def floquet_modes_table(f_modes_0, f_energies, tlist, H, T, args=None):
"""
Pre-calculate the Floquet modes for a range of times spanning the floquet
period. Can later be used as a table to look up the floquet modes for
any time.
Parameters
----------
f_modes_0 : list of :class:`qutip.qobj` (kets)
Floquet modes at :math:`t`
f_energies : list
Floquet energies.
tlist : array
The list of times at which to evaluate the floquet modes.
H : :class:`qutip.qobj`
system Hamiltonian, time-dependent with period `T`
T : float
The period of the time-dependence of the hamiltonian.
args : dictionary
dictionary with variables required to evaluate H
Returns
-------
output : nested list
A nested list of Floquet modes as kets for each time in `tlist`
"""
# truncate tlist to the driving period
tlist_period = tlist[np.where(tlist <= T)]
f_modes_table_t = [[] for t in tlist_period]
opt = Options()
opt.rhs_reuse = True
for n, f_mode in enumerate(f_modes_0):
output = mesolve(H, f_mode, tlist_period, [], [], args, opt)
for t_idx, f_state_t in enumerate(output.states):
f_modes_table_t[t_idx].append(
f_state_t * exp(1j * f_energies[n] * tlist_period[t_idx]))
return f_modes_table_t
def propagator(H, t, c_op_list=[], args={}, options=None,
parallel=False, progress_bar=None, **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.
parallel : bool {False, True}
Run the propagator in parallel mode.
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
td_type = _td_format_check(H, c_op_list, solver='me')[2]
if td_type > 0:
rhs_generate(H, c_op_list, args=args, options=options)
if isinstance(H, (types.FunctionType, types.BuiltinFunctionType,
functools.partial)):
H0 = H(0.0, args)
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
u = np.zeros([N, N, len(tlist)], dtype=complex)
if parallel:
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:
progress_bar.start(N)
for n in range(0, N):
progress_bar.update(n)
psi0 = basis(N, n)
output = sesolve(H, psi0, tlist, [], args, options)
for k, t in enumerate(tlist):
u[:, n, k] = output.states[k].full().T
progress_bar.finished()
# todo: evolving a batch of wave functions:
# psi_0_list = [basis(N, n) for n in range(N)]
# psi_t_list = mesolve(H, psi_0_list, [0, t], [], [], args, options)
# for n in range(0, N):
# u[:,n] = psi_t_list[n][1].full().T
elif len(c_op_list) == 0 and H0.issuper:
# calculate the propagator for the vector representation of the
# density matrix (a superoperator propagator)
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:
progress_bar.start(N)
for n in range(0, N):
progress_bar.update(n)
col_idx, row_idx = np.unravel_index(n,(sqrt_N,sqrt_N))
rho0 = Qobj(sp.csr_matrix(([1],([row_idx],[col_idx])), shape=(sqrt_N,sqrt_N), dtype=complex))
output = mesolve(H, rho0, tlist, [], [], args, options)
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output.states[k].full()).T
progress_bar.finished()
else:
# calculate the propagator for the vector representation of the
# density matrix (a superoperator propagator)
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)
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output.states[k].full()).T
progress_bar.finished()
if len(tlist) == 2:
return Qobj(u[:, :, 1], dims=dims)
else:
return np.array([Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))], dtype=object)
def mcsolve(H,
psi0,
tlist,
c_ops=[],
e_ops=[],
ntraj=0,
args={},
options=None,
progress_bar=True,
map_func=parallel_map,
map_kwargs={},
_safe_mode=True):
r"""Monte Carlo evolution of a state vector :math:`|\psi \rangle` for a
given Hamiltonian and sets of collapse operators, and possibly, operators
for calculating expectation values. Options for the underlying ODE solver
are given by the Options class.
mcsolve supports time-dependent Hamiltonians and collapse operators using
either Python functions of strings to represent time-dependent
coefficients. Note that, the system Hamiltonian MUST have at least one
constant term.
As an example of a time-dependent problem, consider a Hamiltonian with two
terms ``H0`` and ``H1``, where ``H1`` is time-dependent with coefficient
``sin(w*t)``, and collapse operators ``C0`` and ``C1``, where ``C1`` is
time-dependent with coeffcient ``exp(-a*t)``. Here, w and a are constant
arguments with values ``W`` and ``A``.
Using the Python function time-dependent format requires two Python
functions, one for each collapse coefficient. Therefore, this problem could
be expressed as::
def H1_coeff(t,args):
return sin(args['w']*t)
def C1_coeff(t,args):
return exp(-args['a']*t)
H = [H0, [H1, H1_coeff]]
c_ops = [C0, [C1, C1_coeff]]
args={'a': A, 'w': W}
or in String (Cython) format we could write::
H = [H0, [H1, 'sin(w*t)']]
c_ops = [C0, [C1, 'exp(-a*t)']]
args={'a': A, 'w': W}
Constant terms are preferably placed first in the Hamiltonian and collapse
operator lists.
Parameters
----------
H : :class:`qutip.Qobj`, ``list``
System Hamiltonian.
psi0 : :class:`qutip.Qobj`
Initial state vector
tlist : array_like
Times at which results are recorded.
ntraj : int
Number of trajectories to run.
c_ops : :class:`qutip.Qobj`, ``list``
single collapse operator or a ``list`` of collapse operators.
e_ops : :class:`qutip.Qobj`, ``list``
single operator as Qobj or ``list`` or equivalent of Qobj operators
for calculating expectation values.
args : dict
Arguments for time-dependent Hamiltonian and collapse operator terms.
options : Options
Instance of ODE solver options.
progress_bar: BaseProgressBar
Optional instance of BaseProgressBar, or a subclass thereof, for
showing the progress of the simulation. Set to None to disable the
progress bar.
map_func: function
A map function for managing the calls to the single-trajactory solver.
map_kwargs: dictionary
Optional keyword arguments to the map_func function.
Returns
-------
results : :class:`qutip.solver.Result`
Object storing all results from the simulation.
.. note::
It is possible to reuse the random number seeds from a previous run
of the mcsolver by passing the output Result object seeds via the
Options class, i.e. Options(seeds=prev_result.seeds).
"""
if isinstance(c_ops, (Qobj, QobjEvo)):
c_ops = [c_ops]
if options is None:
options = Options()
if options.rhs_reuse and not isinstance(H, SolverSystem):
# TODO: deprecate when going to class based solver.
if "mcsolve" in solver_safe:
# print(" ")
H = solver_safe["mcsolve"]
else:
pass
# raise Exception("Could not find the Hamiltonian to reuse.")
if not ntraj:
ntraj = options.ntraj
if len(c_ops) == 0 and not options.rhs_reuse:
warn("No c_ops, using sesolve")
return sesolve(H,
psi0,
tlist,
e_ops=e_ops,
args=args,
options=options,
progress_bar=progress_bar,
_safe_mode=_safe_mode)
try:
num_traj = int(ntraj)
except TypeError:
num_traj = max(ntraj)
# set the physics
if not psi0.isket:
raise Exception("Initial state must be a state vector.")
# load monte carlo class
mc = _MC(options)
if isinstance(H, SolverSystem):
mc.ss = H
else:
mc.make_system(H, c_ops, tlist, args, options)
mc.reset(tlist[0], psi0)
mc.set_e_ops(e_ops)
if options.seeds is not None:
mc.seed(num_traj, options.seeds)
if _safe_mode:
mc.run_test()
# Run the simulation
mc.run(num_traj=num_traj,
tlist=tlist,
progress_bar=progress_bar,
map_func=map_func,
map_kwargs=map_kwargs)
return mc.get_result(ntraj)
if k != j:
g = np.sqrt(-1j * g_eff(R, a, j, k))
L.append(upD(k, N, g) * downD(k, N, g))
scatterindex.append(j)
#print("L",len(L)-1,": Scattering at j=",j,"due to impurity site k=",k,"at distance=",a*np.abs(k-j),"with g_eff:","%6.5f" % g_eff(R,a,j,k))
return L
N = 3
a = 1
timesteps = 100
ntraj = 200
statelist = []
opts = Options(store_states=True, store_final_state=True, ntraj=200)
tracedEEav = np.zeros(timesteps)
tracedGGav = np.zeros(timesteps)
purityAav = np.zeros(timesteps)
purityBav = np.zeros(timesteps)
purityCav = np.zeros(timesteps)
concav = np.zeros(timesteps)
VNav = np.zeros(timesteps)
disavgs = 1
i = 1
for r in [4.0]:
for t in np.ones(disavgs):
print(i, "of", disavgs)
i = i + 1
def correlation(H,
state0,
tlist,
taulist,
c_ops,
a_op,
b_op,
solver="me",
reverse=False,
args=None,
options=Options(ntraj=[20, 100])):
"""
Calculate the two-operator two-time correlation function:
:math:`\left<A(t+\\tau)B(t)\\right>`
along two time axes using the quantum regression theorem and the
evolution solver indicated by the `solver` parameter.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
state0 : :class:`qutip.qobj.Qobj`
Initial state density matrix :math:`\\rho(t_0)` or state vector
:math:`\\psi(t_0)`. If 'state0' is 'None', then the steady state will
be used as the initial state. The 'steady-state' is only implemented
for the `me` and `es` solvers.
tlist : *list* / *array*
list of times for :math:`t`. tlist must be positive and contain the
element `0`. When taking steady-steady correlations only one tlist
value is necessary, i.e. :math:`t \rightarrow \infty`; here tlist is
automatically set, ignoring user input.
taulist : *list* / *array*
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
operator A.
b_op : :class:`qutip.qobj.Qobj`
operator B.
reverse : bool
If `True`, calculate :math:`\left<A(t)B(t+\\tau)\\right>` instead of
:math:`\left<A(t+\\tau)B(t)\\right>`.
solver : str
choice of solver (`me` for master-equation, `mc` for Monte Carlo, and
`es` for exponential series)
options : :class:`qutip.solver.Options`
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
corr_mat: *array*
An 2-dimensional *array* (matrix) of correlation values for the times
specified by `tlist` (first index) and `taulist` (second index). If
`tlist` is `None`, then a 1-dimensional *array* of correlation values
is returned instead.
References
----------
See, Gardiner, Quantum Noise, Section 5.2.
