qrunch.quantum.algorithms.second_quantization.orbital_optimizers.creators_impls

Module containing builders for orbital optimizers.

Classes

`NewtonOrbitalOptimizerCreator`	Builder for the orbital optimizer algorithm using Newton's method.
`SimpleOrbitalOptimizerCreator`	Creator for the orbital optimizer algorithm.

class NewtonOrbitalOptimizerCreator

Bases: object

Builder for the orbital optimizer algorithm using Newton’s method.

__init__() → None

Initialize builder for the orbital optimizer.

Return type:: None

choose_gradient_calculator() → GradientCalculatorSubCreator[Self]

Choose gradient calculator to use for the orbital optimizer.

Return type:: GradientCalculatorSubCreator[Self]

create() → NewtonOrbitalOptimizer

Create an instance of NewtonOrbitalOptimizer.

Return type:: NewtonOrbitalOptimizer

with_basin_hopping_options(basin_hopping_options: BasinHoppingOptions) → Self

Set the basin-hopping options for global optimization.

Parameters:: basin_hopping_options (BasinHoppingOptions) – Options controlling the basin-hopping wrapper.
Return type:: Self

with_options(options: NewtonMinimizerOptions) → Self

Set the options to use for the orbital optimizer.

Parameters:: options (NewtonMinimizerOptions) – Options to pass to the minimizer
Return type:: Self

with_shots(shots: int | None) → Self

Set the number of shots to use when calling the gradient_calculator to compute the energy, gradient, etc.

This is the number of shots that will be used in each call to the quantum computer or simulator, and not necessarily the total number of shots. The total number of shots will in many cases be much higher.

Parameters:

shots (int | None) – Number of shots to use in the estimator. If None is given, the estimator is assumed to be exact
simulator). ((e.g. the excitation gate)

Return type:

Self

with_total_estimator_shots(shots: int | None) → Self

Set the total number of shots to use when calling the gradient_calculator to compute the energy, gradient, etc.

The total number of shots are then distributed across the different groups in the estimator according to the shots per group strategy of the estimator. This ensures better use of the shot budget, and therefore, better use of the quantum resources.

Parameters:

shots (int | None) – Number of shots to use in the estimator. If None is given, the estimator is assumed to be exact
simulator). ((e.g. the excitation gate)

Return type:

Self

class SimpleOrbitalOptimizerCreator

Bases: object

Creator for the orbital optimizer algorithm.

__init__() → None

Initialize builder for the orbital optimizer.

Return type:: None

choose_minimizer() → MinimizerSubCreator[Self]

Choose minimizer to use for the orbital optimizer.

Return type:: MinimizerSubCreator[Self]

create() → SimpleOrbitalOptimizer

Create an instance of SimpleOrbitalOptimizer.

Return type:: SimpleOrbitalOptimizer

with_estimator(estimator: Estimator) → SimpleOrbitalOptimizerCreator

Set the estimator to use for the orbital optimizer.

Parameters:: estimator (Estimator) – Estimator to use. Can be created using the estimator_creator() builder.
Return type:: SimpleOrbitalOptimizerCreator

with_shots(shots: int | None) → Self

Set the number of shots to use when calling the estimator.

This is the number of shots that will be used in each call to the quantum computer or simulator, and not necessarily the total number of shots. The total number of shots will in many cases be much higher.

Parameters:

shots (int | None) – Number of shots to use in the estimator. If None is given, the estimator is assumed to be exact
simulator). ((e.g. the excitation gate)

Return type:

Self

with_total_estimator_shots(shots: int | None) → Self

Set the number of shots to use when calling the estimator.

The total number of shots are then distributed across the different groups in the estimator according to the shots per group strategy of the estimator. This ensures better use of the shot budget, and therefore, better use of the quantum resources.

Parameters:

shots (int | None) – Number of shots to use in the estimator. If None is given, the estimator is assumed to be exact
simulator). ((e.g. the excitation gate)

Return type:

Self