mirror of https://github.com/Qiskit/qiskit.git
Add gradient methods to FinDiffGradients (#9104)
* implement gradient methods * fix docs * arg order/refactor * fix docs * revert central difference * Update qiskit/algorithms/gradients/finite_diff_estimator_gradient.py * fix docs * Update qiskit/algorithms/gradients/finite_diff_sampler_gradient.py Co-authored-by: Julien Gacon <gaconju@gmail.com> Co-authored-by: Julien Gacon <gaconju@gmail.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
This commit is contained in:
Ikko Hamamura
GitHub
parent
30f45fe79a
commit
2bd4afacdc
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@ -14,6 +14,7 @@
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from __future__ import annotations
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import sys
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from typing import Sequence
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import numpy as np
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@ -28,14 +29,27 @@ from qiskit.quantum_info.operators.base_operator import BaseOperator
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from .base_estimator_gradient import BaseEstimatorGradient
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from .estimator_gradient_result import EstimatorGradientResult
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if sys.version_info >= (3, 8):
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# pylint: disable=no-name-in-module, ungrouped-imports
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from typing import Literal
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else:
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from typing_extensions import Literal
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class FiniteDiffEstimatorGradient(BaseEstimatorGradient):
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"""
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Compute the gradients of the expectation values by finite difference method.
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"""
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def __init__(self, estimator: BaseEstimator, epsilon: float, options: Options | None = None):
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"""
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def __init__(
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self,
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estimator: BaseEstimator,
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epsilon: float,
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options: Options | None = None,
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*,
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method: Literal["central", "forward", "backward"] = "central",
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):
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r"""
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Args:
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estimator: The estimator used to compute the gradients.
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epsilon: The offset size for the finite difference gradients.
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@ -43,14 +57,27 @@ class FiniteDiffEstimatorGradient(BaseEstimatorGradient):
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The order of priority is: options in ``run`` method > gradient's
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default options > primitive's default setting.
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Higher priority setting overrides lower priority setting
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method: The computation method of the gradients.
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- ``central`` computes :math:`\frac{f(x+e)-f(x-e)}{2e}`,
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- ``forward`` computes :math:`\frac{f(x+e) - f(x)}{e}`,
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- ``backward`` computes :math:`\frac{f(x)-f(x-e)}{e}`
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where :math:`e` is epsilon.
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Raises:
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ValueError: If ``epsilon`` is not positive.
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TypeError: If ``method`` is invalid.
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"""
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if epsilon <= 0:
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raise ValueError(f"epsilon ({epsilon}) should be positive.")
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self._epsilon = epsilon
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self._base_parameter_values_dict = {}
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if method not in ("central", "forward", "backward"):
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raise TypeError(
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f"The argument method should be central, forward, or backward: {method} is given."
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)
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self._method = method
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super().__init__(estimator, options)
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def _run(
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@ -74,12 +101,25 @@ class FiniteDiffEstimatorGradient(BaseEstimatorGradient):
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metadata_.append({"parameters": [circuit.parameters[idx] for idx in indices]})
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offset = np.identity(circuit.num_parameters)[indices, :]
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if self._method == "central":
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plus = parameter_values_ + self._epsilon * offset
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minus = parameter_values_ - self._epsilon * offset
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n = 2 * len(indices)
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job = self._estimator.run(
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[circuit] * n, [observable] * n, plus.tolist() + minus.tolist(), **options
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)
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elif self._method == "forward":
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plus = parameter_values_ + self._epsilon * offset
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n = len(indices) + 1
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job = self._estimator.run(
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[circuit] * n, [observable] * n, [parameter_values_] + plus.tolist(), **options
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)
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elif self._method == "backward":
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minus = parameter_values_ - self._epsilon * offset
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n = len(indices) + 1
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job = self._estimator.run(
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[circuit] * n, [observable] * n, [parameter_values_] + minus.tolist(), **options
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)
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jobs.append(job)
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# combine the results
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@ -90,8 +130,13 @@ class FiniteDiffEstimatorGradient(BaseEstimatorGradient):
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gradients = []
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for result in results:
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if self._method == "central":
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n = len(result.values) // 2 # is always a multiple of 2
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gradient_ = (result.values[:n] - result.values[n:]) / (2 * self._epsilon)
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elif self._method == "forward":
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gradient_ = (result.values[1:] - result.values[0]) / self._epsilon
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elif self._method == "backward":
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gradient_ = (result.values[0] - result.values[1:]) / self._epsilon
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gradients.append(gradient_)
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opt = self._get_local_options(options)
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return EstimatorGradientResult(gradients=gradients, metadata=metadata_, options=opt)
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@ -14,6 +14,7 @@
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from __future__ import annotations
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import sys
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from typing import Sequence
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import numpy as np
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@ -26,6 +27,12 @@ from qiskit.providers import Options
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from .base_sampler_gradient import BaseSamplerGradient
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from .sampler_gradient_result import SamplerGradientResult
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if sys.version_info >= (3, 8):
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# pylint: disable=no-name-in-module, ungrouped-imports
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from typing import Literal
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else:
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from typing_extensions import Literal
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class FiniteDiffSamplerGradient(BaseSamplerGradient):
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"""Compute the gradients of the sampling probability by finite difference method."""
