qrunch.quantum.algorithms.second_quantization.vqes.adaptive_orbital_optimization_vqe

Module containing adaptive orbital optimization VQE.

Classes

`BeastSecondQuantizationAdaptiveOrbitalOptimizationVqeCreator`	Builder for the adaptive VQE algorithm.
`SecondQuantizationAdaptiveOrbitalOptimizationVqe`	VQE that combines adaptive and orbital optimization VQE as the underlying algorithm.
`SecondQuantizationAdaptiveOrbitalOptimizationVqeCreator`	Builder for the adaptive VQE algorithm.

class BeastSecondQuantizationAdaptiveOrbitalOptimizationVqeCreator

Bases: VqeCreatorEstimatorMixin[AdaptiveVqeCreator], VqeCreatorMinimizerMixin[AdaptiveVqeCreator], VqeCreatorReminimizerMixin[AdaptiveVqeCreator], VqeCreatorOptionsMixin[AdaptiveVqeCreator], VqeCreatorStoppingCriterionMixin[AdaptiveVqeCreator], VqeCreatorAnalyticalBeastVqeMixin[AdaptiveVqeCreator], VqeCreatorDataPersisterManagerMixin[AdaptiveVqeCreator]

Builder for the adaptive VQE algorithm.

__init__(gate_selector_builder: FastGateSelectorCreator) → None

Initialize builder for the adaptive VQE.

Parameters:: gate_selector_builder (FastGateSelectorCreator)
Return type:: None

choose_data_persister_manager() → DataPersisterManagerSubCreator[Self]

Choose the data persister manager to use and whether to save and/or load the data.

Return type:: DataPersisterManagerSubCreator[Self]

choose_minimizer() → MinimizerSubCreator[Self]

Choose minimizer to use for the VQE.

Return type:: MinimizerSubCreator[Self]

choose_orbital_optimizer_stopping_criterion() → StoppingCriterionSubCreator[Self]

Choose an orbital rotation optimization stopping criterion.

Return type:: StoppingCriterionSubCreator[Self]

choose_reminimizer() → ReMinimizerSubCreator[Self]

Choose reminimizer to use for the VQE.

Return type:: ReMinimizerSubCreator[Self]

choose_stopping_criterion() → StoppingCriterionSubCreator[Self]

Choose stopping criterion to use for the VQE.

Return type:: StoppingCriterionSubCreator[Self]

create() → SecondQuantizationAdaptiveOrbitalOptimizationVqe

Create an instance of SecondQuantizationAdaptiveOrbitalOptimizationVqe.

Return type:: SecondQuantizationAdaptiveOrbitalOptimizationVqe

with_analytical_beast_basic_vqe(*, active: bool = True) → Self

Choose to use the analytical basic vqe inside each adaptive iteration, which only works for BEAST.

Instead of having the minimizer call the estimator directly, the estimator is first called to make an analytical expression for the energy as a function of the gate parameter. This expression is then passed to the minimizer, requiring no more measurements.

Note: This feature only works with BEAST with last parameter optimization.

If a reminimizer is chosen, it will be paired with the normal Basic vqe since, only it, supports multiple parameters at once.

Parameters:: active (bool) – Whether to use the analytical beast basic vqe or not.
Return type:: Self

with_estimator(estimator: Estimator) → Self

Set the estimator to use for the VQE.

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

with_estimator_shots(shots: int | None) → Self

Set the number of shots to use in the estimator.

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_gate_selector_sampler(sampler: Sampler) → Self

Set the sampler for the gate selector to use for the VQE.

Parameters:: sampler (Sampler) – The sampler to use. Can be created using the sampler_creator().
Return type:: Self

with_gate_selector_shots(shots: int | None) → Self

Set the shots on the gate selector to use for the VQE.

Parameters:: shots (int | None) – The number of shots to use in the Gate selector.
Return type:: Self

with_options(options: IterativeVqeOptions) → Self

Set the options to use for the VQE.

