def smesolve_generic(H, rho0, tlist, c_ops, e_ops, rhs, d1, d2, ntraj, nsubsteps):
"""
internal
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(tlist)
N_substeps = nsubsteps
N = N_store * N_substeps
dt = (tlist[1] - tlist[0]) / N_substeps
print("N = %d. dt=%.2e" % (N, dt))
data = Odedata()
data.expect = np.zeros((len(e_ops), N_store), dtype=complex)
# pre-compute collapse operator combinations that are commonly needed
# when evaluating the RHS of stochastic master equations
A_ops = []
for c_idx, c in enumerate(c_ops):
# xxx: precompute useful operator expressions...
cdc = c.dag() * c
Ldt = spre(c) * spost(c.dag()) - 0.5 * spre(cdc) - 0.5 * spost(cdc)
LdW = spre(c) + spost(c.dag())
Lm = spre(c) + spost(c.dag()) # currently same as LdW
A_ops.append([Ldt.data, LdW.data, Lm.data])
# Liouvillian for the unitary part
L = -1.0j * (spre(H) - spost(H)) # XXX: should we split the ME in stochastic
# and deterministic collapse operators here?
progress_acc = 0.0
for n in range(ntraj):
if debug and (100 * float(n) / ntraj) >= progress_acc:
print("Progress: %.2f" % (100 * float(n) / ntraj))
progress_acc += 10.0
rho_t = mat2vec(rho0.full())
states_list = _smesolve_single_trajectory(
L, dt, tlist, N_store, N_substeps, rho_t, A_ops, e_ops, data, rhs, d1, d2
)
# if average -> average...
data.states.append(states_list)
# average
data.expect = data.expect / ntraj
return data
def smepdpsolve_generic(ssdata, options, progress_bar):
"""
For internal use.
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(ssdata.tlist)
N_substeps = ssdata.nsubsteps
N = N_store * N_substeps
dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps
NT = ssdata.ntraj
data = Odedata()
data.solver = "smepdpsolve"
data.times = ssdata.tlist
data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.jump_times = []
data.jump_op_idx = []
# Liouvillian for the deterministic part.
# needs to be modified for TD systems
L = liouvillian_fast(ssdata.H, ssdata.c_ops)
progress_bar.start(ssdata.ntraj)
for n in range(ssdata.ntraj):
progress_bar.update(n)
rho_t = mat2vec(ssdata.rho0.full()).ravel()
states_list, jump_times, jump_op_idx = \
_smepdpsolve_single_trajectory(data, L, dt, ssdata.tlist,
N_store, N_substeps,
rho_t, ssdata.c_ops, ssdata.e_ops)
data.states.append(states_list)
data.jump_times.append(jump_times)
data.jump_op_idx.append(jump_op_idx)
progress_bar.finished()
# average density matrices
if options.average_states and np.any(data.states):
data.states = [sum(state_list).unit() for state_list in data.states]
# average
data.expect = data.expect / ssdata.ntraj
# standard error
if NT > 1:
data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1))
else:
data.se = None
return data
def smesolve_generic(H, rho0, tlist, c_ops, sc_ops, e_ops,
rhs, d1, d2, d2_len, ntraj, nsubsteps,
options, progress_bar):
"""
internal
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(tlist)
N_substeps = nsubsteps
N = N_store * N_substeps
dt = (tlist[1] - tlist[0]) / N_substeps
data = Odedata()
data.solver = "smesolve"
data.times = tlist
data.expect = np.zeros((len(e_ops), N_store), dtype=complex)
# pre-compute collapse operator combinations that are commonly needed
# when evaluating the RHS of stochastic master equations
A_ops = []
for c_idx, c in enumerate(sc_ops):
# xxx: precompute useful operator expressions...
cdc = c.dag() * c
Ldt = spre(c) * spost(c.dag()) - 0.5 * spre(cdc) - 0.5 * spost(cdc)
LdW = spre(c) + spost(c.dag())
Lm = spre(c) + spost(c.dag()) # currently same as LdW
A_ops.append([Ldt.data, LdW.data, Lm.data])
# Liouvillian for the deterministic part
L = liouvillian_fast(H, c_ops) # needs to be modified for TD systems
progress_bar.start(ntraj)
for n in range(ntraj):
progress_bar.update(n)
rho_t = mat2vec(rho0.full())
states_list = _smesolve_single_trajectory(
L, dt, tlist, N_store, N_substeps,
rho_t, A_ops, e_ops, data, rhs, d1, d2, d2_len)
# if average -> average...
data.states.append(states_list)
progress_bar.finished()
# average
data.expect = data.expect / ntraj
return data
def sepdpsolve_generic(ssdata, options, progress_bar):
"""
For internal use.
