forked from qutip/qutip
-
Notifications
You must be signed in to change notification settings - Fork 1
/
steadystate.py
595 lines (491 loc) · 20.9 KB
/
steadystate.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
# 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.
###############################################################################
"""
Module contains functions for solving for the steady state density matrix of
open quantum systems defined by a Liouvillian or Hamiltonian and a list of
collapse operators.
"""
import warnings
import time
import scipy
import numpy as np
from numpy.linalg import svd
from scipy import prod, randn
import scipy.sparse as sp
import scipy.linalg as la
from scipy.sparse.linalg import *
from qutip.qobj import Qobj, issuper, isoper
from qutip.superoperator import liouvillian, vec2mat, mat2vec
from qutip.operators import qeye
from qutip.random_objects import rand_dm
from qutip.sparse import sp_permute, sp_bandwidth, sp_reshape
from qutip.graph import reverse_cuthill_mckee, weighted_bipartite_matching
from qutip.states import ket2dm, maximally_mixed_dm
import qutip.settings as settings
from qutip.utilities import _version2int
if settings.debug:
import inspect
# test if scipy is recent enought to get L & U factors from superLU
_scipy_check = _version2int(scipy.__version__) >= _version2int('0.14.0')
def _default_steadystate_args():
def_args = {'method': 'direct', 'sparse': True, 'use_rcm': False,
'use_wbm': False, 'use_umfpack': False, 'weight': None,
'use_precond': True, 'all_states': False,
'M': None, 'x0' : None, 'drop_tol': 1e-4, 'fill_factor': 100,
'diag_pivot_thresh': None, 'maxiter': 10000, 'tol': 1e-9,
'permc_spec': 'COLAMD', 'ILU_MILU': 'smilu_2'}
return def_args
def steadystate(A, c_op_list=[], **kwargs):
"""Calculates the steady state for quantum evolution subject to the
supplied Hamiltonian or Liouvillian operator and (if given a Hamiltonian) a
list of collapse operators.
If the user passes a Hamiltonian then it, along with the list of collapse
operators, will be converted into a Liouvillian operator in Lindblad form.
Parameters
----------
A : qobj
A Hamiltonian or Liouvillian operator.
c_op_list : list
A list of collapse operators.
method : str {'direct', 'eigen', 'iterative-bicg', 'iterative-bicgstab',
'iterative-gmres', 'iterative-lgmres', 'svd', 'power'}
Method for solving the underlying linear equation. Direct LU solver
'direct' (default), sparse eigenvalue problem 'eigen',
iterative GMRES method 'iterative-gmres', iterative LGMRES method
'iterative-lgmres', SVD 'svd' (dense), or inverse-power method 'power'.
sparse : bool, optional, default=True
Solve for the steady state using sparse algorithms. If set to False,
the underlying Liouvillian operator will be converted into a dense
matrix. Use only for 'smaller' systems.
use_rcm : bool, optional, default=True
Use reverse Cuthill-Mckee reordering to minimize fill-in in the
LU factorization of the Liouvillian.
use_wbm : bool, optional, default=False
Use Weighted Bipartite Matching reordering to make the Liouvillian
diagonally dominant. This is useful for iterative preconditioners
only, and is set to ``True`` by default when finding a preconditioner.
weight : float, optional
Sets the size of the elements used for adding the unity trace condition
to the linear solvers. This is set to the average abs value of the
Liouvillian elements if not specified by the user.
use_umfpack : bool {False, True}
Use umfpack solver instead of SuperLU. For SciPy 0.14+, this option
requires installing scikits.umfpack.
maxiter : int, optional, default=10000
Maximum number of iterations to perform if using an iterative method.
tol : float, optional, default=1e-9
Tolerance used for terminating solver solution when using iterative
solvers.
permc_spec : str, optional, default='COLAMD'
Column ordering used internally by superLU for the 'direct' LU
decomposition method. Options include 'COLAMD' and 'NATURAL'.
