def _steadystate_svd_dense(L, ss_args):
"""
Find the steady state(s) of an open quantum system by solving for the
nullspace of the Liouvillian.
"""
ss_args['info'].pop('weight', None)
atol = 1e-12
rtol = 1e-12
if settings.debug:
logger.debug('Starting SVD solver.')
_svd_start = time.time()
u, s, vh = svd(L.full(), full_matrices=False)
tol = max(atol, rtol * s[0])
nnz = (s >= tol).sum()
ns = vh[nnz:].conj().T
_svd_end = time.time()
ss_args['info']['solution_time'] = _svd_end - _svd_start
if ss_args['all_states']:
rhoss_list = []
for n in range(ns.shape[1]):
rhoss = Qobj(vec2mat(ns[:, n]), dims=L.dims[0])
rhoss_list.append(rhoss / rhoss.tr())
if ss_args['return_info']:
return rhoss_list, ss_args['info']
else:
if ss_args['return_info']:
return rhoss_list, ss_args['info']
else:
return rhoss_list
else:
rhoss = Qobj(vec2mat(ns[:, 0]), dims=L.dims[0])
return rhoss / rhoss.tr()
def _steadystate_direct_dense(L, ss_args):
"""
Direct solver that use numpy dense matrices. Suitable for
small system, with a few states.
"""
if settings.debug:
logger.debug('Starting direct dense solver.')
dims = L.dims[0]
n = int(np.sqrt(L.shape[0]))
b = np.zeros(n ** 2)
b[0] = ss_args['weight']
L = L.data.todense()
L[0, :] = np.diag(ss_args['weight'] * np.ones(n)).reshape((1, n ** 2))
_dense_start = time.time()
v = np.linalg.solve(L, b)
_dense_end = time.time()
ss_args['info']['solution_time'] = _dense_end - _dense_start
if ss_args['return_info']:
ss_args['info']['residual_norm'] = la.norm(b - L * v, np.inf)
data = vec2mat(v)
data = 0.5 * (data + data.conj().T)
return Qobj(data, dims=dims, isherm=True)
def _steadystate_eigen(L, ss_args):
"""
Internal function for solving the steady state problem by
finding the eigenvector corresponding to the zero eigenvalue
of the Liouvillian using ARPACK.
"""
ss_args['info'].pop('weight', None)
if settings.debug:
logger.debug('Starting Eigen solver.')
dims = L.dims[0]
L = L.data.tocsc()
if ss_args['use_rcm']:
ss_args['info']['perm'].append('rcm')
if settings.debug:
old_band = sp_bandwidth(L)[0]
logger.debug('Original bandwidth: %i' % old_band)
perm = reverse_cuthill_mckee(L)
rev_perm = np.argsort(perm)
L = sp_permute(L, perm, perm, 'csc')
if settings.debug:
rcm_band = sp_bandwidth(L)[0]
logger.debug('RCM bandwidth: %i' % rcm_band)
logger.debug('Bandwidth reduction factor: %f' %
(old_band / rcm_band))
_eigen_start = time.time()
eigval, eigvec = eigs(L, k=1, sigma=1e-15, tol=ss_args['tol'],
which='LM', maxiter=ss_args['maxiter'])
_eigen_end = time.time()
ss_args['info']['solution_time'] = _eigen_end - _eigen_start
if ss_args['return_info']:
ss_args['info']['residual_norm'] = la.norm(L * eigvec, np.inf)
if ss_args['use_rcm']:
eigvec = eigvec[np.ix_(rev_perm,)]
_temp = vec2mat(eigvec)
data = dense2D_to_fastcsr_fmode(_temp, _temp.shape[0], _temp.shape[1])
data = 0.5 * (data + data.H)
out = Qobj(data, dims=dims, isherm=True)
if ss_args['return_info']:
return out / out.tr(), ss_args['info']
else:
return out / out.tr()
def _steadystate_power(L, ss_args):
"""
Inverse power method for steady state solving.