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(ssdata.tlist)
N_substeps = ssdata.nsubsteps
N = N_store * N_substeps
dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps
NT = ssdata.ntraj
data = Odedata()
data.solver = "spdpsolve"
data.times = ssdata.tlist
data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.jump_times = []
data.jump_op_idx = []
# effective hamiltonian for deterministic part
Heff = ssdata.H
for c in ssdata.c_ops:
Heff += -0.5j * c.dag() * c
progress_bar.start(ssdata.ntraj)
for n in range(ssdata.ntraj):
progress_bar.update(n)
psi_t = ssdata.psi0.full()
states_list, jump_times, jump_op_idx = \
_sepdpsolve_single_trajectory(Heff, dt, ssdata.tlist,
N_store, N_substeps,
psi_t, ssdata.c_ops, ssdata.e_ops,
data)
# if average -> average...
data.states.append(states_list)
data.jump_times.append(jump_times)
data.jump_op_idx.append(jump_op_idx)
progress_bar.finished()
# average
data.expect = data.expect / NT
# standard error
if NT > 1:
data.ss = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1))
return data
def ssesolve_generic(H, psi0, tlist, c_ops, e_ops, rhs, d1, d2, ntraj, nsubsteps):
"""
internal
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(tlist)
N_substeps = nsubsteps
N = N_store * N_substeps
dt = (tlist[1] - tlist[0]) / N_substeps
print("N = %d. dt=%.2e" % (N, dt))
data = Odedata()
data.expect = np.zeros((len(e_ops), N_store), dtype=complex)
# pre-compute collapse operator combinations that are commonly needed
# when evaluating the RHS of stochastic Schrodinger equations
A_ops = []
for c_idx, c in enumerate(c_ops):
A_ops.append([c.data, (c + c.dag()).data, (c.dag() * c).data])
progress_acc = 0.0
for n in range(ntraj):
if debug and (100 * float(n) / ntraj) >= progress_acc:
print("Progress: %.2f" % (100 * float(n) / ntraj))
progress_acc += 10.0
psi_t = psi0.full()
states_list = _ssesolve_single_trajectory(
H, dt, tlist, N_store, N_substeps, psi_t, A_ops, e_ops, data, rhs, d1, d2
)
# if average -> average...
data.states.append(states_list)
# average
data.expect = data.expect / ntraj
return data
def ssesolve_generic(ssdata, options, progress_bar):
"""
internal
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(ssdata.tlist)
N_substeps = ssdata.nsubsteps
N = N_store * N_substeps
dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps
NT = ssdata.ntraj
data = Odedata()
data.solver = "ssesolve"
data.times = ssdata.tlist
data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
# pre-compute collapse operator combinations that are commonly needed
# when evaluating the RHS of stochastic Schrodinger equations
A_ops = []
for c_idx, c in enumerate(ssdata.c_ops):
A_ops.append([c.data,
(c + c.dag()).data,
(c - c.dag()).data,
(c.dag() * c).data])
progress_bar.start(ssdata.ntraj)
for n in range(ssdata.ntraj):
progress_bar.update(n)
psi_t = ssdata.state0.full()
states_list = _ssesolve_single_trajectory(ssdata.H, dt, ssdata.tlist, N_store,
N_substeps, psi_t, A_ops,
ssdata.e_ops, data, ssdata.rhs_func,
ssdata.d1, ssdata.d2, ssdata.d2_len,
ssdata.homogeneous, ssdata)
# if average -> average...
data.states.append(states_list)
progress_bar.finished()
# average
data.expect = data.expect / NT
# standard error
data.ss = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1))
return data
def _generic_ode_solve(r, psi0, tlist, expt_ops, opt,
state_vectorize, state_norm_func=None):
"""
Internal function for solving ODEs.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
dt = tlist[1] - tlist[0]
output = Odedata()
output.solver = "mesolve"
output.times = tlist
if isinstance(expt_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(expt_ops, list):
n_expt_op = len(expt_ops)
expt_callback = False
if n_expt_op == 0:
output.states = []
else:
output.expect = []
output.num_expect = n_expt_op
for op in expt_ops:
if op.isherm and psi0.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# start evolution
#
psi = Qobj(psi0)
t_idx = 0
for t in tlist:
if not r.successful():
break
if state_norm_func:
psi.data = state_vectorize(r.y)
state_norm = state_norm_func(psi.data)
psi.data = psi.data / state_norm
r.set_initial_value(r.y / state_norm, r.t)
else:
psi.data = state_vectorize(r.y)
if expt_callback:
# use callback method
expt_ops(t, Qobj(psi))
else:
# calculate all the expectation values,
# or output rho if no operators
if n_expt_op == 0:
output.states.append(Qobj(psi)) # copy psi/rho
else:
for m in range(0, n_expt_op):
output.expect[m][t_idx] = expect(expt_ops[m], psi)
r.integrate(r.t + dt)
t_idx += 1
if not opt.rhs_reuse and odeconfig.tdname is not None:
try:
os.remove(odeconfig.tdname + ".pyx")
except:
pass
return output
def sepdpsolve_generic(ssdata, options, progress_bar):
"""
For internal use.
