mqt.qudits.core.micro_dd

Module Contents

class MicroDDNode(label: int | str)

id: int | None = None

value: int | str

children: list[MicroDDNode] = []

reduced: bool | None = None

children_index: list[int] = []

weight: complex = 1

p: complex | None = None

terminal: bool = False

dd_hash: int | None = None

available: bool = True

data: numpy.typing.NDArray[numpy.complex128] | list[complex] = []

__lt__(other: MicroDDNode) → bool

__gt__(other: MicroDDNode) → bool

__le__(other: MicroDDNode) → bool

__ge__(other: MicroDDNode) → bool

zero: MicroDDNode

one: MicroDDNode

NodeContribution

get_node_contributions(root: MicroDDNode, labels: list[int]) → NodeContribution

unique_weights(root: MicroDDNode) → set[complex]

normalize(in_weight: complex, out_weights: list[complex]) → tuple[complex, list[complex]]

create_decision_tree(labels: list[int], cardinalities: list[int], data: numpy.typing.NDArray[numpy.complex128] | list[complex]) → tuple[MicroDDNode, list[int]]

build_decision_tree(labels: list[int], node: MicroDDNode, cardinalities: list[int], data: numpy.typing.NDArray[numpy.complex128] | list[complex], number_of_nodes: list[int], depth: int = 0) → None

dd_approximation(node: MicroDDNode, cardinalities: list[int], tolerance: float, depth: int = 0) → None

remove_children(node: MicroDDNode) → None

cut_branches(contributions: NodeContribution, tolerance: float) → None

normalize_all(node: MicroDDNode, cardinalities: list[int], depth: int = 0) → None

dd_reduction_hashing(node: MicroDDNode, cardinalities: list[int], depth: int = 0) → int

dd_reduction_aggregation(node: MicroDDNode, cardinalities: list[int], depth: int = 0) → None

dd_reduction(root: MicroDDNode, cardinalities: list[int]) → MicroDDNode

count_nodes_after(node: MicroDDNode, counter: list[int], cardinalities: list[int], depth: int = 0) → None

print_decision_weights(node: MicroDDNode, indent: str = '') → None

print_decision_obj_id(node: MicroDDNode, indent: str = '') → None

print_decision_hash(node: MicroDDNode, indent: str = '') → None