C贸digo fuente para qiskit.algorithms.optimizers.aqgd

# This code is part of Qiskit.
# (C) Copyright IBM 2019, 2021.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
# Any modifications or derivative works of this code must retain this
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"""Analytical Quantum Gradient Descent (AQGD) optimizer."""

from __future__ import annotations
import logging
from collections.abc import Callable
from typing import Any

import numpy as np
from qiskit.utils.validation import validate_range_exclusive_max
from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT
from ..exceptions import AlgorithmError

logger = logging.getLogger(__name__)

[documentos]class AQGD(Optimizer): """Analytic Quantum Gradient Descent (AQGD) with Epochs optimizer. Performs gradient descent optimization with a momentum term, analytic gradients, and customized step length schedule for parameterized quantum gates, i.e. Pauli Rotations. See, for example: * K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii. (2018). Quantum circuit learning. Phys. Rev. A 98, 032309. https://arxiv.org/abs/1803.00745 * Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, Nathan Killoran. (2019). Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99, 032331. https://arxiv.org/abs/1811.11184 for further details on analytic gradients of parameterized quantum gates. Gradients are computed "analytically" using the quantum circuit when evaluating the objective function. """ _OPTIONS = ["maxiter", "eta", "tol", "disp", "momentum", "param_tol", "averaging"] def __init__( self, maxiter: int | list[int] = 1000, eta: float | list[float] = 1.0, tol: float = 1e-6, # this is tol momentum: float | list[float] = 0.25, param_tol: float = 1e-6, averaging: int = 10, ) -> None: """ Performs Analytical Quantum Gradient Descent (AQGD) with Epochs. Args: maxiter: Maximum number of iterations (full gradient steps) eta: The coefficient of the gradient update. Increasing this value results in larger step sizes: param = previous_param - eta * deriv tol: Tolerance for change in windowed average of objective values. Convergence occurs when either objective tolerance is met OR parameter tolerance is met. momentum: Bias towards the previous gradient momentum in current update. Must be within the bounds: [0,1) param_tol: Tolerance for change in norm of parameters. averaging: Length of window over which to average objective values for objective convergence criterion Raises: AlgorithmError: If the length of ``maxiter``, `momentum``, and ``eta`` is not the same. """ super().__init__() if isinstance(maxiter, int): maxiter = [maxiter] if isinstance(eta, (int, float)): eta = [eta] if isinstance(momentum, (int, float)): momentum = [momentum] if len(maxiter) != len(eta) or len(maxiter) != len(momentum): raise AlgorithmError( "AQGD input parameter length mismatch. Parameters `maxiter`, " "`eta`, and `momentum` must have the same length." ) for m in momentum: validate_range_exclusive_max("momentum", m, 0, 1) self._eta = eta self._maxiter = maxiter self._momenta_coeff = momentum self._param_tol = param_tol self._tol = tol self._averaging = averaging # state self._avg_objval: float | None = None self._prev_param: np.ndarray | None = None self._eval_count = 0 # function evaluations self._prev_loss: list[float] = [] self._prev_grad: list[list[float]] = []
[documentos] def get_support_level(self) -> dict[str, OptimizerSupportLevel]: """Support level dictionary Returns: Dict[str, int]: gradient, bounds and initial point support information that is ignored/required. """ return { "gradient": OptimizerSupportLevel.ignored, "bounds": OptimizerSupportLevel.ignored, "initial_point": OptimizerSupportLevel.required, }
@property def settings(self) -> dict[str, Any]: return { "maxiter": self._maxiter, "eta": self._eta, "momentum": self._momenta_coeff, "param_tol": self._param_tol, "tol": self._tol, "averaging": self._averaging, } def _compute_objective_fn_and_gradient( self, params: np.ndarray | list[float], obj: Callable ) -> tuple[float, np.ndarray]: """ Obtains the objective function value for params and the analytical quantum derivatives of the objective function with respect to each parameter. Requires 2*(number parameters) + 1 objective evaluations Args: params: Current value of the parameters to evaluate the objective function obj: Objective function of interest Returns: Tuple containing the objective value and array of gradients for the given parameter set. """ num_params = len(params) param_sets_to_eval = params + np.concatenate( ( np.zeros((1, num_params)), # copy of the parameters as is np.eye(num_params) * np.pi / 2, # copy of the parameters with the positive shift -np.eye(num_params) * np.pi / 2, ), # copy of the parameters with the negative shift axis=0, ) # Evaluate, # reshaping to flatten, as expected by objective function values = np.array(obj(param_sets_to_eval.reshape(-1))) # Update number of objective function evaluations self._eval_count += 2 * num_params + 1 # return the objective function value obj_value = values[0] # return the gradient values gradient = 0.5 * (values[1 : num_params + 1] - values[1 + num_params :]) return obj_value, gradient def _update( self, params: np.ndarray, gradient: np.ndarray, mprev: np.ndarray, step_size: float, momentum_coeff: float, ) -> tuple[np.ndarray, np.ndarray]: """ Updates full parameter array based on a step that is a convex combination of the gradient and previous momentum Args: params: Current value of the parameters to evaluate the objective function at gradient: Gradient of objective wrt parameters mprev: Momentum vector for each parameter step_size: The scaling of step to take momentum_coeff: Bias towards previous momentum vector when updating current momentum/step vector Returns: Tuple of the updated parameter and momentum vectors respectively. """ # Momentum update: # Convex combination of previous momentum and current gradient estimate mnew = (1 - momentum_coeff) * gradient + momentum_coeff * mprev params -= step_size * mnew return params, mnew def _converged_objective(self, objval: float, tol: float, window_size: int) -> bool: """ Tests convergence based on the change in a moving windowed average of past objective values Args: objval: Current value of the objective function tol: tolerance below which (average) objective function change must be window_size: size of averaging window Returns: Bool indicating whether or not the optimization has converged. """ # If we haven't reached the required window length, # append the current value, but we haven't converged if len(self._prev_loss) < window_size: self._prev_loss.append(objval) return False # Update last value in list with current value self._prev_loss.append(objval) # (length now = n+1) # Calculate previous windowed average # and current windowed average of objective values prev_avg = np.mean(self._prev_loss[:window_size]) curr_avg = np.mean(self._prev_loss[1 : window_size + 1]) self._avg_objval = curr_avg # Update window of objective values # (Remove earliest value) self._prev_loss.pop(0) if np.absolute(prev_avg - curr_avg) < tol: # converged logger.info("Previous obj avg: %f\nCurr obj avg: %f", prev_avg, curr_avg) return True return False def _converged_parameter(self, parameter: np.ndarray, tol: float) -> bool: """ Tests convergence based on change in parameter Args: parameter: current parameter values tol: tolerance for change in norm of parameters Returns: Bool indicating whether or not the optimization has converged """ if self._prev_param is None: self._prev_param = np.copy(parameter) return False order = np.inf p_change = np.linalg.norm(self._prev_param - parameter, ord=order) if p_change < tol: # converged logger.info("Change in parameters (%f norm): %f", order, p_change) return True return False def _converged_alt(self, gradient: list[float], tol: float, window_size: int) -> bool: """ Tests convergence from norm of windowed average of gradients Args: gradient: current gradient tol: tolerance for average gradient norm window_size: size of averaging window Returns: Bool indicating whether or not the optimization has converged """ # If we haven't reached the required window length, # append the current value, but we haven't converged if len(self._prev_grad) < window_size - 1: self._prev_grad.append(gradient) return False # Update last value in list with current value self._prev_grad.append(gradient) # (length now = n) # Calculate previous windowed average # and current windowed average of objective values avg_grad = np.mean(self._prev_grad, axis=0) # Update window of values # (Remove earliest value) self._prev_grad.pop(0) if np.linalg.norm(avg_grad, ord=np.inf) < tol: # converged logger.info("Avg. grad. norm: %f", np.linalg.norm(avg_grad, ord=np.inf)) return True return False
[documentos] def minimize( self, fun: Callable[[POINT], float], x0: POINT, jac: Callable[[POINT], POINT] | None = None, bounds: list[tuple[float, float]] | None = None, ) -> OptimizerResult: params = np.asarray(x0) momentum = np.zeros(shape=(params.size,)) # empty out history of previous objectives/gradients/parameters # (in case this object is re-used) self._prev_loss = [] self._prev_grad = [] self._prev_param = None self._eval_count = 0 # function evaluations iter_count = 0 logger.info("Initial Params: %s", params) epoch = 0 converged = False for (eta, mom_coeff) in zip(self._eta, self._momenta_coeff): logger.info("Epoch: %4d | Stepsize: %6.4f | Momentum: %6.4f", epoch, eta, mom_coeff) sum_max_iters = sum(self._maxiter[0 : epoch + 1]) while iter_count < sum_max_iters: # update the iteration count iter_count += 1 # Check for parameter convergence before potentially costly function evaluation converged = self._converged_parameter(params, self._param_tol) if converged: break # Calculate objective function and estimate of analytical gradient if jac is None: objval, gradient = self._compute_objective_fn_and_gradient(params, fun) else: objval = fun(params) gradient = jac(params) logger.info( " Iter: %4d | Obj: %11.6f | Grad Norm: %f", iter_count, objval, np.linalg.norm(gradient, ord=np.inf), ) # Check for objective convergence converged = self._converged_objective(objval, self._tol, self._averaging) if converged: break # Update parameters and momentum params, momentum = self._update(params, gradient, momentum, eta, mom_coeff) # end inner iteration # if converged, end iterating over epochs if converged: break epoch += 1 # end epoch iteration result = OptimizerResult() result.x = params result.fun = objval result.nfev = self._eval_count result.nit = iter_count return result