"""
warn("correlation() now legacy, please use correlation_2op_2t()",
FutureWarning)
if debug:
print(inspect.stack()[0][3])
return correlation_2op_2t(H,
state0,
tlist,
taulist,
c_ops,
a_op,
b_op,
solver=solver,
reverse=reverse,
args=args,
options=options)
def correlation_2op_1t(H,
state0,
taulist,
c_ops,
a_op,
b_op,
solver="me",
reverse=False,
args=None,
options=Options(ntraj=[20, 100])):
"""
Calculate the two-operator two-time correlation function:
:math: `\left<A(t+\\tau)B(t)\\right>`
along one time axis using the quantum regression theorem and the evolution
solver indicated by the `solver` parameter.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
state0 : :class:`qutip.qobj.Qobj`
Initial state density matrix :math:`\\rho(t_0)` or state vector
:math:`\\psi(t_0)`. If 'state0' is 'None', then the steady state will
be used as the initial state. The 'steady-state' is only implemented
for the `me` and `es` solvers.
taulist : *list* / *array*
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
operator A.
b_op : :class:`qutip.qobj.Qobj`
operator B.
reverse : bool
If `True`, calculate :math:`\left<A(t)B(t+\\tau)\\right>` instead of
:math:`\left<A(t+\\tau)B(t)\\right>`.
solver : str
choice of solver (`me` for master-equation, `mc` for Monte Carlo, and
`es` for exponential series)
options : :class:`qutip.solver.Options`
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
corr_vec: *array*
An *array* of correlation values for the times specified by `tlist`.
References
----------
See, Gardiner, Quantum Noise, Section 5.2.
"""
if debug:
print(inspect.stack()[0][3])
if reverse:
A_op = a_op
B_op = b_op
C_op = 1
else:
A_op = 1
B_op = a_op
C_op = b_op
return _correlation_2t(H,
state0, [0],
taulist,
c_ops,
A_op,
B_op,
C_op,
solver=solver,
args=args,
options=options)[0]
def _correlation_mc_2t(H,
state0,
tlist,
taulist,
c_ops,
a_op,
b_op,
c_op,
args=None,
options=Options()):
"""
Internal function for calculating the three-operator two-time
correlation function:
<A(t)B(t+tau)C(t)>
using a Monte Carlo solver.
"""
# the solvers only work for positive time differences and the correlators
# require positive tau
if state0 is None:
raise NotImplementedError("steady state not implemented for " +
"mc solver, please use `es` or `me`")
elif not isket(state0):
raise TypeError("state0 must be a state vector.")
psi0 = state0
if debug:
print(inspect.stack()[0][3])
psi_t_mat = mcsolve(H,
psi0,
tlist,
c_ops, [],
args=args,
ntraj=options.ntraj[0],
options=options,
progress_bar=None).states
corr_mat = np.zeros([np.size(tlist), np.size(taulist)], dtype=complex)
H_shifted, _args = _transform_H_t_shift(H, args)
# calculation of <A(t)B(t+tau)C(t)> from only knowledge of psi0 requires
# averaging over both t and tau
for t_idx in range(np.size(tlist)):
if not isinstance(H, Qobj):
_args["_t0"] = tlist[t_idx]
for trial_idx in range(options.ntraj[0]):
if isinstance(a_op, Qobj) and isinstance(c_op, Qobj):
if a_op.dag() == c_op:
# A shortcut here, requires only 1/4 the trials
chi_0 = (options.mc_corr_eps + c_op) * \
psi_t_mat[trial_idx, t_idx]
# evolve these states and calculate expectation value of B
c_tau = chi_0.norm()**2 * mcsolve(
H_shifted,
chi_0 / chi_0.norm(),
taulist,
c_ops, [b_op],
args=_args,
ntraj=options.ntraj[1],
options=options,
progress_bar=None).expect[0]
# final correlation vector computed by combining the
# averages
corr_mat[t_idx, :] += c_tau / options.ntraj[1]
else:
# otherwise, need four trial wavefunctions
# (Ad+C)*psi_t, (Ad+iC)*psi_t, (Ad-C)*psi_t, (Ad-iC)*psi_t
if isinstance(a_op, Qobj):
a_op_dag = a_op.dag()
else:
# assume this is a number, ex. i.e. a_op = 1
# if this is not correct, the over-loaded addition
# operation will raise errors
a_op_dag = a_op
chi_0 = [
(options.mc_corr_eps + a_op_dag +
exp(1j * x * pi / 2) * c_op) * psi_t_mat[trial_idx, t_idx]
for x in range(4)
]
# evolve these states and calculate expectation value of B
c_tau = [
chi.norm()**2 * mcsolve(H_shifted,
chi / chi.norm(),
taulist,
c_ops, [b_op],
args=_args,
ntraj=options.ntraj[1],
options=options,
progress_bar=None).expect[0]
for chi in chi_0
]
# final correlation vector computed by combining the averages
corr_mat[t_idx, :] += \
1/(4*options.ntraj[0]) * (c_tau[0] - c_tau[2] -
1j*c_tau[1] + 1j*c_tau[3])
if t_idx == 1:
options.rhs_reuse = True
return corr_mat
def correlation(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
solver="me",
reverse=False,
args=None,
options=Options()):
"""
Calculate a two-operator two-time correlation function on the form
:math:`\left<A(t+\\tau)B(t)\\right>` or
:math:`\left<A(t)B(t+\\tau)\\right>` (if `reverse=True`), using the
quantum regression theorem and the evolution solver indicated by the
*solver* parameter.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
rho0 : :class:`qutip.qobj.Qobj`
Initial state density matrix (or state vector). If 'rho0' is
'None', then the steady state will be used as initial state.
tlist : *list* / *array*
list of times for :math:`t`.
taulist : *list* / *array*
list of times for :math:`\\tau`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj`
operator A.
b_op : :class:`qutip.qobj`
operator B.
solver : str
choice of solver (`me` for master-equation,
`es` for exponential series and `mc` for Monte-carlo)
Returns
-------
corr_mat: *array*
An 2-dimensional *array* (matrix) of correlation values for the times
specified by `tlist` (first index) and `taulist` (second index). If
`tlist` is `None`, then a 1-dimensional *array* of correlation values
is returned instead.
"""
if debug:
print(inspect.stack()[0][3])
return correlation_2op_2t(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
solver=solver,
reverse=reverse,
args=args,
options=options)
def correlation_4op_2t(H, state0, tlist, taulist, c_ops,
a_op, b_op, c_op, d_op, solver="me", args={},
options=Options(ntraj=[20, 100])):
"""
Calculate the four-operator two-time correlation function:
:math:`\left<A(t)B(t+\\tau)C(t+\\tau)D(t)\\right>`
along two time axes using the quantum regression theorem and the
evolution solver indicated by the `solver` parameter.
Note: it is not possibly to calculate a physically meaningful correlation
of this form where :math:`\\tau<0`.
Parameters
----------
H : Qobj
system Hamiltonian, may be time-dependent for solver choice of `me` or
`mc`.
rho0 : Qobj
Initial state density matrix :math:`\\rho_0` or state vector
:math:`\\psi_0`. If 'state0' is 'None', then the steady state will
be used as the initial state. The 'steady-state' is only implemented
for the `me` and `es` solvers.
tlist : array_like
list of times for :math:`t`. tlist must be positive and contain the
element `0`. When taking steady-steady correlations only one tlist
value is necessary, i.e. when :math:`t \\rightarrow \\infty`; here
tlist is automatically set, ignoring user input.
taulist : array_like
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list
list of collapse operators, may be time-dependent for solver choice of
`me` or `mc`.
a_op : Qobj
operator A.
b_op : Qobj
operator B.
c_op : Qobj
operator C.
d_op : Qobj
operator D.
solver : str
choice of solver (`me` for master-equation, `mc` for Monte Carlo, and
`es` for exponential series).
options : Options
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
corr_mat : array
An 2-dimensional array (matrix) of correlation values for the times
specified by `tlist` (first index) and `taulist` (second index). If
`tlist` is `None`, then a 1-dimensional array of correlation values
is returned instead.
References
----------
See, Gardiner, Quantum Noise, Section 5.2.
"""
warn("correlation_4op_2t() now legacy, please use correlation_3op_2t()",
FutureWarning)
warn("the reverse argument has been removed as it did not contain any" +
"new physical information", DeprecationWarning)
if debug:
print(inspect.stack()[0][3])
return correlation_3op_2t(H, state0, tlist, taulist, c_ops,
a_op, b_op * c_op, d_op,
solver=solver, args=args, options=options)
def coherence_function_g2(H, state0, taulist, c_ops, a_op, solver="me", args={},
options=Options(ntraj=[20, 100])):
"""
Calculate the normalized second-order quantum coherence function:
.. math::
g^{(2)}(\\tau) =
\\frac{\\langle A^\\dagger(0)A^\\dagger(\\tau)A(\\tau)A(0)\\rangle}
{\\langle A^\\dagger(\\tau)A(\\tau)\\rangle
\\langle A^\\dagger(0)A(0)\\rangle}
using the quantum regression theorem and the evolution solver indicated by
the `solver` parameter.
Parameters
----------
H : Qobj
system Hamiltonian, may be time-dependent for solver choice of `me` or
`mc`.
state0 : Qobj
Initial state density matrix :math:`\\rho(t_0)` or state vector
:math:`\\psi(t_0)`. If 'state0' is 'None', then the steady state will
be used as the initial state. The 'steady-state' is only implemented
for the `me` and `es` solvers.
taulist : array_like
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list
list of collapse operators, may be time-dependent for solver choice of
`me` or `mc`.
a_op : Qobj
operator A.
solver : str
choice of solver (`me` for master-equation and
`es` for exponential series).
options : Options
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
g2, G2 : tuple
The normalized and unnormalized second-order coherence function.
"""
# first calculate the photon number
if state0 is None:
state0 = steadystate(H, c_ops)
n = np.array([expect(state0, a_op.dag() * a_op)])
else:
n = mesolve(H, state0, taulist, c_ops, [a_op.dag() * a_op]).expect[0]
# calculate the correlation function G2 and normalize with n to obtain g2
G2 = correlation_3op_1t(H, state0, taulist, c_ops,
a_op.dag(), a_op.dag()*a_op, a_op,
solver=solver, args=args, options=options)
g2 = G2 / (n[0] * n)
return g2, G2
def correlation_2op_2t(H, state0, tlist, taulist, c_ops, a_op, b_op,
solver="me", reverse=False, args={},
options=Options(ntraj=[20, 100])):
"""
Calculate the two-operator two-time correlation function:
:math:`\left<A(t+\\tau)B(t)\\right>`
along two time axes using the quantum regression theorem and the
evolution solver indicated by the `solver` parameter.
Parameters
----------
H : Qobj
system Hamiltonian, may be time-dependent for solver choice of `me` or
`mc`.
state0 : Qobj
Initial state density matrix :math:`\\rho_0` or state vector
:math:`\\psi_0`. If 'state0' is 'None', then the steady state will
be used as the initial state. The 'steady-state' is only implemented
for the `me` and `es` solvers.
tlist : array_like
list of times for :math:`t`. tlist must be positive and contain the
element `0`. When taking steady-steady correlations only one tlist
value is necessary, i.e. when :math:`t \\rightarrow \\infty`; here
tlist is automatically set, ignoring user input.
taulist : array_like
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list
list of collapse operators, may be time-dependent for solver choice of
`me` or `mc`.
a_op : Qobj
operator A.
b_op : Qobj
operator B.
reverse : bool {False, True}
If `True`, calculate :math:`\left<A(t)B(t+\\tau)\\right>` instead of
:math:`\left<A(t+\\tau)B(t)\\right>`.
solver : str
choice of solver (`me` for master-equation, `mc` for Monte Carlo, and
`es` for exponential series).
options : Options
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
corr_mat : ndarray
An 2-dimensional array (matrix) of correlation values for the times
specified by `tlist` (first index) and `taulist` (second index). If
`tlist` is `None`, then a 1-dimensional array of correlation values
is returned instead.
