/search.html @@ -0,0 +1,150 @@ + + + + + + Search — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Search
    • +
  • +
    • +
+
+
+
+
+ + + + +
+ +
+ +
+
+
+ +
+ +
+

© Copyright 2023-2024, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 00000000..3832abd5 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"2D Matrix (fixed size)": [[43, "d-matrix-fixed-size"]], "API": [[1, "api"]], "AST Traslator": [[39, "ast-traslator"]], "Abstraction tools": [[0, "abstraction-tools"]], "Algorithms": [[0, "algorithms"]], "Assign": [[43, "assign"]], "BQM Polynomial solver": [[7, "bqm-polynomial-solver"]], "Bernstein Vazirani": [[0, "bernstein-vazirani"]], "Bernstein Vazirani algorithm": [[5, "bernstein-vazirani-algorithm"]], "Bin Op": [[43, "bin-op"]], "Binary Quadratic Model, Qubo & Ising": [[2, "binary-quadratic-model-qubo-ising"]], "Boolean operators": [[43, "boolean-operators"]], "CLI Tools": [[3, "cli-tools"]], "Cirq": [[17, "cirq"]], "Cite": [[40, "cite"]], "Comparators": [[43, "comparators"]], "Compiler": [[39, "compiler"]], "Constants": [[43, "constants"]], "Decompiler and Circuit Optimizer": [[4, "decompiler-and-circuit-optimizer"]], "Deutsch Jozsa": [[0, "deutsch-jozsa"]], "Deutsch Jozsa algorithm": [[9, "deutsch-jozsa-algorithm"]], "Examples": [[40, null]], "Exporting to other frameworks": [[17, "exporting-to-other-frameworks"]], "Expressions": [[43, "expressions"]], "For loop": [[43, "for-loop"]], "Function call": [[43, "function-call"]], "Function def": [[43, "function-def"]], "Grover search": [[0, "grover-search"], [10, "grover-search"]], "Grover search: hash function preimage attack": [[12, "grover-search-hash-function-preimage-attack"]], "Grover search: subset problem": [[13, "grover-search-subset-problem"]], "Grover search: sudoku solver": [[14, "grover-search-sudoku-solver"]], "Grover: factorize number": [[11, "grover-factorize-number"]], "How it works": [[39, "how-it-works"]], "If expressions": [[43, "if-expressions"]], "If then else": [[43, "if-then-else"]], "Indices and tables": [[40, "indices-and-tables"]], "List": [[43, "list"]], "List (fixed size)": [[43, "list-fixed-size"]], "Matrix": [[43, "matrix"]], "Parameters": [[41, "parameters"]], "Pennylane": [[17, "pennylane"]], "QASM": [[17, "qasm"]], "Qchar": [[43, "qchar"]], "Qfixed": [[43, "qfixed"]], "Qint": [[43, "qint"]], "Qiskit": [[17, "qiskit"]], "Qlasskit": [[40, "qlasskit"], [40, null]], "Quantum Hybrid": [[43, "quantum-hybrid"]], "Quickstart": [[42, "quickstart"]], "Qutip": [[17, "qutip"]], "Result": [[39, "result"]], "Return": [[43, "return"]], "Running on DWave annealer": [[2, "running-on-dwave-annealer"]], "Running on simulated sampler annealer": [[2, "running-on-simulated-sampler-annealer"]], "Simon function periodicity": [[15, "simon-function-periodicity"]], "Simon periodicity": [[0, "simon-periodicity"]], "Statements": [[43, "statements"]], "Subscript": [[43, "subscript"]], "Supported python subset": [[43, "supported-python-subset"]], "Sympy": [[17, "sympy"]], "TSP": [[8, "tsp"]], "Tuple": [[43, "tuple"], [43, "id1"]], "Types": [[43, "types"]], "Unary Op": 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6, 8, 10, 12, 13, 14, 16, 39, 40, 42], "usag": 3, "user": 2, "util": [10, 43], "v": 3, "va": 14, "valid": [2, 14], "valu": [10, 12, 13, 14, 19, 43], "valueerror": 2, "var": [2, 7, 8], "variabl": [2, 41], "vartyp": 2, "vazirani": 40, "version": [2, 3], "viridi": 14, "visual": [5, 9, 10, 11, 12, 13, 14, 15, 16, 42], "wai": [5, 14], "wait": 2, "wait_id": 2, "wait_sampleset": 2, "walk": 39, "want": [10, 13, 14, 19], "warninghandl": 2, "we": [2, 4, 5, 6, 10, 12, 13, 14, 16, 19, 39, 41, 42], "well": 2, "were": 2, "when": 14, "whenev": 28, "where": [10, 13, 14], "which": [0, 2, 12, 13, 39], "while": [2, 39], "whole": 39, "wide": 40, "wire": 17, "within": 43, "without": [2, 6, 12, 14, 