PySparQ.pysparq.algorithms.qda_solver 源代码

"""
QDA (Quantum Discrete Adiabatic) Linear System Solver Implementation
"""

from typing import Callable

import numpy as np
import pysparq as ps




def compute_fs(s: float, kappa: float, p: float) -> float:
    """Compute the interpolation parameter f(s)."""
    ...





def compute_rotation_matrix(fs: float) -> list[complex]:
    """Compute the rotation matrix R_s for block encoding."""
    ...





def chebyshev_T(n: int, x: float) -> float:
    """Compute Chebyshev polynomial T_n(x)."""
    ...





def dolph_chebyshev(epsilon: float, l: int, phi: float) -> float:
    """Compute Dolph-Chebyshev window function."""
    ...





def compute_fourier_coeffs(epsilon: float, l: int) -> list[float]:
    """Compute Fourier coefficients for Dolph-Chebyshev filter."""
    ...





def calculate_angles(coeffs: list[float]) -> list[float]:
    """Calculate rotation angles from coefficients for state preparation."""
    ...





class BlockEncoding:
    """Placeholder for block encoding implementation."""



    A: np.ndarray



    data_size: int

    _condition_regs: list[str]
    _condition_bits: list[tuple[str | int, int]]

    def __init__(self, A: np.ndarray, data_size: int = ...) -> None: ...


    def conditioned_by_all_ones(
        self, conds: str | list[str]
    ) -> "BlockEncoding": ...

    def __call__(self, state: ps.SparseState) -> None: ...


    def dag(self, state: ps.SparseState) -> None: ...






class StatePreparation:
    """Placeholder for state preparation implementation."""



    b: np.ndarray

    _condition_regs: list[str]
    _condition_bits: list[tuple[str | int, int]]

    def __init__(self, b: np.ndarray) -> None: ...


    def conditioned_by_all_ones(
        self, conds: str | list[str]
    ) -> "StatePreparation": ...

    def __call__(self, state: ps.SparseState) -> None: ...


    def dag(self, state: ps.SparseState) -> None: ...






class BlockEncodingHs:
    """Block encoding of the interpolating Hamiltonian H(s)."""



    enc_A: BlockEncoding



    enc_b: StatePreparation



    main_reg: str



    anc_UA: str



    anc_1: str



    anc_2: str



    anc_3: str



    anc_4: str



    fs: float



    R_s: list[complex]

    _condition_regs: list[str]
    _condition_bits: list[tuple[str | int, int]]

    def __init__(
        self,
        enc_A: BlockEncoding,
        enc_b: StatePreparation,
        main_reg: str,
        anc_UA: str,
        anc_1: str,
        anc_2: str,
        anc_3: str,
        anc_4: str,
        fs: float,
    ) -> None: ...


    def conditioned_by_all_ones(
        self, conds: str | list[str]
    ) -> "BlockEncodingHs": ...

    def __call__(self, state: ps.SparseState) -> None: ...


    def dag(self, state: ps.SparseState) -> None: ...






class BlockEncodingHsPD:
    """Positive-definite version of block encoding H(s)."""



    enc_A: BlockEncoding



    enc_b: StatePreparation



    main_reg: str



    anc_UA: str



    anc_1: str



    anc_2: str



    anc_3: str



    anc_4: str



    fs: float



    R_s: list[complex]


    def __init__(
        self,
        enc_A: BlockEncoding,
        enc_b: StatePreparation,
        main_reg: str,
        anc_UA: str,
        anc_1: str,
        anc_2: str,
        anc_3: str,
        anc_4: str,
        fs: float,
    ) -> None: ...
    def __call__(self, state: ps.SparseState) -> None: ...





class WalkS:
    """Quantum walk operator at parameter s."""



    main_reg: str



    anc_UA: str



    anc_1: str



    anc_2: str



    anc_3: str



    anc_4: str



    s: float



    kappa: float



    p: float



    is_positive_definite: bool



    fs: float



    phase: complex



    enc_Hs: BlockEncodingHs | BlockEncodingHsPD

    _condition_regs: list[str]
    _condition_bits: list[tuple[str | int, int]]

    def __init__(
        self,
        enc_A: BlockEncoding,
        enc_b: StatePreparation,
        main_reg: str,
        anc_UA: str,
        anc_1: str,
        anc_2: str,
        anc_3: str,
        anc_4: str,
        s: float,
        kappa: float,
        p: float,
        is_positive_definite: bool = ...,
    ) -> None: ...


    def conditioned_by_all_ones(
        self, conds: str | list[str]
    ) -> "WalkS": ...



    def conditioned_by_bit(self, reg: str, pos: int) -> "WalkS": ...



    def clear_conditions(self) -> None: ...

    def __call__(self, state: ps.SparseState) -> None: ...


    def dag(self, state: ps.SparseState) -> None: ...






class LCU:
    """Linear Combination of Unitaries for QDA."""



    walk: WalkS



    index_reg: str



    log_file: str



    index_size: int


    def __init__(self, walk: WalkS, index_reg: str, log_file: str = ...) -> None: ...
    def __call__(self, state: ps.SparseState) -> None: ...


    def dag(self, state: ps.SparseState) -> None: ...






class Filtering:
    """Dolph-Chebyshev filtering for QDA."""



    walk: WalkS



    index_reg: str



    anc_h: str



    epsilon: float



    l: int



    coeffs: list[float]


    def __init__(
        self,
        walk: WalkS,
        index_reg: str,
        anc_h: str,
        epsilon: float = ...,
        l: int = ...,
    ) -> None: ...
    def __call__(self, state: ps.SparseState) -> float: ...





def classical_to_quantum(
    A: np.ndarray, b: np.ndarray
) -> tuple[np.ndarray, np.ndarray, Callable[[np.ndarray], np.ndarray]]:
    """Convert classical linear system to quantum-compatible form."""
    ...





def qda_solve(
    A: np.ndarray,
    b: np.ndarray,
    kappa: float | None = ...,
    p: float = ...,
    eps: float = ...,
    step_rate: float = ...,
) -> np.ndarray:
    """Solve Ax = b using QDA algorithm."""
    ...





def create_qda_demo() -> str:
    """Generate a demo script for QDA solver."""
    ...