pdft.bases.QFTBasis

class pdft.bases.QFTBasis(m, n, tensors=None, code=None, inv_code=None)

Bases: object

QFT tensor-network basis. Mirror of ParametricDFT.jl/src/basis.jl::QFTBasis.

Stores ONE tensor list. inverse_transform applies conj(tensors) through inv_code — exactly like Julia’s inverse_transform(basis::QFTBasis, ...) which does basis.inverse_code(conj.(basis.tensors)..., ...).

code and inv_code are jit-compiled einsum closures and compare by identity; they are marked compare=False so the dataclass-generated __eq__ doesn’t consider them. Use bases_allclose for semantic comparison.

Pytree contract:

leaves = tensors (one list) aux data = (m, n, len(tensors), code, inv_code)

Parameters:

• m (int)

• n (int)

• tensors (list[Array])

• code (object)

• inv_code (object)

__init__(m, n, tensors=None, code=None, inv_code=None)

Parameters:

• m (int)

• n (int)

• tensors (Sequence[Array] | None)

• code (object | None)

• inv_code (object | None)

Methods

__init__(m, n[, tensors, code, inv_code])
forward_transform(pic)
inverse_transform(pic)

Attributes

image_size
inv_tensors	Julia stores one tensor list.
num_parameters
m
n
tensors
code
inv_code

m: int

n: int

tensors: list[Array]

code: object

inv_code: object

property inv_tensors: list[Array]

Julia stores one tensor list. The “inverse” is computed by applying conj(tensors) through inv_code. We expose this property so older callers that read basis.inv_tensors keep working — they get the same arrays as basis.tensors.

Type:

Back-compat alias

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