42], "work": [40, 43], "worri": 42, "wrapper": [10, 26], "write": [4, 5, 12, 40], "x": [2, 4, 5, 7, 8, 12, 14, 18, 19, 21, 39], "x0": 7, "x1": 7, "x10": 7, "x11": 7, "x12": 7, "x13": 7, "x14": 7, "x2": 7, "x3": 7, "x4": 7, "x5": 7, "x6": 7, "x7": 7, "x8": 7, "x9": 7, "x_": 17, "xag_synth": 39, "xor": 14, "y": [7, 12, 19], "year": 40, "yeld": 10, "you": [6, 43]}, "titles": ["Algorithms", "API", "Binary Quadratic Model, Qubo & Ising", "CLI Tools", "Decompiler and Circuit Optimizer", "Bernstein Vazirani algorithm", "Working with big circuits", "BQM Polynomial solver", "TSP", "Deutsch Jozsa algorithm", "Grover search", "Grover: factorize number", "Grover search: hash function preimage attack", "Grover search: subset problem", "Grover search: sudoku solver", "Simon function periodicity", "Unitary of qlasskit function", "Exporting to other frameworks", "qlasskit.algorithms.deutschjozsa.DeutschJozsa", "qlasskit.algorithms.grover.Grover", "qlasskit.algorithms.qalgorithm", "qlasskit.algorithms.simon.Simon", "qlasskit.bqm.decode_samples", "qlasskit.decompiler.Decompiler", "qlasskit.qcircuit.gates", "qlasskit.qcircuit.qcircuit.QCircuit", "qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper", "qlasskit.qlassfun.QlassF", "qlasskit.qlassfun.qlassf", "qlasskit.qlassfun.qlassfa", "qlasskit.types", "qlasskit.types.parameter", "qlasskit.types.qbool", "qlasskit.types.qchar", "qlasskit.types.qfixed", "qlasskit.types.qint", "qlasskit.types.qlist", "qlasskit.types.qmatrix", "qlasskit.types.qtype", "How it works", "Qlasskit", "Parameters", "Quickstart", "Supported python subset"], "titleterms": {"2d": 43, "For": 43, "If": 43, "Ising": 2, "abstract": 0, "algorithm": [0, 5, 9, 18, 19, 20, 21], "anneal": 2, "api": 1, "assign": 43, "ast": 39, "attack": 12, "bernstein": [0, 5], "big": 6, "bin": 43, "binari": 2, "bool": 43, "boolean": 43, "bqm": [7, 22], "call": 43, "circuit": [4, 6], "cirq": 17, "cite": 40, "cli": 3, "compar": 43, "compil": 39, "constant": 43, "decode_sampl": 22, "decompil": [4, 23], "def": 43, "deutsch": [0, 9], "deutschjozsa": 18, "dwave": 2, "els": 43, "exampl": 40, "export": 17, "express": 43, "factor": 11, "fix": 43, "framework": 17, "function": [12, 15, 16, 43], "gate": 24, "grover": [0, 10, 11, 12, 13, 14, 19], "hash": 12, "how": 39, "hybrid": 43, "indic": 40, "jozsa": [0, 9], "list": 43, "loop": 43, "matrix": 43, "model": 2, "number": 11, "op": 43, "oper": 43, "optim": 4, "other": 17, "paramet": [31, 41], "pennylan": 17, "period": [0, 15], "polynomi": 7, "preimag": 12, "problem": 13, "py2bexp": 3, "py2qasm": 3, "python": 43, "qalgorithm": 20, "qasm": 17, "qbool": 32, "qchar": [33, 43], "qcircuit": [24, 25, 26], "qcircuitwrapp": 26, "qfix": [34, 43], "qint": [35, 43], "qiskit": 17, "qlassf": [27, 28], "qlassfa": 29, "qlassfun": [27, 28, 29], "qlasskit": [16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40], "qlist": 36, "qmatrix": 37, "qtype": 38, "quadrat": 2, "quantum": 43, "qubo": 2, "quickstart": 42, "qutip": 17, "result": 39, "return": 43, "run": 2, "sampler": 2, "search": [0, 10, 12, 13, 14], "simon": [0, 15, 21], "simul": 2, "size": 43, "solver": [7, 14], "statement": 43, "subscript": 43, "subset": [13, 43], "sudoku": 14, "support": 43, "sympi": 17, "tabl": 40, "tool": [0, 3], "traslat": 39, "tsp": 8, "tupl": 43, "type": [30, 31, 32, 33, 34, 35, 36, 37, 38, 43], "unari": 43, "unitari": 16, "vazirani": [0, 5], "work": [6, 39]}}) \ No newline at end of file diff --git a/supported.html b/supported.html new file mode 100644 index 00000000..49bdd4ef --- /dev/null +++ b/supported.html @@ -0,0 +1,398 @@ + + + + + + + Supported python subset — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Supported python subset

+

Qlasskit supports a subset of python. This subset will be expanded, but it is +limited by the linearity of quantum circuits and by the number of qubits.

