qiskit.algorithms.gradients

Gradients (qiskit.algorithms.gradients)

Base Classes

BaseEstimatorGradient(estimator[, options, ...])	Base class for an EstimatorGradient to compute the gradients of the expectation value.
BaseQGT(estimator[, phase_fix, ...])	Base class to computes the Quantum Geometric Tensor (QGT) given a pure, parameterized quantum state.
BaseSamplerGradient(sampler[, options])	Base class for a SamplerGradient to compute the gradients of the sampling probability.
EstimatorGradientResult(gradients, metadata, ...)	Result of EstimatorGradient.
SamplerGradientResult(gradients, metadata, ...)	Result of SamplerGradient.
QGTResult(qgts, derivative_type, metadata, ...)	Result of QGT.

Finite Differences

FiniteDiffEstimatorGradient(estimator, epsilon)	Compute the gradients of the expectation values by finite difference method [1].
FiniteDiffSamplerGradient(sampler, epsilon)	Compute the gradients of the sampling probability by finite difference method [1].

Linear Combination of Unitaries

LinCombEstimatorGradient(estimator[, ...])	Compute the gradients of the expectation values.
LinCombSamplerGradient(sampler[, options])	Compute the gradients of the sampling probability.
LinCombQGT(estimator[, phase_fix, ...])	Computes the Quantum Geometric Tensor (QGT) given a pure, parameterized quantum state.

Parameter Shift Rules

ParamShiftEstimatorGradient(estimator[, ...])	Compute the gradients of the expectation values by the parameter shift rule [1].
ParamShiftSamplerGradient(sampler[, options])	Compute the gradients of the sampling probability by the parameter shift rule [1].

Quantum Fisher Information

QFIResult(qfis, metadata, options)	Result of QFI.
QFI(qgt[, options])	Computes the Quantum Fisher Information (QFI) given a pure, parameterized quantum state.

Classical Methods

ReverseEstimatorGradient([derivative_type])	Estimator gradients with the classically efficient reverse mode.
ReverseQGT([phase_fix, derivative_type])	QGT calculation with the classically efficient reverse mode.

Simultaneous Perturbation Stochastic Approximation

SPSAEstimatorGradient(estimator, epsilon[, ...])	Compute the gradients of the expectation value by the Simultaneous Perturbation Stochastic Approximation (SPSA) [1].
SPSASamplerGradient(sampler, epsilon[, ...])	Compute the gradients of the sampling probability by the Simultaneous Perturbation Stochastic Approximation (SPSA) [1].