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stochastic.py
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stochastic.py
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# -*- coding: utf-8 -*-
#
# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names
# of its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Significant parts of this code were contributed by Denis Vasilyev.
#
###############################################################################
"""
This module contains experimental functions for solving stochastic schrodinger
and master equations. The API should not be considered stable, and is subject
to change when we work more on optimizing this module for performance and
features.
Todo:
* parallelize
"""
__all__ = ['ssesolve', 'ssepdpsolve', 'smesolve', 'smepdpsolve']
import numpy as np
import scipy.sparse as sp
import scipy
from scipy.linalg.blas import get_blas_funcs
try:
norm = get_blas_funcs("znrm2", dtype=np.float64)
except:
from scipy.linalg import norm
from numpy.random import RandomState
from qutip.qobj import Qobj, isket
from qutip.states import ket2dm
from qutip.solver import Result
from qutip.expect import expect, expect_rho_vec
from qutip.superoperator import (spre, spost, mat2vec, vec2mat,
liouvillian, lindblad_dissipator)
from qutip.cy.spmatfuncs import cy_expect_psi_csr, spmv, cy_expect_rho_vec
from qutip.cy.stochastic import (cy_d1_rho_photocurrent,
cy_d2_rho_photocurrent)
from qutip.ui.progressbar import TextProgressBar
from qutip.solver import Options
from qutip.settings import debug
if debug:
import inspect
class StochasticSolverOptions:
"""Class of options for stochastic solvers such as
:func:`qutip.stochastic.ssesolve`, :func:`qutip.stochastic.smesolve`, etc.
Options can be specified either as arguments to the constructor::
sso = StochasticSolverOptions(nsubsteps=100, ...)
or by changing the class attributes after creation::
sso = StochasticSolverOptions()
sso.nsubsteps = 1000
The stochastic solvers :func:`qutip.stochastic.ssesolve`,
:func:`qutip.stochastic.smesolve`, :func:`qutip.stochastic.ssepdpsolve` and
:func:`qutip.stochastic.smepdpsolve` all take the same keyword arguments as
the constructor of these class, and internally they use these arguments to
construct an instance of this class, so it is rarely needed to explicitly
create an instance of this class.
Attributes
----------
H : :class:`qutip.Qobj`
System Hamiltonian.
state0 : :class:`qutip.Qobj`
Initial state vector (ket) or density matrix.
times : *list* / *array*
List of times for :math:`t`. Must be uniformly spaced.
c_ops : list of :class:`qutip.Qobj`
List of deterministic collapse operators.
sc_ops : list of :class:`qutip.Qobj`
List of stochastic collapse operators. Each stochastic collapse
operator will give a deterministic and stochastic contribution
to the equation of motion according to how the d1 and d2 functions
are defined.
e_ops : list of :class:`qutip.Qobj`
Single operator or list of operators for which to evaluate
expectation values.
m_ops : list of :class:`qutip.Qobj`
List of operators representing the measurement operators. The expected
format is a nested list with one measurement operator for each
stochastic increament, for each stochastic collapse operator.
args : dict / list
List of dictionary of additional problem-specific parameters.
ntraj : int
Number of trajectors.
nsubsteps : int
Number of sub steps between each time-spep given in `times`.
d1 : function
Function for calculating the operator-valued coefficient to the
deterministic increment dt.
d2 : function
Function for calculating the operator-valued coefficient to the
stochastic increment(s) dW_n, where n is in [0, d2_len[.
d2_len : int (default 1)
The number of stochastic increments in the process.
dW_factors : array
Array of length d2_len, containing scaling factors for each
measurement operator in m_ops.
rhs : function
Function for calculating the deterministic and stochastic contributions
to the right-hand side of the stochastic differential equation. This
only needs to be specified when implementing a custom SDE solver.
generate_A_ops : function
Function that generates a list of pre-computed operators or super-
operators. These precomputed operators are used in some d1 and d2
functions.
generate_noise : function
Function for generate an array of pre-computed noise signal.
