qiskit-documentation/docs/api/qiskit/0.26/qiskit.opflow.gradients.Der...

99 lines
5.8 KiB
Plaintext
Raw Permalink Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
title: DerivativeBase (v0.26)
description: API reference for qiskit.opflow.gradients.DerivativeBase in qiskit v0.26
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.opflow.gradients.DerivativeBase
---
<span id="qiskit-opflow-gradients-derivativebase" />
# qiskit.opflow\.gradients.DerivativeBase
<Class id="qiskit.opflow.gradients.DerivativeBase" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.17/qiskit/opflow/gradients/derivative_base.py" signature="DerivativeBase" modifiers="class">
Base class for differentiating opflow objects.
Converter for differentiating opflow objects and handling things like properly differentiating combo\_fns and enforcing product rules when operator coefficients are parameterized.
This is distinct from CircuitGradient converters which use quantum techniques such as parameter shifts and linear combination of unitaries to compute derivatives of circuits.
CircuitGradient - uses quantum techniques to get derivatives of circuits DerivativeBase - uses classical techniques to differentiate opflow data structures
### \_\_init\_\_
<Function id="qiskit.opflow.gradients.DerivativeBase.__init__" signature="__init__()">
Initialize self. See help(type(self)) for accurate signature.
</Function>
## Methods
| | |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| [`__init__`](#qiskit.opflow.gradients.DerivativeBase.__init__ "qiskit.opflow.gradients.DerivativeBase.__init__")() | Initialize self. |
| [`convert`](#qiskit.opflow.gradients.DerivativeBase.convert "qiskit.opflow.gradients.DerivativeBase.convert")(operator\[, params]) | **type operator**`OperatorBase` |
| [`gradient_wrapper`](#qiskit.opflow.gradients.DerivativeBase.gradient_wrapper "qiskit.opflow.gradients.DerivativeBase.gradient_wrapper")(operator, bind\_params\[, …]) | Get a callable function which provides the respective gradient, Hessian or QFI for given parameter values. |
| [`parameter_expression_grad`](#qiskit.opflow.gradients.DerivativeBase.parameter_expression_grad "qiskit.opflow.gradients.DerivativeBase.parameter_expression_grad")(param\_expr, param) | Get the derivative of a parameter expression w\.r.t. |
### convert
<Function id="qiskit.opflow.gradients.DerivativeBase.convert" signature="convert(operator, params=None)" modifiers="abstract">
**Parameters**
* **operator** (`OperatorBase`) The operator we are taking the gradient, Hessian or QFI of
* **params** (`Union`\[`ParameterVector`, `ParameterExpression`, `List`\[`ParameterExpression`], `None`]) The parameters we are taking the gradient, Hessian or QFI with respect to.
**Return type**
`OperatorBase`
**Returns**
An operator whose evaluation yields the gradient, Hessian or QFI.
**Raises**
**ValueError** If `params` contains a parameter not present in `operator`.
</Function>
### gradient\_wrapper
<Function id="qiskit.opflow.gradients.DerivativeBase.gradient_wrapper" signature="gradient_wrapper(operator, bind_params, grad_params=None, backend=None)">
Get a callable function which provides the respective gradient, Hessian or QFI for given parameter values. This callable can be used as gradient function for optimizers.
**Parameters**
* **operator** (`OperatorBase`) The operator for which we want to get the gradient, Hessian or QFI.
* **bind\_params** (`Union`\[`ParameterExpression`, `ParameterVector`, `List`\[`ParameterExpression`]]) The operator parameters to which the parameter values are assigned.
* **grad\_params** (`Union`\[`ParameterExpression`, `ParameterVector`, `List`\[`ParameterExpression`], `Tuple`\[`ParameterExpression`, `ParameterExpression`], `List`\[`Tuple`\[`ParameterExpression`, `ParameterExpression`]], `None`]) The parameters with respect to which we are taking the gradient, Hessian or QFI. If grad\_params = None, then grad\_params = bind\_params
* **backend** (`Union`\[`BaseBackend`, `QuantumInstance`, `None`]) The quantum backend or QuantumInstance to use to evaluate the gradient, Hessian or QFI.
**Returns**
Function to compute a gradient, Hessian or QFI. The function takes an iterable as argument which holds the parameter values.
**Return type**
callable(param\_values)
</Function>
### parameter\_expression\_grad
<Function id="qiskit.opflow.gradients.DerivativeBase.parameter_expression_grad" signature="parameter_expression_grad(param_expr, param)" modifiers="static">
Get the derivative of a parameter expression w\.r.t. the given parameter.
**Parameters**
* **param\_expr** (`ParameterExpression`) The Parameter Expression for which we compute the derivative
* **param** (`ParameterExpression`) Parameter w\.r.t. which we want to take the derivative
**Return type**
`Union`\[`ParameterExpression`, `float`]
**Returns**
ParameterExpression representing the gradient of param\_expr w\.r.t. param
</Function>
</Class>