qrunch.tools.minimizers.subset

Optimizer for a subset of parameters.

Classes

SubsetMinimizer

Minimizer that focuses on a subset of parameters while keeping the remaining parameters fixed.
class SubsetMinimizer

Bases: Minimizer

Minimizer that focuses on a subset of parameters while keeping the remaining parameters fixed.

__init__(number_of_parameters_in_subset: int, minimizer: Minimizer | None = None) → None

Initialize the SubsetMinimizer.

Parameters:

  • number_of_parameters_in_subset (int) – The number of parameters to optimize.

  • minimizer (Minimizer | None) – The minimizer to use for the optimization

Return type:

None

minimize(function: Callable[[list[ndarray[Any, dtype[float64]]]], list[float]], initial_guess: ndarray[Any, dtype[float64]] | None, iteration: int | None, bounds: tuple[float, float] | list[tuple[float, float]] | None = None, circuit: Circuit | None = None, operator: HermitianPauliSum | None = None) → MinimizerResult

Minimize the given function, by focusing on a subset of parameters.

Parameters:

  • function (Callable[[list[ndarray[Any, dtype[float64]]]], list[float]]) – The function to minimize. Takes a numpy float as input and outputs a scalar value.

  • initial_guess (ndarray[Any, dtype[float64]] | None) – The initial guess for the values of the parameters.

  • iteration (int | None) – The current adaptive iteration of the VQE algorithm, if applicable.

  • bounds (tuple[float, float] | list[tuple[float, float]] | None) – The bounds for the parameters.

  • circuit (Circuit | None) – The parameterized circuit to be minimized.

  • operator (HermitianPauliSum | None) – The operator used in the expectation value.

Return type:

MinimizerResult