Issue with optimize_pulse_unitary for large sparse matrices

I am using GRAPE to identify optimal pulse sequences to produce a desired unitary, and am running into an error when I use optimize_pulse_unitary for sparse matrices of size 1296x1296 and larger.

For example, the following code works for N=2 but fails for N=>3:

import numpy as np
from qutip import *
from scipy.sparse import csr_matrix
import qutip.logging_utils as logging
logger = logging.get_logger()
#Set this to None or logging.WARN for 'quiet' execution
log_level = logging.INFO
#QuTiP control modules
import qutip.control.pulseoptim as cpo
import math

# Set parameters
chi  = 2.2 * 2 * np.pi  # coupling strength
N = 3             # number of cavity states
n_ts = 21 # number of discrete time steps
evo_time = 4.      # Length of time
fid_err_targ = 1e-10 # Target fidelity error
max_iter = 0 # # Maximum iterations for the optimization algorithm
max_wall_time = 0 # Maximum (elapsed) time
min_grad = 1e-20 # Minimum gradient
p_type = 'SINE' # pulse type (RND|ZERO|LIN|SINE|SQUARE|SAW|TRIANGLE)
f_ext = "pulsedata.txt" # output file

# Set operators and Hamiltonians

# operators
a  = tensor(destroy(N), qeye(N), qeye(N), qeye(N), qeye(2), qeye(2), qeye(2), qeye(2))
b = tensor(qeye(N), destroy(N), qeye(N), qeye(N), qeye(2), qeye(2), qeye(2), qeye(2))
c  = tensor(qeye(N), qeye(N), destroy(N), qeye(N), qeye(2), qeye(2), qeye(2), qeye(2))
d = tensor(qeye(N), qeye(N), qeye(N),  destroy(N), qeye(2), qeye(2), qeye(2), qeye(2))
sm = tensor(qeye(N), qeye(N), qeye(N), qeye(N), destroy(2), qeye(2), qeye(2), qeye(2))
zm = tensor(qeye(N), qeye(N), qeye(N), qeye(N), qeye(2), destroy(2), qeye(2), qeye(2))
tm = tensor(qeye(N), qeye(N), qeye(N), qeye(N), qeye(2), qeye(2), destroy(2), qeye(2))
qm = tensor(qeye(N), qeye(N), qeye(N), qeye(N), qeye(2), qeye(2), qeye(2), destroy(2))
P_se = tensor(qeye(N), qeye(N), qeye(N), qeye(N), basis(2, 1) * basis(2, 1).dag(), qeye(2), qeye(2), qeye(2))
P_ze = tensor(qeye(N), qeye(N), qeye(N), qeye(N), qeye(2), basis(2, 1) * basis(2, 1).dag(), qeye(2), qeye(2))
P_te = tensor(qeye(N), qeye(N), qeye(N), qeye(N), qeye(2), qeye(2), basis(2, 1) * basis(2, 1).dag(), qeye(2))
P_qe = tensor(qeye(N), qeye(N), qeye(N), qeye(N), qeye(2), qeye(2), qeye(2), basis(2, 1) * basis(2, 1).dag())

# Hamiltonians
drift = chi * (a.dag() * a * P_se) + chi * (b.dag() * b * P_ze) + chi * (c.dag() * c * P_te) + chi * (d.dag() * d * P_qe)

row = np.array([1,1,2,2,3,3,3,3,4,4,4,4,5,5,6,6])
column = np.array([3,4,3,4,1,2,5,6,1,2,5,6,3,4,3,4])
data = np.array([-1,1,-1,-1,-1,-1,-1,-1,1,-1,-1,1,-1,-1,-1,1])

mapped=csr_matrix((data,(row,column)),shape=(N**4*2*2*2*2,N**4*2*2*2*2))
mapped=Qobj(mapped)