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(ssdata.tlist)
N_substeps = ssdata.nsubsteps
N = N_store * N_substeps
dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps
NT = ssdata.ntraj
data = Odedata()
data.solver = "sepdpsolve"
data.times = ssdata.tlist
data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.jump_times = []
data.jump_op_idx = []
# effective hamiltonian for deterministic part
Heff = ssdata.H
for c in ssdata.c_ops:
Heff += -0.5j * c.dag() * c
progress_bar.start(ssdata.ntraj)
for n in range(ssdata.ntraj):
progress_bar.update(n)
psi_t = ssdata.psi0.full().ravel()
states_list, jump_times, jump_op_idx = \
_sepdpsolve_single_trajectory(Heff, dt, ssdata.tlist,
N_store, N_substeps,
psi_t, ssdata.c_ops, ssdata.e_ops,
data)
data.states.append(states_list)
data.jump_times.append(jump_times)
data.jump_op_idx.append(jump_op_idx)
progress_bar.finished()
# average density matrices
if options.average_states and np.any(data.states):
data.states = [sum(state_list).unit() for state_list in data.states]
# average
data.expect = data.expect / NT
# standard error
if NT > 1:
data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1))
else:
data.se = None
# convert complex data to real if hermitian
data.expect = [np.real(data.expect[n,:]) if e.isherm else data.expect[n,:]
for n, e in enumerate(ssdata.e_ops)]
return data
def smesolve_generic(ssdata, options, progress_bar):
"""
internal
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(ssdata.tlist)
N_substeps = ssdata.nsubsteps
N = N_store * N_substeps
dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps
NT = ssdata.ntraj
data = Odedata()
data.solver = "smesolve"
data.times = ssdata.tlist
data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.noise = []
data.measurement = []
# pre-compute suporoperator operator combinations that are commonly needed
# when evaluating the RHS of stochastic master equations
A_ops = []
for c_idx, c in enumerate(ssdata.sc_ops):
n = c.dag() * c
A_ops.append([spre(c).data, spost(c).data,
spre(c.dag()).data, spost(c.dag()).data,
spre(n).data, spost(n).data,
(spre(c) * spost(c.dag())).data,
lindblad_dissipator(c, data_only=True)])
s_e_ops = [spre(e) for e in ssdata.e_ops]
# Liouvillian for the deterministic part.
# needs to be modified for TD systems
L = liouvillian_fast(ssdata.H, ssdata.c_ops)
progress_bar.start(ssdata.ntraj)
for n in range(ssdata.ntraj):
progress_bar.update(n)
rho_t = mat2vec(ssdata.state0.full()).ravel()
noise = ssdata.noise[n] if ssdata.noise else None
states_list, dW, m = _smesolve_single_trajectory(
L, dt, ssdata.tlist, N_store, N_substeps,
rho_t, A_ops, s_e_ops, data, ssdata.rhs,
ssdata.d1, ssdata.d2, ssdata.d2_len, ssdata.homogeneous,
ssdata.distribution, ssdata.args,
store_measurement=ssdata.store_measurement,
store_states=ssdata.store_states, noise=noise)
data.states.append(states_list)
data.noise.append(dW)
data.measurement.append(m)
progress_bar.finished()
# average density matrices
if options.average_states and np.any(data.states):
data.states = [sum(state_list).unit() for state_list in data.states]
# average
data.expect = data.expect / NT
# standard error
if NT > 1:
data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1))
else:
data.se = None
# convert complex data to real if hermitian
data.expect = [np.real(data.expect[n,:]) if e.isherm else data.expect[n,:]
for n, e in enumerate(ssdata.e_ops)]
return data
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 = Odeoptions()
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 = Odedata()
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, True)
#
# 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.data = vec2mat(r.y)
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, False))
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, False))
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 _generic_ode_solve(r, rho0, tlist, e_ops, opt, progress_bar):
"""
Internal function for solving ME.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
dt = tlist[1] - tlist[0]
e_sops_data = []
output = Odedata()
output.solver = "mesolve"
output.times = tlist
if opt.store_states:
output.states = []
if isinstance(e_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
if n_expt_op == 0:
# fall back on storing states
output.states = []
opt.store_states = True
else:
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
e_sops_data.append(spre(op).data)
if op.isherm and rho0.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# start evolution
#
progress_bar.start(n_tsteps)
rho = Qobj(rho0)
for t_idx, t in enumerate(tlist):
progress_bar.update(t_idx)
if not r.successful():
break
if opt.store_states or expt_callback:
rho.data = vec2mat(r.y)
if opt.store_states:
output.states.append(Qobj(rho))
if expt_callback:
# use callback method
e_ops(t, rho)
for m in range(n_expt_op):
if output.expect[m].dtype == complex:
output.expect[m][t_idx] = expect_rho_vec(e_sops_data[m], r.y)
else:
output.expect[m][t_idx] = np.real(
expect_rho_vec(e_sops_data[m], r.y))
r.integrate(r.t + dt)
progress_bar.finished()
if not opt.rhs_reuse and odeconfig.tdname is not None:
try:
os.remove(odeconfig.tdname + ".pyx")
except:
pass
if opt.store_final_state:
rho.data = vec2mat(r.y)
output.final_state = Qobj(rho)
return output
def fsesolve(H, psi0, tlist, e_ops=[], T=None, args={}, Tsteps=100):
"""
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.
Returns
-------
output : :class:`qutip.odedata.Odedata`
An instance of the class :class:`qutip.odedata.Odedata`, 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]
# find the floquet modes for the time-dependent hamiltonian
f_modes_0, f_energies = floquet_modes(H, T, args)
# 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)
# setup Odedata for storing the results
output = Odedata()
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, True)
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, False)
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 _gather(sols):
