def run():
N = 101
M = 101
A_pow = np.zeros((M, N))
A_mag = np.zeros((M, N))
A_phase = np.zeros((M, N))
phi = np.linspace(-0.20, -0.10, N)
wd = (2 * np.pi) * np.linspace(5.5, 6.5, M)
for j in range(N):
print(j)
H = TwoQubitNonLin_wInt(17.0, 16.0, 65e-15, 48.8e-15, 20.0, 40.0e-15,
phi[j])
results = qt.parallel_map(task,
wd,
task_args=(H, c),
progress_bar=True)
results = np.array(results)
A_pow[j, :] = results[:, 0]
A_mag[j, :] = results[:, 1]
A_phase[j, :] = results[:, 2]
np.savetxt('Mag2D.txt', A_mag, fmt='%f', delimiter=',')
np.savetxt('Phase2D.txt', A_phase, fmt='%f', delimiter=',')
np.savetxt('Power2D.txt', A_pow, fmt='%f', delimiter=',')
def run(self,parfunc,debug = False):
# self.ask_for_setup_info()
t0 = time.time()
self.generate_parameter_space()
# debugging
if debug:
parfunc(self.parameter_space[0])
if not debug:
raw_data = parallel_map(parfunc,self.parameter_space, progress_bar = True)
self.set_simulation_data_path()
self.save_dat(raw_data)
self.generate_spyview_meta()
p = self.open_spyview()
t1 = time.time()
self.generate_timing_info(t0,t1)
response = self.ask_for_comments()
if response == -1:
os.kill(p.pid, signal.SIGINT)
time.sleep(0.3)
rmtree(self.simulation_data_path)
else:
self.end_comments = response
self.ask_for_png()
self.save_meta()
self.log_on_ppt()
copyfile(os.path.join(self.manager_path,"settings.json"),
os.path.join(self.simulation_data_path,"settings.json"))
def fexpt2(self):
self.power_plot=True
fexpt=parallel_map(self.funcer, self.value_grid2, progress_bar=True)
#print shape(self.value_grid2)
#print self.pwr_arr.shape
#print self.phi_arr.shape
#print shape(fexpt)
return reshape(fexpt, (len(self.pwr_arr), len(self.phi_arr)))
def fexpt2(self):
self.power_plot = True
fexpt = parallel_map(self.funcer, self.value_grid2, progress_bar=True)
#print shape(self.value_grid2)
#print self.pwr_arr.shape
#print self.phi_arr.shape
#print shape(fexpt)
return reshape(fexpt, (len(self.pwr_arr), len(self.phi_arr)))
def entanglement_fidelity(channel, subspace_basis, tlist, progress_bar=False):
d = len(subspace_basis)
u_basis = build_unitary_basis(subspace_basis)
pure_state_basis, T = build_pure_state_basis(subspace_basis, u_basis)
if progress_bar:
F_e = sum(parallel_map(
entanglement_fidelity_term,
range(d**2), (channel, pure_state_basis, tlist, u_basis, d, T),
progress_bar=True),
axis=0)
else:
F_e = sum(parallel_map(entanglement_fidelity_term, range(
d**2), (channel, pure_state_basis, tlist, u_basis, d, T)),
axis=0)
if any(imag(F_e) > 10**-12):
warnings.warn(
'I got a pretty large imaginary number when calculating entanglement fidelity. Are you sure your simulation function is correct?'