+

The structure of a qlasskit function has the following pattern:

+
@qlasskit
+def f(param: type, [...param: type]) -> type:
+   statement
+   ...
+   statement
+
+
+
+

Types

+

All types has a static size.

+
+

bool

+

Boolean type.

+
+
+

Qint

+

Unsigned integers; Qint[2] has 2 bits, and there other sizes are supported. +Single bit of the Qint are accessible by the subscript operator []. +All the supported sizes have a constructor Qintn() defined in qlasskit.types.

+
+
+

Qfixed

+

Fixed point rational number; Qfixed[2,3] has 2 bits for the integer part and 3 bits for the fractional. +All the supported sizes have a constructor Qfixedn_m() defined in qlasskit.types.

+
+
+

Qchar

+

A character.

+
+
+

Tuple

+

Container type holding different types.

+
+
+

List

+

Qlist[T, size] denotes a fixed-size list in qlasskit. +For example, the list [1,2,3] is typed as Qlist[Qint[2],3].

+
+
+

Matrix

+

Qmatrix[T, m, n] denotes a fixed-size list in qlasskit. +For example, the matrix [[1,2],[3,4]] is typed as Qmatrix[Qint[2],2,2].

+
+
+
+

Expressions

+
+

Constants

+
True
+
+
+
42
+
+
+
3.14
+
+
+
'a'
+
+
+
+
+

Tuple

+
(a, b)
+
+
+
+
+

List (fixed size)

+
[a, b]
+
+
+
+
+

2D Matrix (fixed size)

+
[[a, b], [c,d]]
+
+
+
+
+

Subscript

+
a[0]
+
+
+
+
+

Boolean operators

+
not a
+
+
+
a and b
+
+
+
a or b
+
+
+
+
+

If expressions

+
a if b else c
+
+
+
+
+

Comparators

+
a > b or b <= c and c == d or c != a
+
+
+
+
+

Unary Op

+
~a
+
+
+
+
+

Bin Op

+
a << 1
+
+
+
a >> 2
+
+
+
a + b
+
+
+
a - b
+
+
+
a * b
+
+
+
a ** b
+
+
+
a % 2
+
+
+
+

Note

+

Modulo operator only works with 2^n values.

+
+
+
+

Function call

+

Bultin functions:

+
    +

  • print(): debug function, ignore by conversion

    • +

  • len(Tuple), len(Qlist)`: returns the length of a tuple

    • +

  • max(a, b, …), max(Tuple), max(Qlist): returns the max of a tuple

    • +

  • min(a, b, …), min(Tuple), min(Qlist): returns the min of a tuple

    • +

  • sum(Tuple), sum(Qlist): returns the sum of the elemnts of a tuple / list

    • +

  • all(Tuple), all(Qlist): returns True if all of the elemnts are True

    • +

  • any(Tuple), any(Qlist): returns True if any of the elemnts are True

    • +

  • ord(Qchar): returns the integer value of the given Qchar

    • +

  • chr(Qint): returns the char given its ascii code

    • +

  • int(Qfixed | Qint): returns the integer part of a Qfixed

    • +

  • float(Qint | Qfixed): returns a Qfixed representing the Qint

    • +
+
+
+
+

Statements

+
+

Assign

+
c = not a
+
+
+
+
+

Return

+
return b+1
+
+
+
+
+

For loop

+
for i in range(4):
+   a += i
+
+
+
+

Note

+

Please note that in qlasskit, for loops are unrolled during compilation. Therefore, +it is essential that the number of iterations for each for loop is known at the +time of compilation.

+
+
+
+

Function def

+
def f(t: Qlist[Qint[4],2]) -> Qint[4]:
+   return t[0] + t[1]
+
+
+
+
+

If then else

+
c = 0
+if cond:
+   c += 12
+else:
+   c += 13
+
+
+
+

Note

+

At present, the if-then-else statement in qlasskit is designed to support branch bodies +that exclusively contain assignment statements.

+
+
+
+
+

Quantum Hybrid

+

In a qlassf function, you have the option to utilize quantum gates through the Q module. It’s +important to keep in mind that incorporating quantum gates within a qlasskit function leads +to a Python function that exhibits distinct behaviors compared to its quantum counterpart.

+
def bell(a: bool, b: bool) -> bool:
+   return Q.CX(Q.H(a), b)
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file