diff --git a/.vscode/launch.json b/.vscode/launch.json index 3012c60..1769588 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -13,6 +13,7 @@ "args": [ "--debug" ], + "justMyCode": false } ] } \ No newline at end of file diff --git a/app/app.py b/app/app.py index 4fb28cc..89c93f9 100644 --- a/app/app.py +++ b/app/app.py @@ -1,4 +1,14 @@ -from layouts.app_page_layout import sidebar_top, sidebar_bottom, content +from layouts.app_page_layout import ( + sidebar_top, + sidebar_bottom, + content, + DEFAULT_ANSATZ, + DEFAULT_DATA_REUPLOAD, + DEFAULT_N_LAYERS, + DEFAULT_N_QUBITS, + DEFAULT_SEED, +) + import dash import dash_bootstrap_components as dbc from dash import Input, Output, html, callback, State @@ -65,15 +75,21 @@ def on_preference_changed( # Give a default data dict with 0 clicks if there's no data. data = data or {} data["number_qubits"] = ( - max(min(number_qubits, 10), 0) if number_qubits is not None else None + max(min(number_qubits, 10), 0) + if number_qubits is not None + else DEFAULT_N_QUBITS ) data["number_layers"] = ( - max(min(number_layers, 10), 0) if number_layers is not None else None + max(min(number_layers, 10), 0) + if number_layers is not None + else DEFAULT_N_LAYERS + ) + data["circuit_type"] = circuit_type if circuit_type is not None else DEFAULT_ANSATZ + data["data_reupload"] = ( + data_reupload if data_reupload is not None else DEFAULT_DATA_REUPLOAD ) - data["circuit_type"] = circuit_type if circuit_type is not None else "No_Ansatz" - data["data_reupload"] = data_reupload data["tffm"] = False # tffm - data["seed"] = max(min(seed, 999), 100) + data["seed"] = max(min(seed, 999), 1000) if seed is not None else DEFAULT_SEED return data diff --git a/app/layouts/app_page_layout.py b/app/layouts/app_page_layout.py index 511b7c5..f585ec9 100644 --- a/app/layouts/app_page_layout.py +++ b/app/layouts/app_page_layout.py @@ -7,7 +7,7 @@ DEFAULT_N_LAYERS = 1 DEFAULT_SEED = 100 DEFAULT_DATA_REUPLOAD = True -DEFAULT_ANSATZ = "No Ansatz" +DEFAULT_ANSATZ = "No_Ansatz" sidebar_top = [ dcc.Store(id="main-storage", storage_type="session"), diff --git a/app/layouts/training_page_layout.py b/app/layouts/training_page_layout.py index d794d89..11fd3b2 100644 --- a/app/layouts/training_page_layout.py +++ b/app/layouts/training_page_layout.py @@ -4,8 +4,8 @@ ) import dash_bootstrap_components as dbc -DEFAULT_N_STEPS = 10 -DEFAULT_N_FREQS = 3 +DEFAULT_N_STEPS = 50 +DEFAULT_N_FREQS = 4 DEFAULT_STEPSIZE = 0.01 layout = html.Div( @@ -154,7 +154,7 @@ dbc.Input( type="number", min=1, - max=201, + max=801, step=1, value=DEFAULT_N_STEPS, id="training-steps-numeric-input", diff --git a/app/pages/0-main-page.py b/app/pages/0-main-page.py index fbdf4e4..4d75281 100644 --- a/app/pages/0-main-page.py +++ b/app/pages/0-main-page.py @@ -2,4 +2,4 @@ dash.register_page(__name__, name="Home", path="/") -from layouts.main_page_layout import layout # noqa +from layouts.main_page_layout import layout # noqa: E402 diff --git a/app/pages/1-training.py b/app/pages/1-training.py index 2a61b55..b80043a 100644 --- a/app/pages/1-training.py +++ b/app/pages/1-training.py @@ -1,4 +1,5 @@ -from layouts.training_page_layout import layout # noqa +from utils.instructor import Instructor +from layouts.training_page_layout import layout # noqa: E402 from layouts.training_page_layout import ( DEFAULT_N_STEPS, DEFAULT_N_FREQS, @@ -9,8 +10,8 @@ DEFAULT_N_LAYERS, DEFAULT_SEED, DEFAULT_DATA_REUPLOAD, + DEFAULT_ANSATZ, ) -from