)
return real(F_e)
def scan_laser_power(experiment,
start_power,
stop_power,
observables=None,
scan_laser='probe',
steps=100,
parallelize=False,
progress_bar=True):
"""Scans the frequency of a laser and returns transmission by default or user given observables
:param parallelize: Use multiple cores to calculate
:type parallelize: bool
:param experiment: The experiment on which the scan is performed
:type experiment: ntypecqed.simulation.NTypeExperiment
:param start_power: Start power of power scan
:type start_power: float
:param stop_power: Stop power of power scan
:type stop_power: float
:param observables: Observables for which the steadystate is calculated
:type observables: list of qutip.operator
:param scan_laser: Which laser to scan, either *probe*, *signal* or *control*
:type scan_laser: str
:param steps: Number of steps
:type steps: int
:return: tuple(powers, list of lists of the steadystates of the observables)
"""
laser_powers = {'probe': 'eta_p', 'signal': 'eta_s', 'control': 'omega_c'}
powers = np.linspace(start_power, stop_power, steps)
tmp_experiment = experiment.copy()
try:
power_scanned_laser = laser_powers[scan_laser]
except KeyError:
raise (KeyError,
"No valid scan laser, must be one of signal, control, probe")
if observables is None:
observables = tmp_experiment.environment.n_a, tmp_experiment.environment.n_b
if parallelize:
steady_states = parallel_map(ss_power,
powers,
task_args=(experiment,
power_scanned_laser),
progress_bar=progress_bar)
else:
steady_states = serial_map(ss_power,
powers,
task_args=(experiment, power_scanned_laser),
progress_bar=progress_bar)
ob_results = []
for result in steady_states:
ob_results.append(tuple(expect(result, ob) for ob in observables))
return powers, list(map(list, zip(*ob_results)))
def scan_laser_freq(experiment,
start_freq,
stop_freq,
observables=None,
scan_laser='probe',
steps=100,
parallelize=False,
progress_bar=True):
"""Scans the frequency of a laser and returns transmission by default or user given observables
:param parallelize: Use multiple cores to calculate
:type parallelize: bool
:param experiment: The experiment on which the scan is performed
:type experiment: ntypecqed.simulation.NTypeExperiment
:param start_freq: Start frequency of the scan
:type start_freq: float
:param stop_freq: Stop frequency of the scan
:type stop_freq: float
:param observables: Observables for which the steadystate is calculated
:type observables: list(qutip.operator)
:param scan_laser: Which laser to scan, either *probe*, *signal* or *control*
:type scan_laser: str
:param steps: Number of steps
:type steps: int
:return: tuple(frequencies, list of lists of the steadystates of the observables)
"""
freqs = np.linspace(start_freq, stop_freq, steps)
tmp_experiment = experiment.copy()
if scan_laser in ['signal', 'control', 'probe']:
scan_laser += '_detuning'
else:
raise (KeyError,
"No valid scan laser, must be one of signal, control, probe")
if observables is None:
observables = tmp_experiment.environment.n_a, tmp_experiment.environment.n_b
if parallelize:
steady_states = parallel_map(ss_freq,
freqs,
task_args=(experiment, scan_laser),
progress_bar=progress_bar)
else:
steady_states = serial_map(ss_freq,
freqs,
task_args=(experiment, scan_laser),
progress_bar=progress_bar)
ob_results = []
for result in steady_states:
ob_results.append(tuple(expect(result, ob) for ob in observables))
return freqs, list(map(list, zip(*ob_results)))
def generate_cut_states(fd_array, times, params, psi0=None, parallel=False, num_cpus=10, opt=None):
task_args = (times,)
task_kwargs = {'psi0': psi0, 'opt': opt}
params_list = []
for fd in fd_array:
params_copy = deepcopy(params)
params_copy.fd = fd
params_list.append(params_copy)
if parallel:
results = parallel_map(generate_result_states, params_list, task_args=task_args, task_kwargs=task_kwargs,
num_cpus=num_cpus, progress_bar=True)
else:
results = [generate_result(p, *task_args, **task_kwargs) for p in params_list]
results = pd.concat(results)
return results
def fexpt(self):
self.power_plot=False
fexpt=parallel_map(self.funcer, self.value_grid, progress_bar=True)
return reshape(fexpt, (len(self.frq_arr), len(self.phi_arr)))
def fexpt(self):
fexpt = parallel_map(self.funcer, self.value_grid, progress_bar=True)
return reshape(fexpt, (len(self.pwr_arr), len(self.phi_arr)))
#Ej = Ejmax*absolute(cos(pi*phi)) #Josephson energy as function of Phi.
