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.Heisenberg1Denergy(L: int, Pauli: bool = True, maxiters: int = 1000) → float

tensorcircuit.applications.utils.TFIM1Denergy(L: int, Jzz: float = 1.0, Jx: float = 1.0, Pauli: bool = True) → float

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

tensorcircuit.applications.utils.color_svg(circuit: cirq.circuits.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: Optional[Any] = None) → tensorcircuit.circuit.Circuit

tensorcircuit.applications.utils.mnist_amplitude_data(a: int, b: int, binarize: bool = False, index: Optional[Sequence[int]] = None, index_func: Optional[Callable[[int, int], Sequence[int]]] = None, loader: Optional[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: Optional[Sequence[int]] = 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: Optional[Sequence[int]] = None, nqubits: int = 10, epochs: int = 3, batch: int = 64) → Any