tensorcircuit.applications.utils

A collection of useful function snippets that irrelevant with the main modules or await for further refactor

class tensorcircuit.applications.utils.FakeModule

Bases: object

tensorcircuit.applications.utils.amplitude_encoding(fig: Any, qubits: int, index: Sequence[int] | None = None, index_func: Callable[[int, int], Sequence[int]] | None = None) → Any

tensorcircuit.applications.utils.color_svg(circuit: Circuit, *coords: Tuple[int, int]) → Any

color cirq circuit SVG for given gates, a small tool to hack the cirq SVG

Parameters:

• circuit

• coords – integer coordinate which gate is colored

Returns:

tensorcircuit.applications.utils.generate_random_circuit(inputs: Any, nqubits: int = 10, epochs: int = 3, layouts: Any = None) → Circuit

tensorcircuit.applications.utils.mnist_amplitude_data(a: int, b: int, binarize: bool = False, index: Sequence[int] | None = None, index_func: Callable[[int, int], Sequence[int]] | None = None, loader: Any = None, threshold: float = 0.4) → Any

tensorcircuit.applications.utils.mnist_generator(x_train: Any, y_train: Any, batch: int = 1, random: bool = True) → Iterator[Any]

tensorcircuit.applications.utils.naive_qml_vag(gdata: Any, nnp: Any, preset: Sequence[int], nqubits: int = 10, epochs: int = 3, target: int = 0) → Tuple[Any, Any]

tensorcircuit.applications.utils.recursive_index(x: int, y: int) → Sequence[int]

tensorcircuit.applications.utils.repr2array(inputs: str) → Any

transform repr form of an array to real numpy array

Parameters:

inputs

Returns:

tensorcircuit.applications.utils.train_qml_vag(gdata: Any, nnp: Any, preset: Sequence[int] | None = None, nqubits: int = 10, epochs: int = 3, batch: int = 64, validation: bool = False) → Any

tensorcircuit.applications.utils.validate_qml_vag(gdata: Any, nnp: Any, preset: Sequence[int] | None = None, nqubits: int = 10, epochs: int = 3, batch: int = 64) → Any