def testCoherentDensityMatrix(self):
"""
states: coherent density matrix
"""
N = 10
rho = coherent_dm(N, 1)
# make sure rho has trace close to 1.0
assert_(abs(rho.tr() - 1.0) < 1e-12)
def test_driven_cavity_power_bicgstab():
"Steady state: Driven cavity - power-bicgstab solver"
N = 30
Omega = 0.01 * 2 * np.pi
Gamma = 0.05
a = destroy(N)
H = Omega * (a.dag() + a)
c_ops = [np.sqrt(Gamma) * a]
M = build_preconditioner(H, c_ops, method='power')
rho_ss = steadystate(H, c_ops, method='power-bicgstab', M=M, use_precond=1)
rho_ss_analytic = coherent_dm(N, -1.0j * (Omega)/(Gamma/2))
assert_((rho_ss - rho_ss_analytic).norm() < 1e-4)
def test_driven_cavity_lgmres():
"Steady state: Driven cavity - iterative-lgmres solver"
N = 30
Omega = 0.01 * 2 * np.pi
Gamma = 0.05
a = destroy(N)
H = Omega * (a.dag() + a)
c_ops = [np.sqrt(Gamma) * a]
rho_ss = steadystate(H, c_ops, method='iterative-lgmres')
rho_ss_analytic = coherent_dm(N, -1.0j * (Omega)/(Gamma/2))
assert_((rho_ss - rho_ss_analytic).norm() < 1e-4)
def test_driven_cavity_lgmres():
"Steady state: Driven cavity - iterative-lgmres solver"
N = 30
Omega = 0.01 * 2 * np.pi
Gamma = 0.05
a = destroy(N)
H = Omega * (a.dag() + a)
c_ops = [np.sqrt(Gamma) * a]
rho_ss = steadystate(H, c_ops, method='iterative-lgmres')
rho_ss_analytic = coherent_dm(N, -1.0j * (Omega) / (Gamma / 2))
assert_((rho_ss - rho_ss_analytic).norm() < 1e-4)
def compute_item(self, name, model):
name += "_1"
fock_dim = self.editor.fock_dim.value()
timestep = self.editor.timestep.value()
initial_alpha = self.editor.initial_alpha.value()
steps = model.get_steps(fock_dim, timestep)
res0 = coherent_dm(fock_dim, initial_alpha)
qubit0 = ket2dm((basis(2, 0) + basis(2, 1)) / np.sqrt(2))
psi0 = tensor(qubit0, res0)
def add_to_viewer(r):
item = WignerPlotter(name, r)
item.wigners_complete.connect(lambda: self.viewer.add_item(item))
win.statusBar().showMessage("Computing Wigners")
args = (steps, fock_dim, psi0, timestep)
run_in_process(to_state_list, add_to_viewer, args)
#self.thread_is_running.emit()
win.statusBar().showMessage("Computing States")
def test_compare_solvers_coherent_state_mees():
"""
correlation: comparing me and es for oscillator in coherent initial state
"""
N = 20
a = destroy(N)
H = a.dag() * a
G1 = 0.75
n_th = 2.00
c_ops = [np.sqrt(G1 * (1 + n_th)) * a, np.sqrt(G1 * n_th) * a.dag()]
rho0 = coherent_dm(N, np.sqrt(4.0))
taulist = np.linspace(0, 5.0, 100)
corr1 = correlation_2op_2t(H, rho0, None, taulist, c_ops, a.dag(), a,
solver="me")
corr2 = correlation_2op_2t(H, rho0, None, taulist, c_ops, a.dag(), a,
solver="es")
assert_(max(abs(corr1 - corr2)) < 1e-4)
def test_compare_solvers_coherent_state_legacy():
"""
correlation: legacy me and es for oscillator in coherent initial state
"""
N = 20
a = destroy(N)
H = a.dag() * a
G1 = 0.75
n_th = 2.00
c_ops = [np.sqrt(G1 * (1 + n_th)) * a, np.sqrt(G1 * n_th) * a.dag()]
rho0 = coherent_dm(N, np.sqrt(4.0))
taulist = np.linspace(0, 5.0, 100)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
corr1 = correlation(H, rho0, None, taulist, c_ops, a.dag(), a,
solver="me")
corr2 = correlation(H, rho0, None, taulist, c_ops, a.dag(), a,
solver="es")
assert_(max(abs(corr1 - corr2)) < 1e-4)
def test_coherentdm_type():
"State CSR Type: coherent_dm"
st = coherent_dm(25,2+2j)
assert_equal(isspmatrix_csr(st.data), True)
import numpy as np
import matplotlib.pyplot as plt
import qutip
N = 25
taus = np.linspace(0, 25.0, 200)
a = qutip.destroy(N)
H = 2 * np.pi * a.dag() * a
kappa = 0.25
n_th = 2.0 # bath temperature in terms of excitation number
c_ops = [np.sqrt(kappa * (1 + n_th)) * a, np.sqrt(kappa * n_th) * a.dag()]
states = [
{'state': qutip.coherent_dm(N, np.sqrt(2)), 'label': "coherent state"},
{'state': qutip.thermal_dm(N, 2), 'label': "thermal state"},
{'state': qutip.fock_dm(N, 2), 'label': "Fock state"},
]
fig, ax = plt.subplots(1, 1)
for state in states:
rho0 = state['state']
# first calculate the occupation number as a function of time
n = qutip.mesolve(H, rho0, taus, c_ops, [a.dag() * a]).expect[0]
# calculate the correlation function G2 and normalize with n(0)n(t) to
# obtain g2
G2 = qutip.correlation_3op_1t(H, rho0, taus, c_ops, a.dag(), a.dag()*a, a)
g2 = G2 / (n[0] * n)