n_ctrls=4 # Controls
ctrls = [sm.dag()*a+sm*a.dag()+sm.dag()*b+sm*b.dag()+zm.dag()*b+zm*b.dag()+zm.dag()*c+zm*c.dag()+tm.dag()*c+tm*c.dag()+tm.dag()*d+tm*d.dag()+qm.dag()*d+qm*d.dag()+qm.dag()*a+qm*a.dag(),1j*(sm.dag()*a-sm*a.dag())+1j*(sm.dag()*b-sm*b.dag())+1j*(zm.dag()*b-zm*b.dag())+1j*(zm.dag()*c-zm*c.dag())+1j*(tm.dag()*c-tm*c.dag())+1j*(tm.dag()*d-tm*d.dag())+1j*(qm.dag()*d-qm*d.dag())+1j*(qm.dag()*a-qm*a.dag()),sm+sm.dag()+zm+zm.dag()+tm+tm.dag()+qm+qm.dag(), 1j*(sm-sm.dag())+1j*(zm-zm.dag())+1j*(tm-tm.dag())+1j*(qm-qm.dag())]

# Set optimization parameters
U_0=tensor(qeye(N), qeye(N), qeye(N), qeye(N), qeye(2), qeye(2), qeye(2), qeye(2)) # Initial gate
U_targ=mapped # Target gates

# Run the optimization

result = cpo.optimize_pulse_unitary(drift, ctrls, U_0, U_targ, n_ts, evo_time, fid_err_targ=fid_err_targ, min_grad=min_grad, max_iter=max_iter, max_wall_time=max_wall_time, out_file_ext=f_ext, init_pulse_type=p_type, log_level=log_level,gen_stats=True,dyn_params=dict(sparse_eigen_decomp=True))

For N=>3, instead of getting a printout of the optimized controls, I receive the following terminal output error:

Traceback (most recent call last):
  File "MinimalErrorExample.py", line 59, in <module>
    result = cpo.optimize_pulse_unitary(drift, ctrls, U_0, U_targ, n_ts, evo_time, fid_err_targ=fid_err_targ, min_grad=min_grad, max_iter=max_iter, max_wall_time=max_wall_time, out_file_ext=f_ext, init_pulse_type=p_type, log_level=log_level,gen_stats=True,dyn_params=dict(sparse_eigen_decomp=True))
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/pulseoptim.py", line 829, in optimize_pulse_unitary
    gen_stats=gen_stats)
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/pulseoptim.py", line 510, in optimize_pulse
    result = optim.run_optimization()
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/optimizer.py", line 970, in run_optimization
    result = self._create_result()
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/optimizer.py", line 375, in _create_result
    result.initial_fid_err = self.dynamics.fid_computer.get_fid_err()
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/fidcomp.py", line 448, in get_fid_err
    return np.abs(1 - self.get_fidelity())
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/fidcomp.py", line 458, in get_fidelity
    self.fid_norm_func(self.get_fidelity_prenorm())
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/fidcomp.py", line 474, in get_fidelity_prenorm
    dyn.compute_evolution()
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/dynamics.py", line 1421, in compute_evolution
    self.tslot_computer.recompute_evolution()
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/tslotcomp.py", line 299, in recompute_evolution
    prop_comp._compute_prop_grad(k, j)
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/propcomp.py", line 272, in _compute_prop_grad
    dyn._ensure_decomp_curr(k)
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/dynamics.py", line 1437, in _ensure_decomp_curr
    self._spectral_decomp(k)
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/control/dynamics.py", line 1654, in _spectral_decomp
    dims=self.dyn_dims)
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/qutip/qobj.py", line 292, in __init__
    _tmp = sp.csr_matrix(inpt, dtype=complex, copy=do_copy)
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/scipy/sparse/compressed.py", line 88, in __init__
    self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/scipy/sparse/coo.py", line 191, in __init__
    self.row, self.col = M.nonzero()
  File "/Users/msoley/opt/anaconda3/lib/python3.7/site-packages/scipy/sparse/base.py", line 287, in __bool__
    raise ValueError("The truth value of an array with more than one "
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().

I am using the following QuTiP environment: QuTiP Version: 4.4.1 Numpy Version: 1.21.0 Scipy Version: 1.4.1 Cython Version: 0.29.15 Matplotlib Version: 3.4.2 Python Version: 3.7.6 Number of CPUs: 2 BLAS Info: OPENBLAS OPENMP Installed: False INTEL MKL Ext: False Platform Info: Darwin (x86_64)

Thank you for your help.

[michelinesoley]

Thank you for all of your help!

[hodgestar]

#1621 has been merged and should be included in the 4.6.3 release.