homogeneous : bool (True)
Wheter or not the stochastic process is homogenous. Inhomogenous
processes are only supported for poisson distributions.
solver : string
Name of the solver method to use for solving the stochastic
equations. Valid values are: 'euler-maruyama', 'fast-euler-maruyama',
'milstein', 'fast-milstein', 'platen'.
method : string ('homodyne', 'heterodyne', 'photocurrent')
The name of the type of measurement process that give rise to the
stochastic equation to solve. Specifying a method with this keyword
argument is a short-hand notation for using pre-defined d1 and d2
functions for the corresponding stochastic processes.
distribution : string ('normal', 'poission')
The name of the distribution used for the stochastic increments.
store_measurements : bool (default False)
Whether or not to store the measurement results in the
:class:`qutip.solver.SolverResult` instance returned by the solver.
noise : array
Vector specifying the noise.
normalize : bool (default True)
Whether or not to normalize the wave function during the evolution.
options : :class:`qutip.solver.Options`
Generic solver options.
progress_bar : :class:`qutip.ui.BaseProgressBar`
Optional progress bar class instance.
"""
def __init__(self, H=None, state0=None, times=None, c_ops=[], sc_ops=[],
e_ops=[], m_ops=None, args=None, ntraj=1, nsubsteps=1,
d1=None, d2=None, d2_len=1, dW_factors=None, rhs=None,
generate_A_ops=None, generate_noise=None, homogeneous=True,
solver=None, method=None, distribution='normal',
store_measurement=False, noise=None, normalize=True,
options=None, progress_bar=None):
if options is None:
options = Options()
if progress_bar is None:
progress_bar = TextProgressBar()
self.H = H
self.d1 = d1
self.d2 = d2
self.d2_len = d2_len
self.dW_factors = dW_factors if dW_factors else np.ones(d2_len)
self.state0 = state0
self.times = times
self.c_ops = c_ops
self.sc_ops = sc_ops
self.e_ops = e_ops
if m_ops is None:
self.m_ops = [[c for _ in range(d2_len)] for c in sc_ops]
else:
self.m_ops = m_ops
self.ntraj = ntraj
self.nsubsteps = nsubsteps
self.solver = solver
self.method = method
self.distribution = distribution
self.homogeneous = homogeneous
self.rhs = rhs
self.options = options
self.progress_bar = progress_bar
self.store_measurement = store_measurement
self.store_states = options.store_states
self.noise = noise
self.args = args
self.normalize = normalize
self.generate_noise = generate_noise
self.generate_A_ops = generate_A_ops
def ssesolve(H, psi0, times, sc_ops, e_ops, **kwargs):
"""
Solve stochastic Schrödinger equation. Dispatch to specific solvers
depending on the value of the `solver` keyword argument.
Parameters
----------
H : :class:`qutip.Qobj`
System Hamiltonian.
psi0 : :class:`qutip.Qobj`
Initial state vector (ket).
times : *list* / *array*
List of times for :math:`t`. Must be uniformly spaced.
sc_ops : list of :class:`qutip.Qobj`
List of stochastic collapse operators. Each stochastic collapse
operator will give a deterministic and stochastic contribution
to the equation of motion according to how the d1 and d2 functions
are defined.
e_ops : list of :class:`qutip.Qobj`
Single operator or list of operators for which to evaluate
expectation values.
kwargs : *dictionary*
Optional keyword arguments. See
:class:`qutip.stochastic.StochasticSolverOptions`.
Returns
-------
output: :class:`qutip.solver.SolverResult`
An instance of the class :class:`qutip.solver.SolverResult`.