References
----------
See, Gardiner, Quantum Noise, Section 5.2.
"""
if debug:
print(inspect.stack()[0][3])
if tlist is None:
return correlation_2op_1t(H, state0, taulist, c_ops, a_op, b_op,
solver=solver, reverse=reverse, args=args,
options=options)
else:
if reverse:
A_op = a_op
B_op = b_op
C_op = 1
else:
A_op = 1
B_op = a_op
C_op = b_op
return _correlation_2t(H, state0, tlist, taulist,
c_ops, A_op, B_op, C_op,
solver=solver, args=args, options=options)
def sesolve(H, psi0, tlist, e_ops=None, args=None, options=None,
progress_bar=None, _safe_mode=True):
"""
Schrödinger equation evolution of a state vector or unitary matrix for a
given Hamiltonian.
Evolve the state vector (``psi0``) using a given Hamiltonian (``H``), by
integrating the set of ordinary differential equations that define the
system. Alternatively evolve a unitary matrix in solving the Schrodinger
operator equation.
The output is either the state vector or unitary matrix at arbitrary points
in time (``tlist``), or the expectation values of the supplied operators
(``e_ops``). If ``e_ops`` is a callback function, it is invoked for each
time in ``tlist`` with time and the state as arguments, and the function
does not use any return values. ``e_ops`` cannot be used in conjunction
with solving the Schrodinger operator equation
Parameters
----------
H : :class:`~Qobj`, :class:`~QobjEvo`, list, or callable
System Hamiltonian as a :obj:`~Qobj , list of :obj:`Qobj` and
coefficient, :obj:`~QObjEvo`, or a callback function for time-dependent
Hamiltonians. List format and options can be found in QobjEvo's
description.
psi0 : :class:`~Qobj`
Initial state vector (ket) or initial unitary operator ``psi0 = U``.
tlist : array_like of float
List of times for :math:`t`.
e_ops : list of :class:`~Qobj` or callback function, optional
Single operator or list of operators for which to evaluate expectation
values. For operator evolution, the overlap is computed: ::
(e_ops[i].dag() * op(t)).tr()
args : dict, optional
Dictionary of scope parameters for time-dependent Hamiltonians.
options : :obj:`~solver.Options`, optional
Options for the ODE solver.
progress_bar : :obj:`~BaseProgressBar`, optional
Optional instance of :obj:`~BaseProgressBar`, or a subclass thereof,
for showing the progress of the simulation.
Returns
-------
output: :class:`~solver.Result`
An instance of the class :class:`~solver.Result`, which contains either
an array of expectation values for the times specified by ``tlist``, or
an array or state vectors corresponding to the times in ``tlist`` (if
``e_ops`` is an empty list), or nothing if a callback function was
given inplace of operators for which to calculate the expectation
values.
"""
if e_ops is None:
e_ops = []
if isinstance(e_ops, Qobj):
e_ops = [e_ops]
elif isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
if progress_bar is None:
progress_bar = BaseProgressBar()
if progress_bar is True:
progress_bar = TextProgressBar()
if not (psi0.isket or psi0.isunitary):
raise TypeError("The unitary solver requires psi0 to be"
" a ket as initial state"
" or a unitary as initial operator.")
if options is None:
options = Options()
if options.rhs_reuse and not isinstance(H, SolverSystem):
# TODO: deprecate when going to class based solver.
if "sesolve" in solver_safe:
H = solver_safe["sesolve"]
if args is None:
args = {}
check_use_openmp(options)
if isinstance(H, SolverSystem):
ss = H
elif isinstance(H, (list, Qobj, QobjEvo)):
ss = _sesolve_QobjEvo(H, tlist, args, options)
elif callable(H):
ss = _sesolve_func_td(H, args, options)
else:
raise Exception("Invalid H type")
func, ode_args = ss.makefunc(ss, psi0, args, e_ops, options)
if _safe_mode:
v = psi0.full().ravel('F')
func(0., v, *ode_args) + v
res = _generic_ode_solve(func, ode_args, psi0, tlist, e_ops, options,
progress_bar, dims=psi0.dims)
if e_ops_dict:
res.expect = {e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())}
res.SolverSystem = ss
return res
def floquet_markov_mesolve(R,
ekets,
rho0,
tlist,
e_ops,
f_modes_table=None,
options=None,
floquet_basis=True):
"""
Solve the dynamics for the system using the Floquet-Markov master equation.
"""
if options is None:
opt = Options()
else:
opt = options
if opt.tidy:
R.tidyup()
#
# check initial state
#
if isket(rho0):
# Got a wave function as initial state: convert to density matrix.
rho0 = ket2dm(rho0)
#
# prepare output array
#
n_tsteps = len(tlist)
dt = tlist[1] - tlist[0]
output = Result()
output.solver = "fmmesolve"
output.times = tlist
if isinstance(e_ops, 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:
output.states = []
else:
if not f_modes_table:
raise TypeError("The Floquet mode table has to be provided " +
"when requesting expectation values.")
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
if op.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")
#
# transform the initial density matrix to the eigenbasis: from
# computational basis to the floquet basis
#
if ekets is not None:
rho0 = rho0.transform(ekets)
#
# setup integrator
#
initial_vector = mat2vec(rho0.full())
r = scipy.integrate.ode(cy_ode_rhs)
r.set_f_params(R.data.data, R.data.indices, R.data.indptr)
r.set_integrator('zvode',
method=opt.method,
order=opt.order,
atol=opt.atol,
rtol=opt.rtol,
max_step=opt.max_step)
r.set_initial_value(initial_vector, tlist[0])
#
# start evolution
#
rho = Qobj(rho0)
t_idx = 0
for t in tlist:
if not r.successful():
break
rho = Qobj(vec2mat(r.y), rho0.dims, rho0.shape)
if expt_callback:
# use callback method
if floquet_basis:
e_ops(t, Qobj(rho))
else:
f_modes_table_t, T = f_modes_table
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
e_ops(t, Qobj(rho).transform(f_modes_t, True))
else:
# calculate all the expectation values, or output rho if
# no operators
if n_expt_op == 0:
if floquet_basis:
output.states.append(Qobj(rho))
else:
f_modes_table_t, T = f_modes_table
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
output.states.append(Qobj(rho).transform(f_modes_t, True))
else:
f_modes_table_t, T = f_modes_table
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
for m in range(0, n_expt_op):
output.expect[m][t_idx] = \
expect(e_ops[m], rho.transform(f_modes_t, False))
r.integrate(r.t + dt)
t_idx += 1
return output
def odesolve(H, rho0, tlist, c_op_list, e_ops, args=None, options=None):
"""
Master equation evolution of a density matrix for a given Hamiltonian.
Evolution of a state vector or density matrix (`rho0`) for a given
Hamiltonian (`H`) and set of collapse operators (`c_op_list`), by
integrating the set of ordinary differential equations that define the
system. The output is either the state vector at arbitrary points in time
(`tlist`), or the expectation values of the supplied operators
(`e_ops`).
For problems with time-dependent Hamiltonians, `H` can be a callback
function that takes two arguments, time and `args`, and returns the
Hamiltonian at that point in time. `args` is a list of parameters that is
passed to the callback function `H` (only used for time-dependent
Hamiltonians).
Parameters
----------
H : :class:`qutip.qobj`
system Hamiltonian, or a callback function for time-dependent
Hamiltonians.
rho0 : :class:`qutip.qobj`
initial density matrix or state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
c_op_list : list of :class:`qutip.qobj`
list of collapse operators.
e_ops : list of :class:`qutip.qobj` / callback function
list of operators for which to evaluate expectation values.
args : *dictionary*
dictionary of parameters for time-dependent Hamiltonians and
collapse operators.
options : :class:`qutip.Options`
with options for the ODE solver.
Returns
-------
output :array
Expectation values of wavefunctions/density matrices
for the times specified by `tlist`.
Notes
-----
On using callback function: odesolve transforms all :class:`qutip.qobj`
objects to sparse matrices before handing the problem to the integrator
function. In order for your callback function to work correctly, pass
all :class:`qutip.qobj` objects that are used in constructing the
Hamiltonian via args. odesolve will check for :class:`qutip.qobj` in
`args` and handle the conversion to sparse matrices. All other
:class:`qutip.qobj` objects that are not passed via `args` will be
passed on to the integrator to scipy who will raise an NotImplemented
exception.
Deprecated in QuTiP 2.0.0. Use :func:`mesolve` instead.
"""
warnings.warn("odesolve is deprecated since 2.0.0. Use mesolve instead.",
DeprecationWarning)
if debug:
print(inspect.stack()[0][3])
if options is None:
options = Options()
if (c_op_list and len(c_op_list) > 0) or not isket(rho0):
if isinstance(H, list):
output = _mesolve_list_td(H, rho0, tlist,
c_op_list, e_ops, args, options,
BaseProgressBar())
if isinstance(H, (types.FunctionType,
types.BuiltinFunctionType, partial)):
output = _mesolve_func_td(H, rho0, tlist,
c_op_list, e_ops, args, options,
BaseProgressBar())
else:
output = _mesolve_const(H, rho0, tlist,
c_op_list, e_ops, args, options,
BaseProgressBar())
else:
if isinstance(H, list):
output = _sesolve_list_td(H, rho0, tlist, e_ops, args, options,
BaseProgressBar())
if isinstance(H, (types.FunctionType,
types.BuiltinFunctionType, partial)):
output = _sesolve_func_td(H, rho0, tlist, e_ops, args, options,
BaseProgressBar())
else:
output = _sesolve_const(H, rho0, tlist, e_ops, args, options,
BaseProgressBar())
if len(e_ops) > 0:
return output.expect
else:
return output.states
def floquet_modes_table(f_modes_0,
f_energies,
tlist,
H,
T,
args=None,
options=None):
"""
Pre-calculate the Floquet modes for a range of times spanning the floquet
period. Can later be used as a table to look up the floquet modes for
any time.
Parameters
----------
f_modes_0 : list of :class:`qutip.qobj` (kets)
Floquet modes at :math:`t`
f_energies : list
Floquet energies.
tlist : array
The list of times at which to evaluate the floquet modes.
H : :class:`qutip.qobj`
system Hamiltonian, time-dependent with period `T`
T : float
The period of the time-dependence of the hamiltonian.
args : dictionary
dictionary with variables required to evaluate H
options : :class:`qutip.solver`
options for the ODE solver.
