PySparQ.pysparq.algorithms.cks_solver¶

CKS (Childs-Kothari-Somma) Linear System Solver Implementation

Classes¶

`ChebyshevPolynomialCoefficient`	Computes Chebyshev polynomial coefficients for quantum walk.
`CondRotQW`	Conditional rotation for quantum walk.
`LCUContainer`	LCU (Linear Combination of Unitaries) container for CKS.
`QuantumBinarySearch`	Quantum binary search for sparse matrix access.
`QuantumWalk`	Quantum walk operator for CKS algorithm.
`QuantumWalkNSteps`	Multiple quantum walk steps for CKS algorithm.
`SparseMatrix`	Sparse matrix representation for CKS algorithm.
`SparseMatrixData`	Sparse matrix data for quantum simulation.
`TOperator`	T operator for CKS algorithm.

Functions¶

`cks_solve`(→ numpy.ndarray)	Solve Ax = b using CKS quantum linear solver.
`create_cks_demo`(→ str)	Generate a demo script for CKS solver.
`get_coef_common`(→ list[complex])	Get rotation matrix coefficients for general (signed) matrix elements.
`get_coef_positive_only`(→ list[complex])	Get rotation matrix coefficients for positive-only matrix elements.
`make_walk_angle_func`(→ Callable[[int, int, int], ...)	Create walk angle function for a matrix.

Module Contents¶

class PySparQ.pysparq.algorithms.cks_solver.ChebyshevPolynomialCoefficient(b: int)[源代码]¶

Computes Chebyshev polynomial coefficients for quantum walk.

C(Big: int, Small: int) → float[源代码]¶

coef(j: int) → float[源代码]¶

sign(j: int) → bool[源代码]¶

step(j: int) → int[源代码]¶

b: int[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.CondRotQW(j_reg: str, k_reg: str, data_reg: str, output_reg: str, mat: SparseMatrix)[源代码]¶

Conditional rotation for quantum walk.

clear_conditions() → None[源代码]¶

conditioned_by_all_ones(conds: str | int | list[str | int]) → CondRotQW[源代码]¶

conditioned_by_bit(reg: str | int, pos: int) → CondRotQW[源代码]¶

conditioned_by_nonzeros(cond: str | int | list[str | int]) → CondRotQW[源代码]¶

dag(state: pysparq.SparseState) → None[源代码]¶

data_reg: str[源代码]¶

j_reg: str[源代码]¶

k_reg: str[源代码]¶

mat: SparseMatrix[源代码]¶

output_reg: str[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.LCUContainer(mat: SparseMatrix, kappa: float, eps: float, qram: pysparq.QRAMCircuit_qutrit | None = ...)[源代码]¶

LCU (Linear Combination of Unitaries) container for CKS.

clear_conditions() → None[源代码]¶

conditioned_by_all_ones(conds: str | int | list[str | int]) → LCUContainer[源代码]¶

conditioned_by_bit(reg: str | int, pos: int) → LCUContainer[源代码]¶

conditioned_by_nonzeros(cond: str | int | list[str | int]) → LCUContainer[源代码]¶

dag(state: pysparq.SparseState) → None[源代码]¶

external_input(init_op: Callable[[pysparq.SparseState], None]) → None[源代码]¶

get_input_reg() → str[源代码]¶

initialize() → None[源代码]¶

iterate() → bool[源代码]¶

b: int[源代码]¶

chebyshev: ChebyshevPolynomialCoefficient[源代码]¶

current_state: pysparq.SparseState | None[源代码]¶

eps: float[源代码]¶

j0: int[源代码]¶

kappa: float[源代码]¶

step_state: pysparq.SparseState | None[源代码]¶

walk: QuantumWalkNSteps[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.QuantumBinarySearch(qram: pysparq.QRAMCircuit_qutrit, address_offset_reg: str, total_length: int, target_reg: str, result_reg: str)[源代码]¶

Quantum binary search for sparse matrix access.

clear_conditions() → None[源代码]¶

conditioned_by_all_ones(conds: str | int | list[str | int]) → QuantumBinarySearch[源代码]¶

conditioned_by_bit(reg: str | int, pos: int) → QuantumBinarySearch[源代码]¶

conditioned_by_nonzeros(cond: str | int | list[str | int]) → QuantumBinarySearch[源代码]¶

dag(state: pysparq.SparseState) → None[源代码]¶

address_offset_reg: str[源代码]¶

max_step: int[源代码]¶

qram: pysparq.QRAMCircuit_qutrit[源代码]¶

result_reg: str[源代码]¶

target_reg: str[源代码]¶

total_length: int[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.QuantumWalk(qram: pysparq.QRAMCircuit_qutrit, j_reg: str, b1_reg: str, k_reg: str, b2_reg: str, j_comp_reg: str, k_comp_reg: str, data_offset_reg: str, sparse_offset_reg: str, mat: SparseMatrix)[源代码]¶

Quantum walk operator for CKS algorithm.

