def dd_wrapper(H, tau_list, dt, N, mu, sigma, corr_time, seed):
N = 1
e = []
# Initial state
rho_0 = init_qubit([1, 0, 0])
for tau_final in tau_list:
dw_it = normal_autocorr_generator(mu, sigma, corr_time / dt / 2, seed)
tau = np.arange(tau_final, step=dt)
e.append(
dynamical_decoupling(H, rho_0, N, tau_final, tau.shape[0], dw_it))
return e
def test_only_hamiltonian_no_t():
# Simple sigma z Hamiltonian
H = lambda t: sz
# Start in |+> state
rho_0 = init_qubit([1, 0, 0])
tlist = np.linspace(0, 10, 1000)
rho, expect = lindblad_solver(H, rho_0, tlist, e_ops=[si, sx, sy, sz])
expected_sx = np.cos(2 * tlist)
expected_sy = np.sin(2 * tlist)
expected_sz = np.zeros_like(tlist)
assert_almost_equal(expect[0, :], 1, 3)
assert_almost_equal(expect[1, :], expected_sx, 2)
assert_almost_equal(expect[2, :], expected_sy, 2)
assert_almost_equal(expect[3, :], expected_sz, 2)
"legend.fontsize": 14,
"xtick.labelsize": 14,
"ytick.labelsize": 14,
}
mpl.rcParams.update(nice_fonts)
def H(t):
return sz * np.pi
tau_list = np.linspace(0, 3, 100)
e = []
for tau in tqdm(tau_list):
rho = init_qubit([1, 0, 0])
tlist = np.linspace(0, tau, 100)
rho, expect = lindblad_solver(H,
rho,
tlist,
c_ops=[np.sqrt(0.5) * sz],
e_ops=[])
rho = sx @ rho @ sx
rho, expect = lindblad_solver(H,
rho,
tlist,
c_ops=[np.sqrt(0.5) * sz],
e_ops=[sx])
e.append(1 / 2 + np.trace(rho @ sx) / 2)
plt.figure(figsize=(6, 3))
wL = args[0]
w_tilde = np.sqrt((A + wL)**2 + B**2)
mz = (A + wL) / w_tilde
mx = B / w_tilde
alpha = w_tilde * tau
beta = wL * tau
term1 = np.cos(alpha) * np.cos(beta) - mz * np.sin(alpha) * np.sin(beta)
term2 = mx**2 * (1 - np.cos(alpha)) * (1 - np.cos(beta)) / (1 + term1)
M = 1 - term2 * np.power(np.sin(N * np.arccos(term1) / 2), 2)
return (M + 1) / 2
if __name__ == '__main__':
steps = 25
N = 32
rho_0 = np.kron(init_qubit([1, 0, 0]), init_qubit([0, 0, 0]))
taus = np.linspace(9.00, 15.00, 100)
args1 = [1.0, 0.1, np.pi / 4]
parameters1 = list(
product([single_carbon_H], [rho_0], [N], taus, [steps], [args1[0]],
[args1[1]], [args1[2]]))
results1 = parmap.starmap(dynamical_decoupling,
parameters1,
pm_pbar=True,
pm_chunksize=3)
args2 = [1.0, 0.2, np.pi / 6]
parameters2 = list(
product([single_carbon_H], [rho_0], [N], taus, [steps], [args2[0]],
rk_iterator = _runge_kutta_generator(H, rho, tlist, c_ops, *args)
for i, rho in enumerate(rk_iterator):
if len(e_ops): # Compute all the expectations if any
expectations[:, i] = np.trace(rho @ e_ops_np, axis1=1, axis2=2)
return rho, expectations
if __name__ == "__main__":
def H(t, frequency):
return sz * frequency / 2
rho_0 = init_qubit([1, 0, 0])
tlist = np.linspace(0, 3.2, 1000)
frequency = 1 * 2 * np.pi
rho, expect = lindblad_solver(
H,
rho_0,
tlist,
frequency, # Extra argument to H
c_ops=[np.sqrt(0.5) * sz],
e_ops=[si, sx, sy, sz])
rho = sx @ rho @ sx
rho, expect_2 = lindblad_solver(
H,