Returns
-------
output : nested list
A nested list of Floquet modes as kets for each time in `tlist`
"""
options = copy(options) or Options()
# truncate tlist to the driving period
tlist_period = tlist[np.where(tlist <= T)]
f_modes_table_t = [[] for t in tlist_period]
opt = options
opt.rhs_reuse = True
rhs_clear()
for n, f_mode in enumerate(f_modes_0):
output = sesolve(H, f_mode, tlist_period, [], args, opt)
for t_idx, f_state_t in enumerate(output.states):
f_modes_table_t[t_idx].append(
f_state_t * exp(1j * f_energies[n] * tlist_period[t_idx]))
return f_modes_table_t
def sesolve(H,
rho0,
tlist,
e_ops=[],
args={},
options=None,
progress_bar=None,
_safe_mode=True):
"""
Schrodinger equation evolution of a state vector for a given Hamiltonian.
Evolve the state vector or density matrix (`rho0`) using a given
Hamiltonian (`H`), by integrating the set of ordinary differential
equations that define the system.
The output is either the state vector at arbitrary points in time
(`tlist`), or the expectation values of the supplied operators
(`e_ops`). If e_ops is a callback function, it is invoked for each
time in `tlist` with time and the state as arguments, and the function
does not use any return values.
Parameters
----------
H : :class:`qutip.qobj`
system Hamiltonian, or a callback function for time-dependent
Hamiltonians.
rho0 : :class:`qutip.qobj`
initial density matrix or state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
e_ops : list of :class:`qutip.qobj` / callback function single
single operator or list of operators for which to evaluate
expectation values.
args : *dictionary*
dictionary of parameters for time-dependent Hamiltonians and
collapse operators.
options : :class:`qutip.Qdeoptions`
with options for the ODE solver.
progress_bar : BaseProgressBar
Optional instance of BaseProgressBar, or a subclass thereof, for
showing the progress of the simulation.
Returns
-------
output: :class:`qutip.solver`
An instance of the class :class:`qutip.solver`, which contains either
an *array* of expectation values for the times specified by `tlist`, or
an *array* or state vectors or density matrices corresponding to the
times in `tlist` [if `e_ops` is an empty list], or
nothing if a callback function was given inplace of operators for
which to calculate the expectation values.
"""
if isinstance(e_ops, Qobj):
e_ops = [e_ops]
if isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
if _safe_mode:
_solver_safety_check(H, rho0, c_ops=[], e_ops=e_ops, args=args)
if progress_bar is None:
progress_bar = BaseProgressBar()
elif progress_bar is True:
progress_bar = TextProgressBar()
# convert array based time-dependence to string format
H, _, args = _td_wrap_array_str(H, [], args, tlist)
# check for type (if any) of time-dependent inputs
n_const, n_func, n_str = _td_format_check(H, [])
if options is None:
options = Options()
if (not options.rhs_reuse) or (not config.tdfunc):
# reset config time-dependence flags to default values
config.reset()
#check if should use OPENMP
check_use_openmp(options)
if n_func > 0:
res = _sesolve_list_func_td(H, rho0, tlist, e_ops, args, options,
progress_bar)
elif n_str > 0:
res = _sesolve_list_str_td(H, rho0, tlist, e_ops, args, options,
progress_bar)
elif isinstance(H,
(types.FunctionType, types.BuiltinFunctionType, partial)):
res = _sesolve_func_td(H, rho0, tlist, e_ops, args, options,
progress_bar)
else:
res = _sesolve_const(H, rho0, tlist, e_ops, args, options,
progress_bar)
if e_ops_dict:
res.expect = {
e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())
}
return res
def fsesolve(H,
psi0,
tlist,
e_ops=[],
T=None,
args={},
Tsteps=100,
options_modes=None):
"""
Solve the Schrodinger equation using the Floquet formalism.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
System Hamiltonian, time-dependent with period `T`.
psi0 : :class:`qutip.qobj`
Initial state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
e_ops : list of :class:`qutip.qobj` / callback function
list of operators for which to evaluate expectation values. If this
list is empty, the state vectors for each time in `tlist` will be
returned instead of expectation values.
T : float
The period of the time-dependence of the hamiltonian.
args : dictionary
Dictionary with variables required to evaluate H.
Tsteps : integer
The number of time steps in one driving period for which to
precalculate the Floquet modes. `Tsteps` should be an even number.
options_modes : :class:`qutip.solver`
options for the ODE solver.
Returns
-------
output : :class:`qutip.solver.Result`
An instance of the class :class:`qutip.solver.Result`, which
contains either an *array* of expectation values or an array of
state vectors, for the times specified by `tlist`.
"""
if not T:
# assume that tlist span exactly one period of the driving
T = tlist[-1]
if options_modes is None:
options_modes_table = Options()
else:
options_modes_table = options_modes
# find the floquet modes for the time-dependent hamiltonian
f_modes_0, f_energies = floquet_modes(H, T, args, options=options_modes)
# calculate the wavefunctions using the from the floquet modes
f_modes_table_t = floquet_modes_table(f_modes_0,
f_energies,
np.linspace(0, T, Tsteps + 1),
H,
T,
args,
options=options_modes_table)
# setup Result for storing the results
output = Result()
output.times = tlist
output.solver = "fsesolve"
if isinstance(e_ops, FunctionType):
output.num_expect = 0
expt_callback = True
elif isinstance(e_ops, list):
output.num_expect = len(e_ops)
expt_callback = False
if output.num_expect == 0:
output.states = []
else:
output.expect = []
for op in e_ops:
if op.isherm:
output.expect.append(np.zeros(len(tlist)))
else:
output.expect.append(np.zeros(len(tlist), dtype=complex))
else:
raise TypeError("e_ops must be a list Qobj or a callback function")
psi0_fb = psi0.transform(f_modes_0)
for t_idx, t in enumerate(tlist):
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
f_states_t = floquet_states(f_modes_t, f_energies, t)
psi_t = psi0_fb.transform(f_states_t, True)
if expt_callback:
# use callback method
e_ops(t, psi_t)
else:
# calculate all the expectation values, or output psi if
# no expectation value operators where defined
if output.num_expect == 0:
output.states.append(Qobj(psi_t))
else:
for e_idx, e in enumerate(e_ops):
output.expect[e_idx][t_idx] = expect(e, psi_t)
return output
def mesolve(H,
rho0,
tlist,
c_ops=None,
e_ops=None,
args=None,
options=None,
progress_bar=None,
_safe_mode=True):
"""
Master equation evolution of a density matrix for a given Hamiltonian and
set of collapse operators, or a Liouvillian.
Evolve the state vector or density matrix (`rho0`) using a given
Hamiltonian or Liouvillian (`H`) and an optional set of collapse operators
(`c_ops`), by integrating the set of ordinary differential equations
that define the system. In the absence of collapse operators the system is
evolved according to the unitary evolution of the Hamiltonian.
The output is either the state vector at arbitrary points in time
(`tlist`), or the expectation values of the supplied operators
(`e_ops`). If e_ops is a callback function, it is invoked for each
time in `tlist` with time and the state as arguments, and the function
does not use any return values.
If either `H` or the Qobj elements in `c_ops` are superoperators, they
will be treated as direct contributions to the total system Liouvillian.
This allows the solution of master equations that are not in standard
Lindblad form.
**Time-dependent operators**
For time-dependent problems, `H` and `c_ops` can be a specified in a
nested-list format where each element in the list is a list of length 2,
containing an operator (:class:`qutip.qobj`) at the first element and where
the second element is either a string (*list string format*), a callback
function (*list callback format*) that evaluates to the time-dependent
coefficient for the corresponding operator, or a NumPy array (*list
array format*) which specifies the value of the coefficient to the
corresponding operator for each value of t in `tlist`.
Alternatively, `H` (but not `c_ops`) can be a callback function with the
signature `f(t, args) -> Qobj` (*callback format*), which can return the
Hamiltonian or Liouvillian superoperator at any point in time. If the
equation cannot be put in standard Lindblad form, then this time-dependence
format must be used.
*Examples*
H = [[H0, 'sin(w*t)'], [H1, 'sin(2*w*t)']]
H = [[H0, f0_t], [H1, f1_t]]
where f0_t and f1_t are python functions with signature f_t(t, args).
H = [[H0, np.sin(w*tlist)], [H1, np.sin(2*w*tlist)]]
In the *list string format* and *list callback format*, the string
expression and the callback function must evaluate to a real or complex
number (coefficient for the corresponding operator).
In all cases of time-dependent operators, `args` is a dictionary of
parameters that is used when evaluating operators. It is passed to the
callback functions as their second argument.
**Additional options**
Additional options to mesolve can be set via the `options` argument, which
should be an instance of :class:`qutip.solver.Options`. Many ODE
integration options can be set this way, and the `store_states` and
`store_final_state` options can be used to store states even though
expectation values are requested via the `e_ops` argument.
.. note::
If an element in the list-specification of the Hamiltonian or
the list of collapse operators are in superoperator form it will be
added to the total Liouvillian of the problem without further
transformation. This allows for using mesolve for solving master
equations that are not in standard Lindblad form.
.. note::
On using callback functions: mesolve transforms all :class:`qutip.Qobj`
objects to sparse matrices before handing the problem to the integrator
function. In order for your callback function to work correctly, pass
all :class:`qutip.Qobj` objects that are used in constructing the
Hamiltonian via `args`. mesolve will check for :class:`qutip.Qobj` in
`args` and handle the conversion to sparse matrices. All other
:class:`qutip.Qobj` objects that are not passed via `args` will be
passed on to the integrator in scipy which will raise a NotImplemented
exception.
Parameters
----------
H : :class:`qutip.Qobj`
System Hamiltonian, or a callback function for time-dependent
Hamiltonians, or alternatively a system Liouvillian.
rho0 : :class:`qutip.Qobj`
initial density matrix or state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
c_ops : None / list of :class:`qutip.Qobj`
single collapse operator, or list of collapse operators, or a list
of Liouvillian superoperators.
e_ops : None / list of :class:`qutip.Qobj` / callback function single
single operator or list of operators for which to evaluate
expectation values.
args : None / *dictionary*
dictionary of parameters for time-dependent Hamiltonians and
collapse operators.
options : None / :class:`qutip.Options`
with options for the solver.
progress_bar : None / BaseProgressBar
Optional instance of BaseProgressBar, or a subclass thereof, for
showing the progress of the simulation.
Returns
-------
result: :class:`qutip.Result`
An instance of the class :class:`qutip.Result`, which contains
either an *array* `result.expect` of expectation values for the times
specified by `tlist`, or an *array* `result.states` of state vectors or
density matrices corresponding to the times in `tlist` [if `e_ops` is
an empty list], or nothing if a callback function was given in place of
operators for which to calculate the expectation values.
