mqt.qudits.compiler.twodit.variational_twodit_compilation.opt.optimizer¶

Module Contents¶

class Optimizer[source]¶

timer_var: bool = False¶

OBJ_FIDELITY: float = 0.0001¶

SINGLE_DIM_0: int = 0¶

SINGLE_DIM_1: int = 0¶

TARGET_GATE: numpy.typing.NDArray[numpy.complex128]¶

MAX_NUM_LAYERS: int = 0¶

X_SOLUTION: numpy.typing.NDArray[numpy.float64]¶

FUN_SOLUTION: float = 0.0¶

classmethod set_class_variables(target: numpy.typing.NDArray[numpy.complex128], obj_fid: float = 0.0001, dim_0: int = 0, dim_1: int = 0, layers: int = 0) → None[source]¶

static bounds_assigner(b1: tuple[float, float], b2: tuple[float, float], b3: tuple[float, float], num_params_single: int, d: int) → list[tuple[float, float]][source]¶

classmethod return_bounds(num_layer_search: int = 1) → list[tuple[float, float]][source]¶

classmethod obj_fun_core(ansatz: numpy.typing.NDArray[numpy.complex128], lambdas: numpy.typing.NDArray[numpy.float64]) → float[source]¶

classmethod objective_fnc_ms(lambdas: numpy.typing.NDArray[numpy.float64]) → float[source]¶

classmethod objective_fnc_ls(lambdas: numpy.typing.NDArray[numpy.float64]) → float[source]¶

classmethod objective_fnc_cu(lambdas: numpy.typing.NDArray[numpy.float64]) → float[source]¶

classmethod solve_anneal(bounds: list[tuple[float, float]], ansatz_type: str, result_queue: Queue[tuple[float, numpy.typing.NDArray[numpy.float64]]]) → None[source]¶