def __init__(self, dim, basis=None):
if basis is None:
self.dim = dim
self.basis = sparse.stack(gm.get_basis(dim, sparse=True))
else:
self.dim = basis[0].shape[0]
self.basis = COO.from_numpy(np.array(basis))
# Diagonal metric (since we're working with what are assumed to be
# orthogonal but not necessarily normalized basis vectors)
self.sq_norms = COO.from_numpy(
sparse.tensordot(
self.basis, self.basis,
([1, 2], [2, 1])).to_scipy_sparse().diagonal())
# Diagonal inverse metric
sq_norms_inv = COO.from_numpy(1 / self.sq_norms.todense())
# Dual basis obtained from the original basis by the inverse metric
self.dual = self.basis * sq_norms_inv[:,None,None]
# Structure coefficients for the Lie algebra showing how to represent a
# product of two basis elements as a complex-linear combination of basis
# elements
self.struct = sparse.tensordot(sparse.tensordot(self.basis, self.basis,
([2], [1])),
self.dual, ([1, 3], [2, 1]))
if isinstance(self.struct, np.ndarray):
# Sometimes sparse.tensordot returns numpy arrays. We want to force
# it to be sparse, since sparse.tensordot fails when passed two
# numpy arrays.
self.struct = COO.from_numpy(self.struct)
def __init__(self, d_sys, n_max):
self.d_sys = d_sys
self.n_max = n_max
self.d_total = (self.n_max + 1) * self.d_sys
self.basis = gm.get_basis(self.d_total)
# Only want the parts of common_dict pertaining to the basis setup
# (which is time-consuming). Suggests I should split the basis part out
# into a separate function (perhaps a basis object).
zero_op = np.zeros((self.d_total, self.d_total), dtype=np.complex)
self.basis_common_dict = sb.op_calc_setup(zero_op, 0, 0, zero_op,
self.basis[:-1])
def __init__(self, d_sys, n_max):
self.d_sys = d_sys
self.n_max = n_max
self.d_total = (self.n_max + 1) * self.d_sys
self.basis = gm.get_basis(self.d_total)
# Only want the parts of common_dict pertaining to the basis setup
# (which is time-consuming). Suggests I should split the basis part out
# into a separate function (perhaps a basis object).
zero_op = np.zeros((self.d_total, self.d_total), dtype=np.complex)
self.basis_common_dict = sb.op_calc_setup(zero_op, 0, 0, zero_op,
self.basis[:-1])
def __init__(self, c_op, M_sq, N, H, basis=None, drift_rep=None, **kwargs):
if basis is None:
d = c_op.shape[0]
self.basis = gm.get_basis(d)
else:
self.basis = basis
if drift_rep is None:
self.Q = sb.construct_Q(c_op, M_sq, N, H, self.basis[:-1])
else:
self.Q = drift_rep
def __init__(self, c_op, M_sq, N, H, basis=None, drift_rep=None, **kwargs):
if basis is None:
d = c_op.shape[0]
self.basis = gm.get_basis(d)
else:
self.basis = basis
if drift_rep is None:
self.Q = sb.construct_Q(c_op, M_sq, N, H, self.basis[:-1])
else:
self.Q = drift_rep
def __init__(self, dim):
self.dim = dim
self.basis = COO.from_numpy(np.array(gm.get_basis(dim)))
self.sq_norms = COO.from_numpy(np.einsum('jmn,jnm->j',
self.basis.todense(),
self.basis.todense()))
sq_norms_inv = COO.from_numpy(1 / self.sq_norms.todense())
self.dual = self.basis * sq_norms_inv[:,None,None]
self.struct = sparse.tensordot(sparse.tensordot(self.basis, self.basis,
([2], [1])),
self.dual, ([1, 3], [2, 1]))
if type(self.struct) == np.ndarray:
# Sometimes sparse.tensordot returns numpy arrays. We want to force
# it to be sparse, since sparse.tensordot fails when passed two
# numpy arrays.
self.struct = COO.from_numpy(self.struct)
def __init__(self, L, H0):
# The `IntegratorFactory` returns an integrator appropriate for the
# given modelparams.
super(HomodyneQubitPrecessionModel, self).__init__()
basis = gm.get_basis(2)
precomp_args = {'coupling_op': L,
'M_sq': 0,
'N': 0,
'H0': H0,
'partial_basis': basis[:-1]}
precomp_data = precomp_fn(**precomp_args)
self.integrator_factory = \
smeint.IntegratorFactory(smeint.TrDecMilsteinHomodyneIntegrator,
precomp_data, parameter_fn)
self.traceless_basis = \
self.integrator_factory.precomp_data['partial_basis']
self.drifted_particles = {}
def __init__(self, L, H0):
# The `IntegratorFactory` returns an integrator appropriate for the
# given modelparams.
super(HomodyneQubitPrecessionModel, self).__init__()
basis = gm.get_basis(2)
precomp_args = {
'coupling_op': L,
'M_sq': 0,
'N': 0,
'H0': H0,
'partial_basis': basis[:-1]
}
precomp_data = precomp_fn(**precomp_args)
self.integrator_factory = \
smeint.IntegratorFactory(smeint.TrDecMilsteinHomodyneIntegrator,
precomp_data, parameter_fn)
self.traceless_basis = \
self.integrator_factory.precomp_data['partial_basis']
self.drifted_particles = {}