"""
if debug:
print(inspect.stack()[0][3])
if isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
sso = StochasticSolverOptions(H=H, state0=psi0, times=times,
sc_ops=sc_ops, e_ops=e_ops, **kwargs)
if sso.generate_A_ops is None:
sso.generate_A_ops = _generate_psi_A_ops
if (sso.d1 is None) or (sso.d2 is None):
if sso.method == 'homodyne':
sso.d1 = d1_psi_homodyne
sso.d2 = d2_psi_homodyne
sso.d2_len = 1
sso.homogeneous = True
sso.distribution = 'normal'
if "dW_factors" not in kwargs:
sso.dW_factors = np.array([1])
if "m_ops" not in kwargs:
sso.m_ops = [[c + c.dag()] for c in sso.sc_ops]
elif sso.method == 'heterodyne':
sso.d1 = d1_psi_heterodyne
sso.d2 = d2_psi_heterodyne
sso.d2_len = 2
sso.homogeneous = True
sso.distribution = 'normal'
if "dW_factors" not in kwargs:
sso.dW_factors = np.array([np.sqrt(2), np.sqrt(2)])
if "m_ops" not in kwargs:
sso.m_ops = [[(c + c.dag()), (-1j) * (c - c.dag())]
for idx, c in enumerate(sso.sc_ops)]
elif sso.method == 'photocurrent':
sso.d1 = d1_psi_photocurrent
sso.d2 = d2_psi_photocurrent
sso.d2_len = 1
sso.homogeneous = False
sso.distribution = 'poisson'
if "dW_factors" not in kwargs:
sso.dW_factors = np.array([1])
if "m_ops" not in kwargs:
sso.m_ops = [[None] for c in sso.sc_ops]
else:
raise Exception("Unrecognized method '%s'." % sso.method)
if sso.distribution == 'poisson':
sso.homogeneous = False
if sso.solver == 'euler-maruyama' or sso.solver is None:
sso.rhs_func = _rhs_psi_euler_maruyama
elif sso.solver == 'platen':
sso.rhs_func = _rhs_psi_platen
else:
raise Exception("Unrecognized solver '%s'." % sso.solver)
res = _ssesolve_generic(sso, sso.options, sso.progress_bar)
if e_ops_dict:
res.expect = {e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())}
return res
def smesolve(H, rho0, times, c_ops, sc_ops, e_ops, **kwargs):
"""
Solve stochastic master equation. Dispatch to specific solvers
depending on the value of the `solver` keyword argument.
Parameters
----------
H : :class:`qutip.Qobj`
System Hamiltonian.
rho0 : :class:`qutip.Qobj`
Initial density matrix or state vector (ket).
times : *list* / *array*
List of times for :math:`t`. Must be uniformly spaced.
c_ops : list of :class:`qutip.Qobj`
Deterministic collapse operator which will contribute with a standard
Lindblad type of dissipation.
sc_ops : list of :class:`qutip.Qobj`
List of stochastic collapse operators. Each stochastic collapse
operator will give a deterministic and stochastic contribution
to the eqaution of motion according to how the d1 and d2 functions
are defined.
e_ops : list of :class:`qutip.Qobj` / callback function single
single operator or list of operators for which to evaluate
expectation values.
kwargs : *dictionary*
Optional keyword arguments. See
:class:`qutip.stochastic.StochasticSolverOptions`.
Returns
-------
output: :class:`qutip.solver.SolverResult`
An instance of the class :class:`qutip.solver.SolverResult`.
TODO
----
Add checks for commuting jump operators in Milstein method.
