Código fuente para qiskit.algorithms.gradients.spsa_estimator_gradient

# This code is part of Qiskit.
# (C) Copyright IBM 2022.
# 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
# Any modifications or derivative works of this code must retain this
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"""Gradient of Sampler with Finite difference method."""

from __future__ import annotations

from import Sequence

import numpy as np

from qiskit.circuit import Parameter, QuantumCircuit
from qiskit.opflow import PauliSumOp
from qiskit.primitives import BaseEstimator
from qiskit.providers import Options
from qiskit.quantum_info.operators.base_operator import BaseOperator

from .base_estimator_gradient import BaseEstimatorGradient
from .estimator_gradient_result import EstimatorGradientResult

from ..exceptions import AlgorithmError

[documentos]class SPSAEstimatorGradient(BaseEstimatorGradient): """ Compute the gradients of the expectation value by the Simultaneous Perturbation Stochastic Approximation (SPSA) [1]. **Reference:** [1] J. C. Spall, Adaptive stochastic approximation by the simultaneous perturbation method in IEEE Transactions on Automatic Control, vol. 45, no. 10, pp. 1839-1853, Oct 2020, `doi: 10.1109/TAC.2000.880982 <>`_ """ def __init__( self, estimator: BaseEstimator, epsilon: float, batch_size: int = 1, seed: int | None = None, options: Options | None = None, ): """ Args: estimator: The estimator used to compute the gradients. epsilon: The offset size for the SPSA gradients. batch_size: The number of gradients to average. seed: The seed for a random perturbation vector. options: Primitive backend runtime options used for circuit execution. The order of priority is: options in ``run`` method > gradient's default options > primitive's default setting. Higher priority setting overrides lower priority setting Raises: ValueError: If ``epsilon`` is not positive. """ if epsilon <= 0: raise ValueError(f"epsilon ({epsilon}) should be positive.") self._epsilon = epsilon self._batch_size = batch_size self._seed = np.random.default_rng(seed) super().__init__(estimator, options) def _run( self, circuits: Sequence[QuantumCircuit], observables: Sequence[BaseOperator | PauliSumOp], parameter_values: Sequence[Sequence[float]], parameters: Sequence[Sequence[Parameter]], **options, ) -> EstimatorGradientResult: """Compute the estimator gradients on the given circuits.""" job_circuits, job_observables, job_param_values, metadata, offsets = [], [], [], [], [] all_n = [] for circuit, observable, parameter_values_, parameters_ in zip( circuits, observables, parameter_values, parameters ): # Indices of parameters to be differentiated. indices = [ for p in parameters_] metadata.append({"parameters": parameters_}) # Make random perturbation vectors. offset = [ (-1) ** (self._seed.integers(0, 2, len(circuit.parameters))) for _ in range(self._batch_size) ] plus = [parameter_values_ + self._epsilon * offset_ for offset_ in offset] minus = [parameter_values_ - self._epsilon * offset_ for offset_ in offset] offsets.append(offset) # Combine inputs into a single job to reduce overhead. job_circuits.extend([circuit] * 2 * self._batch_size) job_observables.extend([observable] * 2 * self._batch_size) job_param_values.extend(plus + minus) all_n.append(2 * self._batch_size) # Run the single job with all circuits. job = job_circuits, job_observables, job_param_values, **options, ) try: results = job.result() except Exception as exc: raise AlgorithmError("Estimator job failed.") from exc # Compute the gradients. gradients = [] partial_sum_n = 0 for i, n in enumerate(all_n): result = results.values[partial_sum_n : partial_sum_n + n] partial_sum_n += n n = len(result) // 2 diffs = (result[:n] - result[n:]) / (2 * self._epsilon) # Calculate the gradient for each batch. Note that (``diff`` / ``offset``) is the gradient # since ``offset`` is a perturbation vector of 1s and -1s. batch_gradients = np.array([diff / offset for diff, offset in zip(diffs, offsets[i])]) # Take the average of the batch gradients. gradient = np.mean(batch_gradients, axis=0) indices = [circuits[i] for p in metadata[i]["parameters"]] gradients.append(gradient[indices]) opt = self._get_local_options(options) return EstimatorGradientResult(gradients=gradients, metadata=metadata, options=opt)