qlinks.builders package

qlinks.builders package#

Submodules#

qlinks.builders.optimized_sparse module#

class qlinks.builders.optimized_sparse.OptimizedSparseBuildStats(n_basis: 'int', n_terms: 'int', n_raw_actions: 'int', n_kept_actions: 'int', n_missing_actions: 'int', nnz: 'int', n_scratch_arrays: 'int')[source]#

Bases: object

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)
n_scratch_arrays (int)

n_basis: int#

n_terms: int#

n_raw_actions: int#

n_kept_actions: int#

n_missing_actions: int#

nnz: int#

n_scratch_arrays: int#

__init__(n_basis, n_terms, n_raw_actions, n_kept_actions, n_missing_actions, nnz, n_scratch_arrays)#

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)
n_scratch_arrays (int)

Return type:

None

class qlinks.builders.optimized_sparse.OptimizedSparseBuildResult(matrix: 'Any', stats: 'OptimizedSparseBuildStats')[source]#

Bases: object

Parameters:

matrix (Any)
stats (OptimizedSparseBuildStats)

matrix: Any#

stats: OptimizedSparseBuildStats#

__init__(matrix, stats)#

Parameters:

matrix (Any)
stats (OptimizedSparseBuildStats)

Return type:

None

class qlinks.builders.optimized_sparse.OptimizedSparseHamiltonianBuilder(dtype=<class 'numpy.complex128'>, on_missing='skip', drop_zero_atol=0.0, backend='scipy')[source]#

Bases: object

Sparse Hamiltonian builder using LocalUpdateAction.

Main optimization#

Operators do not allocate full output configurations. They only return local updates. The builder owns one scratch array and reuses it for every action.

Matrix convention#

For each column basis state |config_col>, and action

coefficient, variable_indices, new_values

we construct

config_row = config_col with local updates applied

and insert

H[row, col] += coefficient

dtype: DTypeLike#

on_missing: Literal['skip', 'raise']#

drop_zero_atol: float#

backend: Literal['scipy', 'cupy', 'auto'] | SparseBackend#

build(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalUpdateOperator | DiagonalLocalOperator])

Return type:

Any

build_with_stats(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalUpdateOperator | DiagonalLocalOperator])

Return type:

OptimizedSparseBuildResult

__init__(dtype=<class 'numpy.complex128'>, on_missing='skip', drop_zero_atol=0.0, backend='scipy')#

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

None

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

qlinks.builders.optimized_sparse.build_optimized_sparse_hamiltonian(basis, operators, *, dtype=<class 'numpy.complex128'>, on_missing='skip', drop_zero_atol=0.0, backend='scipy')[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalUpdateOperator | DiagonalLocalOperator])
dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

Any

qlinks.builders.sparse module#

class qlinks.builders.sparse.SparseBuildStats(n_basis: 'int', n_terms: 'int', n_raw_actions: 'int', n_kept_actions: 'int', n_missing_actions: 'int', nnz: 'int')[source]#

Bases: object

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)

n_basis: int#

n_terms: int#

n_raw_actions: int#

n_kept_actions: int#

n_missing_actions: int#

nnz: int#

__init__(n_basis, n_terms, n_raw_actions, n_kept_actions, n_missing_actions, nnz)#

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)

Return type:

None

class qlinks.builders.sparse.SparseBuildResult(matrix: 'Any', stats: 'SparseBuildStats')[source]#

Bases: object

Parameters:

matrix (Any)
stats (SparseBuildStats)

matrix: Any#

stats: SparseBuildStats#

__init__(matrix, stats)#

Parameters:

matrix (Any)
stats (SparseBuildStats)

Return type:

None

class qlinks.builders.sparse.SparseHamiltonianBuilder(dtype: 'npt.DTypeLike' = <class 'numpy.complex128'>, on_missing: 'MissingActionPolicy' = 'skip', combine_duplicates: 'bool' = True, drop_zero_atol: 'float' = 0.0, backend: 'SparseBackendName | SparseBackend' = 'scipy')[source]#

Bases: object

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
combine_duplicates (bool)
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

dtype: DTypeLike#

on_missing: Literal['skip', 'raise']#

combine_duplicates: bool#

drop_zero_atol: float#

backend: Literal['scipy', 'cupy', 'auto'] | SparseBackend#

build(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalOperator])

Return type:

Any

build_with_stats(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalOperator])

Return type:

SparseBuildResult

__init__(dtype=<class 'numpy.complex128'>, on_missing='skip', combine_duplicates=True, drop_zero_atol=0.0, backend='scipy')#

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
combine_duplicates (bool)
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

None

qlinks.builders.sparse.build_sparse_hamiltonian(basis, operators, *, dtype=<class 'numpy.complex128'>, on_missing='skip', combine_duplicates=True, drop_zero_atol=0.0, backend='scipy')[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalOperator])
dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
combine_duplicates (bool)
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

Any

qlinks.builders.sparse.is_hermitian_sparse(matrix, *, atol=1e-12, backend='auto')[source]#

Parameters:

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

Return type:

bool

Module contents#

class qlinks.builders.SparseBuildResult(matrix: 'Any', stats: 'SparseBuildStats')[source]#

