93 lines
4.3 KiB
Plaintext
93 lines
4.3 KiB
Plaintext
---
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title: DerivativeBase (v0.29)
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description: API reference for qiskit.opflow.gradients.DerivativeBase in qiskit v0.29
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.opflow.gradients.DerivativeBase
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---
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# DerivativeBase
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<Class id="qiskit.opflow.gradients.DerivativeBase" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.18/qiskit/opflow/gradients/derivative_base.py" signature="DerivativeBase" modifiers="class">
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Bases: `qiskit.opflow.converters.converter_base.ConverterBase`
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Base class for differentiating opflow objects.
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Converter for differentiating opflow objects and handling things like properly differentiating combo\_fn’s and enforcing product rules when operator coefficients are parameterized.
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This is distinct from CircuitGradient converters which use quantum techniques such as parameter shifts and linear combination of unitaries to compute derivatives of circuits.
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CircuitGradient - uses quantum techniques to get derivatives of circuits DerivativeBase - uses classical techniques to differentiate opflow data structures
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## Methods Defined Here
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<span id="qiskit-opflow-gradients-derivativebase-convert" />
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### convert
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<Function id="qiskit.opflow.gradients.DerivativeBase.convert" signature="DerivativeBase.convert(operator, params=None)" modifiers="abstract">
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**Parameters**
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* **operator** (`OperatorBase`) – The operator we are taking the gradient, Hessian or QFI of
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* **params** (`Union`\[`ParameterVector`, `ParameterExpression`, `List`\[`ParameterExpression`], `None`]) – The parameters we are taking the gradient, Hessian or QFI with respect to.
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**Return type**
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`OperatorBase`
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**Returns**
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An operator whose evaluation yields the gradient, Hessian or QFI.
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**Raises**
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**ValueError** – If `params` contains a parameter not present in `operator`.
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</Function>
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<span id="qiskit-opflow-gradients-derivativebase-gradient-wrapper" />
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### gradient\_wrapper
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<Function id="qiskit.opflow.gradients.DerivativeBase.gradient_wrapper" signature="DerivativeBase.gradient_wrapper(operator, bind_params, grad_params=None, backend=None, expectation=None)">
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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.
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**Parameters**
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* **operator** (`OperatorBase`) – The operator for which we want to get the gradient, Hessian or QFI.
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* **bind\_params** (`Union`\[`ParameterExpression`, `ParameterVector`, `List`\[`ParameterExpression`]]) – The operator parameters to which the parameter values are assigned.
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* **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
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* **backend** (`Union`\[`Backend`, `BaseBackend`, `QuantumInstance`, `None`]) – The quantum backend or QuantumInstance to use to evaluate the gradient, Hessian or QFI.
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* **expectation** (`Optional`\[`ExpectationBase`]) – The expectation converter to be used. If none is set then PauliExpectation() is used.
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**Return type**
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`Callable`\[\[`Iterable`], `ndarray`]
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**Returns**
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Function to compute a gradient, Hessian or QFI. The function takes an iterable as argument which holds the parameter values.
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</Function>
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<span id="qiskit-opflow-gradients-derivativebase-parameter-expression-grad" />
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### parameter\_expression\_grad
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<Function id="qiskit.opflow.gradients.DerivativeBase.parameter_expression_grad" signature="DerivativeBase.parameter_expression_grad(param_expr, param)" modifiers="static">
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Get the derivative of a parameter expression w\.r.t. the given parameter.
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**Parameters**
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* **param\_expr** (`ParameterExpression`) – The Parameter Expression for which we compute the derivative
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* **param** (`ParameterExpression`) – Parameter w\.r.t. which we want to take the derivative
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**Return type**
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`Union`\[`ParameterExpression`, `float`]
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**Returns**
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ParameterExpression representing the gradient of param\_expr w\.r.t. param
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</Function>
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</Class>
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