"""
ss_args['info'].pop('weight', None)
if settings.debug:
logger.debug('Starting iterative inverse-power method solver.')
tol = ss_args['tol']
maxiter = ss_args['maxiter']
use_solver(assumeSortedIndices=True)
rhoss = Qobj()
sflag = issuper(L)
if sflag:
rhoss.dims = L.dims[0]
else:
rhoss.dims = [L.dims[0], 1]
n = L.shape[0]
# Build Liouvillian
if settings.has_mkl and ss_args['method'] == 'power':
has_mkl = 1
else:
has_mkl = 0
L, perm, perm2, rev_perm, ss_args = _steadystate_power_liouvillian(L,
ss_args, has_mkl)
orig_nnz = L.nnz
# start with all ones as RHS
v = np.ones(n, dtype=complex)
if ss_args['use_rcm']:
v = v[np.ix_(perm2,)]
# Do preconditioning
if ss_args['M'] is None and ss_args['use_precond'] and \
ss_args['method'] in ['power-gmres',
'power-lgmres', 'power-bicgstab']:
ss_args['M'], ss_args = _iterative_precondition(
L, int(np.sqrt(n)), ss_args)
if ss_args['M'] is None:
warnings.warn("Preconditioning failed. Continuing without.",
UserWarning)
ss_iters = {'iter': 0}
def _iter_count(r):
ss_iters['iter'] += 1
return
_power_start = time.time()
# Get LU factors
if ss_args['method'] == 'power':
if settings.has_mkl:
lu = mkl_splu(L)
else:
lu = splu(L, permc_spec=ss_args['permc_spec'],
diag_pivot_thresh=ss_args['diag_pivot_thresh'],
options=dict(ILU_MILU=ss_args['ILU_MILU']))
if settings.debug and _scipy_check:
L_nnz = lu.L.nnz
U_nnz = lu.U.nnz
logger.debug('L NNZ: %i ; U NNZ: %i' % (L_nnz, U_nnz))
logger.debug('Fill factor: %f' % ((L_nnz + U_nnz) / orig_nnz))
it = 0
_tol = max(ss_args['tol'] / 10, 1e-15) # Should make this user accessible
while (la.norm(L * v, np.inf) > tol) and (it < maxiter):
if ss_args['method'] == 'power':
v = lu.solve(v)
elif ss_args['method'] == 'power-gmres':
v, check = gmres(L, v, tol=_tol, M=ss_args['M'],
x0=ss_args['x0'], restart=ss_args['restart'],
maxiter=ss_args['maxiter'], callback=_iter_count)
elif ss_args['method'] == 'power-lgmres':
v, check = lgmres(L, v, tol=_tol, M=ss_args['M'],
x0=ss_args['x0'], maxiter=ss_args['maxiter'],
callback=_iter_count)
elif ss_args['method'] == 'power-bicgstab':
v, check = bicgstab(L, v, tol=_tol, M=ss_args['M'],
x0=ss_args['x0'],
maxiter=ss_args['maxiter'], callback=_iter_count)
else:
raise Exception("Invalid iterative solver method.")
v = v / la.norm(v, np.inf)
it += 1
if ss_args['method'] == 'power' and settings.has_mkl:
lu.delete()
if it >= maxiter:
raise Exception('Failed to find steady state after ' +
str(maxiter) + ' iterations')
_power_end = time.time()
ss_args['info']['solution_time'] = _power_end - _power_start
ss_args['info']['iterations'] = it
if ss_args['return_info']:
ss_args['info']['residual_norm'] = la.norm(L * v, np.inf)
if settings.debug:
logger.debug('Number of iterations: %i' % it)
if ss_args['use_rcm']:
v = v[np.ix_(rev_perm,)]
# normalise according to type of problem
if sflag:
trow = v[::rhoss.shape[0] + 1]
data = v / np.sum(trow)
else:
data = data / la.norm(v)
data = dense2D_to_fastcsr_fmode(
vec2mat(data), rhoss.shape[0], rhoss.shape[0])
rhoss.data = 0.5 * (data + data.H)
rhoss.isherm = True
if ss_args['return_info']:
return rhoss, ss_args['info']
else:
return rhoss
def _steadystate_iterative(L, ss_args):
"""
Iterative steady state solver using the GMRES, LGMRES, or BICGSTAB
algorithm and a sparse incomplete LU preconditioner.