Bases: object

Parameters:

matrix (Any)
stats (SparseBuildStats)

matrix: Any#

stats: SparseBuildStats#

__init__(matrix, stats)#

Parameters:

matrix (Any)
stats (SparseBuildStats)

Return type:

None

class qlinks.builders.SparseBuildStats(n_basis: 'int', n_terms: 'int', n_raw_actions: 'int', n_kept_actions: 'int', n_missing_actions: 'int', nnz: 'int')[source]#

Bases: object

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)

n_basis: int#

n_terms: int#

n_raw_actions: int#

n_kept_actions: int#

n_missing_actions: int#

nnz: int#

__init__(n_basis, n_terms, n_raw_actions, n_kept_actions, n_missing_actions, nnz)#

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)

Return type:

None

class qlinks.builders.SparseHamiltonianBuilder(dtype: 'npt.DTypeLike' = <class 'numpy.complex128'>, on_missing: 'MissingActionPolicy' = 'skip', combine_duplicates: 'bool' = True, drop_zero_atol: 'float' = 0.0, backend: 'SparseBackendName | SparseBackend' = 'scipy')[source]#

Bases: object

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
combine_duplicates (bool)
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

dtype: DTypeLike#

on_missing: Literal['skip', 'raise']#

combine_duplicates: bool#

drop_zero_atol: float#

backend: Literal['scipy', 'cupy', 'auto'] | SparseBackend#

build(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalOperator])

Return type:

Any

build_with_stats(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalOperator])

Return type:

SparseBuildResult

__init__(dtype=<class 'numpy.complex128'>, on_missing='skip', combine_duplicates=True, drop_zero_atol=0.0, backend='scipy')#

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
combine_duplicates (bool)
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

None

qlinks.builders.build_sparse_hamiltonian(basis, operators, *, dtype=<class 'numpy.complex128'>, on_missing='skip', combine_duplicates=True, drop_zero_atol=0.0, backend='scipy')[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalOperator])
dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
combine_duplicates (bool)
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

Any

qlinks.builders.is_hermitian_sparse(matrix, *, atol=1e-12, backend='auto')[source]#

Parameters:

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

Return type:

bool

class qlinks.builders.OptimizedSparseBuildResult(matrix: 'Any', stats: 'OptimizedSparseBuildStats')[source]#

Bases: object

Parameters:

matrix (Any)
stats (OptimizedSparseBuildStats)

matrix: Any#

stats: OptimizedSparseBuildStats#

__init__(matrix, stats)#

Parameters:

matrix (Any)
stats (OptimizedSparseBuildStats)

Return type:

None

class qlinks.builders.OptimizedSparseBuildStats(n_basis: 'int', n_terms: 'int', n_raw_actions: 'int', n_kept_actions: 'int', n_missing_actions: 'int', nnz: 'int', n_scratch_arrays: 'int')[source]#

Bases: object

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)
n_scratch_arrays (int)

n_basis: int#

n_terms: int#

n_raw_actions: int#

n_kept_actions: int#

n_missing_actions: int#

nnz: int#

n_scratch_arrays: int#

__init__(n_basis, n_terms, n_raw_actions, n_kept_actions, n_missing_actions, nnz, n_scratch_arrays)#

Parameters:

n_basis (int)
n_terms (int)
n_raw_actions (int)
n_kept_actions (int)
n_missing_actions (int)
nnz (int)
n_scratch_arrays (int)

Return type:

None

class qlinks.builders.OptimizedSparseHamiltonianBuilder(dtype=<class 'numpy.complex128'>, on_missing='skip', drop_zero_atol=0.0, backend='scipy')[source]#

Bases: object

Sparse Hamiltonian builder using LocalUpdateAction.

Main optimization#

Operators do not allocate full output configurations. They only return local updates. The builder owns one scratch array and reuses it for every action.

Matrix convention#

For each column basis state |config_col>, and action

coefficient, variable_indices, new_values

we construct

config_row = config_col with local updates applied

and insert

H[row, col] += coefficient

dtype: DTypeLike#

on_missing: Literal['skip', 'raise']#

drop_zero_atol: float#

backend: Literal['scipy', 'cupy', 'auto'] | SparseBackend#

build(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalUpdateOperator | DiagonalLocalOperator])

Return type:

Any

build_with_stats(basis, operators)[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalUpdateOperator | DiagonalLocalOperator])

Return type:

OptimizedSparseBuildResult

__init__(dtype=<class 'numpy.complex128'>, on_missing='skip', drop_zero_atol=0.0, backend='scipy')#

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

None

Parameters:

dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

qlinks.builders.build_optimized_sparse_hamiltonian(basis, operators, *, dtype=<class 'numpy.complex128'>, on_missing='skip', drop_zero_atol=0.0, backend='scipy')[source]#

Parameters:

basis (Basis)
operators (Sequence[LocalUpdateOperator | DiagonalLocalOperator])
dtype (DTypeLike)
on_missing (Literal['skip', 'raise'])
drop_zero_atol (float)
backend (Literal['scipy', 'cupy', 'auto'] | ~qlinks.backends.sparse.SparseBackend)

Return type:

Any

qlinks.builders package

Contents

qlinks.builders package#

Submodules#

qlinks.builders.optimized_sparse module#

Main optimization#

Matrix convention#

qlinks.builders.sparse module#

Module contents#

Main optimization#

Matrix convention#