clear_conditions() → None[源代码]¶

conditioned_by_all_ones(conds: str | int | list[str | int]) → QuantumWalk[源代码]¶

conditioned_by_bit(reg: str | int, pos: int) → QuantumWalk[源代码]¶

conditioned_by_nonzeros(cond: str | int | list[str | int]) → QuantumWalk[源代码]¶

dag(state: pysparq.SparseState) → None[源代码]¶

b1_reg: str[源代码]¶

b2_reg: str[源代码]¶

data_offset_reg: str[源代码]¶

j_comp_reg: str[源代码]¶

j_reg: str[源代码]¶

k_comp_reg: str[源代码]¶

k_reg: str[源代码]¶

mat: SparseMatrix[源代码]¶

qram: pysparq.QRAMCircuit_qutrit[源代码]¶

sparse_offset_reg: str[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.QuantumWalkNSteps(mat: SparseMatrix, qram: pysparq.QRAMCircuit_qutrit | None = ...)[源代码]¶

Multiple quantum walk steps for CKS algorithm.

clear_conditions() → None[源代码]¶

conditioned_by_all_ones(conds: str | int | list[str | int]) → QuantumWalkNSteps[源代码]¶

conditioned_by_bit(reg: str | int, pos: int) → QuantumWalkNSteps[源代码]¶

conditioned_by_nonzeros(cond: str | int | list[str | int]) → QuantumWalkNSteps[源代码]¶

create_state() → pysparq.SparseState[源代码]¶

dag(state: pysparq.SparseState) → None[源代码]¶

first_step(state: pysparq.SparseState) → None[源代码]¶

init_environment(state: pysparq.SparseState) → None[源代码]¶

make_n_step_state(n_steps: int) → pysparq.SparseState[源代码]¶

step(state: pysparq.SparseState) → None[源代码]¶

addr_size: int[源代码]¶

b1: str[源代码]¶

b2: str[源代码]¶

data_offset: str[源代码]¶

data_size: int[源代码]¶

default_reg_size: int[源代码]¶

j: str[源代码]¶

j_comp: str[源代码]¶

k: str[源代码]¶

k_comp: str[源代码]¶

mat: SparseMatrix[源代码]¶

n_row: int[源代码]¶

nnz_col: int[源代码]¶

qram: pysparq.QRAMCircuit_qutrit[源代码]¶

sparse_offset: str[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.SparseMatrix(n_row: int, nnz_col: int, data: list[int], data_size: int, positive_only: bool = ...)[源代码]¶

Sparse matrix representation for CKS algorithm.

classmethod from_dense(matrix: numpy.ndarray, data_size: int = ..., positive_only: bool | None = ...) → SparseMatrix[源代码]¶

get_data() → list[int][源代码]¶

get_sparsity_offset() → int[源代码]¶

get_walk_angle_func() → Callable[[int, int, int], list[complex]][源代码]¶

data: list[int][源代码]¶

data_size: int[源代码]¶

n_row: int[源代码]¶

nnz_col: int[源代码]¶

positive_only: bool[源代码]¶

sparsity_offset: int[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.SparseMatrixData(n_row: int, nnz_col: int, data: list[int], data_size: int, positive_only: bool = ..., sparsity_offset: int = ...)[源代码]¶

Sparse matrix data for quantum simulation.

data: list[int][源代码]¶

data_size: int[源代码]¶

n_row: int[源代码]¶

nnz_col: int[源代码]¶

positive_only: bool[源代码]¶

sparsity_offset: int[源代码]¶

class PySparQ.pysparq.algorithms.cks_solver.TOperator(qram: pysparq.QRAMCircuit_qutrit, data_offset_reg: str, sparse_offset_reg: str, j_reg: str, b1_reg: str, k_reg: str, b2_reg: str, search_result_reg: str, nnz_col: int, data_size: int, mat: SparseMatrix)[源代码]¶

T operator for CKS algorithm.

clear_conditions() → None[源代码]¶

conditioned_by_all_ones(conds: str | int | list[str | int]) → TOperator[源代码]¶

conditioned_by_bit(reg: str | int, pos: int) → TOperator[源代码]¶

conditioned_by_nonzeros(cond: str | int | list[str | int]) → TOperator[源代码]¶

dag(state: pysparq.SparseState) → None[源代码]¶

b1_reg: str[源代码]¶

b2_reg: str[源代码]¶

data_offset_reg: str[源代码]¶

data_size: int[源代码]¶

j_reg: str[源代码]¶

k_reg: str[源代码]¶

mat: SparseMatrix[源代码]¶

nnz_col: int[源代码]¶

qram: pysparq.QRAMCircuit_qutrit[源代码]¶

search_result_reg: str[源代码]¶

sparse_offset_reg: str[源代码]¶

PySparQ.pysparq.algorithms.cks_solver.cks_solve(A: numpy.ndarray, b: numpy.ndarray, kappa: float | None = ..., eps: float = ..., data_size: int = ...) → numpy.ndarray[源代码]¶: Solve Ax = b using CKS quantum linear solver.

PySparQ.pysparq.algorithms.cks_solver.create_cks_demo() → str[源代码]¶: Generate a demo script for CKS solver.

PySparQ.pysparq.algorithms.cks_solver.get_coef_common(mat_data_size: int, v: int, row: int, col: int) → list[complex][源代码]¶: Get rotation matrix coefficients for general (signed) matrix elements.

PySparQ.pysparq.algorithms.cks_solver.get_coef_positive_only(mat_data_size: int, v: int, row: int, col: int) → list[complex][源代码]¶: Get rotation matrix coefficients for positive-only matrix elements.

PySparQ.pysparq.algorithms.cks_solver.make_walk_angle_func(mat_data_size: int, positive_only: bool) → Callable[[int, int, int], list[complex]][源代码]¶: Create walk angle function for a matrix.