"""
if debug:
print(inspect.stack()[0][3])
if isket(rho0):
rho0 = ket2dm(rho0)
if isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
sso = StochasticSolverOptions(H=H, state0=rho0, times=times, c_ops=c_ops,
sc_ops=sc_ops, e_ops=e_ops, **kwargs)
if (sso.d1 is None) or (sso.d2 is None):
if sso.method == 'homodyne' or sso.method is None:
sso.d1 = d1_rho_homodyne
sso.d2 = d2_rho_homodyne
sso.d2_len = 1
sso.homogeneous = True
sso.distribution = 'normal'
if "dW_factors" not in kwargs:
sso.dW_factors = np.array([np.sqrt(1)])
if "m_ops" not in kwargs:
sso.m_ops = [[c + c.dag()] for c in sso.sc_ops]
elif sso.method == 'heterodyne':
sso.d1 = d1_rho_heterodyne
sso.d2 = d2_rho_heterodyne
sso.d2_len = 2
sso.homogeneous = True
sso.distribution = 'normal'
if "dW_factors" not in kwargs:
sso.dW_factors = np.array([np.sqrt(2), np.sqrt(2)])
if "m_ops" not in kwargs:
sso.m_ops = [[(c + c.dag()), -1j * (c - c.dag())]
for c in sso.sc_ops]
elif sso.method == 'photocurrent':
sso.d1 = cy_d1_rho_photocurrent
sso.d2 = cy_d2_rho_photocurrent
sso.d2_len = 1
sso.homogeneous = False
sso.distribution = 'poisson'
if "dW_factors" not in kwargs:
sso.dW_factors = np.array([1])
if "m_ops" not in kwargs:
sso.m_ops = [[None] for c in sso.sc_ops]
else:
raise Exception("Unrecognized method '%s'." % sso.method)
if sso.distribution == 'poisson':
sso.homogeneous = False
if sso.generate_A_ops is None:
sso.generate_A_ops = _generate_rho_A_ops
if sso.rhs is None:
if sso.solver == 'euler-maruyama' or sso.solver is None:
sso.rhs = _rhs_rho_euler_maruyama
elif sso.solver == 'milstein':
if sso.method == 'homodyne' or sso.method is None:
if len(sc_ops) == 1:
sso.rhs = _rhs_rho_milstein_homodyne_single
else:
sso.rhs = _rhs_rho_milstein_homodyne
elif sso.method == 'heterodyne':
sso.rhs = _rhs_rho_milstein_homodyne
sso.d2_len = 1
sso.sc_ops = []
for sc in iter(sc_ops):
sso.sc_ops += [sc / np.sqrt(2), -1.0j * sc / np.sqrt(2)]
elif sso.solver == 'fast-euler-maruyama' and sso.method == 'homodyne':
sso.rhs = _rhs_rho_euler_homodyne_fast
sso.generate_A_ops = _generate_A_ops_Euler
elif sso.solver == 'fast-milstein':
sso.generate_A_ops = _generate_A_ops_Milstein
sso.generate_noise = _generate_noise_Milstein
if sso.method == 'homodyne' or sso.method is None:
if len(sc_ops) == 1:
sso.rhs = _rhs_rho_milstein_homodyne_single_fast
elif len(sc_ops) == 2:
sso.rhs = _rhs_rho_milstein_homodyne_two_fast
else:
sso.rhs = _rhs_rho_milstein_homodyne_fast
elif sso.method == 'heterodyne':
sso.d2_len = 1
sso.sc_ops = []
for sc in iter(sc_ops):
sso.sc_ops += [sc / np.sqrt(2), -1.0j * sc / np.sqrt(2)]
if len(sc_ops) == 1:
sso.rhs = _rhs_rho_milstein_homodyne_two_fast
else:
sso.rhs = _rhs_rho_milstein_homodyne_fast
else:
raise Exception("Unrecognized solver '%s'." % sso.solver)
res = _smesolve_generic(sso, sso.options, sso.progress_bar)
if e_ops_dict:
res.expect = {e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())}
return res
def ssepdpsolve(H, psi0, times, c_ops, e_ops, **kwargs):
"""
A stochastic (piecewse deterministic process) PDP solver for wavefunction
evolution. For most purposes, use :func:`qutip.mcsolve` instead for quantum
trajectory simulations.
Parameters
----------
H : :class:`qutip.Qobj`
System Hamiltonian.
psi0 : :class:`qutip.Qobj`
Initial state vector (ket).
times : *list* / *array*
List of times for :math:`t`. Must be uniformly spaced.
c_ops : list of :class:`qutip.Qobj`
Deterministic collapse operator which will contribute with a standard
Lindblad type of dissipation.
e_ops : list of :class:`qutip.Qobj` / callback function single
single operator or list of operators for which to evaluate
expectation values.
kwargs : *dictionary*
Optional keyword arguments. See
:class:`qutip.stochastic.StochasticSolverOptions`.