If using RCM then this is set to 'NATURAL' automatically unless
explicitly specified.
use_precond : bool optional, default = True
ITERATIVE ONLY. Use an incomplete sparse LU decomposition as a
preconditioner for the 'iterative' GMRES and BICG solvers.
Speeds up convergence time by orders of magnitude in many cases.
M : {sparse matrix, dense matrix, LinearOperator}, optional
Preconditioner for A. The preconditioner should approximate the inverse
of A. Effective preconditioning dramatically improves the rate of
convergence, for iterative methods only . If no preconditioner is
given and ``use_precond=True``, then one is generated automatically.
fill_factor : float, optional, default=10
ITERATIVE ONLY. Specifies the fill ratio upper bound (>=1) of the iLU
preconditioner. Lower values save memory at the cost of longer
execution times and a possible singular factorization.
drop_tol : float, optional, default=1e-3
ITERATIVE ONLY. Sets the threshold for the magnitude of preconditioner
elements that should be dropped. Can be reduced for a courser
factorization at the cost of an increased number of iterations, and a
possible singular factorization.
diag_pivot_thresh : float, optional, default=None
ITERATIVE ONLY. Sets the threshold between [0,1] for which diagonal
elements are considered acceptable pivot points when using a
preconditioner. A value of zero forces the pivot to be the diagonal
element.
ILU_MILU : str, optional, default='smilu_2'
Selects the incomplete LU decomposition method algoithm used in
creating the preconditoner. Should only be used by advanced users.
Returns
-------
dm : qobj
Steady state density matrix.
Notes
-----
The SVD method works only for dense operators (i.e. small systems).
"""
ss_args = _default_steadystate_args()
for key in kwargs.keys():
if key in ss_args.keys():
ss_args[key] = kwargs[key]
else:
raise Exception(
"Invalid keyword argument '"+key+"' passed to steadystate.")
# Set column perm to NATURAL if using RCM and not specified by user
if ss_args['use_rcm'] and ('permc_spec' not in kwargs.keys()):
ss_args['permc_spec'] = 'NATURAL'
# Set use_wbm=True if using iterative solver with preconditioner and
# not explicitly set to False by user
if (ss_args['method'] in ['iterative-lgmres', 'iterative-gmres']) \
and ('use_wbm' not in kwargs.keys()):
ss_args['use_wbm'] = True
n_op = len(c_op_list)
if isoper(A):
if n_op == 0:
raise TypeError('Cannot calculate the steady state for a ' +
'non-dissipative system ' +
'(no collapse operators given)')
else:
A = liouvillian(A, c_op_list)
if not issuper(A):
raise TypeError('Solving for steady states requires ' +
'Liouvillian (super) operators')
# Set weight parameter to avg abs val in L if not set explicitly
if 'weight' not in kwargs.keys():
ss_args['weight'] = np.mean(np.abs(A.data.data.max()))
if ss_args['method'] == 'direct':
if ss_args['sparse']:
return _steadystate_direct_sparse(A, ss_args)
else:
return _steadystate_direct_dense(A)
elif ss_args['method'] == 'eigen':
return _steadystate_eigen(A, ss_args)
elif ss_args['method'] in ['iterative-gmres', 'iterative-lgmres',
'iterative-bicg', 'iterative-bicgstab']:
return _steadystate_iterative(A, ss_args)
elif ss_args['method'] == 'svd':
return _steadystate_svd_dense(A, ss_args)
elif ss_args['method'] == 'power':
return _steadystate_power(A, ss_args)
else:
raise ValueError('Invalid method argument for steadystate.')
def steady(L, maxiter=10, tol=1e-6, itertol=1e-5, method='solve',
use_umfpack=False, use_precond=False):
"""
Deprecated. See steadystate instead.
