def __init__(self, factors):
super(TensorProductDistribution, self).__init__(
basis=tensor_product_basis(
factor.basis for factor in factors
)
)
self._factors = tuple(factors)
def __init__(self, factors):
super(TensorProductDistribution, self).__init__(
basis=tensor_product_basis(
factor.basis for factor in factors
)
)
self._factors = tuple(factors)
def __init__(self, basis, rank=None, enforce_tp=True):
if isinstance(basis, int):
basis = gell_mann_basis(basis)
self._hdim = basis.dim
# TODO: take basis on underlying space, tensor up?
channel_basis = tensor_product_basis(basis, basis)
# FIXME: this is a hack to get another level of nesting.
channel_basis.dims = [basis.dims, basis.dims]
channel_basis.superrep = 'choi'
super(BCSZChoiDistribution, self).__init__(channel_basis)
self._rank = rank
self._enforce_tp = enforce_tp
def __init__(self, basis, rank=None, enforce_tp=True):
if isinstance(basis, int):
basis = gell_mann_basis(basis)
self._hdim = basis.dim
# TODO: take basis on underlying space, tensor up?
channel_basis = tensor_product_basis(basis, basis)
# FIXME: this is a hack to get another level of nesting.
channel_basis.dims = [basis.dims, basis.dims]
channel_basis.superrep = 'choi'
super(BCSZChoiDistribution, self).__init__(channel_basis)
self._rank = rank
self._enforce_tp = enforce_tp