"""
ss_iters = {'iter': 0}
def _iter_count(r):
ss_iters['iter'] += 1
return
if settings.debug:
logger.debug('Starting %s solver.' % ss_args['method'])
dims = L.dims[0]
n = int(np.sqrt(L.shape[0]))
b = np.zeros(n ** 2)
b[0] = ss_args['weight']
L, perm, perm2, rev_perm, ss_args = _steadystate_LU_liouvillian(L, ss_args)
if np.any(perm):
b = b[np.ix_(perm,)]
if np.any(perm2):
b = b[np.ix_(perm2,)]
use_solver(assumeSortedIndices=True)
if ss_args['M'] is None and ss_args['use_precond']:
ss_args['M'], ss_args = _iterative_precondition(L, n, ss_args)
if ss_args['M'] is None:
warnings.warn("Preconditioning failed. Continuing without.",
UserWarning)
# Select iterative solver type
_iter_start = time.time()
if ss_args['method'] == 'iterative-gmres':
v, check = gmres(L, b, tol=ss_args['tol'], M=ss_args['M'],
x0=ss_args['x0'], restart=ss_args['restart'],
maxiter=ss_args['maxiter'], callback=_iter_count)
elif ss_args['method'] == 'iterative-lgmres':
v, check = lgmres(L, b, tol=ss_args['tol'], M=ss_args['M'],
x0=ss_args['x0'], maxiter=ss_args['maxiter'],
callback=_iter_count)
elif ss_args['method'] == 'iterative-bicgstab':
v, check = bicgstab(L, b, tol=ss_args['tol'], M=ss_args['M'],
x0=ss_args['x0'],
maxiter=ss_args['maxiter'], callback=_iter_count)
else:
raise Exception("Invalid iterative solver method.")
_iter_end = time.time()
ss_args['info']['iter_time'] = _iter_end - _iter_start
if ss_args['info']['precond_time'] is not None:
ss_args['info']['solution_time'] = (ss_args['info']['iter_time'] +
ss_args['info']['precond_time'])
else:
ss_args['info']['solution_time'] = ss_args['info']['iter_time']
ss_args['info']['iterations'] = ss_iters['iter']
if ss_args['return_info']:
ss_args['info']['residual_norm'] = la.norm(b - L * v, np.inf)
if settings.debug:
logger.debug('Number of Iterations: %i' % ss_iters['iter'])
logger.debug('Iteration. time: %f' % (_iter_end - _iter_start))
if check > 0:
raise Exception("Steadystate error: Did not reach tolerance after " +
str(ss_args['maxiter']) + " steps." +
"\nResidual norm: " +
str(ss_args['info']['residual_norm']))
elif check < 0:
raise Exception(
"Steadystate error: Failed with fatal error: " + str(check) + ".")
if ss_args['use_rcm']:
v = v[np.ix_(rev_perm,)]
data = vec2mat(v)
data = 0.5 * (data + data.conj().T)
if ss_args['return_info']:
return Qobj(data, dims=dims, isherm=True), ss_args['info']
else:
return Qobj(data, dims=dims, isherm=True)
def _steadystate_direct_sparse(L, ss_args):
"""
Direct solver that uses scipy sparse matrices
"""
if settings.debug:
logger.debug('Starting direct LU solver.')
