Core (Rust extension)

The ferrmion.core module contains the Rust-accelerated functions that power the encoding and optimisation pipelines. All functions are compiled via PyO3 and available directly on the ferrmion namespace through re-exports in ferrmion/__init__.py.

ferrmion.core.anneal_enumerations(ipowers: ndarray[tuple[int, ...], dtype[uint8]], symplectics: ndarray[tuple[int, ...], dtype[bool]], signatures: list[str], coeffs: list[ndarray], temperature: float, initial_guess: ndarray[tuple[int, ...], dtype[uint64]], coefficient_weighted: bool, seed: int | None = None) → tuple[ndarray[tuple[int, ...], dtype[uint8]], ndarray[tuple[int, ...], dtype[bool]]]

ferrmion.core.batch_pauli_weights(ipowers: ndarray[tuple[int, ...], dtype[uint8]], symplectics: ndarray[tuple[int, ...], dtype[bool]], vacuum_state: ndarray[tuple[int, ...], dtype[bool]], signatures: list[str], coeffs: list[ndarray], permutations: ndarray[tuple[int, ...], dtype[uint64]]) → tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]]]

ferrmion.core.clifford_heuristic(qham: dict[str, complex], n_qubits: int, temperature: float, coefficient_weighted: bool, seed: int | None = None, clifford_subset: str = Ellipsis) → dict

ferrmion.core.decode(states: ndarray[tuple[int, ...], dtype[bool]], ipowers: ndarray[tuple[int, ...], dtype[uint8]], symplectic_matrix: ndarray[tuple[int, ...], dtype[bool]], vacuum_state: ndarray[tuple[int, ...], dtype[bool]]) → ndarray[tuple[int, ...], dtype[bool]]

ferrmion.core.encode(ipowers: ndarray[tuple[int, ...], dtype[uint8]], symplectics: ndarray[tuple[int, ...], dtype[bool]], vacuum_state: ndarray[tuple[int, ...], dtype[bool]], signatures: list[str], coeffs: list[ndarray], constant_energy: float) → dict

ferrmion.core.encode_fermion_product(ipowers: ndarray[tuple[int, ...], dtype[uint8]], symplectics: ndarray[tuple[int, ...], dtype[bool]], signatures: str, indices: list[int], coefficient: complex) → dict

ferrmion.core.encode_topphatt(flatpack: list[tuple[uint64, tuple[uint64, uint64, uint64]]], n_qubits: int, signatures: list[str], coeffs: list[ndarray], constant_energy: float, parallelize: bool, heuristic: str = 'min_weight', seed: int | None = None) → tuple[ndarray[tuple[int, ...], dtype[uint8]], ndarray[tuple[int, ...], dtype[bool]], dict, ndarray[tuple[int, ...], dtype[bool]]]

ferrmion.core.fermionic_to_sparse_majorana(signatures: list[str], coeffs: list[ndarray], constant_energy: float) → dict

ferrmion.core.flatpack_symplectic_matrix(flatpack: TTFlatpack, n_qubits: None | int) → tuple[ndarray[tuple[int, ...], dtype[uint8]], ndarray[tuple[int, ...], dtype[bool]], ndarray[tuple[int, ...], dtype[bool]]]

ferrmion.core.hartree_fock_state(fermionic_hf_state: ndarray[tuple[int, ...], dtype[bool]], mode_op_map: ndarray[tuple[int, ...], dtype[uint64]], ipowers: ndarray[tuple[int, ...], dtype[uint8]], symplectic_matrix: ndarray[tuple[int, ...], dtype[bool]], vacuum_state: ndarray[tuple[int, ...], dtype[bool]]) → ndarray[tuple[int, ...], dtype[bool]]

ferrmion.core.hatt(n_modes: int, signatures: list[str], coeffs: list[ndarray]) → tuple[TTFlatpack, int]

ferrmion.core.maxnto_symplectic_matrix(n_modes: int) → ndarray[tuple[int, ...], dtype[bool]]

ferrmion.core.pauli_to_symplectic(pauli: str, ipower: int) → tuple[ndarray[tuple[int, ...], dtype[bool]], int]

ferrmion.core.randomised_subsystem_descent(qham: dict[str, complex], n_qubits: int, iterations: int, temperature: float, subsystem_dimension: int, coefficient_weighted: bool = Ellipsis, sampler: str = Ellipsis, seed: int | None = None, clifford_subset: str = Ellipsis) → dict

ferrmion.core.symplectic_product(left: ndarray[tuple[int, ...], dtype[bool]], right: ndarray[tuple[int, ...], dtype[bool]]) → tuple[int, ndarray[tuple[int, ...], dtype[bool]]]

ferrmion.core.symplectic_to_pauli(symplectic: ndarray[tuple[int, ...], dtype[bool]], ipower: int) → tuple[str, int]

ferrmion.core.symplectic_to_sparse(symplectic: ndarray[tuple[int, ...], dtype[bool]], ipower: int) → tuple[str, ndarray[tuple[int, ...], dtype[uint64]], complex]

ferrmion.core.topphatt(flatpack: list[tuple[uint64, tuple[uint64, uint64, uint64]]], n_qubits: int, signatures: list[str], coeffs: list[ndarray], parallelize: bool, heuristic: str = 'min_weight', seed: int | None = None) → tuple[ndarray[tuple[int, ...], dtype[uint8]], ndarray[tuple[int, ...], dtype[bool]], ndarray[tuple[int, ...], dtype[bool]]]