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]. |
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