# gather list of Odedata objects, sols, into one.
sol = Odedata()
# sol = sols[0]
ntraj = sum([a.ntraj for a in sols])
sol.col_times = np.zeros((ntraj), dtype=np.ndarray)
sol.col_which = np.zeros((ntraj), dtype=np.ndarray)
sol.col_times[0:sols[0].ntraj] = sols[0].col_times
sol.col_which[0:sols[0].ntraj] = sols[0].col_which
sol.states = np.array(sols[0].states)
sol.expect = np.array(sols[0].expect)
if (hasattr(sols[0], 'entropy')):
sol.entropy = np.array(sols[0].entropy)
sofar = 0
for j in range(1, len(sols)):
sofar = sofar + sols[j - 1].ntraj
sol.col_times[sofar:sofar + sols[j].ntraj] = (
sols[j].col_times)
sol.col_which[sofar:sofar + sols[j].ntraj] = (
sols[j].col_which)
if (odeconfig.e_num == 0):
if (odeconfig.options.average_states):
# collect states, averaged over trajectories
sol.states += np.array(sols[j].states)
else:
# collect states, all trajectories
sol.states = np.vstack((sol.states,
np.array(sols[j].states)))
else:
if (odeconfig.options.average_expect):
# collect expectation values, averaged
for i in range(odeconfig.e_num):
sol.expect[i] += np.array(sols[j].expect[i])
else:
# collect expectation values, all trajectories
sol.expect = np.vstack((sol.expect,
np.array(sols[j].expect)))
if (hasattr(sols[j], 'entropy')):
if (odeconfig.options.average_states or odeconfig.options.average_expect):
# collect entropy values, averaged
sol.entropy += np.array(sols[j].entropy)
else:
# collect entropy values, all trajectories
sol.entropy = np.vstack((sol.entropy,
np.array(sols[j].entropy)))
if (odeconfig.options.average_states or odeconfig.options.average_expect):
if (odeconfig.e_num == 0):
sol.states = sol.states / len(sols)
else:
sol.expect = list(sol.expect / len(sols))
inds=np.where(odeconfig.e_ops_isherm)[0]
for jj in inds:
sol.expect[jj]=np.real(sol.expect[jj])
if (hasattr(sols[0], 'entropy')):
sol.entropy = sol.entropy / len(sols)
#convert sol.expect array to list and fix dtypes of arrays
if (not odeconfig.options.average_expect) and odeconfig.e_num!=0:
temp=[list(sol.expect[ii]) for ii in range(ntraj)]
for ii in range(ntraj):
for jj in np.where(odeconfig.e_ops_isherm)[0]:
temp[ii][jj]=np.real(temp[ii][jj])
sol.expect=temp
# convert to list/array to be consistent with qutip mcsolve
sol.states = list(sol.states)
return sol
def brmesolve(H, psi0, tlist, a_ops, e_ops=[], spectra_cb=[],
args={}, options=Odeoptions()):
"""
Solve the dynamics for the system using the Bloch-Redfeild master equation.
.. note::
This solver does not currently support time-dependent Hamiltonian or
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`.
a_ops : list of :class:`qutip.qobj`
List of system operators that couple to bath degrees of freedom.
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.Qdeoptions`
Options for the ODE solver.
Returns
-------
output: :class:`qutip.odedata`
An instance of the class :class:`qutip.odedata`, which contains either
a list of expectation values, for operators given in e_ops, or a list
of states for the times specified by `tlist`.
"""
if not spectra_cb:
# default to infinite temperature white noise
spectra_cb = [lambda w: 1.0 for _ in a_ops]
R, ekets = bloch_redfield_tensor(H, a_ops, spectra_cb)
output = Odedata()
output.times = tlist
results = bloch_redfield_solve(R, ekets, psi0, tlist, e_ops, options)
if e_ops:
output.expect = results
else:
output.states = results
return output
def brmesolve(H,
psi0,
tlist,
a_ops,
e_ops=[],
spectra_cb=[],
args={},
options=Odeoptions()):
"""
Solve the dynamics for the system using the Bloch-Redfeild master equation.
.. note::
This solver does not currently support time-dependent Hamiltonian or
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`.
a_ops : list of :class:`qutip.qobj`
List of system operators that couple to bath degrees of freedom.
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.Qdeoptions`
Options for the ODE solver.
Returns
-------
output: :class:`qutip.odedata`
An instance of the class :class:`qutip.odedata`, which contains either
a list of expectation values, for operators given in e_ops, or a list
of states for the times specified by `tlist`.
"""
if not spectra_cb:
# default to infinite temperature white noise
spectra_cb = [lambda w: 1.0 for _ in a_ops]
R, ekets = bloch_redfield_tensor(H, a_ops, spectra_cb)
output = Odedata()
output.solver = "brmesolve"
output.times = tlist
results = bloch_redfield_solve(R, ekets, psi0, tlist, e_ops, options)
if e_ops:
output.expect = results
else:
output.states = results
return output
def _generic_ode_solve(r, rho0, tlist, e_ops, opt, progress_bar):
"""
Internal function for solving ME.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
e_sops_data = []
output = Odedata()
output.solver = "mesolve"
output.times = tlist
if opt.store_states:
output.states = []
if isinstance(e_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
if n_expt_op == 0:
# fall back on storing states
output.states = []
opt.store_states = True
else:
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
e_sops_data.append(spre(op).data)
if op.isherm and rho0.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# start evolution
#
progress_bar.start(n_tsteps)
rho = Qobj(rho0)
dt = np.diff(tlist)
for t_idx, t in enumerate(tlist):
progress_bar.update(t_idx)
if not r.successful():
break
if opt.store_states or expt_callback:
rho.data = vec2mat(r.y)
if opt.store_states:
output.states.append(Qobj(rho))
if expt_callback:
# use callback method
e_ops(t, rho)
for m in range(n_expt_op):
if output.expect[m].dtype == complex:
output.expect[m][t_idx] = expect_rho_vec(
e_sops_data[m], r.y, 0)
else:
output.expect[m][t_idx] = expect_rho_vec(
e_sops_data[m], r.y, 1)
if t_idx < n_tsteps - 1:
r.integrate(r.t + dt[t_idx])
progress_bar.finished()
if not opt.rhs_reuse and odeconfig.tdname is not None:
try:
os.remove(odeconfig.tdname + ".pyx")
except:
pass
if opt.store_final_state:
rho.data = vec2mat(r.y)
output.final_state = Qobj(rho)
return output
def mcsolve(H,psi0,tlist,c_ops,e_ops,ntraj=500,args={},options=Odeoptions()):
"""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 Odeoptions 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_op_list=[C0,[C1,C1_coeff]]
args={'a':A,'w':W}
or in String (Cython) format we could write::
H=[H0,[H1,'sin(w*t)']]
c_op_list=[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 : qobj
System Hamiltonian.