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@ -35,8 +42,10 @@ class FiniteDiffSamplerGradient(BaseSamplerGradient):
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sampler: BaseSampler,
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epsilon: float,
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options: Options | None = None,
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*,
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method: Literal["central", "forward", "backward"] = "central",
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):
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"""
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r"""
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Args:
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sampler: The sampler used to compute the gradients.
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epsilon: The offset size for the finite difference gradients.
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@ -44,13 +53,26 @@ class FiniteDiffSamplerGradient(BaseSamplerGradient):
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The order of priority is: options in ``run`` method > gradient's
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default options > primitive's default setting.
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Higher priority setting overrides lower priority setting
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method: The computation method of the gradients.
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- ``central`` computes :math:`\frac{f(x+e)-f(x-e)}{2e}`,
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- ``forward`` computes :math:`\frac{f(x+e) - f(x)}{e}`,
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- ``backward`` computes :math:`\frac{f(x)-f(x-e)}{e}`
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where :math:`e` is epsilon.
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Raises:
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ValueError: If ``epsilon`` is not positive.
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TypeError: If ``method`` is invalid.
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"""
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if epsilon <= 0:
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raise ValueError(f"epsilon ({epsilon}) should be positive.")
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self._epsilon = epsilon
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if method not in ("central", "forward", "backward"):
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raise TypeError(
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f"The argument method should be central, forward, or backward: {method} is given."
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)
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self._method = method
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super().__init__(sampler, options)
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def _run(
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@ -70,10 +92,23 @@ class FiniteDiffSamplerGradient(BaseSamplerGradient):
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indices = [circuit.parameters.data.index(p) for p in parameters_]
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metadata_.append({"parameters": [circuit.parameters[idx] for idx in indices]})
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offset = np.identity(circuit.num_parameters)[indices, :]
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if self._method == "central":
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plus = parameter_values_ + self._epsilon * offset
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minus = parameter_values_ - self._epsilon * offset
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n = 2 * len(indices)
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job = self._sampler.run([circuit] * n, plus.tolist() + minus.tolist(), **options)
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elif self._method == "forward":
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plus = parameter_values_ + self._epsilon * offset
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n = len(indices) + 1
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job = self._sampler.run(
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[circuit] * n, [parameter_values_] + plus.tolist(), **options
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)
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elif self._method == "backward":
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minus = parameter_values_ - self._epsilon * offset
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n = len(indices) + 1
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job = self._sampler.run(
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[circuit] * n, [parameter_values_] + minus.tolist(), **options
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)
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jobs.append(job)
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# combine the results
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@ -84,6 +119,7 @@ class FiniteDiffSamplerGradient(BaseSamplerGradient):
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gradients = []
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for i, result in enumerate(results):
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if self._method == "central":
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n = len(result.quasi_dists) // 2
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gradient_ = []