#wTvec = -Ej + sqrt(8.0*Ej*Ec)*(nvec+0.5)+Ecvec
#wT = wTvec-wdvec
#transmon_levels = Qobj(diag(wT[range(N)]))
#H=transmon_levels +Omega_op
final_state = steadystate(H, c_op_list)
return expect(a, final_state)
if 0:
value_grid = array(meshgrid(phi_arr, Omega_sim_vec))
vg = zip(value_grid[0, :, :].flatten(), value_grid[1, :, :].flatten())
fexpt = parallel_map(find_expect, vg, progress_bar=True)
fexpt = reshape(fexpt, (31, 101))
pcolormesh(phi_arr, sample_power_sim_dBm, absolute(fexpt), cmap="RdBu_r")
show()
#w01_vec(i) = wT_vec(2)- wT_vec(1); % Transition energies.
#w12_vec(i) = wT_vec(3)- wT_vec(2);
#w23_vec(i) = wT_vec(4)- wT_vec(3);
#w34_vec(i) = wT_vec(5)- wT_vec(4);
H = make_H(0.2, 190, 4000.0)
print H
L = liouvillian(H, c_op_list)
print L
tr_mat = tensor([qeye(n) for n in L.dims[0][0]])
print tr_mat
tr_vec = transpose(mat2vec(tr_mat.full()))
# %%
if __name__ == '__main__':
# Simple CR, in qubit 2 frame
print('Calculate simple CR gate')
result_down = CR(tlist_simple, psidown)
result_up = CR(tlist_simple, psiup)
# Save result to file
print('Save data')
data = np.vstack((tlist_simple, result_down, result_up))
qt.file_data_store('CR0.dat', data, numtype='real')
print('Finished')
# Echoed CR, in qubit 2 frame
print('Calculate echoed CR gate')
print('|psi0> = |00>')
result_down = \
np.array(qt.parallel_map(CR_echo, tlist_echo, task_args=(psidown,),
progress_bar=True)).T
print('|psi0> = |10>')
result_up = \
np.array(qt.parallel_map(CR_echo, tlist_echo, task_args=(psiup,),
progress_bar=True)).T
print('Finished')
# Save result to file
print('Save data')
data = np.vstack((tlist_echo, result_down, result_up))
qt.file_data_store('CR2.dat', data, numtype='real')
# %% Plot results
data = qt.file_data_read('CR0.dat')
tlist = data[0]
result_down = data[1:7]
result_up = data[7:13]
def fexpt(self):
fexpt=parallel_map(self.funcer, self.value_grid, progress_bar=True)
return reshape(fexpt, (len(self.pwr_arr), len(self.phi_arr)))
#print "tm_l", tm_l.full().shape
#print "Lint", Lint.shape
#print "pl", p_l.full().shape
#print "pr", p_r.full().shape
#print rho.shape, to_super(rho_ss).full().shape
#print "rho", to_super(rho_ss) #rho_ss.full().shape #rho.shape
#print (tm_l.full()*Lint*(p_l.full() - p_r.full())*rho).shape
#raise Exception
return 1j*trace(tm_l*Lint*(p_l_m_p_r)*rho_ss)
value_grid=array(meshgrid(phi_arr, wd_vec))
vg=zip(value_grid[0, :, :].flatten(), value_grid[1, :, :].flatten())
print "1"
two_tone((0.23, 4000.0))
print "2"
fexpt=parallel_map(two_tone, vg, progress_bar=True)
fexpt=reshape(fexpt, (len(wd_vec), len(phi_arr)))
pcolormesh(absolute(fexpt), cmap="RdBu_r")
colorbar()
show()
#arange(1,7).reshape(-1,2).transpose()
