FiniteDiffEstimatorGradient

class FiniteDiffEstimatorGradient(estimator, epsilon, precision=None, *, method='central', transpiler=None, transpiler_options=None)

Bases: BaseEstimatorGradient

Compute the gradients of the expectation values by finite difference method [1].

Reference: [1] Finite difference method

Parameters:

• estimator (BaseEstimatorV2) – The estimator used to compute the gradients.

• epsilon (float) – The offset size for the finite difference gradients.

• precision (float | None) – Precision to be used by the underlying Estimator. If provided, this number takes precedence over the default precision of the primitive. If None, the default precision of the primitive is used.

• method (Literal['central', 'forward', 'backward']) –

  The computation method of the gradients.

  • central computes \(\frac{f(x+e)-f(x-e)}{2e}\),

  • forward computes \(\frac{f(x+e) - f(x)}{e}\),

  • backward computes \(\frac{f(x)-f(x-e)}{e}\)

  where \(e\) is epsilon.

• transpiler (Transpiler | None) – An optional object with a run method allowing to transpile the circuits that are run when using this algorithm. If set to None, these won’t be transpiled.

• transpiler_options (dict[str, Any] | None) – A dictionary of options to be passed to the transpiler’s run method as keyword arguments.

Raises:

• ValueError – If epsilon is not positive.

• TypeError – If method is invalid.

Attributes

derivative_type

Return the derivative type (real, imaginary or complex).

Returns:

The derivative type.

precision

Return the precision used by the run method of the Estimator primitive. If None, the default precision of the primitive is used.

Returns:

The default precision.

Methods

run(circuits, observables, parameter_values, parameters=None, *, precision=None)

Run the job of the estimator gradient on the given circuits.

Parameters:

• circuits (Sequence[QuantumCircuit]) – The list of quantum circuits to compute the gradients.

• observables (Sequence[BaseOperator]) – The list of observables.

• parameter_values (Sequence[Sequence[float]]) – The list of parameter values to be bound to the circuit.

• parameters (Sequence[Sequence[Parameter] | None] | None) – The sequence of parameters to calculate only the gradients of the specified parameters. Each sequence of parameters corresponds to a circuit in circuits. Defaults to None, which means that the gradients of all parameters in each circuit are calculated. None in the sequence means that the gradients of all parameters in the corresponding circuit are calculated.

• precision (float | Sequence[float] | None) – Precision to be used by the underlying Estimator. If a single float is provided, this number will be used for all circuits. If a sequence of floats is provided, they will be used on a per-circuit basis. If not set, the gradient’s default precision will be used for all circuits, and if that is None (not set) then the underlying primitive’s (default) precision will be used for all circuits.

Returns:

The job object of the gradients of the expectation values. The i-th result corresponds to circuits[i] evaluated with parameters bound as parameter_values[i]. The j-th element of the i-th result corresponds to the gradient of the i-th circuit with respect to the j-th parameter.

Raises:

ValueError – Invalid arguments are given.

Return type:

AlgorithmJob