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for dist_plus, dist_minus in zip(result.quasi_dists[:n], result.quasi_dists[n:]):
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@ -92,6 +128,24 @@ class FiniteDiffSamplerGradient(BaseSamplerGradient):
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grad_dist[list(dist_minus.keys())] -= list(dist_minus.values())
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grad_dist /= 2 * self._epsilon
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gradient_.append(dict(enumerate(grad_dist)))
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elif self._method == "forward":
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gradient_ = []
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dist_zero = result.quasi_dists[0]
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for dist_plus in result.quasi_dists[1:]:
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grad_dist = np.zeros(2 ** circuits[i].num_qubits)
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grad_dist[list(dist_plus.keys())] += list(dist_plus.values())
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grad_dist[list(dist_zero.keys())] -= list(dist_zero.values())
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grad_dist /= self._epsilon
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gradient_.append(dict(enumerate(grad_dist)))
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elif self._method == "backward":
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gradient_ = []
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dist_zero = result.quasi_dists[0]
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for dist_minus in result.quasi_dists[1:]:
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grad_dist = np.zeros(2 ** circuits[i].num_qubits)
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grad_dist[list(dist_zero.keys())] += list(dist_zero.values())
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grad_dist[list(dist_minus.keys())] -= list(dist_minus.values())
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grad_dist /= self._epsilon
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gradient_.append(dict(enumerate(grad_dist)))
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gradients.append(gradient_)
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opt = self._get_local_options(options)
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@ -0,0 +1,10 @@
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---
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features:
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- |
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:class:`.FiniteDiffEstimatorGradient` and :class:`FiniteDiffSamplerGradient`
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have new argument method.
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There are three methods, "central", "forward", and "backward".
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This option changes the gradient calculation methods.
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"central" calculates :math:`\frac{f(x+e)-f(x-e)}{2e}`, "forward"
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:math:`\frac{f(x+e) - f(x)}{e}`, and "backward" :math:`\frac{f(x)-f(x-e)}{e}` where
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:math:`e` is the offset epsilon.
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@ -35,14 +35,20 @@ from qiskit.quantum_info import Operator, SparsePauliOp, Pauli
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from qiskit.quantum_info.random import random_pauli_list
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from qiskit.test import QiskitTestCase
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gradient_factories = [
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lambda estimator: FiniteDiffEstimatorGradient(estimator, epsilon=1e-6, method="central"),
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lambda estimator: FiniteDiffEstimatorGradient(estimator, epsilon=1e-6, method="forward"),
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lambda estimator: FiniteDiffEstimatorGradient(estimator, epsilon=1e-6, method="backward"),
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ParamShiftEstimatorGradient,
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LinCombEstimatorGradient,
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]
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@ddt
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class TestEstimatorGradient(QiskitTestCase):
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"""Test Estimator Gradient"""
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
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)
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@combine(grad=gradient_factories)
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def test_gradient_operators(self, grad):
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"""Test the estimator gradient for different operators"""
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estimator = Estimator()
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@ -51,9 +57,6 @@ class TestEstimatorGradient(QiskitTestCase):
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qc.h(0)
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qc.p(a, 0)
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qc.h(0)
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if grad is FiniteDiffEstimatorGradient:
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gradient = grad(estimator, epsilon=1e-6)
