qiskit.opflow.gradients.Gradient

__init__(grad_method='param_shift', **kwargs)

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.

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

OperatorBase

Returns

An operator whose evaluation yields the Gradient.

Raises

• ValueError – If params contains a parameter not present in operator.
• ValueError – If operator is not parameterized.

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

OperatorBase

Returns

Operator which represents the gradient w.r.t. the given params.

Raises

• ValueError – If params contains a parameter not present in operator.
• 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

Returns CircuitGradient.

Return type

CircuitGradient

Returns

CircuitGradient.

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)

static parameter_expression_grad(param_expr, param)

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