qrunch.quantum.algorithms.pauli.vqes.basic_vqe

Implementation of the basic VQE algorithm.

Classes

`BasicVqe`	Class for running a basic VQE.
`BasicVqeCreator`	Builder for the basic VQE algorithm.

class BasicVqe

Bases: object

Class for running a basic VQE.

__init__(estimator: Estimator, minimizer: Minimizer, shots: int | None) → None

Initialize the VQE.

Parameters:

Return type:

None

clear_cache() → None

Clear the cache of the estimator.

does_vqe_support_number_of_simultanously_parameters(number_of_parameters: int) → bool

Check if the vqe supports the given number of parameters.

Parameters:: number_of_parameters (int) – The number of parameters to check.
Return type:: bool

run(observable: HermitianPauliSum, circuit: Circuit, iteration: int | None = None, initial_guess: dict[Parameter, float] | None = None) → VqeResult

Run the VQE algorithm.

The VQE algorithm finds optimal parameters for the circuit such that the expectation value of the observable is minimized.

Parameters:

observable (int | float | complex | Expression[PauliOperators] | HermitianPauliSum) – The observable whose expectation value should be minimized.
circuit (Circuit) – The parametrized circuit to find optimal parameters for. Should contain unspecified parameters.
iteration (int | None) – If applicable, the current adaptive iteration. If not applicable, set to None.
initial_guess (dict[Parameter, float] | None) – Dict specifying the initial value of each Parameter. If None is given initial guess is 0.0.

Return type:

VqeResult

class BasicVqeCreator

Bases: object

Builder for the basic VQE algorithm.

__init__() → None

Initialize builder for the basic VQE.

choose_minimizer() → MinimizerSubCreator[Self]

Chose minimizer to use for the VQE.

create() → BasicVqe

Create an instance of BasicVqe (fixed ansatz).

with_estimator(estimator: Estimator) → Self

Choose estimator to use for the VQE.

with_estimator_shots(shots: int | None) → Self

Choose the number of shots to use in the estimator.