"""
if c_ops is None:
c_ops = []
if isinstance(c_ops, (Qobj, QobjEvo)):
c_ops = [c_ops]
if e_ops is None:
e_ops = []
if isinstance(e_ops, Qobj):
e_ops = [e_ops]
if isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
if progress_bar is None:
progress_bar = BaseProgressBar()
if progress_bar is True:
progress_bar = TextProgressBar()
# check if rho0 is a superoperator, in which case e_ops argument should
# be empty, i.e., e_ops = []
# TODO: e_ops for superoperator
if issuper(rho0) and not e_ops == []:
raise TypeError("Must have e_ops = [] when initial condition rho0 is" +
" a superoperator.")
if options is None:
options = Options()
if options.rhs_reuse and not isinstance(H, SolverSystem):
# TODO: deprecate when going to class based solver.
if "mesolve" in solver_safe:
# print(" ")
H = solver_safe["mesolve"]
else:
pass
# raise Exception("Could not find the Hamiltonian to reuse.")
if args is None:
args = {}
check_use_openmp(options)
use_mesolve = ((c_ops and len(c_ops) > 0) or (not isket(rho0))
or (isinstance(H, Qobj) and issuper(H))
or (isinstance(H, QobjEvo) and issuper(H.cte))
or (isinstance(H, list) and isinstance(H[0], Qobj)
and issuper(H[0]))
or (not isinstance(H, (Qobj, QobjEvo)) and callable(H)
and not options.rhs_with_state and issuper(H(0., args)))
or (not isinstance(H, (Qobj, QobjEvo)) and callable(H)
and options.rhs_with_state))
if not use_mesolve:
return sesolve(H,
rho0,
tlist,
e_ops=e_ops,
args=args,
options=options,
progress_bar=progress_bar,
_safe_mode=_safe_mode)
if isket(rho0):
rho0 = ket2dm(rho0)
if (not (rho0.isoper or rho0.issuper)) or (rho0.dims[0] != rho0.dims[1]):
raise ValueError(
"input state must be a pure state vector, square density matrix, "
"or superoperator")
if isinstance(H, SolverSystem):
ss = H
elif isinstance(H, (list, Qobj, QobjEvo)):
ss = _mesolve_QobjEvo(H, c_ops, tlist, args, options)
elif callable(H):
ss = _mesolve_func_td(H, c_ops, rho0, tlist, args, options)
else:
raise Exception("Invalid H type")
func, ode_args = ss.makefunc(ss, rho0, args, e_ops, options)
if _safe_mode:
# This is to test safety of the function before starting the loop.
v = rho0.full().ravel('F')
func(0., v, *ode_args) + v
res = _generic_ode_solve(func,
ode_args,
rho0,
tlist,
e_ops,
options,
progress_bar,
dims=rho0.dims)
res.num_collapse = len(c_ops)
if e_ops_dict:
res.expect = {
e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())
}
return res
def floquet_markov_mesolve(
R,
rho0,
tlist,
e_ops,
options=None,
floquet_basis=True,
f_modes_0=None,
f_modes_table_t=None,
f_energies=None,
T=None,
):
"""
Solve the dynamics for the system using the Floquet-Markov master equation.
.. note::
It is important to understand in which frame and basis the results
are returned here.
Parameters
----------
R : array
The Floquet-Markov master equation tensor `R`.
rho0 : :class:`qutip.qobj`
Initial density matrix. If ``f_modes_0`` is not passed, this density
matrix is assumed to be in the Floquet picture.
tlist : *list* / *array*
list of times for :math:`t`.
e_ops : list of :class:`qutip.qobj` / callback function
list of operators for which to evaluate expectation values.
options : :class:`qutip.solver`
options for the ODE solver.
floquet_basis: bool, True
If ``True``, states and expectation values will be returned in the
Floquet basis. If ``False``, a transformation will be made to the
computational basis; this will be in the lab frame if
``f_modes_table``, ``T` and ``f_energies`` are all supplied, or the
interaction picture (defined purely be f_modes_0) if they are not.
f_modes_0 : list of :class:`qutip.qobj` (kets), optional
A list of initial Floquet modes, used to transform the given starting
density matrix into the Floquet basis. If this is not passed, it is
assumed that ``rho`` is already in the Floquet basis.
f_modes_table_t : nested list of :class:`qutip.qobj` (kets), optional
A lookup-table of Floquet modes at times precalculated by
:func:`qutip.floquet.floquet_modes_table`. Necessary if
``floquet_basis`` is ``False`` and the transformation should be made
back to the lab frame.
f_energies : array_like of float, optional
The precalculated Floquet quasienergies. Necessary if
``floquet_basis`` is ``False`` and the transformation should be made
back to the lab frame.
T : float, optional
The time period of driving. Necessary if ``floquet_basis`` is
``False`` and the transformation should be made back to the lab frame.
Returns
-------
output : :class:`qutip.solver.Result`
An instance of the class :class:`qutip.solver.Result`, which
contains either an *array* of expectation values or an array of
state vectors, for the times specified by `tlist`.
"""
opt = options or Options()
if opt.tidy:
R.tidyup()
rho0 = rho0.proj() if rho0.isket else rho0
# Prepare output object.
dt = tlist[1] - tlist[0]
output = Result()
output.solver = "fmmesolve"
output.times = tlist
if isinstance(e_ops, FunctionType):
expt_callback = True
store_states = opt.store_states or False
else:
expt_callback = False
try:
e_ops = list(e_ops)
except TypeError:
raise TypeError("`e_ops` must be iterable or a function") from None
n_expt_op = len(e_ops)
if n_expt_op == 0:
store_states = True
else:
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
dtype = np.float64 if op.isherm else np.complex128
output.expect.append(np.zeros(len(tlist), dtype=dtype))
store_states = opt.store_states or (n_expt_op == 0)
if store_states:
output.states = []
# Choose which frame transformations should be done on the initial and
# evolved states.
lab_lookup = [f_modes_table_t, f_energies, T]
if (any(x is None for x in lab_lookup)
and not all(x is None for x in lab_lookup)):
warnings.warn(
"if transformation back to the computational basis in the lab"
"frame is desired, all of `f_modes_t`, `f_energies` and `T` must"
"be supplied.")
f_modes_table_t = f_energies = T = None
# Initial state.
if f_modes_0 is not None:
rho0 = rho0.transform(f_modes_0)
# Evolved states.
if floquet_basis:
def transform(rho, t):
return rho
elif f_modes_table_t is not None:
# Lab frame, computational basis.
def transform(rho, t):
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
f_states_t = floquet_states(f_modes_t, f_energies, t)
return rho.transform(f_states_t, True)
elif f_modes_0 is not None:
# Interaction picture, computational basis.
def transform(rho, t):
return rho.transform(f_modes_0, False)
else:
raise ValueError(
"cannot transform out of the Floquet basis without some knowledge "
"of the Floquet modes. Pass `f_modes_0`, or all of `f_modes_t`, "
"`f_energies` and `T`.")
# Setup integrator.
initial_vector = mat2vec(rho0.full())
r = scipy.integrate.ode(cy_ode_rhs)
r.set_f_params(R.data.data, R.data.indices, R.data.indptr)
r.set_integrator('zvode',
method=opt.method,
order=opt.order,
atol=opt.atol,
rtol=opt.rtol,
max_step=opt.max_step)
r.set_initial_value(initial_vector, tlist[0])
# Main evolution loop.
for t_idx, t in enumerate(tlist):
if not r.successful():
break
rho = transform(Qobj(vec2mat(r.y), rho0.dims, rho0.shape), t)
if expt_callback:
e_ops(t, rho)
else:
for m, e_op in enumerate(e_ops):
output.expect[m][t_idx] = expect(e_op, rho)
if store_states:
output.states.append(rho)
r.integrate(r.t + dt)
return output
def coherence_function_g2(H,
taulist,
c_ops,
a_op,
solver="me",
args=None,
options=Options(ntraj=[20, 100])):
"""
Calculate the normalized second-order quantum coherence function:
.. math::
g^{(2)}(\\tau) = \lim_{t \to \infty}
\\frac{\\langle a^\\dagger(t)a^\\dagger(t+\\tau)
a(t+\\tau)a(t)\\rangle}
{\\langle a^\\dagger(t)a(t)\\rangle^2}
using the quantum regression theorem and the evolution solver indicated by
the `solver` parameter. Note: g2 is only defined for stationary
statistics (uses steady state rho0).
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
taulist : *list* / *array*
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
The annihilation operator of the mode.
solver : str
choice of solver (`me` for master-equation and
`es` for exponential series)
options : :class:`qutip.solver.Options`
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
g2: *array*
The normalized second-order coherence function.
"""
# first calculate the the steady state photon number
rho0 = steadystate(H, c_ops)
n = np.array([expect(rho0, a_op.dag() * a_op)])
# calculate the correlation function G2 and normalize with n to obtain g2
G2 = correlation_3op_1t(H,
None,
taulist,
c_ops,
a_op.dag(),
a_op.dag() * a_op,
a_op,
solver=solver,
args=args,
options=options)
g2 = G2 / n**2
return g2
def fmmesolve(H,
rho0,
tlist,
c_ops=[],
e_ops=[],
spectra_cb=[],
T=None,
args={},
options=Options(),
floquet_basis=True,
kmax=5,
_safe_mode=True,
options_modes=None):
"""
Solve the dynamics for the system using the Floquet-Markov master equation.
.. note::
This solver currently does not support multiple collapse operators.
Parameters
----------
H : :class:`qutip.qobj`
system Hamiltonian.
rho0 / psi0 : :class:`qutip.qobj`
initial density matrix or state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
c_ops : list of :class:`qutip.qobj`
list of collapse operators.
e_ops : list of :class:`qutip.qobj` / callback function
list of operators for which to evaluate expectation values.
spectra_cb : list callback functions
List of callback functions that compute the noise power spectrum as
a function of frequency for the collapse operators in `c_ops`.
T : float
The period of the time-dependence of the hamiltonian. The default value
'None' indicates that the 'tlist' spans a single period of the driving.
args : *dictionary*
dictionary of parameters for time-dependent Hamiltonians and
collapse operators.
This dictionary should also contain an entry 'w_th', which is
the temperature of the environment (if finite) in the
energy/frequency units of the Hamiltonian. For example, if
the Hamiltonian written in units of 2pi GHz, and the
temperature is given in K, use the following conversion
>>> temperature = 25e-3 # unit K # doctest: +SKIP
>>> h = 6.626e-34 # doctest: +SKIP
>>> kB = 1.38e-23 # doctest: +SKIP
>>> args['w_th'] = temperature * (kB / h) * 2 * pi * 1e-9 \
#doctest: +SKIP
options : :class:`qutip.solver`
options for the ODE solver. For solving the master equation.
floquet_basis : bool
Will return results in Floquet basis or computational basis
(optional).
k_max : int
The truncation of the number of sidebands (default 5).
options_modes : :class:`qutip.solver`
options for the ODE solver. For computing Floquet modes.
Returns
-------
output : :class:`qutip.solver`
An instance of the class :class:`qutip.solver`, which contains either
an *array* of expectation values for the times specified by `tlist`.
