qrunch.quantum.algorithms.second_quantization.orbital_optimizers.protocols

Protocols for orbital optimizers.

Classes

`GradientCalculator`	Protocol for calculating gradients and Hessians for orbital optimization.
`OrbitalOptimizerAlgorithm`	Protocol class for an orbital optimizer.
`OrbitalOptimizerResult`	Result of orbital optimization.

class GradientCalculator

Bases: Protocol

Protocol for calculating gradients and Hessians for orbital optimization.

__init__(*args, **kwargs)

clear_cache() → None

Clear the cache of the underlying estimator.

Return type:: None

compute_energy(operator: FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol, circuit: Circuit, shots: int | None = None) → float

Compute the energy.

Parameters:

operator (FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol) – Operator to estimate the energy of.
circuit (Circuit) – The circuit ansatz with minimized parameters set.
shots (int | None) – The number of shots to use in the estimator.

Return type:

float

compute_gradient(operator: FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol, circuit: Circuit, shots: int | None = None) → ndarray[Any, dtype[float64]]

Compute the gradient.

Parameters:

operator (FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol) – Operator to estimate the gradient from.
circuit (Circuit) – The circuit ansatz with minimized parameters set.
shots (int | None) – The number of shots to use in the estimator.

Return type:

ndarray[Any, dtype[float64]]

compute_hessian(operator: FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol, circuit: Circuit, shots: int | None = None) → ndarray[Any, dtype[float64]]

Compute the Hessian.

Parameters:

operator (FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol) – Operator to estimate the Hessian from.
circuit (Circuit) – The circuit ansatz with minimized parameters set.
shots (int | None) – The number of shots to use in the estimator.

Return type:

ndarray[Any, dtype[float64]]

get_estimator_shots() → int

Get the total amount of shots made by the estimator.

Return type:: int

class OrbitalOptimizerAlgorithm

Bases: ABC

Protocol class for an orbital optimizer.

The orbital optimization algorithm rotates the observable with an ansatz unitary so that the expectation value is minimized.

abstractmethod clear_cache() → None

Clear the cache of the underlying estimator.

Return type:: None

abstractmethod run(operator: T, circuit: Circuit) → OrbitalOptimizerResult[T]

Run the orbital optimizer and find the eigenvalue.

Parameters:

operator (T) – The operator whose expectation value should be minimized.
circuit (Circuit) – The parametrized circuit to find optimal parameters for. Should contain unspecified parameters.

Return type:

OrbitalOptimizerResult[T]

class OrbitalOptimizerResult

Bases: Generic[T_COV]

Result of orbital optimization.

All fields are immutable (frozen=True) so an instance can be safely reused.

Parameters:

sampler_shots – The total amount of shots used by the sampler
rotation_matrices – Optimal rotation_matrices
estimated_value – Estimated expectation value of the rotated operator
rotated_operator – The rotated second quantized operator.
rotation_parameters – Optimal parameters in the exponential of the rotation matrices (default=None)

__init__(sampler_shots: int, rotation_matrices: tuple[ndarray[Any, dtype[float64]], ...], estimated_value: float, rotated_operator: T_COV, rotation_parameters: tuple[dict[tuple[int, int], float64], ...] | None = None) → None

Parameters:

sampler_shots (int)
rotation_matrices (tuple[ndarray[Any, dtype[float64]], ...])
estimated_value (float)
rotated_operator (T_COV)
rotation_parameters (tuple[dict[tuple[int, int], float64], ...] | None)

Return type:

None

estimated_value: float

rotated_operator: T_COV

rotation_matrices: tuple[ndarray[Any, dtype[float64]], ...]

rotation_parameters: tuple[dict[tuple[int, int], float64], ...] | None = None

sampler_shots: int