PySparQ.pysparq.algorithms.block_encoding

Matrix block encoding algorithms (tridiagonal and QRAM-based).

Classes

BlockEncodingTridiagonal	Block encoding of the tridiagonal matrix alpha*I + beta*T.
BlockEncodingViaQRAM	Block encoding of an arbitrary matrix via QRAM.
PlusOneAndOverflow	Add 1 to a register and track overflow.
UL	Left-multiplication operator based on QRAM block encoding.
UR	Right-multiplication operator based on QRAM block encoding.

Functions

create_block_encoding_demo(→ str)	Return a demo script string showing block encoding usage.
get_tridiagonal_matrix(→ numpy.ndarray)	Return the dim x dim tridiagonal matrix alpha*I + beta*T.
get_u_minus(→ numpy.ndarray)	Return the size x size up-shift (superdiagonal) matrix.
get_u_plus(→ numpy.ndarray)	Return the size x size down-shift (subdiagonal) matrix.

Module Contents

class PySparQ.pysparq.algorithms.block_encoding.BlockEncodingTridiagonal(main_reg: str, anc_UA: str, alpha: float, beta: float)

Block encoding of the tridiagonal matrix alpha*I + beta*T.

clear_conditions() → None

conditioned_by_all_ones(conds: str | list[str]) → BlockEncodingTridiagonal

conditioned_by_bit(reg: str | int, pos: int) → BlockEncodingTridiagonal

conditioned_by_nonzeros(conds: str | list[str]) → BlockEncodingTridiagonal

dag(state: SparseState) → None

alpha: float

anc_UA: str

beta: float

main_reg: str

prep_state: list[complex]

class PySparQ.pysparq.algorithms.block_encoding.BlockEncodingViaQRAM(qram: QRAMCircuit_qutrit, column_index: str, row_index: str, data_size: int, rational_size: int)

Block encoding of an arbitrary matrix via QRAM.

clear_conditions() → None

conditioned_by_all_ones(conds: str | list[str]) → BlockEncodingViaQRAM

conditioned_by_bit(reg: str | int, pos: int) → BlockEncodingViaQRAM

conditioned_by_nonzeros(conds: str | list[str]) → BlockEncodingViaQRAM

dag(state: SparseState) → None

column_index: str

data_size: int

qram: QRAMCircuit_qutrit

rational_size: int

row_index: str

class PySparQ.pysparq.algorithms.block_encoding.PlusOneAndOverflow(main_reg: str, overflow: str)

Add 1 to a register and track overflow.

clear_conditions() → None

conditioned_by_all_ones(conds: str | list[str]) → PlusOneAndOverflow

conditioned_by_bit(reg: str | int, pos: int) → PlusOneAndOverflow

conditioned_by_nonzeros(conds: str | list[str]) → PlusOneAndOverflow

conditioned_by_value(reg: str | int, value: int) → PlusOneAndOverflow

dag(state: SparseState) → None

main_reg: str

overflow: str

class PySparQ.pysparq.algorithms.block_encoding.UL(qram: QRAMCircuit_qutrit, row_index: str, column_index: str, data_size: int, rational_size: int)

Left-multiplication operator based on QRAM block encoding.

clear_conditions() → None

conditioned_by_all_ones(conds: str | list[str]) → UL

conditioned_by_bit(reg: str | int, pos: int) → UL

conditioned_by_nonzeros(conds: str | list[str]) → UL

dag(state: SparseState) → None

addr_size: int

column_index: str

data_size: int

qram: QRAMCircuit_qutrit

rational_size: int

row_index: str

class PySparQ.pysparq.algorithms.block_encoding.UR(qram: QRAMCircuit_qutrit, column_index: str, data_size: int, rational_size: int)

Right-multiplication operator based on QRAM block encoding.

clear_conditions() → None

conditioned_by_all_ones(conds: str | list[str]) → UR

conditioned_by_bit(reg: str | int, pos: int) → UR

conditioned_by_nonzeros(conds: str | list[str]) → UR

dag(state: SparseState) → None

addr_size: int

column_index: str

data_size: int

qram: QRAMCircuit_qutrit

rational_size: int

PySparQ.pysparq.algorithms.block_encoding.create_block_encoding_demo() → str

Return a demo script string showing block encoding usage.

PySparQ.pysparq.algorithms.block_encoding.get_tridiagonal_matrix(alpha: float, beta: float, dim: int) → numpy.ndarray

Return the dim x dim tridiagonal matrix alpha*I + beta*T.

PySparQ.pysparq.algorithms.block_encoding.get_u_minus(size: int) → numpy.ndarray

Return the size x size up-shift (superdiagonal) matrix.

PySparQ.pysparq.algorithms.block_encoding.get_u_plus(size: int) → numpy.ndarray

Return the size x size down-shift (subdiagonal) matrix.