psi0 : qobj
Initial state vector
tlist : array_like
Times at which results are recorded.
ntraj : int
Number of trajectories to run.
c_ops : array_like
single collapse operator or ``list`` or ``array`` of collapse operators.
e_ops : array_like
single operator or ``list`` or ``array`` of operators for calculating expectation values.
args : dict
Arguments for time-dependent Hamiltonian and collapse operator terms.
options : Odeoptions
Instance of ODE solver options.
Returns
-------
results : Odedata
Object storing all results from simulation.
"""
# if single operator is passed for c_ops or e_ops, convert it to
# list containing only that operator
if isinstance(c_ops, Qobj):
c_ops = [c_ops]
if isinstance(e_ops, Qobj):
e_ops = [e_ops]
if psi0.type!='ket':
raise Exception("Initial state must be a state vector.")
odeconfig.options=options
#set num_cpus to the value given in qutip.settings if none in Odeoptions
if not odeconfig.options.num_cpus:
odeconfig.options.num_cpus=qutip.settings.num_cpus
#set initial value data
if options.tidy:
odeconfig.psi0=psi0.tidyup(options.atol).full()
else:
odeconfig.psi0=psi0.full()
odeconfig.psi0_dims=psi0.dims
odeconfig.psi0_shape=psi0.shape
#set general items
odeconfig.tlist=tlist
if isinstance(ntraj,(list,ndarray)):
odeconfig.ntraj=sort(ntraj)[-1]
else:
odeconfig.ntraj=ntraj
#set norm finding constants
odeconfig.norm_tol=options.norm_tol
odeconfig.norm_steps=options.norm_steps
#----
#----------------------------------------------
# SETUP ODE DATA IF NONE EXISTS OR NOT REUSING
#----------------------------------------------
if (not options.rhs_reuse) or (not odeconfig.tdfunc):
#reset odeconfig collapse and time-dependence flags to default values
_reset_odeconfig()
#check for type of time-dependence (if any)
time_type,h_stuff,c_stuff=_ode_checks(H,c_ops,'mc')
h_terms=len(h_stuff[0])+len(h_stuff[1])+len(h_stuff[2])
c_terms=len(c_stuff[0])+len(c_stuff[1])+len(c_stuff[2])
#set time_type for use in multiprocessing
odeconfig.tflag=time_type
#-Check for PyObjC on Mac platforms
if sys.platform=='darwin' and odeconfig.options.gui:
try:
import Foundation
except:
odeconfig.options.gui=False
#check if running in iPython and using Cython compiling (then no GUI to work around error)
if odeconfig.options.gui and odeconfig.tflag in array([1,10,11]):
try:
__IPYTHON__
except:
pass
else:
odeconfig.options.gui=False
if qutip.settings.qutip_gui=="NONE":
odeconfig.options.gui=False
#check for collapse operators
if c_terms>0:
odeconfig.cflag=1
else:
odeconfig.cflag=0
#Configure data
_mc_data_config(H,psi0,h_stuff,c_ops,c_stuff,args,e_ops,options)
if odeconfig.tflag in array([1,10,11]): #compile time-depdendent RHS code
os.environ['CFLAGS'] = '-O3 -w'
import pyximport
pyximport.install(setup_args={'include_dirs':[numpy.get_include()]})
if odeconfig.tflag in array([1,11]):
code = compile('from '+odeconfig.tdname+' import cyq_td_ode_rhs,col_spmv,col_expect', '<string>', 'exec')
exec(code, globals())
odeconfig.tdfunc=cyq_td_ode_rhs
odeconfig.colspmv=col_spmv
odeconfig.colexpect=col_expect
else:
code = compile('from '+odeconfig.tdname+' import cyq_td_ode_rhs', '<string>', 'exec')
exec(code, globals())
odeconfig.tdfunc=cyq_td_ode_rhs
try:
os.remove(odeconfig.tdname+".pyx")
except:
print("Error removing pyx file. File not found.")