"""
if _safe_mode:
_solver_safety_check(H, rho0, c_ops, e_ops, args)
if options_modes is None:
options_modes_table = Options()
else:
options_modes_table = options_modes
if T is None:
T = max(tlist)
if len(spectra_cb) == 0:
# add white noise callbacks if absent
spectra_cb = [lambda w: 1.0] * len(c_ops)
f_modes_0, f_energies = floquet_modes(H, T, args, options=options_modes)
f_modes_table_t = floquet_modes_table(f_modes_0,
f_energies,
np.linspace(0, T, 500 + 1),
H,
T,
args,
options=options_modes_table)
# get w_th from args if it exists
if 'w_th' in args:
w_th = args['w_th']
else:
w_th = 0
# TODO: loop over input c_ops and spectra_cb, calculate one R for each set
# calculate the rate-matrices for the floquet-markov master equation
Delta, X, Gamma, Amat = floquet_master_equation_rates(
f_modes_0, f_energies, c_ops[0], H, T, args, spectra_cb[0], w_th, kmax,
f_modes_table_t)
# the floquet-markov master equation tensor
R = floquet_master_equation_tensor(Amat, f_energies)
return floquet_markov_mesolve(R,
rho0,
tlist,
e_ops,
options=options,
floquet_basis=floquet_basis,
f_modes_0=f_modes_0,
f_modes_table_t=f_modes_table_t,
T=T,
f_energies=f_energies)
def correlation_ss(H,
taulist,
c_ops,
a_op,
b_op,
solver="me",
reverse=False,
args=None,
options=Options(ntraj=[20, 100])):
"""
Calculate the two-operator two-time correlation function:
.. math::
\lim_{t \to \infty} \left<A(t+\\tau)B(t)\\right>
along one time axis (given steady-state initial conditions) using the
quantum regression theorem and the evolution solver indicated by the
`solver` parameter.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
taulist : *list* / *array*
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
operator A.
b_op : :class:`qutip.qobj.Qobj`
operator B.
reverse : bool
If `True`, calculate
:math:`\lim_{t \to \infty} \left<A(t)B(t+\\tau)\\right>` instead of
:math:`\lim_{t \to \infty} \left<A(t+\\tau)B(t)\\right>`.
solver : str
choice of solver (`me` for master-equation and
`es` for exponential series)
options : :class:`qutip.solver.Options`
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
corr_vec: *array*
An *array* of correlation values for the times specified by `tlist`.
References
----------
See, Gardiner, Quantum Noise, Section 5.2.
"""
warn(
"correlation_ss() now legacy, please use correlation_2op_1t() with" +
"initial state as None", FutureWarning)
if debug:
print(inspect.stack()[0][3])
return correlation_2op_1t(H,
None,
taulist,
c_ops,
a_op,
b_op,
solver=solver,
reverse=reverse,
args=args,
options=options)
def rhs_generate(H,
c_ops,
args={},
options=Options(),
name=None,
cleanup=True):
"""
Generates the Cython functions needed for solving the dynamics of a
given system using the mesolve function inside a parfor loop.
Parameters
----------
H : qobj
System Hamiltonian.
c_ops : list
``list`` of collapse operators.
args : dict
Arguments for time-dependent Hamiltonian and collapse operator terms.
options : Options
Instance of ODE solver options.
name: str
Name of generated RHS
cleanup: bool
Whether the generated cython file should be automatically removed or
not.
Notes
-----
Using this function with any solver other than the mesolve function
will result in an error.
"""
config.reset()
config.options = options
if name:
config.tdname = name
else:
config.tdname = "rhs" + str(os.getpid()) + str(config.cgen_num)
Lconst = 0
Ldata = []
Linds = []
Lptrs = []
Lcoeff = []
Lobj = []
# loop over all hamiltonian terms, convert to superoperator form and
# add the data of sparse matrix represenation to
msg = "Incorrect specification of time-dependence: "
for h_spec in H:
if isinstance(h_spec, Qobj):
h = h_spec
if not isinstance(h, Qobj):
raise TypeError(msg + "expected Qobj")
if h.isoper:
Lconst += -1j * (spre(h) - spost(h))
elif h.issuper:
Lconst += h
else:
raise TypeError(msg + "expected operator or superoperator")
elif isinstance(h_spec, list):
h = h_spec[0]
h_coeff = h_spec[1]
if not isinstance(h, Qobj):
raise TypeError(msg + "expected Qobj")
if h.isoper:
L = -1j * (spre(h) - spost(h))
elif h.issuper:
L = h
else:
raise TypeError(msg + "expected operator or superoperator")
Ldata.append(L.data.data)
Linds.append(L.data.indices)
Lptrs.append(L.data.indptr)
if isinstance(h_coeff, Cubic_Spline):
Lobj.append(h_coeff.coeffs)
Lcoeff.append(h_coeff)
else:
raise TypeError(msg + "expected string format")
# loop over all collapse operators
for c_spec in c_ops:
if isinstance(c_spec, Qobj):
c = c_spec
if not isinstance(c, Qobj):
raise TypeError(msg + "expected Qobj")
if c.isoper:
cdc = c.dag() * c
Lconst += spre(c) * spost(c.dag()) - 0.5 * spre(cdc) \
- 0.5 * spost(cdc)
elif c.issuper:
Lconst += c
else:
raise TypeError(msg + "expected operator or superoperator")
elif isinstance(c_spec, list):
c = c_spec[0]
c_coeff = c_spec[1]
if not isinstance(c, Qobj):
raise TypeError(msg + "expected Qobj")
if c.isoper:
cdc = c.dag() * c
L = spre(c) * spost(c.dag()) - 0.5 * spre(cdc) \
- 0.5 * spost(cdc)
c_coeff = "(" + c_coeff + ")**2"
elif c.issuper:
L = c
else:
raise TypeError(msg + "expected operator or superoperator")
Ldata.append(L.data.data)
Linds.append(L.data.indices)
Lptrs.append(L.data.indptr)
Lcoeff.append(c_coeff)
else:
raise TypeError(msg + "expected string format")
# add the constant part of the lagrangian
if Lconst != 0:
Ldata.append(Lconst.data.data)
Linds.append(Lconst.data.indices)
Lptrs.append(Lconst.data.indptr)
Lcoeff.append("1.0")
# the total number of liouvillian terms (hamiltonian terms + collapse
# operators)
n_L_terms = len(Ldata)
cgen = Codegen(h_terms=n_L_terms,
h_tdterms=Lcoeff,
args=args,
config=config)
cgen.generate(config.tdname + ".pyx")
code = compile('from ' + config.tdname + ' import cy_td_ode_rhs',
'<string>', 'exec')
exec(code, globals())
config.tdfunc = cy_td_ode_rhs
if cleanup:
try:
os.remove(config.tdname + ".pyx")
except:
pass
def correlation_4op_1t(H,
state0,
taulist,
c_ops,
a_op,
b_op,
c_op,
d_op,
solver="me",
args=None,
options=Options(ntraj=[20, 100])):
"""
Calculate the four-operator two-time correlation function:
:math:`\left<A(t)B(t+\\tau)C(t+\\tau)D(t)\\right>`
along one time axis using the quantum regression theorem and the
evolution solver indicated by the `solver` parameter.
Note: it is not possibly to calculate a physically meaningful correlation
of this form where :math:`\\tau<0`.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
rho0 : :class:`qutip.qobj.Qobj`
Initial state density matrix :math:`\\rho(t_0)` or state vector
:math:`\\psi(t_0)`. If 'state0' is 'None', then the steady state will
be used as the initial state. The 'steady-state' is only implemented
for the `me` and `es` solvers.
taulist : *list* / *array*
list of times for :math:`\\tau`. taulist must be positive and contain
the element `0`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
operator A.
b_op : :class:`qutip.qobj.Qobj`
operator B.
c_op : :class:`qutip.qobj.Qobj`
operator C.
d_op : :class:`qutip.qobj.Qobj`
operator D.
solver : str
choice of solver (`me` for master-equation, `mc` for Monte Carlo, and
`es` for exponential series)
options : :class:`qutip.solver.Options`
solver options class. `ntraj` is taken as a two-element list because
the `mc` correlator calls `mcsolve()` recursively; by default,
`ntraj=[20, 100]`. `mc_corr_eps` prevents divide-by-zero errors in
the `mc` correlator; by default, `mc_corr_eps=1e-10`.
Returns
-------
corr_vec: *array*
An *array* of correlation values for the times specified by `taulist`
References
----------
See, Gardiner, Quantum Noise, Section 5.2.
"""
warn("correlation_4op_1t() now legacy, please use correlation_3op_1t()",
FutureWarning)
warn(
"the reverse argument has been removed as it did not contain any" +
"new physical information", DeprecationWarning)
if debug:
print(inspect.stack()[0][3])
return correlation_3op_1t(H,
state0,
taulist,
c_ops,
a_op,
b_op * c_op,
d_op,
solver=solver,
args=args,
options=options)
def correlation_2op_2t(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
solver="me",
reverse=False,
args=None,
options=Options()):
"""
Calculate a two-operator two-time correlation function on the form
:math:`\left<A(t+\\tau)B(t)\\right>` or
:math:`\left<A(t)B(t+\\tau)\\right>` (if `reverse=True`), using the
quantum regression theorem and the evolution solver indicated by the
*solver* parameter.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
rho0 : :class:`qutip.qobj.Qobj`
Initial state density matrix :math:`\\rho(t_0)` (or state vector). If
'rho0' is 'None', then the steady state will be used as initial state.
tlist : *list* / *array*
list of times for :math:`t`.
taulist : *list* / *array*
list of times for :math:`\\tau`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
operator A.
b_op : :class:`qutip.qobj.Qobj`
operator B.
solver : str
choice of solver (`me` for master-equation,
`es` for exponential series and `mc` for Monte-carlo)
reverse : bool
If `True`, calculate :math:`\left<A(t)B(t+\\tau)\\right>` instead of
:math:`\left<A(t+\\tau)B(t)\\right>`.
Returns
-------
corr_mat: *array*
An 2-dimensional *array* (matrix) of correlation values for the times
specified by `tlist` (first index) and `taulist` (second index). If
`tlist` is `None`, then a 1-dimensional *array* of correlation values
is returned instead.
"""
if debug:
print(inspect.stack()[0][3])
if tlist is None:
# only interested in correlation vs one time coordinate, so we can use
# the ss solver with the supplied density matrix as initial state (in
# place of the steady state)
return correlation_2op_1t(H,
rho0,
taulist,
c_ops,
a_op,
b_op,
solver,
reverse,
args=args,
options=options)
if solver == "me":
return _correlation_me_2op_2t(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
reverse,
args=args,
options=options)
elif solver == "es":
return _correlation_es_2op_2t(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
reverse,
args=args,
options=options)
elif solver == "mc":
return _correlation_mc_2op_2t(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
reverse,
args=args,
options=options)
else:
raise "Unrecognized choice of solver %s (use me, es or mc)." % solver
def mesolve_checkpoint(H, rho0, tlist, c_ops, e_ops, save, subdir, args={}, options=None,
progress_bar=None):
"""
Master equation evolution of a density matrix for a given Hamiltonian and
set of collapse operators, or a Liouvillian.