utils.instructor import Instructor import dash import numpy as np @@ -25,9 +26,24 @@ from typing import Dict, Any, List, Optional +import logging + +log = logging.getLogger(__name__) + dash.register_page(__name__, name="Training") +instructor = Instructor( + DEFAULT_N_QUBITS, + DEFAULT_N_LAYERS, + n_freqs=DEFAULT_N_FREQS, + stepsize=DEFAULT_STEPSIZE, + seed=DEFAULT_SEED, + circuit_type=DEFAULT_ANSATZ, + data_reupload=DEFAULT_DATA_REUPLOAD, +) + + def reset_log() -> Dict[str, list]: """ Resets the training log to contain empty lists for the following keys: @@ -120,9 +136,11 @@ def on_preference_changed( "PhaseDamping": pd, "Depolarization": dp, }, - "steps": steps, - "n_freqs": n_freqs, - "stepsize": stepsize, + "steps": steps if steps is not None and steps > 0 else DEFAULT_N_STEPS, + "n_freqs": n_freqs if n_freqs is not None and n_freqs > 0 else DEFAULT_N_FREQS, + "stepsize": ( + stepsize if stepsize is not None and stepsize > 0 else DEFAULT_STEPSIZE + ), "running": state != "Reset Training", } page_log_training = reset_log() @@ -297,12 +315,15 @@ def update_expval( x=page_log_training["x"], y=page_log_training["y"], name="Target" ) + miny = np.min(page_log_training["y"]) if len(page_log_training["y"]) > 0 else -1 + maxy = np.max(page_log_training["y"]) if len(page_log_training["y"]) > 0 else 1 + fig_expval.update_layout( title="Output", template="simple_white", xaxis_title="X Domain", yaxis_title="Expectation Value", - yaxis_range=[-0.5, 0.5], + yaxis_range=[miny, maxy], legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01), ) @@ -386,48 +407,61 @@ def training( Returns: The updated data in the training log storage. """ + global instructor + if page_log_training is None or page_data is None: raise PreventUpdate() if len(page_log_training["loss"]) > page_data["steps"]: page_log_training = reset_log() - instructor = Instructor( - main_data["number_qubits"], - main_data["number_layers"], - n_freqs=page_data["n_freqs"], - stepsize=page_data["stepsize"], - seed=main_data["seed"], - circuit_type=main_data["circuit_type"], - data_reupload=main_data["data_reupload"], - ) - - page_log_training["params"], cost, pred = instructor.step( - page_log_training["params"], page_data["noise_params"] - ) - - page_log_training["loss"].append(cost.item()) - page_log_training["y_hat"] = pred + if ( + instructor.seed != main_data["seed"] + or instructor.model.n_qubits != main_data["number_qubits"] + or instructor.model.n_layers != main_data["number_layers"] + or instructor.stepsize != page_data["stepsize"] + or instructor.n_freqs != page_data["n_freqs"] + or instructor.circuit_type != main_data["circuit_type"] + or instructor.model.data_reupload != main_data["data_reupload"] + ): + print("Re-init inst") + instructor = Instructor( + main_data["number_qubits"], + main_data["number_layers"], + n_freqs=page_data["n_freqs"], + stepsize=page_data["stepsize"], + seed=main_data["seed"], + circuit_type=main_data["circuit_type"], + data_reupload=main_data["data_reupload"], + ) page_log_training["x"] = instructor.x_d page_log_training["y"] = instructor.y_d - data = instructor.calc_hist( - params=page_log_training["params"], - noise_params=page_data["noise_params"], - ) - - page_log_training["X"] = np.arange( - -len(data) // 2 + 1, len(data) // 2 + 1, 1 - ).tolist() - page_log_training["steps"] = [i for i in range(len(page_log_training["loss"]))] - page_log_training["Y"].append(data.tolist()) - - if