Source code for ferrmion.core

import numpy as np
import numpy.typing as npt

from .encode.ternary_tree import TTFlatpack

# Rust-accelerated functions exposed to Python


[docs]
def symplectic_product(

    left: npt.NDArray[np.bool], right: npt.NDArray[np.bool]
) -> tuple[int, npt.NDArray[np.bool]]: ...

[docs]
def hartree_fock_state(

    fermionic_hf_state: npt.NDArray[np.bool],
    mode_op_map: npt.NDArray[np.uint],
    ipowers: npt.NDArray[np.uint8],
    symplectic_matrix: npt.NDArray[np.bool],
    vacuum_state: npt.NDArray[np.bool],
) -> npt.NDArray[np.bool]: ...

[docs]
def symplectic_to_pauli(

    symplectic: npt.NDArray[np.bool], ipower: int
) -> tuple[str, int]: ...

[docs]
def pauli_to_symplectic(

    pauli: str, ipower: int
) -> tuple[npt.NDArray[np.bool], int]: ...

[docs]
def symplectic_to_sparse(

    symplectic: npt.NDArray[np.bool],
    ipower: int,
) -> tuple[str, npt.NDArray[np.uintp], complex]: ...

[docs]
def clifford_heuristic(

    qham: dict[str, complex],
    n_qubits: int,
    temperature: float,
    coefficient_weighted: bool,
    seed: int | None = None,
    clifford_subset: str = ...,
) -> dict: ...

[docs]
def randomised_subsystem_descent(

    qham: dict[str, complex],
    n_qubits: int,
    iterations: int,
    temperature: float,
    subsystem_dimension: int,
    coefficient_weighted: bool = ...,
    sampler: str = ...,
    seed: int | None = None,
    clifford_subset: str = ...,
) -> dict: ...

[docs]
def anneal_enumerations(

    ipowers: npt.NDArray[np.uint8],
    symplectics: npt.NDArray[np.bool],
    signatures: list[str],
    coeffs: list[np.ndarray],
    temperature: float,
    initial_guess: npt.NDArray[np.uint],
    coefficient_weighted: bool,
    seed: int | None = None,
) -> tuple[npt.NDArray[np.uint8], npt.NDArray[np.bool]]: ...

[docs]
def batch_pauli_weights(

    ipowers: npt.NDArray[np.uint8],
    symplectics: npt.NDArray[np.bool],
    vacuum_state: npt.NDArray[np.bool],
    signatures: list[str],
    coeffs: list[np.ndarray],
    permutations: npt.NDArray[np.uint],
) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]: ...

[docs]
def topphatt(

    flatpack: list[tuple[np.uint, tuple[np.uint, np.uint, np.uint]]],
    n_qubits: int,
    signatures: list[str],
    coeffs: list[np.ndarray],
    parallelize: bool,
    heuristic: str = "min_weight",
    seed: int | None = None,
) -> tuple[npt.NDArray[np.uint8], npt.NDArray[np.bool], npt.NDArray[np.bool]]: ...

[docs]
def hatt(

    n_modes: int,
    signatures: list[str],
    coeffs: list[np.ndarray],
) -> tuple[TTFlatpack, int]: ...

[docs]
def flatpack_symplectic_matrix(

    flatpack: TTFlatpack,
    n_qubits: None | int,
) -> tuple[npt.NDArray[np.uint8], npt.NDArray[np.bool], npt.NDArray[np.bool]]: ...

[docs]
def encode_fermion_product(

    ipowers: npt.NDArray[np.uint8],
    symplectics: npt.NDArray[np.bool],
    signatures: str,
    indices: list[int],
    coefficient: complex,
) -> dict: ...

[docs]
def encode(

    ipowers: npt.NDArray[np.uint8],
    symplectics: npt.NDArray[np.bool],
    vacuum_state: npt.NDArray[np.bool],
    signatures: list[str],
    coeffs: list[np.ndarray],
    constant_energy: float,
) -> dict: ...

[docs]
def encode_topphatt(

    flatpack: list[tuple[np.uint, tuple[np.uint, np.uint, np.uint]]],
    n_qubits: int,
    signatures: list[str],
    coeffs: list[np.ndarray],
    constant_energy: float,
    parallelize: bool,
    heuristic: str = "min_weight",
    seed: int | None = None,
) -> tuple[npt.NDArray[np.uint8], npt.NDArray[np.bool], dict, npt.NDArray[np.bool]]: ...

[docs]
def fermionic_to_sparse_majorana(

    signatures: list[str],
    coeffs: list[np.ndarray],
    constant_energy: float,
) -> dict: ...

[docs]
def decode(

    states: npt.NDArray[np.bool],
    ipowers: npt.NDArray[np.uint8],
    symplectic_matrix: npt.NDArray[np.bool],
    vacuum_state: npt.NDArray[np.bool],
) -> npt.NDArray[np.bool]: ...

[docs]
def maxnto_symplectic_matrix(

    n_modes: int,
) -> npt.NDArray[np.bool]: ...