pdft.bases.EntangledQFTBasis

class pdft.bases.EntangledQFTBasis(m, n, tensors=None, entangle_phases=None, entangle_position='back', code=None, inv_code=None, seed=None)

Bases: object

QFT + appended entanglement layer on min(m, n) row/col qubit pairs.

Mirror of upstream src/basis.jl:280-500 (entangle_position=:back only).

Parameters:

• m (int)

• n (int)

• tensors (list[Array])

• entangle_phases (Sequence[float] | None)

• entangle_position (str)

• code (object)

• inv_code (object)

• seed (int | None)

__init__(m, n, tensors=None, entangle_phases=None, entangle_position='back', code=None, inv_code=None, seed=None)

Mirror of _init_circuit(::Type{EntangledQFTBasis}, ...) from ParametricDFT.jl/src/training.jl. When seed is provided AND entangle_phases is None, draws phases from np.random.default_rng(seed).normal(0, 0.1, n_entangle) — Julia’s randn(n_gates) * 0.1 convention. This breaks the symmetry-collapse where EntangledQFTBasis initialises identically to QFTBasis when all entanglement phases are zero.

Parameters:

• m (int)

• n (int)

• tensors (Sequence[Array] | None)

• entangle_phases (Sequence[float] | None)

• entangle_position (str)

• code (object | None)

• inv_code (object | None)

• seed (int | None)

Methods

__init__(m, n[, tensors, entangle_phases, ...])	Mirror of _init_circuit(::Type{EntangledQFTBasis}, ...) from ParametricDFT.jl/src/training.jl.
forward_transform(pic)
inverse_transform(pic)

Attributes

image_size
inv_tensors
num_parameters
m
n
n_entangle
tensors
code
inv_code

m: int

n: int

n_entangle: int

tensors: list[Array]

code: object

inv_code: object

property inv_tensors: list[Array]

property image_size: tuple[int, int]

property num_parameters: int

forward_transform(pic)

Parameters:

pic (Array)

Return type:

Array

inverse_transform(pic)

Parameters:

pic (Array)

Return type:

Array