main_data["number_qubits"] > 1: - instructor.model.params = page_log_training["params"] - ent_cap = instructor.meyer_wallach( + try: + data = instructor.calc_hist( + params=instructor.model.params, noise_params=page_data["noise_params"], ) + page_log_training["X"] = np.arange( + -len(data) // 2 + 1, len(data) // 2 + 1, 1 + ).tolist() + + page_log_training["Y"].append(data.tolist()) + + if main_data["number_qubits"] > 1: + instructor.model.params = instructor.model.params + ent_cap = instructor.meyer_wallach( + noise_params=page_data["noise_params"], + ) + + page_log_training["ent_cap"].append(ent_cap) + except Exception as e: + log.error(e) - page_log_training["ent_cap"].append(ent_cap) + cost, pred = instructor.step(page_data["noise_params"]) + + page_log_training["loss"].append(cost.item()) + page_log_training["steps"] = [i for i in range(len(page_log_training["loss"]))] + page_log_training["y_hat"] = pred return page_log_training diff --git a/app/pages/2-expressibility.py b/app/pages/2-expressibility.py index e0dbebf..0ef5ab3 100644 --- a/app/pages/2-expressibility.py +++ b/app/pages/2-expressibility.py @@ -13,7 +13,7 @@ dash.register_page(__name__, name="Expressibility") -from layouts.expressibility_page_layout import layout # noqa +from layouts.expressibility_page_layout import layout # noqa: E402 @callback( diff --git a/app/utils/instructor.py b/app/utils/instructor.py index 1344db7..9415af0 100644 --- a/app/utils/instructor.py +++ b/app/utils/instructor.py @@ -8,6 +8,10 @@ from qml_essentials.entanglement import Entanglement from qml_essentials.expressibility import Expressibility +import logging + +log = logging.getLogger(__name__) + class Instructor: def __init__( @@ -16,7 +20,7 @@ def __init__( n_layers: int, n_freqs: int = 3, stepsize: float = 0.01, - seed: int = 1000, + seed: int = 100, circuit_type: str = "Circuit_19", data_reupload: bool = True, **kwargs, @@ -33,6 +37,9 @@ def __init__( tffm: Whether or not to use trainable frequency feature mapping. """ self.seed = seed + self.stepsize = stepsize + self.n_freqs = n_freqs + self.circuit_type = circuit_type self.model = Model( n_qubits=n_qubits, @@ -43,20 +50,96 @@ def __init__( **kwargs, ) self.x_domain = [-1 * np.pi, 1 * np.pi] # [-4 * np.pi, 4 * np.pi] - omega_d = np.array([i for i in range(1, n_freqs + 1)]) - n_d = int(np.ceil(2 * np.max(np.abs(self.x_domain)) * np.max(omega_d))) - self.x_d = np.linspace( - self.x_domain[0], self.x_domain[1], n_d, requires_grad=False - ) - amplitude = 0.5 + self.x_d = self.sample_domain(self.x_domain, n_freqs) + self.y_d = self.generate_fourier_series(self.x_d, n_freqs, 0.5) + self.opt = qml.AdamOptimizer(stepsize=stepsize) + + def sample_domain( + self, domain: List[float], omegas: List[List[float]] + ) -> np.ndarray: + """ + Generates a flattened grid of (x,y,...) coordinates in a range of -1 to 1. - def y_fct(x): - return 1 / np.linalg.norm(omega_d) * np.sum(amplitude * np.cos(omega_d * x)) + Parameters + ---------- + sidelen : int + Side length of the grid + dim : int, optional + Dimensionality of the grid, by default 2 - self.y_d = np.array([y_fct(x) for x in self.x_d], requires_grad=False) + Returns + ------- + np.Tensor + Grid tensor of shape (sidelen^dim, dim) + """ + dimensions = 1 # len(omega) - self.opt = qml.AdamOptimizer(stepsize=stepsize) + if isinstance(omegas, int): + omegas = [o for o in range(omegas)] + # using the