dims = L.dims[0]
n = int(np.sqrt(L.shape[0]))
b = np.zeros(n ** 2, dtype=complex)
b[0] = ss_args['weight']
if settings.has_mkl:
has_mkl = 1
else:
has_mkl = 0
L, perm, perm2, rev_perm, ss_args = _steadystate_LU_liouvillian(
L, ss_args, has_mkl)
if np.any(perm):
b = b[np.ix_(perm,)]
if np.any(perm2):
b = b[np.ix_(perm2,)]
ss_args['info']['permc_spec'] = ss_args['permc_spec']
ss_args['info']['drop_tol'] = ss_args['drop_tol']
ss_args['info']['diag_pivot_thresh'] = ss_args['diag_pivot_thresh']
ss_args['info']['fill_factor'] = ss_args['fill_factor']
ss_args['info']['ILU_MILU'] = ss_args['ILU_MILU']
if not has_mkl:
# Use superLU solver
orig_nnz = L.nnz
_direct_start = time.time()
lu = splu(L, permc_spec=ss_args['permc_spec'],
diag_pivot_thresh=ss_args['diag_pivot_thresh'],
options=dict(ILU_MILU=ss_args['ILU_MILU']))
v = lu.solve(b)
_direct_end = time.time()
ss_args['info']['solution_time'] = _direct_end - _direct_start
if (settings.debug or ss_args['return_info']) and _scipy_check:
L_nnz = lu.L.nnz
U_nnz = lu.U.nnz
ss_args['info']['l_nnz'] = L_nnz
ss_args['info']['u_nnz'] = U_nnz
ss_args['info']['lu_fill_factor'] = (L_nnz + U_nnz) / L.nnz
if settings.debug:
logger.debug('L NNZ: %i ; U NNZ: %i' % (L_nnz, U_nnz))
logger.debug('Fill factor: %f' % ((L_nnz + U_nnz) / orig_nnz))
else: # Use MKL solver
if len(ss_args['info']['perm']) != 0:
in_perm = np.arange(n**2, dtype=np.int32)
else:
in_perm = None
_direct_start = time.time()
v = mkl_spsolve(L, b, perm=in_perm, verbose=ss_args['verbose'])
_direct_end = time.time()
ss_args['info']['solution_time'] = _direct_end - _direct_start
if ss_args['return_info']:
ss_args['info']['residual_norm'] = la.norm(b - L * v, np.inf)
if ss_args['use_rcm']:
v = v[np.ix_(rev_perm,)]
data = dense2D_to_fastcsr_fmode(vec2mat(v), n, n)
data = 0.5 * (data + data.H)
if ss_args['return_info']:
return Qobj(data, dims=dims, isherm=True), ss_args['info']
else:
return Qobj(data, dims=dims, isherm=True)
def _generic_ode_solve(r, rho0, tlist, e_ops, opt, progress_bar):
"""
Internal function for solving ME. Solve an ODE which solver parameters
already setup (r). Calculate the required expectation values or invoke
callback function at each time step.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
e_sops_data = []
output = Result()
output.solver = "mesolve"
output.times = tlist
if opt.store_states:
output.states = []
if isinstance(e_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
if n_expt_op == 0:
# fall back on storing states
output.states = []
opt.store_states = True
else:
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
e_sops_data.append(spre(op).data)
if op.isherm and rho0.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# start evolution
#
progress_bar.start(n_tsteps)
rho = Qobj(rho0)
dt = np.diff(tlist)
for t_idx, t in enumerate(tlist):
progress_bar.update(t_idx)
if not r.successful():
raise Exception("ODE integration error: Try to increase "
"the allowed number of substeps by increasing "
"the nsteps parameter in the Options class.")
if opt.store_states or expt_callback:
rho.data = dense2D_to_fastcsr_fmode(vec2mat(r.y), rho.shape[0], rho.shape[1])
if opt.store_states:
output.states.append(Qobj(rho, isherm=True))
if expt_callback:
# use callback method
e_ops(t, rho)
for m in range(n_expt_op):
if output.expect[m].dtype == complex:
output.expect[m][t_idx] = expect_rho_vec(e_sops_data[m],
r.y, 0)
else:
output.expect[m][t_idx] = expect_rho_vec(e_sops_data[m],
r.y, 1)
if t_idx < n_tsteps - 1:
r.integrate(r.t + dt[t_idx])
progress_bar.finished()
if (not opt.rhs_reuse) and (config.tdname is not None):
_cython_build_cleanup(config.tdname)
if opt.store_final_state:
rho.data = dense2D_to_fastcsr_fmode(vec2mat(r.y), rho.shape[0], rho.shape[1])
output.final_state = Qobj(rho, dims=rho0.dims, isherm=True)
return output
def _ode_super_func(t, y, data):
ym = vec2mat(y)
return (data*ym).ravel('F')