elif odeconfig.tflag==0:
odeconfig.tdfunc=cyq_ode_rhs
else:#setup args for new parameters when rhs_reuse=True and tdfunc is given
#string based
if odeconfig.tflag in array([1,10,11]):
if any(args):
odeconfig.c_args=[]
arg_items=args.items()
for k in range(len(args)):
odeconfig.c_args.append(arg_items[k][1])
#function based
elif odeconfig.tflag in array([2,3,20,22]):
odeconfig.h_func_args=args
#load monte-carlo class
mc=_MC_class()
#RUN THE SIMULATION
mc.run()
#AFTER MCSOLVER IS DONE --------------------------------------
#-------COLLECT AND RETURN OUTPUT DATA IN ODEDATA OBJECT --------------#
output=Odedata()
output.solver='mcsolve'
#state vectors
if mc.psi_out is not None and odeconfig.options.mc_avg and odeconfig.cflag:
output.states=parfor(_mc_dm_avg,mc.psi_out.T)
elif mc.psi_out is not None:
output.states=mc.psi_out
#expectation values
elif mc.expect_out is not None and odeconfig.cflag and odeconfig.options.mc_avg:#averaging if multiple trajectories
if isinstance(ntraj,int):
output.expect=mean(mc.expect_out,axis=0)
elif isinstance(ntraj,(list,ndarray)):
output.expect=[]
for num in ntraj:
expt_data=mean(mc.expect_out[:num],axis=0)
data_list=[]
if any([op.isherm==False for op in e_ops]):
for k in range(len(e_ops)):
if e_ops[k].isherm:
data_list.append(real(expt_data[k]))
else:
data_list.append(expt_data[k])
else:
data_list=[data for data in expt_data]
output.expect.append(data_list)
else:#no averaging for single trajectory or if mc_avg flag (Odeoptions) is off
if mc.expect_out is not None:
output.expect=mc.expect_out
#simulation parameters
output.times=odeconfig.tlist
output.num_expect=odeconfig.e_num
output.num_collapse=odeconfig.c_num
output.ntraj=odeconfig.ntraj
output.col_times=mc.collapse_times_out
output.col_which=mc.which_op_out
return output
def evolve_serial(self, args):
if debug:
print(inspect.stack()[0][3] + ":" + str(os.getpid()))
# run ntraj trajectories for one process via fortran
# get args
queue, ntraj, instanceno, rngseed = args
# initialize the problem in fortran
_init_tlist()
_init_psi0()
if (self.ptrace_sel != []):
_init_ptrace_stuff(self.ptrace_sel)
_init_hamilt()
if (odeconfig.c_num != 0):
_init_c_ops()
if (odeconfig.e_num != 0):
_init_e_ops()
# set options
qtf90.qutraj_run.n_c_ops = odeconfig.c_num
qtf90.qutraj_run.n_e_ops = odeconfig.e_num
qtf90.qutraj_run.ntraj = ntraj
qtf90.qutraj_run.unravel_type = self.unravel_type
qtf90.qutraj_run.average_states = odeconfig.options.average_states
qtf90.qutraj_run.average_expect = odeconfig.options.average_expect
qtf90.qutraj_run.init_odedata(odeconfig.psi0_shape[0],
odeconfig.options.atol,
odeconfig.options.rtol, mf=self.mf,
norm_steps=odeconfig.norm_steps,
norm_tol=odeconfig.norm_tol)
# set optional arguments
qtf90.qutraj_run.order = odeconfig.options.order
qtf90.qutraj_run.nsteps = odeconfig.options.nsteps
qtf90.qutraj_run.first_step = odeconfig.options.first_step
qtf90.qutraj_run.min_step = odeconfig.options.min_step
qtf90.qutraj_run.max_step = odeconfig.options.max_step
qtf90.qutraj_run.norm_steps = odeconfig.options.norm_steps
qtf90.qutraj_run.norm_tol = odeconfig.options.norm_tol
# use sparse density matrices during computation?
qtf90.qutraj_run.rho_return_sparse = self.sparse_dms
# calculate entropy of reduced density matrice?
qtf90.qutraj_run.calc_entropy = self.calc_entropy
# run
show_progress = 1 if debug else 0
qtf90.qutraj_run.evolve(instanceno, rngseed, show_progress)
# construct Odedata instance
sol = Odedata()
sol.ntraj = ntraj
# sol.col_times = qtf90.qutraj_run.col_times
# sol.col_which = qtf90.qutraj_run.col_which-1
sol.col_times, sol.col_which = self.get_collapses(ntraj)
if (odeconfig.e_num == 0):
sol.states = self.get_states(len(odeconfig.tlist), ntraj)
else:
sol.expect = self.get_expect(len(odeconfig.tlist), ntraj)
if (self.calc_entropy):
sol.entropy = self.get_entropy(len(odeconfig.tlist))
if (not self.serial_run):
# put to queue
queue.put(sol)
queue.join()
# deallocate stuff
# finalize()
return sol
def brmesolve(H, psi0, tlist, c_ops, e_ops=[], spectra_cb=[],
args={}, options=Odeoptions()):
"""
Solve the dynamics for the system using the Bloch-Redfeild master equation.
.. note::
This solver does not currently support time-dependent Hamiltonian or
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.
expt_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.Qdeoptions`
Options for the ODE solver.
Returns
-------
output: :class:`qutip.odedata`
An instance of the class :class:`qutip.odedata`, which contains either
an *array* of expectation values for the times specified by `tlist`.
"""
if len(spectra_cb) == 0:
for n in range(len(c_ops)):
# add white noise callbacks if absent
spectra_cb.append(lambda w: 1.0)
R, ekets = bloch_redfield_tensor(H, c_ops, spectra_cb)
output = Odedata()
output.times = tlist
results = bloch_redfield_solve(R, ekets, psi0, tlist, e_ops, options)
if len(e_ops):
output.expect = results
else:
output.states = results
return output
def mcsolve_f90(H, psi0, tlist, c_ops, e_ops, ntraj=None,
options=Odeoptions(), sparse_dms=True, serial=False,
ptrace_sel=[], calc_entropy=False):
"""
Monte-Carlo wave function solver with fortran 90 backend.
