我正在尝试将用于绝热量子计算的官方 QuTip 示例的 IPython 笔记本转换为独立的程序 Python 模块。
不幸的是，我在使用回调函数时收到以下错误process_rho。有人可以帮我吗？谢谢。
import gia gia.main() Traceback（最近一次调用最后）：文件“”，第 1 行，在文件“gia.py”中，第 146 行，在 main mesolve(h_t, psi0, taulist, [], process_rho, args)文件“/opt/anaconda/lib/python2.7/site-packages/qutip/mesolve.py”，第 226 行，在 mesolve _solver_safety_check(H, rho0, c_ops, e_ops, args) 文件“/opt/anaconda/lib/ python2.7/site-packages/qutip/solver.py"，第 836 行，_solver_safety_check for ii in range(len(e_ops)): TypeError: 'function' 类型的对象没有 len()
以下是我的代码，也可在此链接中找到。
import matplotlib.pyplot as plt
import numpy as np
import logging
from logging.handlers import RotatingFileHandler
import time
from qutip import *
from scipy import *
# Setting up the logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# create a log file handler
logFile = 'logs/gia_' + time.strftime("%d-%m-%Y") + '.log'
# handler = logging.FileHandler(logFile)
handler = RotatingFileHandler(logFile, mode='a', maxBytes=100*1024*1024, backupCount=100, encoding=None, delay=0)
handler.setLevel(logging.INFO)
# create a logging format
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(handler)
N = 6 # number of spins
M = 20 # number of eigenenergies to plot
# array of spin energy splittings and coupling strengths (random values).
h = 1.0 * 2 * pi * (1 - 2 * rand(N))
logger.info("Type of h: " + str(type(h)))
logger.info(str(h))
Jz = 1.0 * 2 * pi * (1 - 2 * rand(N))
logger.info("Type of Jz: " + str(type(Jz)))
logger.info(str(Jz))
Jx = 1.0 * 2 * pi * (1 - 2 * rand(N))
logger.info("Type of Jx: " + str(type(Jx)))
logger.info(str(Jx))
Jy = 1.0 * 2 * pi * (1 - 2 * rand(N))
logger.info("Type of Jy: " + str(type(Jy)))
logger.info(str(Jy))
# increase taumax to get make the sweep more adiabatic
taumax = 5.0
taulist = np.linspace(0, taumax, 100)
logger.info("Type of taulist: " + str(type(taulist)))
logger.info(str(taulist))
# pre-allocate operators
si = qeye(2)
logger.info("Type of taulist: " + str(type(si)))
logger.info(str(si))
sx = sigmax()
logger.info("Type of taulist: " + str(type(sx)))
logger.info(str(sx))
sy = sigmay()
logger.info("Type of taulist: " + str(type(sy)))
logger.info(str(sy))
sz = sigmaz()
logger.info("Type of taulist: " + str(type(sz)))
logger.info(str(sz))
sx_list = []
sy_list = []
sz_list = []
for n in range(N):
op_list = []
for m in range(N):
op_list.append(si)
op_list[n] = sx
sx_list.append(tensor(op_list))
op_list[n] = sy
sy_list.append(tensor(op_list))
op_list[n] = sz
sz_list.append(tensor(op_list))
psi_list = [basis(2,0) for n in range(N)]
logger.info("Type of psi_list: " + str(type(psi_list)))
logger.info(str(psi_list))
psi0 = tensor(psi_list)
logger.info("Type of psi0: " + str(type(psi0)))
logger.info(str(psi0))
H0 = 0
for n in range(N):
H0 += - 0.5 * 2.5 * sz_list[n]
logger.info("Type of H0: " + str(type(H0)))
logger.info(str(H0))
# energy splitting terms
H1 = 0
for n in range(N):
H1 += - 0.5 * h[n] * sz_list[n]
logger.info("Energy splitting terms...")
logger.info("Type of H0: " + str(type(H1)))
logger.info(str(H1))
H1 = 0
for n in range(N-1):
# interaction terms
H1 += - 0.5 * Jx[n] * sx_list[n] * sx_list[n+1]
H1 += - 0.5 * Jy[n] * sy_list[n] * sy_list[n+1]
H1 += - 0.5 * Jz[n] * sz_list[n] * sz_list[n+1]
logger.info("Interaction terms...")