max of all dimensions because we want uniform sampling + n_d = int(np.ceil(2 * np.max(np.abs(domain)) * np.max(omegas))) + + log.info(f"Using {n_d} data points on {len(omegas)} dimensions") + + tensors = tuple(dimensions * [np.linspace(domain[0], domain[1], num=n_d)]) + + return np.meshgrid(*tensors)[0].reshape(-1) # .reshape(-1, dimensions) + + def generate_fourier_series( + self, + domain_samples: np.ndarray, + omegas: List[List[float]], + coefficients: List[List[float]], + ) -> np.ndarray: + """ + Generates the Fourier series representation of a function. + + Parameters + ---------- + domain_samples : np.ndarray + Grid of domain samples. + omega : List[List[float]] + List of frequencies for each dimension. + + Returns + ------- + np.ndarray + Fourier series representation of the function. + """ + if not isinstance(omegas, list): + omegas = [o for o in range(omegas)] + if not isinstance(coefficients, list): + coefficients = [coefficients for _ in omegas] + + assert len(omegas) == len( + coefficients + ), "Number of frequencies and coefficients must match" + + omegas = np.array(omegas) + coefficients = np.array(coefficients) + + def y(x: np.ndarray) -> float: + """ + Calculates the Fourier series representation of a function at a given point. + + Parameters + ---------- + x : np.ndarray + Point at which to evaluate the function. + + Returns + ------- + float + Value of the Fourier series representation at the given point. + """ + return ( + 1 / np.linalg.norm(omegas) * np.sum(coefficients * np.cos(omegas.T * x)) + ) # transpose! + + values = np.stack([y(x) for x in domain_samples]) + + return values def calc_hist( self, @@ -142,8 +225,9 @@ def kullback_leibler(self, a, b): def cost( self, params: np.ndarray, + x_d: np.ndarray, y_d: np.ndarray, - noise_params: Optional[Dict[str, float]] = None, + **kwargs, ) -> float: """Compute the cost of the model. @@ -161,17 +245,14 @@ def cost( """ y_pred = self.model( params=params, - inputs=self.x_d, - noise_params=noise_params, - cache=False, - execution_type="expval", + inputs=x_d, + **kwargs, ) return np.mean((y_d - y_pred) ** 2) def step( self, - params: List[float], noise_params: Optional[Dict[str, float]] = None, ) -> Tuple[np.ndarray, float]: """Perform a single optimization step. @@ -187,19 +268,24 @@ def step( Returns: Tuple[np.ndarray, float]: The updated params and the cost of the model. """ - if len(params) == 0: - params = self.model.params - y_pred = self.model( - params=params, + params=self.model.params, inputs=self.x_d, noise_params=noise_params, cache=False, execution_type="expval", + force_mean=True, ) - params = np.array(params, requires_grad=True) - params, cost = self.opt.step_and_cost( - self.cost, params, y_d=self.y_d, noise_params=noise_params + self.model.params, cost = self.opt.step_and_cost( + self.cost, + self.model.params, + x_d=self.x_d, + y_d=self.y_d, + noise_params=noise_params, + cache=False, + execution_type="expval", + force_mean=True, ) - return (params, cost, y_pred[0]) + + return (cost, y_pred) diff --git a/poetry.lock b/poetry.lock index c5a510c..888fab5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -108,13 +108,13 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "blinker" -version = "1.8.2" +version = "1.9.0" description = "Fast, simple object-to-object and broadcast signaling" optional = false -python-versions = ">=3.8" 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