"""
message = "steady has been deprecated, use steadystate instead"
warnings.warn(message, DeprecationWarning)
return steadystate(L, [], maxiter=maxiter, tol=tol,
use_umfpack=use_umfpack, use_precond=use_precond)
def _steadystate_direct_sparse(L, ss_args):
"""
Direct solver that uses scipy sparse matrices
"""
if settings.debug:
print('Starting direct LU solver...')
dims = L.dims[0]
n = prod(L.dims[0][0])
b = np.zeros(n ** 2, dtype=complex)
b[0] = ss_args['weight']
L = L.data.tocsc() + sp.csc_matrix(
(ss_args['weight']*np.ones(n), (np.zeros(n), [nn * (n + 1)
for nn in range(n)])),
shape=(n ** 2, n ** 2))
L.sort_indices()
use_solver(assumeSortedIndices=True, useUmfpack=ss_args['use_umfpack'])
if not ss_args['use_umfpack']:
# Use superLU solver
orig_nnz = L.nnz
if settings.debug:
old_band = sp_bandwidth(L)[0]
print('Original NNZ:', orig_nnz)
if ss_args['use_rcm']:
print('Original bandwidth:', old_band)
if ss_args['use_wbm']:
perm = weighted_bipartite_matching(L)
L = sp_permute(L, perm, [], 'csc')
b = b[np.ix_(perm,)]
if ss_args['use_rcm']:
perm2 = reverse_cuthill_mckee(L)
rev_perm = np.argsort(perm2)
L = sp_permute(L, perm2, perm2, 'csc')
b = b[np.ix_(perm2,)]
if settings.debug:
rcm_band = sp_bandwidth(L)[0]
print('RCM bandwidth:', rcm_band)
print('Bandwidth reduction factor:', round(
old_band/rcm_band, 1))
lu = splu(L, permc_spec=ss_args['permc_spec'],
diag_pivot_thresh=ss_args['diag_pivot_thresh'],
options=dict(ILU_MILU=ss_args['ILU_MILU']))
v = lu.solve(b)
if settings.debug and _scipy_check:
L_nnz = lu.L.nnz
U_nnz = lu.U.nnz
print('L NNZ:', L_nnz, ';', 'U NNZ:', U_nnz)
print('Fill factor:', (L_nnz+U_nnz)/orig_nnz)
else:
# Use umfpack solver
v = spsolve(L, b)
if (not ss_args['use_umfpack']) and ss_args['use_rcm']:
v = v[np.ix_(rev_perm,)]
data = vec2mat(v)
data = 0.5 * (data + data.conj().T)
return Qobj(data, dims=dims, isherm=True)
def _steadystate_direct_dense(L):
"""
Direct solver that use numpy dense matrices. Suitable for
small system, with a few states.
"""
if settings.debug:
print('Starting direct dense solver...')
dims = L.dims[0]
n = prod(L.dims[0][0])
b = np.zeros(n ** 2)
b[0] = ss_args['weight']
L = L.data.todense()
L[0, :] = np.diag(ss_args['weight']*np.ones(n)).reshape((1, n ** 2))
v = np.linalg.solve(L, b)
data = vec2mat(v)
data = 0.5 * (data + data.conj().T)
return Qobj(data, dims=dims, isherm=True)
def _steadystate_eigen(L, ss_args):
"""
Internal function for solving the steady state problem by
finding the eigenvector corresponding to the zero eigenvalue
of the Liouvillian using ARPACK.
"""
if settings.debug:
print('Starting Eigen solver...')
dims = L.dims[0]
shape = prod(dims[0])
L = L.data.tocsc()
if ss_args['use_rcm']:
if settings.debug:
old_band = sp_bandwidth(L)[0]
print('Original bandwidth:', old_band)
perm = reverse_cuthill_mckee(L)
rev_perm = np.argsort(perm)
L = sp_permute(L, perm, perm, 'csc')
if settings.debug:
rcm_band = sp_bandwidth(L)[0]
print('RCM bandwidth:', rcm_band)
print('Bandwidth reduction factor:', round(old_band/rcm_band, 1))
eigval, eigvec = eigs(L, k=1, sigma=1e-15, tol=ss_args['tol'],
which='LM', maxiter=ss_args['maxiter'])
if ss_args['use_rcm']:
eigvec = eigvec[np.ix_(rev_perm,)]
data = vec2mat(eigvec)
data = 0.5 * (data + data.conj().T)
out = Qobj(data, dims=dims, isherm=True)
return out/out.tr()
def _iterative_precondition(A, n, ss_args):
"""
Internal function for preconditioning the steadystate problem for use
with iterative solvers.