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else:
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gradient = grad(estimator)
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op = SparsePauliOp.from_list([("Z", 1)])
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correct_result = -1 / np.sqrt(2)
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@ -67,9 +70,7 @@ class TestEstimatorGradient(QiskitTestCase):
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value = gradient.run([qc], [op], [param]).result().gradients[0]
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self.assertAlmostEqual(value[0], correct_result, 3)
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
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)
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@combine(grad=gradient_factories)
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def test_gradient_p(self, grad):
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"""Test the estimator gradient for p"""
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estimator = Estimator()
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@ -78,9 +79,6 @@ class TestEstimatorGradient(QiskitTestCase):
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qc.h(0)
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qc.p(a, 0)
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qc.h(0)
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if grad is FiniteDiffEstimatorGradient:
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gradient = grad(estimator, epsilon=1e-6)
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else:
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gradient = grad(estimator)
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op = SparsePauliOp.from_list([("Z", 1)])
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param_list = [[np.pi / 4], [0], [np.pi / 2]]
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@ -90,9 +88,7 @@ class TestEstimatorGradient(QiskitTestCase):
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for j, value in enumerate(gradients):
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self.assertAlmostEqual(value, correct_results[i][j], 3)
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
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)
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@combine(grad=gradient_factories)
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def test_gradient_u(self, grad):
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"""Test the estimator gradient for u"""
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estimator = Estimator()
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@ -103,9 +99,6 @@ class TestEstimatorGradient(QiskitTestCase):
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qc.h(0)
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qc.u(a, b, c, 0)
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qc.h(0)
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if grad is FiniteDiffEstimatorGradient:
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gradient = grad(estimator, epsilon=1e-6)
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else:
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gradient = grad(estimator)
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op = SparsePauliOp.from_list([("Z", 1)])
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@ -116,17 +109,12 @@ class TestEstimatorGradient(QiskitTestCase):
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for j, value in enumerate(gradients):
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self.assertAlmostEqual(value, correct_results[i][j], 3)
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
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)
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@combine(grad=gradient_factories)
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def test_gradient_efficient_su2(self, grad):
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"""Test the estimator gradient for EfficientSU2"""
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estimator = Estimator()
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qc = EfficientSU2(2, reps=1)
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op = SparsePauliOp.from_list([("ZI", 1)])
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if grad is FiniteDiffEstimatorGradient:
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gradient = grad(estimator, epsilon=1e-6)
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else:
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gradient = grad(estimator)
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param_list = [
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[np.pi / 4 for param in qc.parameters],
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@ -149,9 +137,7 @@ class TestEstimatorGradient(QiskitTestCase):
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gradients = gradient.run([qc], [op], [param]).result().gradients[0]
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np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient],
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)
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@combine(grad=gradient_factories)
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def test_gradient_2qubit_gate(self, grad):
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"""Test the estimator gradient for 2 qubit gates"""
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estimator = Estimator()
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@ -165,9 +151,6 @@ class TestEstimatorGradient(QiskitTestCase):
