def _partial_transpose_sparse(rho, mask):
"""
Implement the partial transpose using the CSR sparse matrix.
"""
data = sp.lil_matrix((rho.shape[0], rho.shape[1]), dtype=complex)
for m in range(len(rho.data.indptr) - 1):
n1 = rho.data.indptr[m]
n2 = rho.data.indptr[m + 1]
psi_A = state_index_number(rho.dims[0], m)
for idx, n in enumerate(rho.data.indices[n1:n2]):
psi_B = state_index_number(rho.dims[1], n)
m_pt = state_number_index(
rho.dims[1], np.choose(mask, [psi_A, psi_B]))
n_pt = state_number_index(
rho.dims[0], np.choose(mask, [psi_B, psi_A]))
data[m_pt, n_pt] = rho.data.data[n1 + idx]
return Qobj(data.tocsr(), dims=rho.dims)
def _partial_transpose_sparse(rho, mask):
"""
Implement the partial transpose using the CSR sparse matrix.
"""
data = sp.lil_matrix((rho.shape[0], rho.shape[1]), dtype=complex)
for m in range(len(rho.data.indptr) - 1):
n1 = rho.data.indptr[m]
n2 = rho.data.indptr[m + 1]
psi_A = state_index_number(rho.dims[0], m)
for idx, n in enumerate(rho.data.indices[n1:n2]):
psi_B = state_index_number(rho.dims[1], n)
m_pt = state_number_index(
rho.dims[1], np.choose(mask, [psi_A, psi_B]))
n_pt = state_number_index(
rho.dims[0], np.choose(mask, [psi_B, psi_A]))
data[m_pt, n_pt] = rho.data.data[n1 + idx]
return Qobj(data.tocsr(), dims=rho.dims)