def test_mcf90_dtypes2():
"mcsolve_f90: check for correct dtypes (average_states=False)"
# set system parameters
kappa = 2.0 # mirror coupling
gamma = 0.2 # spontaneous emission rate
g = 1 # atom/cavity coupling strength
wc = 0 # cavity frequency
w0 = 0 # atom frequency
wl = 0 # driving frequency
E = 0.5 # driving amplitude
N = 5 # number of cavity energy levels (0->3 Fock states)
tlist = np.linspace(0, 10, 5) # times for expectation values
# construct Hamiltonian
ida = qeye(N)
idatom = qeye(2)
a = tensor(destroy(N), idatom)
sm = tensor(ida, sigmam())
H = (w0 - wl) * sm.dag() * sm + (wc - wl) * a.dag() * a + \
1j * g * (a.dag() * sm - sm.dag() * a) + E * (a.dag() + a)
# collapse operators
C1 = np.sqrt(2 * kappa) * a
C2 = np.sqrt(gamma) * sm
C1dC1 = C1.dag() * C1
C2dC2 = C2.dag() * C2
# intial state
psi0 = tensor(basis(N, 0), basis(2, 1))
opts = Options(average_expect=False)
data = mcsolve_f90(
H, psi0, tlist, [C1, C2], [C1dC1, C2dC2, a], ntraj=5, options=opts)
assert_equal(isinstance(data.expect[0][0][1], float), True)
assert_equal(isinstance(data.expect[0][1][1], float), True)
assert_equal(isinstance(data.expect[0][2][1], complex), True)
def test_mcf90_dtypes2():
"mcsolve_f90: check for correct dtypes (average_states=False)"
# set system parameters
kappa = 2.0 # mirror coupling
gamma = 0.2 # spontaneous emission rate
g = 1 # atom/cavity coupling strength
wc = 0 # cavity frequency
w0 = 0 # atom frequency
wl = 0 # driving frequency
E = 0.5 # driving amplitude
N = 5 # number of cavity energy levels (0->3 Fock states)
tlist = np.linspace(0, 10, 5) # times for expectation values
# construct Hamiltonian
ida = qeye(N)
idatom = qeye(2)
a = tensor(destroy(N), idatom)
sm = tensor(ida, sigmam())
H = (w0 - wl) * sm.dag() * sm + (wc - wl) * a.dag() * a + \
1j * g * (a.dag() * sm - sm.dag() * a) + E * (a.dag() + a)
# collapse operators
C1 = np.sqrt(2 * kappa) * a
C2 = np.sqrt(gamma) * sm
C1dC1 = C1.dag() * C1
C2dC2 = C2.dag() * C2
# intial state
psi0 = tensor(basis(N, 0), basis(2, 1))
opts = Options(average_expect=False)
data = mcsolve_f90(
H, psi0, tlist, [C1, C2], [C1dC1, C2dC2, a], ntraj=5, options=opts)
assert_equal(isinstance(data.expect[0][0][1], float), True)
assert_equal(isinstance(data.expect[0][1][1], float), True)
assert_equal(isinstance(data.expect[0][2][1], complex), True)
def test_MCNoCollExpt():
"mcsolve_f90: Constant H with no collapse ops (expect)"
error = 1e-8
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
c_op_list = []
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve_f90(H, psi0, tlist, c_op_list, [a.dag() * a])
expt = mcdata.expect[0]
actual_answer = 9.0 * np.ones(len(tlist))
diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(diff < error, True)
def test_MCSimpleSingleExpect():
"""mcsolve_f90: Constant H with single expect operator"""
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
kappa = 0.2 # coupling to oscillator
c_op_list = [np.sqrt(kappa) * a]
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve_f90(H, psi0, tlist, c_op_list, [a.dag() * a])
expt = mcdata.expect[0]
actual_answer = 9.0 * np.exp(-kappa * tlist)
avg_diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(avg_diff < mc_error, True)
def test_MCNoCollExpt():
"mcsolve_f90: Constant H with no collapse ops (expect)"
error = 1e-8
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
c_op_list = []
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve_f90(H, psi0, tlist, c_op_list, [a.dag() * a])
expt = mcdata.expect[0]
actual_answer = 9.0 * np.ones(len(tlist))
diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(diff < error, True)
def test_MCSimpleSingleExpect():
"""mcsolve_f90: Constant H with single expect operator"""
N = 10 # number of basis states to consider
a = destroy(N)
H = a.dag() * a
psi0 = basis(N, 9) # initial state
kappa = 0.2 # coupling to oscillator
c_op_list = [np.sqrt(kappa) * a]
tlist = np.linspace(0, 10, 100)
mcdata = mcsolve_f90(H, psi0, tlist, c_op_list, [a.dag() * a])
expt = mcdata.expect[0]
actual_answer = 9.0 * np.exp(-kappa * tlist)
avg_diff = np.mean(abs(actual_answer - expt) / actual_answer)
assert_equal(avg_diff < mc_error, True)