Returns
-------
output: :class:`qutip.solver.SolverResult`
An instance of the class :class:`qutip.solver.SolverResult`.
"""
if debug:
print(inspect.stack()[0][3])
if isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
sso = StochasticSolverOptions(H=H, state0=psi0, times=times, c_ops=c_ops,
e_ops=e_ops, **kwargs)
res = _ssepdpsolve_generic(sso, sso.options, sso.progress_bar)
if e_ops_dict:
res.expect = {e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())}
return res
def smepdpsolve(H, rho0, times, c_ops, e_ops, **kwargs):
"""
A stochastic (piecewse deterministic process) PDP solver for density matrix
evolution.
Parameters
----------
H : :class:`qutip.Qobj`
System Hamiltonian.
rho0 : :class:`qutip.Qobj`
Initial density matrix.
times : *list* / *array*
List of times for :math:`t`. Must be uniformly spaced.
c_ops : list of :class:`qutip.Qobj`
Deterministic collapse operator which will contribute with a standard
Lindblad type of dissipation.
sc_ops : list of :class:`qutip.Qobj`
List of stochastic collapse operators. Each stochastic collapse
operator will give a deterministic and stochastic contribution
to the eqaution of motion according to how the d1 and d2 functions
are defined.
e_ops : list of :class:`qutip.Qobj` / callback function single
single operator or list of operators for which to evaluate
expectation values.
kwargs : *dictionary*
Optional keyword arguments. See
:class:`qutip.stochastic.StochasticSolverOptions`.
Returns
-------
output: :class:`qutip.solver.SolverResult`
An instance of the class :class:`qutip.solver.SolverResult`.
"""
if debug:
print(inspect.stack()[0][3])
if isinstance(e_ops, dict):
e_ops_dict = e_ops
e_ops = [e for e in e_ops.values()]
else:
e_ops_dict = None
sso = StochasticSolverOptions(H=H, state0=rho0, times=times, c_ops=c_ops,
e_ops=e_ops, **kwargs)
res = _smepdpsolve_generic(sso, sso.options, sso.progress_bar)
if e_ops_dict:
res.expect = {e: res.expect[n]
for n, e in enumerate(e_ops_dict.keys())}
return res
# -----------------------------------------------------------------------------
# Generic parameterized stochastic Schrodinger equation solver
#
def _ssesolve_generic(sso, options, progress_bar):
"""
Internal function for carrying out a sse integration. Used by ssesolve.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(sso.times)
N_substeps = sso.nsubsteps
dt = (sso.times[1] - sso.times[0]) / N_substeps
NT = sso.ntraj
data = Result()
data.solver = "ssesolve"
data.times = sso.times
data.expect = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(sso.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 = sso.generate_A_ops(sso.sc_ops, sso.H)
progress_bar.start(sso.ntraj)
for n in range(sso.ntraj):
progress_bar.update(n)
psi_t = sso.state0.full().ravel()
noise = sso.noise[n] if sso.noise else None
states_list, dW, m = _ssesolve_single_trajectory(
data, sso.H, dt, sso.times, N_store, N_substeps, psi_t,
sso.state0.dims, A_ops, sso.e_ops, sso.m_ops, sso.rhs_func, sso.d1,
sso.d2, sso.d2_len, sso.dW_factors, sso.homogeneous,
sso.distribution, sso.args,
store_measurement=sso.store_measurement, noise=noise,
normalize=sso.normalize)
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([ket2dm(data.states[m][n])
for m in range(NT)]).unit()
for n in range(len(data.times))]
# 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(sso.e_ops)]
return data
def _ssesolve_single_trajectory(data, H, dt, times, N_store, N_substeps, psi_t,
dims, A_ops, e_ops, m_ops, rhs, d1, d2, d2_len,
dW_factors, homogeneous, distribution, args,
store_measurement=False, noise=None,
normalize=True):
"""
Internal function. See ssesolve.