Usage is identical to qutip.mcsolve, for problems without explicit
time-dependence, and with some optional input:
Parameters
----------
H : qobj
System Hamiltonian.
psi0 : qobj
Initial state vector
tlist : array_like
Times at which results are recorded.
ntraj : int
Number of trajectories to run.
c_ops : array_like
``list`` or ``array`` of collapse operators.
e_ops : array_like
``list`` or ``array`` of operators for calculating expectation values.
options : Odeoptions
Instance of ODE solver options.
sparse_dms : boolean
If averaged density matrices are returned, they will be stored as
sparse (Compressed Row Format) matrices during computation if
sparse_dms = True (default), and dense matrices otherwise. Dense
matrices might be preferable for smaller systems.
serial : boolean
If True (default is False) the solver will not make use of the
multiprocessing module, and simply run in serial.
ptrace_sel: list
This optional argument specifies a list of components to keep when
returning a partially traced density matrix. This can be convenient for
large systems where memory becomes a problem, but you are only
interested in parts of the density matrix.
calc_entropy : boolean
If ptrace_sel is specified, calc_entropy=True will have the solver
return the averaged entropy over trajectories in results.entropy. This
can be interpreted as a measure of entanglement. See Phys. Rev. Lett.
93, 120408 (2004), Phys. Rev. A 86, 022310 (2012).
Returns
-------
results : Odedata
Object storing all results from simulation.
"""
if ntraj is None:
ntraj = options.ntraj
if psi0.type != 'ket':
raise Exception("Initial state must be a state vector.")
odeconfig.options = options
# set num_cpus to the value given in qutip.settings
# if none in Odeoptions
if not odeconfig.options.num_cpus:
odeconfig.options.num_cpus = qutip.settings.num_cpus
# set initial value data
if options.tidy:
odeconfig.psi0 = psi0.tidyup(options.atol).full()
else:
odeconfig.psi0 = psi0.full()
odeconfig.psi0_dims = psi0.dims
odeconfig.psi0_shape = psi0.shape
# set general items
odeconfig.tlist = tlist
if isinstance(ntraj, (list, np.ndarray)):
raise Exception("ntraj as list argument is not supported.")
else:
odeconfig.ntraj = ntraj
# ntraj_list = [ntraj]
# set norm finding constants
odeconfig.norm_tol = options.norm_tol
odeconfig.norm_steps = options.norm_steps
if not options.rhs_reuse:
odeconfig.soft_reset()
# no time dependence
odeconfig.tflag = 0
# check for collapse operators
if len(c_ops) > 0:
odeconfig.cflag = 1
else:
odeconfig.cflag = 0
# Configure data
_mc_data_config(H, psi0, [], c_ops, [], [], e_ops, options, odeconfig)
# Load Monte Carlo class
mc = _MC_class()
# Set solver type
if (options.method == 'adams'):
mc.mf = 10
elif (options.method == 'bdf'):
mc.mf = 22
else:
if debug:
print('Unrecognized method for ode solver, using "adams".')
mc.mf = 10
# store ket and density matrix dims and shape for convenience
mc.psi0_dims = psi0.dims
mc.psi0_shape = psi0.shape
mc.dm_dims = (psi0 * psi0.dag()).dims
mc.dm_shape = (psi0 * psi0.dag()).shape
# use sparse density matrices during computation?
mc.sparse_dms = sparse_dms
# run in serial?
mc.serial_run = serial or (ntraj == 1)
# are we doing a partial trace for returned states?
mc.ptrace_sel = ptrace_sel
if (ptrace_sel != []):
if debug:
print("ptrace_sel set to " + str(ptrace_sel))
print("We are using dense density matrices during computation " +
"when performing partial trace. Setting sparse_dms = False")
print("This feature is experimental.")
mc.sparse_dms = False
mc.dm_dims = psi0.ptrace(ptrace_sel).dims
mc.dm_shape = psi0.ptrace(ptrace_sel).shape
if (calc_entropy):
if (ptrace_sel == []):
if debug:
print("calc_entropy = True, but ptrace_sel = []. Please set " +
"a list of components to keep when calculating average " +
"entropy of reduced density matrix in ptrace_sel. " +
"Setting calc_entropy = False.")
calc_entropy = False
mc.calc_entropy = calc_entropy
# construct output Odedata object
output = Odedata()
# Run
mc.run()
output.states = mc.sol.states
output.expect = mc.sol.expect
output.col_times = mc.sol.col_times
output.col_which = mc.sol.col_which
if (hasattr(mc.sol, 'entropy')):
output.entropy = mc.sol.entropy
output.solver = 'Fortran 90 Monte Carlo solver'
# simulation parameters
output.times = odeconfig.tlist
output.num_expect = odeconfig.e_num
output.num_collapse = odeconfig.c_num
output.ntraj = odeconfig.ntraj
return output
def essolve(H, rho0, tlist, c_op_list, e_ops):
"""
Evolution of a state vector or density matrix (`rho0`) for a given
Hamiltonian (`H`) and set of collapse operators (`c_op_list`), by
expressing the ODE as an exponential series. The output is either
the state vector at arbitrary points in time (`tlist`), or the
expectation values of the supplied operators (`e_ops`).
