Gradient
Gradient(grad_method='param_shift', **kwargs)
Bases: qiskit.opflow.gradients.gradient_base.GradientBase
Convert an operator expression to the first-order gradient.
Parameters
- grad_method (
Union[str,CircuitGradient]) – The method used to compute the state/probability gradient. Can be either'param_shift'or'lin_comb'or'fin_diff'. Ignored for gradients w.r.t observable parameters. - kwargs (dict) – Optional parameters for a CircuitGradient
Raises
ValueError – If method != fin_diff and epsilon is not None.
Methods Defined Here
convert
Gradient.convert(operator, params=None)
Parameters
- operator (
OperatorBase) – The operator we are taking the gradient of. - params (
Union[ParameterVector,ParameterExpression,List[ParameterExpression],None]) – The parameters we are taking the gradient with respect to. If not explicitly passed, they are inferred from the operator and sorted by name.
Return type
Returns
An operator whose evaluation yields the Gradient.
Raises
- ValueError – If
paramscontains a parameter not present inoperator. - ValueError – If
operatoris not parameterized.
get_gradient
Gradient.get_gradient(operator, params)
Get the gradient for the given operator w.r.t. the given parameters
Parameters
- operator (
OperatorBase) – Operator w.r.t. which we take the gradient. - params (
Union[ParameterExpression,ParameterVector,List[ParameterExpression]]) – Parameters w.r.t. which we compute the gradient.
Return type
Returns
Operator which represents the gradient w.r.t. the given params.
Raises
- ValueError – If
paramscontains a parameter not present inoperator. - OpflowError – If the coefficient of the operator could not be reduced to 1.
- OpflowError – If the differentiation of a combo_fn requires JAX but the package is not installed.
- TypeError – If the operator does not include a StateFn given by a quantum circuit
- Exception – Unintended code is reached
- MissingOptionalLibraryError – jax not installed
Attributes
grad_method
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