"""
if noise is None:
if homogeneous:
if distribution == 'normal':
dW = np.sqrt(dt) * \
scipy.randn(len(A_ops), N_store, N_substeps, d2_len)
else:
raise TypeError('Unsupported increment distribution for ' +
'homogeneous process.')
else:
if distribution != 'poisson':
raise TypeError('Unsupported increment distribution for ' +
'inhomogeneous process.')
dW = np.zeros((len(A_ops), N_store, N_substeps, d2_len))
else:
dW = noise
states_list = []
measurements = np.zeros((len(times), len(m_ops), d2_len), dtype=complex)
for t_idx, t in enumerate(times):
if e_ops:
for e_idx, e in enumerate(e_ops):
s = cy_expect_psi_csr(e.data.data,
e.data.indices,
e.data.indptr, psi_t, 0)
data.expect[e_idx, t_idx] += s
data.ss[e_idx, t_idx] += s ** 2
else:
states_list.append(Qobj(psi_t, dims=dims))
for j in range(N_substeps):
if noise is None and not homogeneous:
for a_idx, A in enumerate(A_ops):
# dw_expect = norm(spmv(A[0], psi_t)) ** 2 * dt
dw_expect = cy_expect_psi_csr(A[3].data,
A[3].indices,
A[3].indptr, psi_t, 1) * dt
dW[a_idx, t_idx, j, :] = np.random.poisson(dw_expect,
d2_len)
psi_t = rhs(H.data, psi_t, t + dt * j,
A_ops, dt, dW[:, t_idx, j, :], d1, d2, args)
# optionally renormalize the wave function
if normalize:
psi_t /= norm(psi_t)
if store_measurement:
for m_idx, m in enumerate(m_ops):
for dW_idx, dW_factor in enumerate(dW_factors):
if m[dW_idx]:
m_data = m[dW_idx].data
m_expt = cy_expect_psi_csr(m_data.data,
m_data.indices,
m_data.indptr,
psi_t, 0)
else:
m_expt = 0
measurements[t_idx, m_idx, dW_idx] = (m_expt +
dW_factor * dW[m_idx, t_idx, :, dW_idx].sum() /
(dt * N_substeps))
if d2_len == 1:
measurements = measurements.squeeze(axis=(2))
return states_list, dW, measurements
# -----------------------------------------------------------------------------
# Generic parameterized stochastic master equation solver
#
def _smesolve_generic(sso, options, progress_bar):
"""
Internal function. See smesolve.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(sso.times)
N_substeps = sso.nsubsteps
dt = (sso.times[1] - sso.times[0]) / N_substeps
NT = sso.ntraj
data = Result()
data.solver = "smesolve"
data.times = sso.times
data.expect = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.noise = []
data.measurement = []
# Liouvillian for the deterministic part.
# needs to be modified for TD systems
L = liouvillian(sso.H, sso.c_ops)
# pre-compute suporoperator operator combinations that are commonly needed
# when evaluating the RHS of stochastic master equations
A_ops = sso.generate_A_ops(sso.sc_ops, L.data, dt)
# use .data instead of Qobj ?
s_e_ops = [spre(e) for e in sso.e_ops]
if sso.m_ops:
s_m_ops = [[spre(m) if m else None for m in m_op]
for m_op in sso.m_ops]
else:
s_m_ops = [[spre(c) for _ in range(sso.d2_len)]
for c in sso.sc_ops]
progress_bar.start(sso.ntraj)
for n in range(sso.ntraj):
progress_bar.update(n)
rho_t = mat2vec(sso.state0.full()).ravel()
# noise = sso.noise[n] if sso.noise else None
if sso.noise:
noise = sso.noise[n]
elif sso.generate_noise:
noise = sso.generate_noise(len(A_ops), N_store, N_substeps,
sso.d2_len, dt)
else:
noise = None
states_list, dW, m = _smesolve_single_trajectory(
data, L, dt, sso.times, N_store, N_substeps, rho_t, sso.state0.dims,
A_ops, s_e_ops, s_m_ops, sso.rhs, sso.d1, sso.d2, sso.d2_len,
sso.dW_factors, sso.homogeneous, sso.distribution, sso.args,
store_measurement=sso.store_measurement,
store_states=sso.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([data.states[m][n] for m in range(NT)]).unit()
for n in range(len(data.times))]
# 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(sso.e_ops)]
return data
def _smesolve_single_trajectory(data, L, dt, times, N_store, N_substeps, rho_t,
dims, A_ops, e_ops, m_ops, rhs, d1, d2, d2_len,
dW_factors, homogeneous, distribution, args,
store_measurement=False,
store_states=False, noise=None):
"""
Internal function. See smesolve.