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for i, param in enumerate(param_list):
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a = Parameter("a")
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qc = QuantumCircuit(2)
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if grad is FiniteDiffEstimatorGradient:
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gradient = grad(estimator, epsilon=1e-6)
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else:
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gradient = grad(estimator)
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if gate is RZZGate:
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@ -179,9 +162,7 @@ class TestEstimatorGradient(QiskitTestCase):
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gradients = gradient.run([qc], [op], [param]).result().gradients[0]
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np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
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)
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@combine(grad=gradient_factories)
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def test_gradient_parameter_coefficient(self, grad):
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"""Test the estimator gradient for parameter variables with coefficients"""
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estimator = Estimator()
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@ -191,9 +172,6 @@ class TestEstimatorGradient(QiskitTestCase):
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qc.u(qc.parameters[0], qc.parameters[1], qc.parameters[3], 1)
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qc.p(2 * qc.parameters[0] + 1, 0)
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qc.rxx(qc.parameters[0] + 2, 0, 1)
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if grad is FiniteDiffEstimatorGradient:
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gradient = grad(estimator, epsilon=1e-6)
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else:
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gradient = grad(estimator)
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param_list = [[np.pi / 4 for _ in qc.parameters], [np.pi / 2 for _ in qc.parameters]]
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correct_results = [
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@ -205,9 +183,7 @@ class TestEstimatorGradient(QiskitTestCase):
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gradients = gradient.run([qc], [op], [param]).result().gradients[0]
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np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
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)
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@combine(grad=gradient_factories)
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def test_gradient_parameters(self, grad):
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"""Test the estimator gradient for parameters"""
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estimator = Estimator()
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@ -216,9 +192,6 @@ class TestEstimatorGradient(QiskitTestCase):
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qc = QuantumCircuit(1)
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qc.rx(a, 0)
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qc.rx(b, 0)
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if grad is FiniteDiffEstimatorGradient:
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gradient = grad(estimator, epsilon=1e-6)
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else:
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gradient = grad(estimator)
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param_list = [[np.pi / 4, np.pi / 2]]
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correct_results = [
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@ -229,9 +202,7 @@ class TestEstimatorGradient(QiskitTestCase):
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gradients = gradient.run([qc], [op], [param], parameters=[[a]]).result().gradients[0]
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np.testing.assert_allclose(gradients, correct_results[i], atol=1e-3)
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@combine(
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grad=[FiniteDiffEstimatorGradient, ParamShiftEstimatorGradient, LinCombEstimatorGradient]
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)
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@combine(grad=gradient_factories)
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def test_gradient_multi_arguments(self, grad):
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"""Test the estimator gradient for multiple arguments"""
|
||||
estimator = Estimator()
|
||||
|
|
@ -241,9 +212,6 @@ class TestEstimatorGradient(QiskitTestCase):
|
|||
qc.rx(a, 0)
|
||||
qc2 = QuantumCircuit(1)
|
||||
qc2.rx(b, 0)
|
||||
if grad is FiniteDiffEstimatorGradient:
|
||||
gradient = grad(estimator, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(estimator)
|
||||
param_list = [[np.pi / 4], [np.pi / 2]]
|
||||
correct_results = [
|
||||
|
|
|
|||
|
|
@ -34,12 +34,20 @@ from qiskit.primitives import Sampler
|
|||
from qiskit.result import QuasiDistribution
|
||||
from qiskit.test import QiskitTestCase
|
||||
|
||||
gradient_factories = [
|
||||
lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="central"),
|
||||
lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="forward"),
|
||||
lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="backward"),
|
||||
ParamShiftSamplerGradient,
