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# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names
# of its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
###############################################################################
__all__ = ['propagator', 'propagator_steadystate']
import types
import numpy as np
import scipy.linalg as la
import functools
import scipy.sparse as sp
from qutip.qobj import Qobj
from qutip.tensor import tensor
from qutip.operators import qeye
from qutip.rhs_generate import (rhs_generate, rhs_clear, _td_format_check)
from qutip.superoperator import (vec2mat, mat2vec,
vector_to_operator, operator_to_vector)
from qutip.sparse import sp_reshape
from qutip.cy.sparse_utils import unit_row_norm
from qutip.mesolve import mesolve
from qutip.sesolve import sesolve
from qutip.states import basis
from qutip.solver import Options, _solver_safety_check, config
from qutip.parallel import parallel_map, _default_kwargs
from qutip.ui.progressbar import BaseProgressBar, TextProgressBar
def propagator(H, t, c_op_list=[], args={}, options=None,
unitary_mode='batch', parallel=False,
progress_bar=None, _safe_mode=True,
**kwargs):
"""
Calculate the propagator U(t) for the density matrix or wave function such
that :math:`\psi(t) = U(t)\psi(0)` or
:math:`\\rho_{\mathrm vec}(t) = U(t) \\rho_{\mathrm vec}(0)`
where :math:`\\rho_{\mathrm vec}` is the vector representation of the
density matrix.
Parameters
----------
H : qobj or list
Hamiltonian as a Qobj instance of a nested list of Qobjs and
coefficients in the list-string or list-function format for
time-dependent Hamiltonians (see description in :func:`qutip.mesolve`).
t : float or array-like
Time or list of times for which to evaluate the propagator.
c_op_list : list
List of qobj collapse operators.
args : list/array/dictionary
Parameters to callback functions for time-dependent Hamiltonians and
collapse operators.
options : :class:`qutip.Options`
with options for the ODE solver.
unitary_mode = str ('batch', 'single')
Solve all basis vectors simulaneously ('batch') or individually
('single').
parallel : bool {False, True}
Run the propagator in parallel mode. This will override the
unitary_mode settings if set to True.
progress_bar: BaseProgressBar
Optional instance of BaseProgressBar, or a subclass thereof, for
showing the progress of the simulation. By default no progress bar
is used, and if set to True a TextProgressBar will be used.
Returns
-------
a : qobj
Instance representing the propagator :math:`U(t)`.
"""
kw = _default_kwargs()
if 'num_cpus' in kwargs:
num_cpus = kwargs['num_cpus']
else:
num_cpus = kw['num_cpus']
if progress_bar is None:
progress_bar = BaseProgressBar()
elif progress_bar is True:
progress_bar = TextProgressBar()
if options is None:
options = Options()
options.rhs_reuse = True
rhs_clear()
if isinstance(t, (int, float, np.integer, np.floating)):
tlist = [0, t]
else:
tlist = t
if _safe_mode:
_solver_safety_check(H, None, c_ops=c_op_list, e_ops=[], args=args)
td_type = _td_format_check(H, c_op_list, solver='me')
if isinstance(H, (types.FunctionType, types.BuiltinFunctionType,
functools.partial)):
H0 = H(0.0, args)
if unitary_mode == 'batch':
# batch don't work with function Hamiltonian
unitary_mode = 'single'
elif isinstance(H, list):
H0 = H[0][0] if isinstance(H[0], list) else H[0]
else:
H0 = H
if len(c_op_list) == 0 and H0.isoper:
# calculate propagator for the wave function
N = H0.shape[0]
dims = H0.dims
if parallel:
unitary_mode = 'single'
u = np.zeros([N, N, len(tlist)], dtype=complex)
output = parallel_map(_parallel_sesolve, range(N),
task_args=(N, H, tlist, args, options),
progress_bar=progress_bar, num_cpus=num_cpus)
for n in range(N):
for k, t in enumerate(tlist):
u[:, n, k] = output[n].states[k].full().T
else:
if unitary_mode == 'single':
output = sesolve(H, qeye(dims[0]), tlist, [], args, options,
_safe_mode=False)
return output.states[-1] if len(tlist) == 2 else output.states
elif unitary_mode == 'batch':
u = np.zeros(len(tlist), dtype=object)
_rows = np.array([(N+1)*m for m in range(N)])
_cols = np.zeros_like(_rows)
_data = np.ones_like(_rows, dtype=complex)
psi0 = Qobj(sp.coo_matrix((_data, (_rows, _cols))).tocsr())
if td_type[1] > 0 or td_type[2] > 0:
H2 = []
for k in range(len(H)):
if isinstance(H[k], list):
H2.append([tensor(qeye(N), H[k][0]), H[k][1]])
else:
H2.append(tensor(qeye(N), H[k]))
else:
H2 = tensor(qeye(N), H)
options.normalize_output = False
output = sesolve(H2, psi0, tlist, [],
args=args, options=options,
_safe_mode=False)
for k, t in enumerate(tlist):
u[k] = sp_reshape(output.states[k].data, (N, N))
unit_row_norm(u[k].data, u[k].indptr, u[k].shape[0])
u[k] = u[k].T.tocsr()
else:
raise ValueError('Invalid unitary mode.')