Parameters:: options (IterativeVqeOptions) – Options to use.
Return type:: Self

with_orbital_optimizer(orbital_optimizer: OrbitalOptimizerAlgorithm | None) → Self

Configure to use orbital optimizer provided.

Parameters:: orbital_optimizer (OrbitalOptimizerAlgorithm | None)
Return type:: Self

class SecondQuantizationAdaptiveOrbitalOptimizationVqe

Bases: SecondQuantizationAdaptiveVqeAlgorithm

VQE that combines adaptive and orbital optimization VQE as the underlying algorithm.

__init__(orbital_optimization_vqe: SecondQuantizationOrbitalOptimizationVqe, reminimizer_orbital_optimization_vqe: SecondQuantizationOrbitalOptimizationVqe | None, gate_selector: GateSelector, options: IterativeVqeOptions = IterativeVqeOptions(max_iterations=5, gates_per_iteration=1, new_parameter_guess=0.0, force_all_iterations=False), data_persister_manager: DataPersisterManager | None = None, stopping_criterion: StoppingCriterion | None = None, orbital_optimizer_stopping_criterion: StoppingCriterion | None = None) → None

Initialize adaptive orbital optimization VQE.

Parameters:

orbital_optimization_vqe (SecondQuantizationOrbitalOptimizationVqe) – Orbital optimization VQE to use.
reminimizer_orbital_optimization_vqe (SecondQuantizationOrbitalOptimizationVqe | None) – The VQE object running the parameter re-optimization at the end of an adaptive run. If None, no reminimization step is performed.
gate_selector (GateSelector) – The gate selector to use.
options (IterativeVqeOptions) – Options for the adaptive VQE.
data_persister_manager (DataPersisterManager | None) – Manager to handle saving and loading heavy vqe optimizations.
stopping_criterion (StoppingCriterion | None) – The stopping criterion to use when performing early stopping.
orbital_optimizer_stopping_criterion (StoppingCriterion | None) – The stopping criterion to use when performing early stopping of orbital optimization.

Return type:

None

static build_circuit_to_optimize(iteration_data: AdaptiveOrbitalOrbitalOptimizedIterationDataHandler[T], new_gates: list[GatePoolOperator], iteration_count: int) → Circuit

Build circuit to optimize.

The full unoptimized circuit with the new gates are provided.

Parameters:

iteration_data (AdaptiveOrbitalOrbitalOptimizedIterationDataHandler[T]) – The iteration data handler.
new_gates (list[GatePoolOperator]) – The new_gates to optimize.
iteration_count (int) – The iteration count.

Return type:

Circuit

static build_metadata(second_quantized_operator: FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol, initial_ansatz: Circuit) → Mapping[str, str | Mapping[str, str | NestedStrDict]]

Create meta-data that represent the input.

Parameters:

second_quantized_operator (FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol) – The operator whose expectation value should be minimized.
initial_ansatz (Circuit) – The starting ansatz circuit. Gates from the gate_pool is appended to this.

Return type:

Mapping[str, str | Mapping[str, str | NestedStrDict]]

clear_cache() → None

Clear the cache of the underlying VQE.

Return type:: None

classmethod persistence_checkpoints() → list[str]

Define the persistence checkpoints used during the orbital optimization adaptive vqe.

These checkpoints specify computational stages where intermediate results can be saved and loaded to optimize computations and ensure reproducibility.

Return type:: list[str]

run(second_quantized_operator: T, gate_pool: GatePool, initial_ansatz: Circuit, callback: AdaptiveIterationCallback | None = None, input_result: SecondQuantizationAdaptiveVqeResult[T] | None = None) → SecondQuantizationAdaptiveVqeResult[T]

Run the VQE and find the eigenvalue.

