qrunch.chemistry.orbital_optimizers.newton_orbital_optimizer

Module containing an orbital optimizer implementation using Newton’s method.

Module Attributes

DEFAULT_OPTIONS

The default options for the Newton Orbital Optimizer Scipy minimizer.

Classes

`NewtonMinimizerOptions`	The default options for SciPy minimizer.
`NewtonOrbitalOptimizer`	Implement a solver to find and apply Newton's method for orbital optimization.
`NewtonOrbitalOptimizerOrbitalOptimizerCreator`	Builder for the orbital optimizer algorithm using Newton's method.

DEFAULT_OPTIONS: The default options for the Newton Orbital Optimizer Scipy minimizer.

class NewtonMinimizerOptions

Bases: DataclassPublicAPI

The default options for SciPy minimizer.

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

Parameters:

relative_error_tolerance – Convergence tolerance (default=1.0e-5)
max_iterations_per_parameter – Maximum number of iterations per parameter. (default=10000)
jacobian_step_size – Jacobian step size (default=1e-6)

__init__(*, relative_error_tolerance: float = 1e-05, max_iterations_per_parameter: int = 10000, jacobian_step_size: float = 1e-06) → None

Parameters:

relative_error_tolerance (float)
max_iterations_per_parameter (int)
jacobian_step_size (float)

Return type:

None

static __new__(cls, *args: Any, **kwargs: Any) → Any

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

jacobian_step_size: float = 1e-06

max_iterations_per_parameter: int = 10000

relative_error_tolerance: float = 1e-05

class NewtonOrbitalOptimizer

Bases: OrbitalOptimizerAlgorithm

Implement a solver to find and apply Newton’s method for orbital optimization.

__init__(gradient_calculator: GradientCalculator, shots: int | None = None, options: NewtonMinimizerOptions = NewtonMinimizerOptions(relative_error_tolerance=1e-05, max_iterations_per_parameter=10000, jacobian_step_size=1e-06)) → None

Initialize builder for the orbital optimizer.

Parameters:

gradient_calculator (GradientCalculator) – The object that can calculate gradients and Hessians.
shots (int | None) – Number of shots to use.
options (NewtonMinimizerOptions) – Options to pass to the minimizer

Return type:

None

clear_cache() → None

Clear the cache of the underlying estimator.

Return type:: None

run(operator: FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol, circuit: Circuit) → OrbitalOptimizerResult

Compute kappa analytically at zero kappa using Newton’s method.

The method uses the gradient and Hessian in from the gradient calculator.

Parameters:

operator (FermionHermitianSumProtocol | PairedHardcoreBosonHermitianSumProtocol) – The operator to rotate.
circuit (Circuit) – The circuit used to evaluate the operator.

Return type:

OrbitalOptimizerResult

class NewtonOrbitalOptimizerOrbitalOptimizerCreator

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_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.

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