qlinks.backends package

Submodules

qlinks.backends.sparse module

class qlinks.backends.sparse.SparseBackend(*args, **kwargs)

Bases: Protocol

Minimal backend protocol for sparse Hamiltonian construction.

name: str

as_data_array(data, dtype)

Parameters:

• data (list[complex])

• dtype (DTypeLike)

Return type:

Any

as_index_array(indices)

Parameters:

indices (list[int] | ndarray[tuple[Any, ...], dtype[integer]])

Return type:

Any

coo_matrix(data, rows, cols, shape, dtype)

Parameters:

• data (Any)

• rows (Any)

• cols (Any)

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

Any

empty_csr(shape, dtype)

Parameters:

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

Any

to_cpu_array(array)

Parameters:

array (Any)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

max_abs_data(sparse_matrix)

Parameters:

sparse_matrix (Any)

Return type:

float

__init__(*args, **kwargs)

class qlinks.backends.sparse.ScipySparseBackend(name: 'str' = 'scipy')

Bases: object

Parameters:

name (str)

name: str

as_data_array(data, dtype)

Parameters:

• data (list[complex])

• dtype (DTypeLike)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

as_index_array(indices)

Parameters:

indices (list[int] | ndarray[tuple[Any, ...], dtype[integer]])

Return type:

ndarray[tuple[Any, …], dtype[int64]]

coo_matrix(data, rows, cols, shape, dtype)

Parameters:

• data (ndarray[tuple[Any, ...], dtype[_ScalarT]])

• rows (ndarray[tuple[Any, ...], dtype[int64]])

• cols (ndarray[tuple[Any, ...], dtype[int64]])

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

csr_array

empty_csr(shape, dtype)

Parameters:

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

csr_array

to_cpu_array(array)

Parameters:

array (Any)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

max_abs_data(sparse_matrix)

Parameters:

sparse_matrix (Any)

Return type:

float

__init__(name='scipy')

Parameters:

name (str)

Return type:

None

class qlinks.backends.sparse.CupySparseBackend(name='cupy')

Bases: object

CuPy/CuPyX sparse backend.

Imported lazily so qlinks does not require CuPy unless backend=”cupy” is used.

Note

cupyx.scipy.sparse currently follows the scipy sparse matrix-style API more closely than scipy’s newer sparse array API. Therefore this backend creates cupyx.scipy.sparse.coo_matrix/csr_matrix, not csr_array.

Parameters:

name (str)

name: str

property cp

property cupyx_sparse

as_data_array(data, dtype)

Parameters:

• data (list[complex])

• dtype (DTypeLike)

as_index_array(indices)

Parameters:

indices (list[int] | ndarray[tuple[Any, ...], dtype[integer]])

coo_matrix(data, rows, cols, shape, dtype)

Parameters:

• data (Any)

• rows (Any)

• cols (Any)

• shape (tuple[int, int])

• dtype (DTypeLike)

empty_csr(shape, dtype)

Parameters:

• shape (tuple[int, int])

• dtype (DTypeLike)

to_cpu_array(array)

Parameters:

array (Any)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

max_abs_data(sparse_matrix)

Parameters:

sparse_matrix (Any)

Return type:

float

__init__(name='cupy')

Parameters:

name (str)

Return type:

None

qlinks.backends.sparse.get_sparse_backend(backend='scipy')

Resolve a sparse backend.

backend=”auto” currently chooses scipy. This keeps behavior deterministic. Later, you can change “auto” to use cupy if an input basis lives on GPU.

Parameters:

backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

SparseBackend

Module contents

class qlinks.backends.CupySparseBackend(name='cupy')

Bases: object

CuPy/CuPyX sparse backend.

Imported lazily so qlinks does not require CuPy unless backend=”cupy” is used.

Note

cupyx.scipy.sparse currently follows the scipy sparse matrix-style API more closely than scipy’s newer sparse array API. Therefore this backend creates cupyx.scipy.sparse.coo_matrix/csr_matrix, not csr_array.

Parameters:

name (str)

name: str

property cp

property cupyx_sparse

as_data_array(data, dtype)

Parameters:

• data (list[complex])

• dtype (DTypeLike)

as_index_array(indices)

Parameters:

indices (list[int] | ndarray[tuple[Any, ...], dtype[integer]])

coo_matrix(data, rows, cols, shape, dtype)

Parameters:

• data (Any)

• rows (Any)

• cols (Any)

• shape (tuple[int, int])

• dtype (DTypeLike)

empty_csr(shape, dtype)

Parameters:

• shape (tuple[int, int])

• dtype (DTypeLike)

to_cpu_array(array)

Parameters:

array (Any)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

max_abs_data(sparse_matrix)

Parameters:

sparse_matrix (Any)

Return type:

float

__init__(name='cupy')

Parameters:

name (str)

Return type:

None

class qlinks.backends.ScipySparseBackend(name: 'str' = 'scipy')

Bases: object

Parameters:

name (str)

name: str

as_data_array(data, dtype)

Parameters:

• data (list[complex])

• dtype (DTypeLike)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

as_index_array(indices)

Parameters:

indices (list[int] | ndarray[tuple[Any, ...], dtype[integer]])

Return type:

ndarray[tuple[Any, …], dtype[int64]]

coo_matrix(data, rows, cols, shape, dtype)

Parameters:

• data (ndarray[tuple[Any, ...], dtype[_ScalarT]])

• rows (ndarray[tuple[Any, ...], dtype[int64]])

• cols (ndarray[tuple[Any, ...], dtype[int64]])

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

csr_array

empty_csr(shape, dtype)

Parameters:

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

csr_array

to_cpu_array(array)

Parameters:

array (Any)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

max_abs_data(sparse_matrix)

Parameters:

sparse_matrix (Any)

Return type:

float

__init__(name='scipy')

Parameters:

name (str)

Return type:

None

class qlinks.backends.SparseBackend(*args, **kwargs)

Bases: Protocol

Minimal backend protocol for sparse Hamiltonian construction.

name: str

as_data_array(data, dtype)

Parameters:

• data (list[complex])

• dtype (DTypeLike)

Return type:

Any

as_index_array(indices)

Parameters:

indices (list[int] | ndarray[tuple[Any, ...], dtype[integer]])

Return type:

Any

coo_matrix(data, rows, cols, shape, dtype)

Parameters:

• data (Any)

• rows (Any)

• cols (Any)

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

Any

empty_csr(shape, dtype)

Parameters:

• shape (tuple[int, int])

• dtype (DTypeLike)

Return type:

Any

to_cpu_array(array)

Parameters:

array (Any)

Return type:

ndarray[tuple[Any, …], dtype[_ScalarT]]

max_abs_data(sparse_matrix)

Parameters:

sparse_matrix (Any)

Return type:

float

__init__(*args, **kwargs)

qlinks.backends.get_sparse_backend(backend='scipy')

Resolve a sparse backend.

backend=”auto” currently chooses scipy. This keeps behavior deterministic. Later, you can change “auto” to use cupy if an input basis lives on GPU.

Parameters:

backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

SparseBackend