Parameters
----------
H : qobj/function_type
System Hamiltonian.
rho0 : :class:`qutip.qobj`
Initial state density matrix.
tlist : list/array
``list`` of times for :math:`t`.
c_op_list : list of :class:`qutip.qobj`
``list`` of :class:`qutip.qobj` collapse operators.
e_ops : list of :class:`qutip.qobj`
``list`` of :class:`qutip.qobj` operators for which to evaluate
expectation values.
Returns
-------
expt_array : array
Expectation values of wavefunctions/density matrices for the
times specified in ``tlist``.
.. note:: This solver does not support time-dependent Hamiltonians.
"""
n_expt_op = len(e_ops)
n_tsteps = len(tlist)
# Calculate the Liouvillian
if (c_op_list is None or len(c_op_list) == 0) and isket(rho0):
L = H
else:
L = liouvillian(H, c_op_list)
es = ode2es(L, rho0)
# evaluate the expectation values
if n_expt_op == 0:
result_list = [Qobj()] * n_tsteps # XXX
else:
result_list = np.zeros([n_expt_op, n_tsteps], dtype=complex)
for n, e in enumerate(e_ops):
result_list[n, :] = expect(e, esval(es, tlist))
data = Odedata()
data.solver = "essolve"
data.times = tlist
data.expect = [np.real(result_list[n, :]) if e.isherm else result_list[n, :]
for n, e in enumerate(e_ops)]
return data
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 = Odeoptions()
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 = Odedata()
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, True)
#
# 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.data = vec2mat(r.y)
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, False))
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, False))
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 ssesolve_generic(ssdata, options, progress_bar):
"""
internal
.. note::
Experimental.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(ssdata.tlist)
N_substeps = ssdata.nsubsteps
N = N_store * N_substeps
dt = (ssdata.tlist[1] - ssdata.tlist[0]) / N_substeps
NT = ssdata.ntraj
data = Odedata()
data.solver = "ssesolve"
data.times = ssdata.tlist
data.expect = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(ssdata.e_ops), N_store), dtype=complex)
data.noise = []
data.measurement = []
# pre-compute collapse operator combinations that are commonly needed
# when evaluating the RHS of stochastic Schrodinger equations
A_ops = []
for c_idx, c in enumerate(ssdata.sc_ops):
A_ops.append([c.data,
(c + c.dag()).data,
(c - c.dag()).data,
(c.dag() * c).data])
progress_bar.start(ssdata.ntraj)
for n in range(ssdata.ntraj):
progress_bar.update(n)
psi_t = ssdata.state0.full().ravel()
noise = ssdata.noise[n] if ssdata.noise else None
states_list, dW, m = _ssesolve_single_trajectory(
ssdata.H, dt, ssdata.tlist, N_store, N_substeps, psi_t, A_ops,
ssdata.e_ops, data, ssdata.rhs_func, ssdata.d1, ssdata.d2,
ssdata.d2_len, ssdata.homogeneous, ssdata.distribution, ssdata.args,
store_measurement=ssdata.store_measurement, noise=noise)
data.states.append(states_list)
data.noise.append(dW)
data.measurement.append(m)
progress_bar.finished()
# average density matrices
if options.average_states and np.any(data.states):
data.states = [sum(state_list).unit() for state_list in data.states]
# average
data.expect = data.expect / NT
# standard error
if NT > 1:
data.se = (data.ss - NT * (data.expect ** 2)) / (NT * (NT - 1))
else:
data.se = None
# convert complex data to real if hermitian
data.expect = [np.real(data.expect[n,:]) if e.isherm else data.expect[n,:]
for n, e in enumerate(ssdata.e_ops)]
return data
def _generic_ode_solve(r, psi0, tlist, e_ops, opt, progress_bar,
state_norm_func=None, dims=None):
"""
Internal function for solving ODEs.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
output = Odedata()
output.solver = "sesolve"
output.times = tlist
if opt.store_states:
output.states = []
if isinstance(e_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
if n_expt_op == 0:
# fallback on storing states
output.states = []
opt.store_states = True
else:
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")
#
# start evolution
#
progress_bar.start(n_tsteps)
dt = np.diff(tlist)
for t_idx, t in enumerate(tlist):
progress_bar.update(t_idx)
if not r.successful():
break
if state_norm_func:
data = r.y / state_norm_func(r.y)
r.set_initial_value(data, r.t)
if opt.store_states:
output.states.append(Qobj(r.y,dims=dims))
if expt_callback:
# use callback method
e_ops(t, Qobj(r.y, dims=psi0.dims))
for m in range(n_expt_op):
output.expect[m][t_idx] = cy_expect_psi(e_ops[m].data, r.y, e_ops[m].isherm)
if t_idx < n_tsteps - 1:
r.integrate(r.t + dt[t_idx])
progress_bar.finished()
if not opt.rhs_reuse and odeconfig.tdname is not None:
try:
os.remove(odeconfig.tdname + ".pyx")
except:
pass
if opt.store_final_state:
output.final_state = Qobj(r.y)
return output
def fsesolve(H, psi0, tlist, e_ops=[], T=None, args={}, Tsteps=100):
"""
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.
Returns
-------
output : :class:`qutip.odedata.Odedata`
An instance of the class :class:`qutip.odedata.Odedata`, 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]
# find the floquet modes for the time-dependent hamiltonian
f_modes_0, f_energies = floquet_modes(H, T, args)
# 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)
# setup Odedata for storing the results
output = Odedata()
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, True)
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, False)
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