Evolve the state vector or density matrix (`rho0`) using a given
Hamiltonian (`H`) and an [optional] set of collapse operators
(`c_ops`), by integrating the set of ordinary differential equations
that define the system. In the absence of collapse operators the system is
evolved according to the unitary evolution of the Hamiltonian.
The output is either the state vector at arbitrary points in time
(`tlist`), or the expectation values of the supplied operators
(`e_ops`). If e_ops is a callback function, it is invoked for each
time in `tlist` with time and the state as arguments, and the function
does not use any return values.
If either `H` or the Qobj elements in `c_ops` are superoperators, they
will be treated as direct contributions to the total system Liouvillian.
This allows to solve master equations that are not on standard Lindblad
form by passing a custom Liouvillian in place of either the `H` or `c_ops`
elements.
**Time-dependent operators**
For time-dependent problems, `H` and `c_ops` can be callback
functions that takes two arguments, time and `args`, and returns the
Hamiltonian or Liouvillian for the system at that point in time
(*callback format*).
Alternatively, `H` and `c_ops` can be a specified in a nested-list format
where each element in the list is a list of length 2, containing an
operator (:class:`qutip.qobj`) at the first element and where the
second element is either a string (*list string format*), a callback
function (*list callback format*) that evaluates to the time-dependent
coefficient for the corresponding operator, or a NumPy array (*list
array format*) which specifies the value of the coefficient to the
corresponding operator for each value of t in tlist.
*Examples*
H = [[H0, 'sin(w*t)'], [H1, 'sin(2*w*t)']]
H = [[H0, f0_t], [H1, f1_t]]
where f0_t and f1_t are python functions with signature f_t(t, args).
H = [[H0, np.sin(w*tlist)], [H1, np.sin(2*w*tlist)]]
In the *list string format* and *list callback format*, the string
expression and the callback function must evaluate to a real or complex
number (coefficient for the corresponding operator).
In all cases of time-dependent operators, `args` is a dictionary of
parameters that is used when evaluating operators. It is passed to the
callback functions as second argument.
**Additional options**
Additional options to mesolve can be set via the `options` argument, which
should be an instance of :class:`qutip.solver.Options`. Many ODE
integration options can be set this way, and the `store_states` and
`store_final_state` options can be used to store states even though
expectation values are requested via the `e_ops` argument.
.. note::
If an element in the list-specification of the Hamiltonian or
the list of collapse operators are in superoperator form it will be
added to the total Liouvillian of the problem with out further
transformation. This allows for using mesolve for solving master
equations that are not on standard Lindblad form.
.. note::
On using callback function: mesolve transforms all :class:`qutip.qobj`
objects to sparse matrices before handing the problem to the integrator
function. In order for your callback function to work correctly, pass
all :class:`qutip.qobj` objects that are used in constructing the
Hamiltonian via args. mesolve will check for :class:`qutip.qobj` in
`args` and handle the conversion to sparse matrices. All other
:class:`qutip.qobj` objects that are not passed via `args` will be
passed on to the integrator in scipy which will raise an NotImplemented
exception.
Parameters
----------
H : :class:`qutip.Qobj`
System Hamiltonian, or a callback function for time-dependent
Hamiltonians, or alternatively a system Liouvillian.
rho0 : :class:`qutip.Qobj`
initial density matrix or state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
c_ops : list of :class:`qutip.Qobj`
single collapse operator, or list of collapse operators, or a list
of Liouvillian superoperators.
e_ops : list of :class:`qutip.Qobj` / callback function single
single operator or list of operators for which to evaluate
expectation values.
args : *dictionary*
dictionary of parameters for time-dependent Hamiltonians and
collapse operators.
options : :class:`qutip.Options`
with options for the solver.
progress_bar: BaseProgressBar
Optional instance of BaseProgressBar, or a subclass thereof, for
showing the progress of the simulation.
Returns
-------
result: :class:`qutip.Result`
An instance of the class :class:`qutip.Result`, which contains
either an *array* `result.expect` of expectation values for the times
specified by `tlist`, or an *array* `result.states` of state vectors or
density matrices corresponding to the times in `tlist` [if `e_ops` is
an empty list], or nothing if a callback function was given in place of
operators for which to calculate the expectation values.
"""
if progress_bar is None:
progress_bar = BaseProgressBar()
elif progress_bar is True:
progress_bar = TextProgressBar()
# check whether c_ops or e_ops is is a single operator
# if so convert it to a list containing only that operator
if isinstance(c_ops, Qobj):
c_ops = [c_ops]
# convert array based time-dependence to string format
H, c_ops, args = _td_wrap_array_str(H, c_ops, args, tlist)
# check for type (if any) of time-dependent inputs
_, n_func, n_str = _td_format_check(H, c_ops)
if options is None:
options = Options()
if (not options.rhs_reuse) or (not config.tdfunc):
# reset config collapse and time-dependence flags to default values
config.reset()
res = None
#
# dispatch the appropriate solver
#
if ((c_ops and len(c_ops) > 0)
or (not isket(rho0))
or (isinstance(H, Qobj) and issuper(H))
or (isinstance(H, list) and
isinstance(H[0], Qobj) and issuper(H[0]))):
#
# we have collapse operators
#
#
# find out if we are dealing with all-constant hamiltonian and
# collapse operators or if we have at least one time-dependent
# operator. Then delegate to appropriate solver...
#
if isinstance(H, Qobj):
# constant hamiltonian
if n_func == 0 and n_str == 0:
# constant collapse operators
res = mesolve_const_checkpoint(H, rho0, tlist, c_ops,
e_ops, args, options,
progress_bar, save, subdir)
res.expect = {e: res.expect[n]
for n, e in enumerate(e_ops.keys())}
return res
def correlation_4op_2t(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
c_op,
d_op,
solver="me",
args=None,
options=Options()):
"""
Calculate the four-operator two-time correlation function on the from
:math:`\left<A(t)B(t+\\tau)C(t+\\tau)D(t)\\right>` using the quantum
regression theorem and the solver indicated by the 'solver' parameter.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
rho0 : :class:`qutip.qobj.Qobj`
Initial state density matrix (or state vector). If 'rho0' is
'None', then the steady state will be used as initial state.
tlist : *list* / *array*
list of times for :math:`t`.
taulist : *list* / *array*
list of times for :math:`\\tau`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
operator A.
b_op : :class:`qutip.qobj.Qobj`
operator B.
c_op : :class:`qutip.qobj.Qobj`
operator C.
d_op : :class:`qutip.qobj.Qobj`
operator D.
solver : str
choice of solver (currently only `me` for master-equation)
Returns
-------
corr_mat: *array*
An 2-dimensional *array* (matrix) of correlation values for the times
specified by `tlist` (first index) and `taulist` (second index). If
`tlist` is `None`, then a 1-dimensional *array* of correlation values
is returned instead.
References
----------
See, Gardiner, Quantum Noise, Section 5.2.1.
"""
if debug:
print(inspect.stack()[0][3])
if solver == "me":
return _correlation_me_4op_2t(H,
rho0,
tlist,
taulist,
c_ops,
a_op,
b_op,
c_op,
d_op,
args=args,
options=options)
else:
raise NotImplementedError("Unrecognized choice of solver %s." % solver)
def propagator(H,
t,
c_op_list=[],
args={},
options=None,
unitary_mode='batch',
parallel=False,
progress_bar=None,
_safe_mode=True,
**kwargs):
r"""
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
if parallel:
output = parallel_map(_parallel_mesolve,
range(N),
task_args=(sqrt_N, H, tlist, c_op_list, args,
options),
task_kwargs={"dims": H0.dims[0]},
progress_bar=progress_bar,
num_cpus=num_cpus)
u = np.zeros([N, N, len(tlist)], dtype=complex)
for n in range(N):
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output[n].states[k].full()).T
else:
rho0 = qeye(H0.dims[0])
output = mesolve(H,
rho0,
tlist,
args=args,
options=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),
task_kwargs={"dims": H0.dims},
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 = projection(N, row_idx, col_idx)
rho0.dims = H0.dims
output = mesolve(H,
rho0,
tlist,
c_ops=c_op_list,
args=args,
options=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 coherence_function_g2(H,
rho0,
taulist,
c_ops,
a_op,
solver="me",
args=None,
options=Options()):
"""
Calculate the second-order quantum coherence function:
.. math::
g^{(2)}(\\tau) =
\\frac{\\langle a^\\dagger(0)a^\\dagger(\\tau)a(\\tau)a(0)\\rangle}
{\\langle a^\\dagger(\\tau)a(\\tau)\\rangle
\\langle a^\\dagger(0)a(0)\\rangle}
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
rho0 : :class:`qutip.qobj.Qobj`
Initial state density matrix (or state vector). If 'rho0' is
'None', then the steady state will be used as initial state.
taulist : *list* / *array*
list of times for :math:`\\tau`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
The annihilation operator of the mode.
solver : str
choice of solver (currently only 'me')
Returns
-------
g2, G2: tuble of *array*
The normalized and unnormalized second-order coherence function.
"""
# first calculate the photon number
if rho0 is None:
rho0 = steadystate(H, c_ops)
n = np.array([expect(rho0, a_op.dag() * a_op)])
else:
n = mesolve(H,
rho0,
taulist,
c_ops, [a_op.dag() * a_op],
args=args,
options=options).expect[0]
# calculate the correlation function G2 and normalize with n to obtain g2
G2 = correlation_4op_1t(H,
rho0,
taulist,
c_ops,
a_op.dag(),
a_op.dag(),
a_op,
a_op,
solver=solver,
args=args,
options=options)
g2 = G2 / (n[0] * n)
return g2, G2
def propagator(H, t, c_op_list=[], args={}, options=None,
unitary_mode='batch', parallel=False,
progress_bar=None, **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
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)
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':
u = np.zeros([N, N, len(tlist)], dtype=complex)
progress_bar.start(N)
for n in range(0, N):
progress_bar.update(n)
psi0 = basis(N, n)
output = sesolve(H, psi0, tlist, [], args, options, _safe_mode=False)
for k, t in enumerate(tlist):
u[:, n, k] = output.states[k].full().T
progress_bar.finished()
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)
output = sesolve(H2, psi0, tlist, [] , args = args, _safe_mode=False,
options=Options(normalize_output=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 Exception('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:
progress_bar.start(N)
for n in range(0, N):
progress_bar.update(n)
col_idx, row_idx = np.unravel_index(n,(sqrt_N,sqrt_N))
rho0 = Qobj(sp.csr_matrix(([1],([row_idx],[col_idx])), shape=(sqrt_N,sqrt_N), dtype=complex))
output = mesolve(H, rho0, tlist, [], [], args, options, _safe_mode=False)
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output.states[k].full()).T
progress_bar.finished()
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:
if unitary_mode == 'batch':
return Qobj(u[-1], dims=dims)
else:
return Qobj(u[:, :, 1], dims=dims)
else:
if unitary_mode == 'batch':
return np.array([Qobj(u[k], dims=dims) for k in range(len(tlist))], dtype=object)
else:
return np.array([Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))], dtype=object)
def correlation_ss(H,
taulist,
c_ops,
a_op,
b_op,
rho0=None,
solver="me",
reverse=False,
args=None,
options=Options()):
"""
Calculate a two-operator two-time correlation function
:math:`\left<A(\\tau)B(0)\\right>` or
:math:`\left<A(0)B(\\tau)\\right>` (if `reverse=True`),
using the quantum regression theorem and the evolution solver indicated by
the *solver* parameter.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
system Hamiltonian.