"""
if noise is None:
if homogeneous:
if distribution == 'normal':
dW = np.sqrt(dt) * scipy.randn(len(A_ops),
N_store, N_substeps, d2_len)
else:
raise TypeError('Unsupported increment distribution for ' +
'homogeneous process.')
else:
if distribution != 'poisson':
raise TypeError('Unsupported increment distribution for ' +
'inhomogeneous process.')
dW = np.zeros((len(A_ops), N_store, N_substeps, d2_len))
else:
dW = noise
states_list = []
measurements = np.zeros((len(times), len(m_ops), d2_len), dtype=complex)
for t_idx, t in enumerate(times):
if e_ops:
for e_idx, e in enumerate(e_ops):
s = cy_expect_rho_vec(e.data, rho_t, 0)
data.expect[e_idx, t_idx] += s
data.ss[e_idx, t_idx] += s ** 2
if store_states or not e_ops:
states_list.append(Qobj(vec2mat(rho_t), dims=dims))
rho_prev = np.copy(rho_t)
for j in range(N_substeps):
if noise is None and not homogeneous:
for a_idx, A in enumerate(A_ops):
dw_expect = cy_expect_rho_vec(A[4], rho_t, 1) * dt
if dw_expect > 0:
dW[a_idx, t_idx, j, :] = np.random.poisson(dw_expect,
d2_len)
else:
dW[a_idx, t_idx, j, :] = np.zeros(d2_len)
rho_t = rhs(L.data, rho_t, t + dt * j,
A_ops, dt, dW[:, t_idx, j, :], d1, d2, args)
if store_measurement:
for m_idx, m in enumerate(m_ops):
for dW_idx, dW_factor in enumerate(dW_factors):
if m[dW_idx]:
m_expt = cy_expect_rho_vec(m[dW_idx].data, rho_prev, 0)
else:
m_expt = 0
measurements[t_idx, m_idx, dW_idx] = m_expt + dW_factor * \
dW[m_idx, t_idx, :, dW_idx].sum() / (dt * N_substeps)
if d2_len == 1:
measurements = measurements.squeeze(axis=(2))
return states_list, dW, measurements
# -----------------------------------------------------------------------------
# Generic parameterized stochastic SE PDP solver
#
def _ssepdpsolve_generic(sso, options, progress_bar):
"""
For internal use. See ssepdpsolve.
"""
if debug:
print(inspect.stack()[0][3])
N_store = len(sso.times)
N_substeps = sso.nsubsteps
dt = (sso.times[1] - sso.times[0]) / N_substeps
NT = sso.ntraj
data = Result()
data.solver = "sepdpsolve"
data.times = sso.tlist
data.expect = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.jump_times = []
data.jump_op_idx = []
# effective hamiltonian for deterministic part
Heff = sso.H
for c in sso.c_ops:
Heff += -0.5j * c.dag() * c
progress_bar.start(sso.ntraj)
for n in range(sso.ntraj):
progress_bar.update(n)
psi_t = sso.state0.full().ravel()
states_list, jump_times, jump_op_idx = \
_ssepdpsolve_single_trajectory(data, Heff, dt, sso.times,
N_store, N_substeps,
psi_t, sso.state0.dims,