Parameters:

second_quantized_operator (T) – The operator whose expectation value should be minimized.
gate_pool (GatePool) – The set of gates to choose from when building the ansatz.
initial_ansatz (Circuit) – The starting ansatz circuit. Gates from the gate_pool is appended to this.
callback (AdaptiveIterationCallback | None) – An optional callback function that is called at each minimizer iteration.
input_result (SecondQuantizationAdaptiveVqeResult[T] | None) – A prior results that should be refined.

Return type:

SecondQuantizationAdaptiveVqeResult[T]

class SecondQuantizationAdaptiveOrbitalOptimizationVqeCreator

Bases: VqeCreatorEstimatorMixin[AdaptiveVqeCreator], VqeCreatorMinimizerMixin[AdaptiveVqeCreator], VqeCreatorReminimizerMixin[AdaptiveVqeCreator], VqeCreatorOptionsMixin[AdaptiveVqeCreator], VqeCreatorStoppingCriterionMixin[AdaptiveVqeCreator], VqeCreatorGateSelectorMixin[AdaptiveVqeCreator], VqeCreatorAnalyticalBeastVqeMixin[AdaptiveVqeCreator], VqeCreatorDataPersisterManagerMixin[AdaptiveVqeCreator]

Builder for the adaptive VQE algorithm.

__init__() → None

Initialize builder for the adaptive VQE.

Return type:: None

choose_data_persister_manager() → DataPersisterManagerSubCreator[Self]

Choose the data persister manager to use and whether to save and/or load the data.

Return type:: DataPersisterManagerSubCreator[Self]

choose_minimizer() → MinimizerSubCreator[Self]

Choose minimizer to use for the VQE.

Return type:: MinimizerSubCreator[Self]

choose_orbital_optimizer_stopping_criterion() → StoppingCriterionSubCreator[Self]

Choose an orbital rotation optimization stopping criterion.

Return type:: StoppingCriterionSubCreator[Self]

choose_reminimizer() → ReMinimizerSubCreator[Self]

Choose reminimizer to use for the VQE.

Return type:: ReMinimizerSubCreator[Self]

choose_stopping_criterion() → StoppingCriterionSubCreator[Self]

Choose stopping criterion to use for the VQE.

Return type:: StoppingCriterionSubCreator[Self]

create() → SecondQuantizationAdaptiveOrbitalOptimizationVqe

Create an instance of SecondQuantizationAdaptiveOrbitalOptimizationVqe.

Return type:: SecondQuantizationAdaptiveOrbitalOptimizationVqe

with_analytical_beast_basic_vqe(*, active: bool = True) → Self

Choose to use the analytical basic vqe inside each adaptive iteration, which only works for BEAST.

Instead of having the minimizer call the estimator directly, the estimator is first called to make an analytical expression for the energy as a function of the gate parameter. This expression is then passed to the minimizer, requiring no more measurements.

Note: This feature only works with BEAST with last parameter optimization.

If a reminimizer is chosen, it will be paired with the normal Basic vqe since, only it, supports multiple parameters at once.

Parameters:: active (bool) – Whether to use the analytical beast basic vqe or not.
Return type:: Self

with_estimator(estimator: Estimator) → Self

Set the estimator to use for the VQE.

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

with_estimator_shots(shots: int | None) → Self

Set the number of shots to use in the estimator.

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_gate_selector(gate_selector: GateSelector) → Self

Choose the gate selector to use for the VQE.

Parameters:: gate_selector (GateSelector) – Gate selector to use. Can be created using the gate_selector_creator().
Return type:: Self

with_options(options: IterativeVqeOptions) → Self

Set the options to use for the VQE.

Parameters:: options (IterativeVqeOptions) – Options to use.
Return type:: Self

with_orbital_optimizer(orbital_optimizer: OrbitalOptimizerAlgorithm | None) → Self

Configure to use orbital optimizer provided.

Parameters:: orbital_optimizer (OrbitalOptimizerAlgorithm | None)
Return type:: Self