rho0 : :class:`qutip.qobj.Qobj`
Initial state density matrix (or state vector). If 'rho0' is
'None', then the steady state will be used as initial state.
taulist : *list* / *array*
list of times for :math:`\\tau`.
c_ops : list of :class:`qutip.qobj.Qobj`
list of collapse operators.
a_op : :class:`qutip.qobj.Qobj`
operator A.
b_op : :class:`qutip.qobj.Qobj`
operator B.
reverse : bool
If `True`, calculate :math:`\left<A(0)B(\\tau)\\right>` instead of
:math:`\left<A(\\tau)B(0)\\right>`.
solver : str
choice of solver (`me` for master-equation,
`es` for exponential series and `mc` for Monte-carlo)
Returns
-------
corr_vec: *array*
An *array* of correlation values for the times specified by `tlist`
"""
if debug:
print(inspect.stack()[0][3])
return correlation_2op_1t(H,
rho0,
taulist,
c_ops,
a_op,
b_op,
solver,
reverse=reverse,
args=args,
options=options)
def propagator(H, t, c_op_list, args=None, options=None, sparse=False):
"""
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.
Returns
-------
a : qobj
Instance representing the propagator :math:`U(t)`.
"""
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 isinstance(H, (types.FunctionType, types.BuiltinFunctionType,
functools.partial)):
H0 = H(0.0, args)
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
u = np.zeros([N, N, len(tlist)], dtype=complex)
for n in range(0, N):
psi0 = basis(N, n)
output = sesolve(H, psi0, tlist, [], args, options)
for k, t in enumerate(tlist):
u[:, n, k] = output.states[k].full().T
# todo: evolving a batch of wave functions:
# psi_0_list = [basis(N, n) for n in range(N)]
# psi_t_list = mesolve(H, psi_0_list, [0, t], [], [], args, options)
# for n in range(0, N):
# u[:,n] = psi_t_list[n][1].full().T
elif len(c_op_list) == 0 and H0.issuper:
# calculate the propagator for the vector representation of the
# density matrix (a superoperator propagator)
N = H0.shape[0]
dims = H0.dims
u = np.zeros([N, N, len(tlist)], dtype=complex)
for n in range(0, N):
psi0 = basis(N, n)
rho0 = Qobj(vec2mat(psi0.full()))
output = mesolve(H, rho0, tlist, [], [], args, options)
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output.states[k].full()).T
else:
# calculate the propagator for the vector representation of the
# density matrix (a superoperator propagator)
N = H0.shape[0]
dims = [H0.dims, H0.dims]
u = np.zeros([N * N, N * N, len(tlist)], dtype=complex)
if sparse:
for n in range(N * N):
psi0 = basis(N * N, n)
psi0.dims = [dims[0], 1]
rho0 = vector_to_operator(psi0)
output = mesolve(H, rho0, tlist, c_op_list, [], args, options)
for k, t in enumerate(tlist):
u[:, n, k] = operator_to_vector(
output.states[k]).full(squeeze=True)
else:
for n in range(N * N):
psi0 = basis(N * N, n)
rho0 = Qobj(vec2mat(psi0.full()))
output = mesolve(H, rho0, tlist, c_op_list, [], args, options)
for k, t in enumerate(tlist):
u[:, n, k] = mat2vec(output.states[k].full()).T
if len(tlist) == 2:
return Qobj(u[:, :, 1], dims=dims)
else:
return [Qobj(u[:, :, k], dims=dims) for k in range(len(tlist))]
def mesolve(H, rho0, tlist, c_ops, e_ops, args={}, options=None,
progress_bar=BaseProgressBar()):
"""
Master equation evolution of a density matrix for a given Hamiltonian.
Evolve the state vector or density matrix (`rho0`) using a given
Hamiltonian (`H`) and an [optional] set of collapse operators
(`c_op_list`), by integrating the set of ordinary differential equations
that define the system. In the absence of collapse operators the system is
evolved according to the unitary evolution of the Hamiltonian.
The output is either the state vector at arbitrary points in time
(`tlist`), or the expectation values of the supplied operators
(`e_ops`). If e_ops is a callback function, it is invoked for each
time in `tlist` with time and the state as arguments, and the function
does not use any return values.
**Time-dependent operators**
For problems with time-dependent problems `H` and `c_ops` can be callback
functions that takes two arguments, time and `args`, and returns the
Hamiltonian or Liouvillian for the system at that point in time
(*callback format*).
Alternatively, `H` and `c_ops` can be a specified in a nested-list format
where each element in the list is a list of length 2, containing an
operator (:class:`qutip.qobj`) at the first element and where the
second element is either a string (*list string format*), a callback
function (*list callback format*) that evaluates to the time-dependent
coefficient for the corresponding operator, or a numpy array (*list
array format*) which specifies the value of the coefficient to the
corresponding operator for each value of t in tlist.
*Examples*
H = [[H0, 'sin(w*t)'], [H1, 'sin(2*w*t)']]
H = [[H0, sin(w*tlist)], [H1, sin(2*w*tlist)]]
H = [[H0, f0_t], [H1, f1_t]]
where f0_t and f1_t are python functions with signature f_t(t, args).
In the *list string format* and *list callback format*, the string
expression and the callback function must evaluate to a real or complex
number (coefficient for the corresponding operator).
In all cases of time-dependent operators, `args` is a dictionary of
parameters that is used when evaluating operators. It is passed to the
callback functions as second argument
.. note::
If an element in the list-specification of the Hamiltonian or
the list of collapse operators are in super-operator form it will be
added to the total Liouvillian of the problem with out further
transformation. This allows for using mesolve for solving master
equations that are not on standard Lindblad form.
.. note::
On using callback function: mesolve transforms all :class:`qutip.qobj`
objects to sparse matrices before handing the problem to the integrator
function. In order for your callback function to work correctly, pass
all :class:`qutip.qobj` objects that are used in constructing the
Hamiltonian via args. mesolve will check for :class:`qutip.qobj` in
`args` and handle the conversion to sparse matrices. All other
:class:`qutip.qobj` objects that are not passed via `args` will be
passed on to the integrator in scipy which will raise an NotImplemented
exception.
Parameters
----------
H : :class:`qutip.Qobj`
system Hamiltonian, or a callback function for time-dependent
Hamiltonians.
rho0 : :class:`qutip.Qobj`
initial density matrix or state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
c_ops : list of :class:`qutip.Qobj`
single collapse operator, or list of collapse operators.
e_ops : list of :class:`qutip.Qobj` / callback function single
single operator or list of operators for which to evaluate
expectation values.
args : *dictionary*
dictionary of parameters for time-dependent Hamiltonians and
collapse operators.
options : :class:`qutip.Options`
with options for the ODE solver.
progress_bar: TextProgressBar
Optional instance of BaseProgressBar, or a subclass thereof, for
showing the progress of the simulation.
Returns
-------
output: :class:`qutip.solver`
An instance of the class :class:`qutip.solver`, which contains either
an *array* of expectation values for the times specified by `tlist`, or
an *array* or state vectors or density matrices corresponding to the
times in `tlist` [if `e_ops` is an empty list], or
nothing if a callback function was given in place of operators for
which to calculate the expectation values.
"""
#Cast Qobj instances to lists of one element
c_ops, e_ops = map(qobj_list, [c_ops, e_ops])
if isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
# convert array based time-dependence to string format
H, c_ops, args = _td_wrap_array_str(H, c_ops, args, tlist)
# check for type (if any) of time-dependent inputs
_, n_func, n_str = _td_format_check(H, c_ops)
if options is None:
options = Options()
if (not options.rhs_reuse) or (not config.tdfunc):
# reset config collapse and time-dependence flags to default values
config.reset()
res = None
#
# dispatch the appropriate solver
#
if ((c_ops and len(c_ops) > 0)
or (not isket(rho0))
or (isinstance(H, Qobj) and issuper(H))
or (isinstance(H, list) and
isinstance(H[0], Qobj) and issuper(H[0]))):
#
# we have collapse operators
#
#
# find out if we are dealing with all-constant hamiltonian and
# collapse operators or if we have at least one time-dependent
# operator. Then delegate to appropriate solver...
#
if isinstance(H, Qobj):
# constant hamiltonian
if n_func == 0 and n_str == 0:
# constant collapse operators
res = _mesolve_const(H, rho0, tlist, c_ops,
e_ops, args, options,
progress_bar)
elif n_str > 0:
# constant hamiltonian but time-dependent collapse
# operators in list string format
res = _mesolve_list_str_td([H], rho0, tlist, c_ops,
e_ops, args, options,
progress_bar)
elif n_func > 0:
# constant hamiltonian but time-dependent collapse
# operators in list function format
res = _mesolve_list_func_td([H], rho0, tlist, c_ops,
e_ops, args, options,
progress_bar)
elif isinstance(H, (types.FunctionType,
types.BuiltinFunctionType, partial)):
# function-callback style time-dependence: must have constant
# collapse operators
if n_str > 0: # or n_func > 0:
raise TypeError("Incorrect format: function-format " +
"Hamiltonian cannot be mixed with " +
"time-dependent collapse operators.")
else:
res = _mesolve_func_td(H, rho0, tlist, c_ops,
e_ops, args, options,
progress_bar)
elif isinstance(H, list):
# determine if we are dealing with list of [Qobj, string] or
# [Qobj, function] style time-dependencies (for pure python and
# cython, respectively)
if n_func > 0:
res = _mesolve_list_func_td(H, rho0, tlist, c_ops,
e_ops, args, options,
progress_bar)
else:
res = _mesolve_list_str_td(H, rho0, tlist, c_ops,
e_ops, args, options,
progress_bar)
else:
raise TypeError("Incorrect specification of Hamiltonian " +
"or collapse operators.")
else:
#
# no collapse operators: unitary dynamics
#
if n_func > 0:
res = _sesolve_list_func_td(H, rho0, tlist,
e_ops, args, options, progress_bar)
elif n_str > 0:
res = _sesolve_list_str_td(H, rho0, tlist,
e_ops, args, options, progress_bar)
elif isinstance(H, (types.FunctionType,
types.BuiltinFunctionType, partial)):
res = _sesolve_func_td(H, rho0, tlist,
e_ops, args, options, progress_bar)
else:
res = _sesolve_const(H, rho0, tlist,
e_ops, args, options, progress_bar)
if e_ops_dict:
res.expect = {e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())}
return res