77 lines
8.9 KiB
Plaintext
77 lines
8.9 KiB
Plaintext
---
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title: gradients
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description: API reference for qiskit.algorithms.gradients
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in_page_toc_min_heading_level: 2
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python_api_type: module
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python_api_name: qiskit.algorithms.gradients
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---
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<span id="module-qiskit.algorithms.gradients" />
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<span id="qiskit-algorithms-gradients" />
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# qiskit.algorithms.gradients
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<span id="gradients-qiskit-algorithms-gradients" />
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## Gradients
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<span id="module-qiskit.algorithms.gradients" />
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`qiskit.algorithms.gradients`
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### Base Classes
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| ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
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| [`BaseEstimatorGradient`](qiskit.algorithms.gradients.BaseEstimatorGradient "qiskit.algorithms.gradients.BaseEstimatorGradient")(estimator\[, options, ...]) | Base class for an `EstimatorGradient` to compute the gradients of the expectation value. |
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| [`BaseQGT`](qiskit.algorithms.gradients.BaseQGT "qiskit.algorithms.gradients.BaseQGT")(estimator\[, phase\_fix, ...]) | Base class to computes the Quantum Geometric Tensor (QGT) given a pure, parameterized quantum state. |
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| [`BaseSamplerGradient`](qiskit.algorithms.gradients.BaseSamplerGradient "qiskit.algorithms.gradients.BaseSamplerGradient")(sampler\[, options]) | Base class for a `SamplerGradient` to compute the gradients of the sampling probability. |
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| [`EstimatorGradientResult`](qiskit.algorithms.gradients.EstimatorGradientResult "qiskit.algorithms.gradients.EstimatorGradientResult")(gradients, metadata, ...) | Result of EstimatorGradient. |
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| [`SamplerGradientResult`](qiskit.algorithms.gradients.SamplerGradientResult "qiskit.algorithms.gradients.SamplerGradientResult")(gradients, metadata, ...) | Result of SamplerGradient. |
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| [`QGTResult`](qiskit.algorithms.gradients.QGTResult "qiskit.algorithms.gradients.QGTResult")(qgts, derivative\_type, metadata, ...) | Result of QGT. |
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### Finite Differences
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| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- |
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| [`FiniteDiffEstimatorGradient`](qiskit.algorithms.gradients.FiniteDiffEstimatorGradient "qiskit.algorithms.gradients.FiniteDiffEstimatorGradient")(estimator, epsilon) | Compute the gradients of the expectation values by finite difference method \[1]. |
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| [`FiniteDiffSamplerGradient`](qiskit.algorithms.gradients.FiniteDiffSamplerGradient "qiskit.algorithms.gradients.FiniteDiffSamplerGradient")(sampler, epsilon) | Compute the gradients of the sampling probability by finite difference method \[1]. |
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### Linear Combination of Unitaries
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- |
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| [`LinCombEstimatorGradient`](qiskit.algorithms.gradients.LinCombEstimatorGradient "qiskit.algorithms.gradients.LinCombEstimatorGradient")(estimator\[, ...]) | Compute the gradients of the expectation values. |
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| [`LinCombSamplerGradient`](qiskit.algorithms.gradients.LinCombSamplerGradient "qiskit.algorithms.gradients.LinCombSamplerGradient")(sampler\[, options]) | Compute the gradients of the sampling probability. |
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| [`LinCombQGT`](qiskit.algorithms.gradients.LinCombQGT "qiskit.algorithms.gradients.LinCombQGT")(estimator\[, phase\_fix, ...]) | Computes the Quantum Geometric Tensor (QGT) given a pure, parameterized quantum state. |
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### Parameter Shift Rules
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| --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- |
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| [`ParamShiftEstimatorGradient`](qiskit.algorithms.gradients.ParamShiftEstimatorGradient "qiskit.algorithms.gradients.ParamShiftEstimatorGradient")(estimator\[, ...]) | Compute the gradients of the expectation values by the parameter shift rule \[1]. |
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| [`ParamShiftSamplerGradient`](qiskit.algorithms.gradients.ParamShiftSamplerGradient "qiskit.algorithms.gradients.ParamShiftSamplerGradient")(sampler\[, options]) | Compute the gradients of the sampling probability by the parameter shift rule \[1]. |
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### Quantum Fisher Information
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| --------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- |
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| [`QFIResult`](qiskit.algorithms.gradients.QFIResult "qiskit.algorithms.gradients.QFIResult")(qfis, metadata, options) | Result of QFI. |
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| [`QFI`](qiskit.algorithms.gradients.QFI "qiskit.algorithms.gradients.QFI")(qgt\[, options]) | Computes the Quantum Fisher Information (QFI) given a pure, parameterized quantum state. |
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### Classical Methods
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| -------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
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| [`ReverseEstimatorGradient`](qiskit.algorithms.gradients.ReverseEstimatorGradient "qiskit.algorithms.gradients.ReverseEstimatorGradient")(\[derivative\_type]) | Estimator gradients with the classically efficient reverse mode. |
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| [`ReverseQGT`](qiskit.algorithms.gradients.ReverseQGT "qiskit.algorithms.gradients.ReverseQGT")(\[phase\_fix, derivative\_type]) | QGT calculation with the classically efficient reverse mode. |
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### Simultaneous Perturbation Stochastic Approximation
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------ |
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| [`SPSAEstimatorGradient`](qiskit.algorithms.gradients.SPSAEstimatorGradient "qiskit.algorithms.gradients.SPSAEstimatorGradient")(estimator, epsilon\[, ...]) | Compute the gradients of the expectation value by the Simultaneous Perturbation Stochastic Approximation (SPSA) \[1]. |
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| [`SPSASamplerGradient`](qiskit.algorithms.gradients.SPSASamplerGradient "qiskit.algorithms.gradients.SPSASamplerGradient")(sampler, epsilon\[, ...]) | Compute the gradients of the sampling probability by the Simultaneous Perturbation Stochastic Approximation (SPSA) \[1]. |
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