# return (1.0/theta_steps)*1j*trace(reshape(tm_l*Lint*(p_l - p_r)*rho_ss_c,N,N))
# Chi_temp(i,j) = Chi_temp(i,j) + (1/theta_steps)*1i*trace(reshape(tm_l*Lint*(p_l - p_r)*rho_ss_c,dim,dim));
# r = (gamma/2)*Chi; % The reflection coefficient.
def fexpt(self):
self.power_plot = False
fexpt = parallel_map(self.funcer, self.value_grid, progress_bar=True)
return reshape(fexpt, (len(self.frq_arr), len(self.phi_arr)))
# Construct all combinations of omegaD and omegaP for parallelization
omegazip = []
for omegaP in omegaPlist:
omegazip.extend(zip(omegaDlist, np.repeat(omegaP, len(omegaDlist))))
# Divide into batches
bsize = 4 * len(omegaDlist)
nbatch = int(np.ceil(len(omegazip) / bsize))
filename = 'transSweep.dat'
result = []
# Run simulation and save data batch by batch
for ii in range(0, nbatch):
print('Calculating batch %d of %d ...' % (ii + 1, nbatch))
result.extend(
qt.parallel_map(transmission,
omegazip[ii * bsize:(ii + 1) * bsize],
task_args=(ampD, ampP),
num_cpus=40,
progress_bar=True))
qt.file_data_store(filename,
np.array(result).reshape((-1, 1)),
numtype='real')
print('Saved to %s\n' % filename)
# Save data
# data is an numpy array with M rows and N columns
# M = len(omegaPlist)+1, N = len(omegaDlist)+1
# data[0, 1:] = omegaDlist, data[1:, 0] = omegaPlist
# data[1:, 1:] = transmission data
result = np.array(result).reshape((len(omegaPlist), len(omegaDlist)))
data = np.vstack((omegaDlist, result))
data = np.hstack((np.append([0], omegaPlist).reshape(-1, 1), data))
def expect_update(Omega):
tstart=time()
Omega_vec=- 0.5j*(Omega*adag - conj(Omega)*a)
fexp=parallel_map(find_expect, phi_arr, task_kwargs=dict(Omega_vec=Omega_vec))# for phi in phi_arr]
print time()-tstart
return fexp
H=transmon_levels +Omega_vec #- 0.5j*(Omega_true*adag - conj(Omega_true)*a)
final_state = steadystate(H, c_op_list) #solve master equation
return expect( a, final_state) #expectation value of relaxation operator
#Omega=\alpha\sqrt{2\Gamma_10} where |\alpha|^2=phonon flux=number of phonons per second
#Omega=2\alpha\sqrt{\gamma} where |\alpha|^2=phonon flux=number of phonons per second
def expect_update(Omega):
tstart=time()
Omega_vec=- 0.5j*(Omega*adag - conj(Omega)*a)
fexp=parallel_map(find_expect, phi_arr, task_kwargs=dict(Omega_vec=Omega_vec))# for phi in phi_arr]
print time()-tstart
return fexp
vg=zip(value_grid[0, :, :].flatten(), value_grid[1, :, :].flatten())
fexpt=parallel_map(find_expect, vg, progress_bar=True)
fexpt=reshape(fexpt, (31, 101))
#fexpt=[expect_update(Omega) for Omega in Omega_sim_vec]
#fexpt=[[find_expect(phi, Omega) for phi in phi_arr] for Omega in Omega_sim_vec]
#print fexpt
pcolormesh(phi_arr, sample_power_sim_dBm, absolute(fexpt), cmap="RdBu_r")
figure()
pcolormesh(absolute(fexpt), cmap="RdBu_r")
figure()
plot(sample_power_sim_dBm, absolute(fexpt)[:, 77])
figure()
plot(sample_power_sim_dBm, 20*log10(absolute(fexpt)[:, 77])-sample_power_sim_dBm)
figure()
plot(sample_power_sim_dBm, 10**((20*log10(absolute(fexpt)[:, 77])-sample_power_sim_dBm)/10.0))