def mcsolve_f90(H, psi0, tlist, c_ops, e_ops, ntraj=None,
options=Options(), 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 : Options
Instance of 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 : Result
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.")
config.options = options
# set num_cpus to the value given in qutip.settings
# if none in Options
if not config.options.num_cpus:
config.options.num_cpus = qutip.settings.num_cpus
# set initial value data
if options.tidy:
config.psi0 = psi0.tidyup(options.atol).full()
else:
config.psi0 = psi0.full()
config.psi0_dims = psi0.dims
config.psi0_shape = psi0.shape
# set general items
config.tlist = tlist
if isinstance(ntraj, (list, np.ndarray)):
raise Exception("ntraj as list argument is not supported.")
else:
config.ntraj = ntraj
# ntraj_list = [ntraj]
# set norm finding constants
config.norm_tol = options.norm_tol
config.norm_steps = options.norm_steps
if not options.rhs_reuse:
config.soft_reset()
# no time dependence
config.tflag = 0
# check for collapse operators
if len(c_ops) > 0:
config.cflag = 1
else:
config.cflag = 0
# Configure data
_mc_data_config(H, psi0, [], c_ops, [], [], e_ops, options, config)
# 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 Result object
output = Result()
# 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 = config.tlist
output.num_expect = config.e_num
output.num_collapse = config.c_num
output.ntraj = config.ntraj
return output
def _gather(sols):
# gather list of Result objects, sols, into one.
sol = Result()
# 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 (config.e_num == 0):
if (config.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 (config.options.average_expect):
# collect expectation values, averaged
for i in range(config.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 (config.options.average_states or
config.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 (config.options.average_states or config.options.average_expect):
if (config.e_num == 0):
sol.states = sol.states / len(sols)
else:
sol.expect = list(sol.expect / len(sols))
inds = np.where(config.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 config.options.average_expect) and config.e_num != 0:
temp = [list(sol.expect[ii]) for ii in range(ntraj)]
for ii in range(ntraj):
for jj in np.where(config.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 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 (config.c_num != 0):
_init_c_ops()
if (config.e_num != 0):
_init_e_ops()
# set options
qtf90.qutraj_run.n_c_ops = config.c_num
qtf90.qutraj_run.n_e_ops = config.e_num
qtf90.qutraj_run.ntraj = ntraj
qtf90.qutraj_run.unravel_type = self.unravel_type
qtf90.qutraj_run.average_states = config.options.average_states
qtf90.qutraj_run.average_expect = config.options.average_expect
qtf90.qutraj_run.init_result(config.psi0_shape[0],
config.options.atol,
config.options.rtol, mf=self.mf,
norm_steps=config.norm_steps,
norm_tol=config.norm_tol)
# set optional arguments
qtf90.qutraj_run.order = config.options.order
qtf90.qutraj_run.nsteps = config.options.nsteps
qtf90.qutraj_run.first_step = config.options.first_step
qtf90.qutraj_run.min_step = config.options.min_step
qtf90.qutraj_run.max_step = config.options.max_step
qtf90.qutraj_run.norm_steps = config.options.norm_steps
qtf90.qutraj_run.norm_tol = config.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 Result instance
sol = Result()
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 (config.e_num == 0):
sol.states = self.get_states(len(config.tlist), ntraj)
else:
sol.expect = self.get_expect(len(config.tlist), ntraj)
if (self.calc_entropy):
sol.entropy = self.get_entropy(len(config.tlist))
if (not self.serial_run):
# put to queue
queue.put(sol)
queue.join()
# deallocate stuff
# finalize()
return sol
def mcsolve_f90(H,
psi0,
tlist,
c_ops,
e_ops,
ntraj=None,
options=Options(),
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 : Options
Instance of 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 : Result
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.")
config.options = options
# set num_cpus to the value given in qutip.settings
# if none in Options
if not config.options.num_cpus:
config.options.num_cpus = qutip.settings.num_cpus
# set initial value data
if options.tidy:
config.psi0 = psi0.tidyup(options.atol).full()
else:
config.psi0 = psi0.full()
config.psi0_dims = psi0.dims
config.psi0_shape = psi0.shape
# set general items
config.tlist = tlist
if isinstance(ntraj, (list, np.ndarray)):
raise Exception("ntraj as list argument is not supported.")
else:
config.ntraj = ntraj
# ntraj_list = [ntraj]
# set norm finding constants
config.norm_tol = options.norm_tol
config.norm_steps = options.norm_steps
if not options.rhs_reuse:
config.soft_reset()
# no time dependence
config.tflag = 0
# check for collapse operators
if len(c_ops) > 0:
config.cflag = 1
else:
config.cflag = 0
# Configure data
_mc_data_config(H, psi0, [], c_ops, [], [], e_ops, options, config)
# 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 Result object
output = Result()
# 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 = config.tlist
output.num_expect = config.e_num
output.num_collapse = config.c_num
output.ntraj = config.ntraj
return output
def _gather(sols):
# gather list of Result objects, sols, into one.
sol = Result()
# 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, dtype=object)
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 (config.e_num == 0):
if (config.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 (config.options.average_expect):
# collect expectation values, averaged
for i in range(config.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 (config.options.average_states
or config.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 (config.options.average_states or config.options.average_expect):
if (config.e_num == 0):
sol.states = sol.states / len(sols)
else:
sol.expect = list(sol.expect / len(sols))
inds = np.where(config.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 config.options.average_expect) and config.e_num != 0:
temp = [list(sol.expect[ii]) for ii in range(ntraj)]
for ii in range(ntraj):
for jj in np.where(config.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 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 (config.c_num != 0):
_init_c_ops()
if (config.e_num != 0):
_init_e_ops()
# set options
qtf90.qutraj_run.n_c_ops = config.c_num
qtf90.qutraj_run.n_e_ops = config.e_num
qtf90.qutraj_run.ntraj = ntraj
qtf90.qutraj_run.unravel_type = self.unravel_type
qtf90.qutraj_run.average_states = config.options.average_states
qtf90.qutraj_run.average_expect = config.options.average_expect
qtf90.qutraj_run.init_result(config.psi0_shape[0],
config.options.atol,
config.options.rtol,
mf=self.mf,
norm_steps=config.norm_steps,
norm_tol=config.norm_tol)
# set optional arguments
qtf90.qutraj_run.order = config.options.order
qtf90.qutraj_run.nsteps = config.options.nsteps
qtf90.qutraj_run.first_step = config.options.first_step
qtf90.qutraj_run.min_step = config.options.min_step
qtf90.qutraj_run.max_step = config.options.max_step
qtf90.qutraj_run.norm_steps = config.options.norm_steps
qtf90.qutraj_run.norm_tol = config.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 Result instance
sol = Result()
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 (config.e_num == 0):
sol.states = self.get_states(len(config.tlist), ntraj)
else:
sol.expect = self.get_expect(len(config.tlist), ntraj)
if (self.calc_entropy):
sol.entropy = self.get_entropy(len(config.tlist))
if (not self.serial_run):
# put to queue
queue.put(sol)
queue.join()
# deallocate stuff
# finalize()
return sol
