def test_against_matrix_implementation():
r'''Compare an Euler trajectory computed naively using matrices to the
Euler trajectory computed by our implementation for a particular
realization of noise.
'''
c_loaded = np.load('tests/matrix_euler_test_0/coupling_op.npy')
MN_arr = np.load('tests/matrix_euler_test_0/M_N.npy')
M_loaded = MN_arr[0]
N_loaded = MN_arr[1]
H_loaded = np.load('tests/matrix_euler_test_0/H.npy')
times_loaded = np.load('tests/matrix_euler_test_0/times.npy')
U1s_loaded = np.load('tests/matrix_euler_test_0/U1s.npy')
rho0_loaded = np.load('tests/matrix_euler_test_0/rho_0.npy')
rhos_loaded = np.load('tests/matrix_euler_test_0/rhos.npy')
test_integrator = integrate.EulerHomodyneIntegrator(
c_loaded, M_loaded, N_loaded, H_loaded)
test_soln = test_integrator.integrate(rho0_loaded, times_loaded,
U1s_loaded)
test_rhos = test_soln.get_density_matrices()
test_errors = test_rhos - rhos_loaded
error_norms = [
sb.norm_squared(test_errors[j]) for j in range(test_errors.shape[0])
]
assert_almost_equal(max(error_norms), 0.0, 7)
def test_against_matrix_implementation():
r'''Compare an Euler trajectory computed naively using matrices to the
Euler trajectory computed by our implementation for a particular
realization of noise.
'''
c_loaded = np.load('tests/matrix_euler_test_0/coupling_op.npy')
MN_arr = np.load('tests/matrix_euler_test_0/M_N.npy')
M_loaded = MN_arr[0]
N_loaded = MN_arr[1]
H_loaded = np.load('tests/matrix_euler_test_0/H.npy')
times_loaded = np.load('tests/matrix_euler_test_0/times.npy')
U1s_loaded = np.load('tests/matrix_euler_test_0/U1s.npy')
rho0_loaded = np.load('tests/matrix_euler_test_0/rho_0.npy')
rhos_loaded = np.load('tests/matrix_euler_test_0/rhos.npy')
test_integrator = integrate.EulerHomodyneIntegrator(c_loaded, M_loaded,
N_loaded, H_loaded)
test_soln = test_integrator.integrate(rho0_loaded, times_loaded,
U1s_loaded)
test_rhos = test_soln.get_density_matrices()
test_errors = test_rhos - rhos_loaded
error_norms = [sb.norm_squared(test_errors[j])
for j in range(test_errors.shape[0])]
assert_almost_equal(max(error_norms), 0.0, 7)
def test_against_random_spin1_matrix_euler_test_vector():
with open('tests/random-spin1-matrix-euler-test-vector.npz', 'rb') as f:
test_vec = np.load(f)
Ls = test_vec['Ls']
H = test_vec['H']
rho0 = test_vec['rho0']
times = test_vec['times']
loaded_rhos = test_vec['rhos']
integrator = integrate.UncondLindbladIntegrator(Ls, H)
soln = integrator.integrate(rho0, times)
rhos = soln.get_density_matrices()
errors = rhos - loaded_rhos
error_norms = [sb.norm_squared(errors[j])
for j in range(errors.shape[0])]
# Normally I'm checking to 7 decimal places, but my Euler integrator
# seems to only be able to get within 6 decimal places of the vectorized
# solution. Analytic solution for a random instance like this is probably
# not going to happen.
assert_almost_equal(max(error_norms), 0.0, 6)
def test_against_t1_t2_matrix_euler_test_vector():
with open('tests/t1-t2-matrix-euler-test-vector.npz', 'rb') as f:
test_vec = np.load(f)
Ls = test_vec['Ls']
H = test_vec['H']
rho0 = test_vec['rho0']
times = test_vec['times']
loaded_rhos = test_vec['rhos']
integrator = integrate.UncondLindbladIntegrator(Ls, H)
soln = integrator.integrate(rho0, times)
rhos = soln.get_density_matrices()
errors = rhos - loaded_rhos
error_norms = [sb.norm_squared(errors[j])
for j in range(errors.shape[0])]
# Normally I'm checking to 7 decimal places, but my Euler integrator
# seems to only be able to get within 6 decimal places of the vectorized
# solution. Maybe I'll bother to get an analytic expression one of these
# days.
assert_almost_equal(max(error_norms), 0.0, 6)
def get_phys_dual_basis(self, field_rho_0):
return np.array([
sb.dualize(np.kron(basis_el, field_rho_0), self.basis).real /
sb.norm_squared(basis_el) for basis_el in self.basis_sys
])