|
||||
LinCombSamplerGradient,
|
||||
]
|
||||
|
||||
|
||||
@ddt
|
||||
class TestSamplerGradient(QiskitTestCase):
|
||||
"""Test Sampler Gradient"""
|
||||
|
||||
@combine(grad=[FiniteDiffSamplerGradient, ParamShiftSamplerGradient, LinCombSamplerGradient])
|
||||
@combine(grad=gradient_factories)
|
||||
def test_gradient_p(self, grad):
|
||||
"""Test the sampler gradient for p"""
|
||||
sampler = Sampler()
|
||||
|
|
@ -49,9 +57,6 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
qc.p(a, 0)
|
||||
qc.h(0)
|
||||
qc.measure_all()
|
||||
if grad is FiniteDiffSamplerGradient:
|
||||
gradient = grad(sampler, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(sampler)
|
||||
param_list = [[np.pi / 4], [0], [np.pi / 2]]
|
||||
correct_results = [
|
||||
|
|
@ -65,7 +70,7 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
for k in quasi_dist:
|
||||
self.assertAlmostEqual(quasi_dist[k], correct_results[i][j][k], 3)
|
||||
|
||||
@combine(grad=[FiniteDiffSamplerGradient, ParamShiftSamplerGradient, LinCombSamplerGradient])
|
||||
@combine(grad=gradient_factories)
|
||||
def test_gradient_u(self, grad):
|
||||
"""Test the sampler gradient for u"""
|
||||
sampler = Sampler()
|
||||
|
|
@ -77,9 +82,6 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
qc.u(a, b, c, 0)
|
||||
qc.h(0)
|
||||
qc.measure_all()
|
||||
if grad is FiniteDiffSamplerGradient:
|
||||
gradient = grad(sampler, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(sampler)
|
||||
param_list = [[np.pi / 4, 0, 0], [np.pi / 4, np.pi / 4, np.pi / 4]]
|
||||
correct_results = [
|
||||
|
|
@ -92,15 +94,12 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
for k in quasi_dist:
|
||||
self.assertAlmostEqual(quasi_dist[k], correct_results[i][j][k], 3)
|
||||
|
||||
@combine(grad=[FiniteDiffSamplerGradient, ParamShiftSamplerGradient, LinCombSamplerGradient])
|
||||
@combine(grad=gradient_factories)
|
||||
def test_gradient_efficient_su2(self, grad):
|
||||
"""Test the sampler gradient for EfficientSU2"""
|
||||
sampler = Sampler()
|
||||
qc = EfficientSU2(2, reps=1)
|
||||
qc.measure_all()
|
||||
if grad is FiniteDiffSamplerGradient:
|
||||
gradient = grad(sampler, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(sampler)
|
||||
param_list = [
|
||||
[np.pi / 4 for param in qc.parameters],
|
||||
|
|
@ -189,7 +188,7 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
for k in quasi_dist:
|
||||
self.assertAlmostEqual(quasi_dist[k], correct_results[i][j][k], 3)
|
||||
|
||||
@combine(grad=[FiniteDiffSamplerGradient, ParamShiftSamplerGradient, LinCombSamplerGradient])
|
||||
@combine(grad=gradient_factories)
|
||||
def test_gradient_2qubit_gate(self, grad):
|
||||
"""Test the sampler gradient for 2 qubit gates"""
|
||||
sampler = Sampler()
|
||||
|
|
@ -211,16 +210,13 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
qc = QuantumCircuit(2)
|
||||
qc.append(gate(a), [qc.qubits[0], qc.qubits[1]], [])
|
||||
qc.measure_all()
|
||||
if grad is FiniteDiffSamplerGradient:
|
||||
gradient = grad(sampler, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(sampler)
|
||||
gradients = gradient.run([qc], [param]).result().gradients[0]
|
||||
for j, quasi_dist in enumerate(gradients):
|
||||
for k in quasi_dist:
|
||||
self.assertAlmostEqual(quasi_dist[k], correct_results[i][j][k], 3)
|
||||
|
||||
@combine(grad=[FiniteDiffSamplerGradient, ParamShiftSamplerGradient, LinCombSamplerGradient])
|
||||
@combine(grad=gradient_factories)
|
||||
def test_gradient_parameter_coefficient(self, grad):
|
||||
"""Test the sampler gradient for parameter variables with coefficients"""
|
||||
sampler = Sampler()
|
||||
|
|
@ -231,9 +227,6 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
qc.p(2 * qc.parameters[0] + 1, 0)
|
||||
qc.rxx(qc.parameters[0] + 2, 0, 1)
|
||||
qc.measure_all()
|
||||
if grad is FiniteDiffSamplerGradient:
|
||||
gradient = grad(sampler, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(sampler)
|
||||
param_list = [[np.pi / 4 for _ in qc.parameters], [np.pi / 2 for _ in qc.parameters]]
|
||||
correct_results = [
|
||||
|
|
@ -297,7 +290,7 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
for k in quasi_dist:
|
||||
self.assertAlmostEqual(quasi_dist[k], correct_results[i][j][k], 2)
|
||||
|
||||
@combine(grad=[FiniteDiffSamplerGradient, ParamShiftSamplerGradient, LinCombSamplerGradient])
|
||||
@combine(grad=gradient_factories)
|
||||
def test_gradient_parameters(self, grad):
|
||||
"""Test the sampler gradient for parameters"""
|
||||
sampler = Sampler()
|
||||
|
|
@ -307,9 +300,6 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
qc.rx(a, 0)
|
||||
qc.rz(b, 0)
|
||||
qc.measure_all()
|
||||
if grad is FiniteDiffSamplerGradient:
|
||||
gradient = grad(sampler, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(sampler)
|
||||
param_list = [[np.pi / 4, np.pi / 2]]
|
||||
correct_results = [
|
||||
|
|
@ -321,7 +311,7 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
for k in quasi_dist:
|
||||
self.assertAlmostEqual(quasi_dist[k], correct_results[i][j][k], 3)
|
||||
|
||||
@combine(grad=[FiniteDiffSamplerGradient, ParamShiftSamplerGradient, LinCombSamplerGradient])
|
||||
@combine(grad=gradient_factories)
|
||||
def test_gradient_multi_arguments(self, grad):
|
||||
"""Test the sampler gradient for multiple arguments"""
|
||||
sampler = Sampler()
|
||||
|
|
@ -333,9 +323,6 @@ class TestSamplerGradient(QiskitTestCase):
|
|||
qc2 = QuantumCircuit(1)
|
||||
qc2.rx(b, 0)
|
||||
qc2.measure_all()
|
||||
if grad is FiniteDiffSamplerGradient:
|
||||
gradient = grad(sampler, epsilon=1e-6)
|
||||
else:
|
||||
gradient = grad(sampler)
|
||||
param_list = [[np.pi / 4], [np.pi / 2]]
|
||||
correct_results = [
|
||||
|
|
|
|||
Loading…
Reference in New Issue