elif len(c_op_list) == 0 and H0.issuper:
# calculate the propagator for the vector representation of the
# density matrix (a superoperator propagator)
unitary_mode = 'single'
N = H0.shape[0]
sqrt_N = int(np.sqrt(N))
dims = H0.dims
u = np.zeros([N, N, len(tlist)], dtype=complex)
if parallel:
output = parallel_map(_parallel_mesolve, range(N * N),
task_args=(
sqrt_N, H, tlist, c_op_list, args,
options),
progress_bar=progress_bar, num_cpus=num_cpus)
for n in range(N * N):
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output[n].states[k].full()).T
else:
rho0 = qeye(N, N)
rho0.dims = [[sqrt_N, sqrt_N], [sqrt_N, sqrt_N]]
output = mesolve(H, psi0, tlist, [], args, options,
_safe_mode=False)
return output.states[-1] if len(tlist) == 2 else output.states
else:
# calculate the propagator for the vector representation of the
# density matrix (a superoperator propagator)
unitary_mode = 'single'
N = H0.shape[0]
dims = [H0.dims, H0.dims]
u = np.zeros([N * N, N * N, len(tlist)], dtype=complex)
if parallel:
output = parallel_map(_parallel_mesolve, range(N * N),
task_args=(
N, H, tlist, c_op_list, args, options),
progress_bar=progress_bar, num_cpus=num_cpus)
for n in range(N * N):
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output[n].states[k].full()).T
else:
progress_bar.start(N * N)
for n in range(N * N):
progress_bar.update(n)
col_idx, row_idx = np.unravel_index(n, (N, N))
rho0 = Qobj(sp.csr_matrix(([1], ([row_idx], [col_idx])),
shape=(N, N), dtype=complex))
output = mesolve(H, rho0, tlist, c_op_list, [], args, options,
_safe_mode=False)
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output.states[k].full()).T
progress_bar.finished()
if len(tlist) == 2:
data = u[-1] if unitary_mode == 'batch' else u[:, :, 1]
return Qobj(data, dims=dims)
out = np.empty((len(tlist),), dtype=object)
if unitary_mode == 'batch':
out[:] = [Qobj(u[k], dims=dims) for k in range(len(tlist))]
else:
out[:] = [Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))]
return out
def _get_min_and_index(lst):
"""
Private function for obtaining min and max indicies.
"""
minval, minidx = lst[0], 0
for i, v in enumerate(lst[1:]):
if v < minval:
minval, minidx = v, i + 1
return minval, minidx
def propagator_steadystate(U):
"""Find the steady state for successive applications of the propagator
:math:`U`.
Parameters
----------
U : qobj
Operator representing the propagator.
Returns
-------
a : qobj
Instance representing the steady-state density matrix.
"""
evals, evecs = la.eig(U.full())
shifted_vals = np.abs(evals - 1.0)
ev_idx = np.argmin(shifted_vals)
ev_min = shifted_vals[ev_idx]
evecs = evecs.T
rho = Qobj(vec2mat(evecs[ev_idx]), dims=U.dims[0])
rho = rho * (1.0 / rho.tr())
rho = 0.5 * (rho + rho.dag()) # make sure rho is herm
rho.isherm = True
return rho
def _parallel_sesolve(n, N, H, tlist, args, options):
psi0 = basis(N, n)
output = sesolve(H, psi0, tlist, [], args, options, _safe_mode=False)
return output
def _parallel_mesolve(n, N, H, tlist, c_op_list, args, options):
col_idx, row_idx = np.unravel_index(n, (N, N))
rho0 = Qobj(sp.csr_matrix(([1], ([row_idx], [col_idx])),
shape=(N, N), dtype=complex))
output = mesolve(H, rho0, tlist, c_op_list, [], args, options,
_safe_mode=False)
return output
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