"""
if settings.debug:
print('Starting preconditioner...',)
_precond_start = time.time()
try:
P = spilu(A, permc_spec=ss_args['permc_spec'],
drop_tol=ss_args['drop_tol'],
diag_pivot_thresh=ss_args['diag_pivot_thresh'],
fill_factor=ss_args['fill_factor'],
options=dict(ILU_MILU=ss_args['ILU_MILU']))
P_x = lambda x: P.solve(x)
M = LinearOperator((n ** 2, n ** 2), matvec=P_x)
if settings.debug:
print('Preconditioning succeeded.')
print('Precond. time:',time.time()-_precond_start)
if _scipy_check:
L_nnz = P.L.nnz
U_nnz = P.U.nnz
print('L NNZ:', L_nnz, ';', 'U NNZ:', U_nnz)
print('Fill factor:', (L_nnz+U_nnz)/A.nnz)
e = np.ones(n ** 2,dtype=int)
condest = la.norm(M*e,np.inf)
print('iLU Condest:', condest)
except:
warnings.warn("Preconditioning failed. Continuing without.",
UserWarning)
M = None
return M
def _steadystate_iterative(L, ss_args):
"""
Iterative steady state solver using the GMRES or LGMRES algorithm
and a sparse incomplete LU preconditioner.
"""
if settings.debug:
print('Starting '+ss_args['method']+' solver...')
dims = L.dims[0]
n = prod(L.dims[0][0])
b = np.zeros(n ** 2)
b[0] = ss_args['weight']
L = L.data.tocsc() + sp.csc_matrix(
(ss_args['weight']*np.ones(n), (np.zeros(n), [nn * (n + 1)
for nn in range(n)])),
shape=(n ** 2, n ** 2))
if ss_args['use_wbm']:
perm = weighted_bipartite_matching(L)
L = sp_permute(L, perm, [], 'csc')
b = b[np.ix_(perm,)]
if ss_args['use_rcm']:
if settings.debug:
old_band = sp_bandwidth(L)[0]
print('Original bandwidth ', old_band)
perm2 = reverse_cuthill_mckee(L)
rev_perm = np.argsort(perm2)
L = sp_permute(L, perm2, perm2, 'csc')
b = b[np.ix_(perm2,)]
if settings.debug:
rcm_band = sp_bandwidth(L)[0]
print('RCM bandwidth ', rcm_band)
print('Bandwidth reduction factor:', round(old_band/rcm_band, 1))
L.sort_indices()
if ss_args['M'] is None and ss_args['use_precond']:
ss_args['M'] = _iterative_precondition(L, n, ss_args)
# Select iterative solver type
if settings.debug:
_iter_start = time.time()
if ss_args['method'] == 'iterative-gmres':
v, check = gmres(L, b, tol=ss_args['tol'], M=ss_args['M'],
x0=ss_args['x0'],
maxiter=ss_args['maxiter'])
elif ss_args['method'] == 'iterative-lgmres':
v, check = lgmres(L, b, tol=ss_args['tol'], M=ss_args['M'],
x0=ss_args['x0'],
maxiter=ss_args['maxiter'])
elif ss_args['method'] == 'iterative-bicg':
v, check = bicg(L, b, tol=ss_args['tol'], M=ss_args['M'],
x0=ss_args['x0'],
maxiter=ss_args['maxiter'])
elif ss_args['method'] == 'iterative-bicgstab':
v, check = bicgstab(L, b, tol=ss_args['tol'], M=ss_args['M'],
x0=ss_args['x0'],
maxiter=ss_args['maxiter'])
else:
raise Exception("Invalid iterative solver method.")