logger.info("Type of H1: " + str(type(H1)))
logger.info(str(H1))
# the time-dependent hamiltonian in list-function format
args = {'t_max': max(taulist)}
logger.info("the time-dependent hamiltonian in list-function format...")
logger.info("Type of args: " + str(args))
logger.info(str(args))
h_t = [[H0, lambda t, args : (args['t_max']-t)/args['t_max']],
[H1, lambda t, args : t/args['t_max']]]
logger.info("Type of args: " + str(h_t))
logger.info(str(h_t))
def main():
# Start the timer
experiment_start = time.time()
logger.info("\n\n\n\n>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Experiment started --------------------------------------------------------------------------------")
# Evolve the system, request the solver to call process_rho at each time step.
mesolve(h_t, psi0, taulist, [], process_rho, args)
#rc('font', family='serif')
#rc('font', size='10')
fig, axes = plt.subplots(2, 1, figsize=(12,10))
#
# plot the energy eigenvalues
#
# first draw thin lines outlining the energy spectrum
for n in range(len(evals_mat[0,:])):
ls,lw = ('b',1) if n == 0 else ('k', 0.25)
axes[0].plot(taulist/max(taulist), evals_mat[:,n] / (2*pi), ls, lw=lw)
# second, draw line that encode the occupation probability of each state in
# its linewidth. thicker line => high occupation probability.
for idx in range(len(taulist)-1):
for n in range(len(P_mat[0,:])):
lw = 0.5 + 4*P_mat[idx,n]
if lw > 0.55:
axes[0].plot(array([taulist[idx], taulist[idx+1]])/taumax,
array([evals_mat[idx,n], evals_mat[idx+1,n]])/(2*pi),
'r', linewidth=lw)
axes[0].set_xlabel(r'$\tau$')
axes[0].set_ylabel('Eigenenergies')
axes[0].set_title("Energyspectrum (%d lowest values) of a chain of %d spins.\n " % (M,N)
+ "The occupation probabilities are encoded in the red line widths.")
#
# plot the occupation probabilities for the few lowest eigenstates
#
for n in range(len(P_mat[0,:])):
if n == 0:
axes[1].plot(taulist/max(taulist), 0 + P_mat[:,n], 'r', linewidth=2)
else:
axes[1].plot(taulist/max(taulist), 0 + P_mat[:,n])
axes[1].set_xlabel(r'$\tau$')
axes[1].set_ylabel('Occupation probability')
axes[1].set_title("Occupation probability of the %d lowest " % M +
"eigenstates for a chain of %d spins" % N)
axes[1].legend(("Ground state",));
logger.info("Title set")
logger.info("Saving the plot...")
plot_file_name = "gia_" + time.strftime("%d-%m-%Y") + ".png"
plt.savefig("plots/" + plot_file_name, dpi=1200)
experiment_end = time.time()
elapsed = experiment_end - experiment_start
hours, rem = divmod(elapsed, 3600)
minutes, seconds = divmod(rem, 60)
log_string = "Time spent: " + "{:0>2}:{:0>2}:{:05.2f}".format(int(hours), int(minutes), seconds)
logger.info( log_string)
log_string = "------|||||| END OF GIA EXPERIMENT ||||||------"
logger.info( log_string)
# me == the sender's email address
# you == the recipient's email address
# Send the message via our own SMTP server, but don't include the
# envelope header.
s = smtplib.SMTP('localhost')
if email_report:
s.sendmail("bluewave@chmpr.umbc.edu", ["shehab1@umbc.edu"], log_string)
s.quit()
#
# callback function for each time-step
#
evals_mat = np.zeros((len(taulist),M))
P_mat = np.zeros((len(taulist),M))
idx = [0]
def process_rho(tau, psi):
# evaluate the Hamiltonian with gradually switched on interaction
H = qobj_list_evaluate(h_t, tau, args)
# find the M lowest eigenvalues of the system
evals, ekets = H.eigenstates(eigvals=M)
evals_mat[idx[0],:] = real(evals)
# find the overlap between the eigenstates and psi
for n, eket in enumerate(ekets):
P_mat[idx[0],n] = abs((eket.dag().data * psi.data)[0,0])**2
idx[0] += 1
if __name__ == "__main__":
main()