Source code for qiskit_algorithms.gradients.spsa.spsa_sampler_gradient

# This code is part of a Qiskit project.
# (C) Copyright IBM 2022, 2023.
# 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 collections import defaultdict
from import Sequence

import numpy as np

from qiskit.circuit import Parameter, QuantumCircuit
from qiskit.primitives import BaseSampler
from qiskit.providers import Options

from ..base.base_sampler_gradient import BaseSamplerGradient
from ..base.sampler_gradient_result import SamplerGradientResult

from ...exceptions import AlgorithmError

[docs]class SPSASamplerGradient(BaseSamplerGradient): """ Compute the gradients of the sampling probability 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, sampler: BaseSampler, epsilon: float, batch_size: int = 1, seed: int | None = None, options: Options | None = None, ): """ Args: sampler: The sampler used to compute the gradients. epsilon: The offset size for the SPSA gradients. batch_size: 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._batch_size = batch_size self._epsilon = epsilon self._seed = np.random.default_rng(seed) super().__init__(sampler, options) def _run( self, circuits: Sequence[QuantumCircuit], parameter_values: Sequence[Sequence[float]], parameters: Sequence[Sequence[Parameter]], **options, ) -> SamplerGradientResult: """Compute the sampler gradients on the given circuits.""" job_circuits, job_param_values, metadata, offsets = [], [], [], [] all_n = [] for circuit, parameter_values_, parameters_ in zip(circuits, parameter_values, parameters): # Indices of parameters to be differentiated. indices = [ for p in parameters_] metadata.append({"parameters": parameters_}) offset = np.array( [ (-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. n = 2 * self._batch_size job_circuits.extend([circuit] * n) job_param_values.extend(plus + minus) all_n.append(n) # Run the single job with all circuits. job =, job_param_values, **options) try: results = job.result() except Exception as exc: raise AlgorithmError("Sampler job failed.") from exc # Compute the gradients. gradients = [] partial_sum_n = 0 for i, n in enumerate(all_n): dist_diffs = {} result = results.quasi_dists[partial_sum_n : partial_sum_n + n] for j, (dist_plus, dist_minus) in enumerate(zip(result[: n // 2], result[n // 2 :])): dist_diff: dict[int, float] = defaultdict(float) for key, value in dist_plus.items(): dist_diff[key] += value / (2 * self._epsilon) for key, value in dist_minus.items(): dist_diff[key] -= value / (2 * self._epsilon) dist_diffs[j] = dist_diff gradient = [] indices = [circuits[i] for p in metadata[i]["parameters"]] for j in indices: gradient_j: dict[int, float] = defaultdict(float) for k in range(self._batch_size): for key, value in dist_diffs[k].items(): gradient_j[key] += value * offsets[i][k][j] gradient_j = {key: value / self._batch_size for key, value in gradient_j.items()} gradient.append(gradient_j) gradients.append(gradient) partial_sum_n += n opt = self._get_local_options(options) return SamplerGradientResult(gradients=gradients, metadata=metadata, options=opt)