if settings.debug:
_iter_end = time.time()
print('Iteration. time:',_iter_end-_iter_start)
if check > 0:
raise Exception("Steadystate solver did not reach tolerance after " +
str(check) + " steps.")
elif check < 0:
raise Exception(
"Steadystate solver failed with fatal error: " + str(check) + ".")
if ss_args['use_rcm']:
v = v[np.ix_(rev_perm,)]
data = vec2mat(v)
data = 0.5 * (data + data.conj().T)
return Qobj(data, dims=dims, isherm=True)
def _steadystate_svd_dense(L, ss_args):
"""
Find the steady state(s) of an open quantum system by solving for the
nullspace of the Liouvillian.
"""
atol = 1e-12
rtol = 1e-12
if settings.debug:
print('Starting SVD solver...')
u, s, vh = svd(L.full(), full_matrices=False)
tol = max(atol, rtol * s[0])
nnz = (s >= tol).sum()
ns = vh[nnz:].conj().T
if ss_args['all_states']:
rhoss_list = []
for n in range(ns.shape[1]):
rhoss = Qobj(vec2mat(ns[:, n]), dims=L.dims[0])
rhoss_list.append(rhoss / rhoss.tr())
return rhoss_list
else:
rhoss = Qobj(vec2mat(ns[:, 0]), dims=L.dims[0])
return rhoss / rhoss.tr()
def _steadystate_power(L, ss_args):
"""
Inverse power method for steady state solving.
"""
if settings.debug:
print('Starting iterative inverse-power method solver...')
tol = ss_args['tol']
maxiter = ss_args['maxiter']
use_solver(assumeSortedIndices=True)
rhoss = Qobj()
sflag = issuper(L)
if sflag:
rhoss.dims = L.dims[0]
else:
rhoss.dims = [L.dims[0], 1]
n = prod(rhoss.shape)
L = L.data.tocsc() - (1e-15) * sp.eye(n, n, format='csc')
L.sort_indices()
orig_nnz = L.nnz
# start with all ones as RHS
v = np.ones(n,dtype=complex)
if ss_args['use_rcm']:
if settings.debug:
old_band = sp_bandwidth(L)[0]
print('Original bandwidth:', old_band)
perm = reverse_cuthill_mckee(L)
rev_perm = np.argsort(perm)
L = sp_permute(L, perm, perm, 'csc')
v = v[np.ix_(perm,)]
if settings.debug:
new_band = sp_bandwidth(L)[0]
print('RCM bandwidth:', new_band)
print('Bandwidth reduction factor:', round(old_band/new_band, 2))
# Get LU factors
lu = splu(L, permc_spec=ss_args['permc_spec'],
diag_pivot_thresh=ss_args['diag_pivot_thresh'],
options=dict(ILU_MILU=ss_args['ILU_MILU']))
if settings.debug and _scipy_check:
L_nnz = lu.L.nnz
U_nnz = lu.U.nnz
print('L NNZ:', L_nnz, ';', 'U NNZ:', U_nnz)
print('Fill factor:', (L_nnz+U_nnz)/orig_nnz)
it = 0
while (la.norm(L * v, np.inf) > tol) and (it < maxiter):
v = lu.solve(v)
v = v / la.norm(v, np.inf)
it += 1
if it >= maxiter:
raise Exception('Failed to find steady state after ' +
str(maxiter) + ' iterations')
if settings.debug:
print('Number of iterations:', it)
if ss_args['use_rcm']:
v = v[np.ix_(rev_perm,)]
# normalise according to type of problem
if sflag:
trow = sp.eye(rhoss.shape[0], rhoss.shape[0], format='coo')
trow = sp_reshape(trow, (1, n))
data = v / sum(trow.dot(v))
else:
data = data / la.norm(v)
data = sp.csr_matrix(vec2mat(data))
rhoss.data = 0.5 * (data + data.conj().T)
rhoss.isherm = True
return rhoss