def add_nuclear_and_charge_noise(sim_object): ''' Adds the niose to the simulation. Configured to be: T2* for J oscillations 1.6 us if white, 1.2 us if static gaussian T2* qubit 1 = 1 us T2* qubit 2 = 0.6 us ''' # # # J = 6MHZ @ epsilon 835e9, t = 210e6 chargingE = 850e9*np.pi*2 detuningE = 828.6e9*np.pi*2 # T2* qubit 1 -- nuclear B_noise_1 = me.noise_py() B_noise_1.init_gauss(sim_object.H_B_field1,1.1e-6) # T2* qubit 2 -- nuclear B_noise_2 = me.noise_py() B_noise_2.init_gauss(sim_object.H_B_field2,0.75e-6) # Add noise object to simulation object. sim_object.add_noise_object(B_noise_1) sim_object.add_noise_object(B_noise_2) # Electrical noise ++ (static with realtionchip form the J plots). --- note here everything expressed in form of pauli matrices and not spin matrices (bi) charge_noise = me.noise_py() charge_noise.init_white(np.zeros([6,6],dtype=np.complex), 1.08e3) charge_noise.add_param_matrix_dep(sim_object.H_B_field1*2.4 + 0.78*sim_object.H_B_field2 + sim_object.H_B_field1*sim_object.H_B_field2, (4,4), np.array([[0,-1/detuningE],[0,chargingE]], dtype=np.complex)) charge_noise.add_param_matrix_dep(sim_object.H_B_field1*0.45 + 0.93*sim_object.H_B_field2, (4, 4), np.array([[0,1/detuningE],[0,chargingE-detuningE]], dtype=np.complex)) sim_object.add_noise_object(charge_noise) return sim_object
def add_nuclear_and_charge_noise(sim_object, T2_electric=0.82e-6):
# Adds the noise. Charge noise is taken so T2* of J oscillations is 1.6us.
# # # J = 6MHZ @ epsilon 835e9, t = 210e6
chargingE = 850e9 * np.pi * 2
detuningE = 828.6e9 * np.pi * 2
# T2* qubit 1 -- nuclear
B_noise_1 = me.noise_py()
B_noise_1.init_gauss(sim_object.H_B_field1, 1.2e-6)
# T2* qubit 2 -- nuclear
B_noise_2 = me.noise_py()
B_noise_2.init_gauss(sim_object.H_B_field2, 0.8e-6)
# Add noise object to simulation object.
sim_object.add_noise_object(B_noise_1)
sim_object.add_noise_object(B_noise_2)
# Electrical noise ++ (static with realtionchip form the J plots). --- note here everything expressed in form of pauli matrices and not spin matrices (bi)
charge_noise = me.noise_py()
charge_noise.init_gauss(np.zeros([6, 6], dtype=np.complex), T2_electric)
charge_noise.add_param_matrix_dep(
2.4 * sim_object.H_B_field1 + 0.78 * sim_object.H_B_field2 +
sim_object.H_B_field1 * sim_object.H_B_field2, (4, 4),
np.array([[0, 1 / detuningE], [0, chargingE]], dtype=np.complex))
charge_noise.add_param_matrix_dep(
0.45 * sim_object.H_B_field1 + 0.93 * sim_object.H_B_field2, (4, 4),
np.array([[0, -1 / detuningE], [0, chargingE - detuningE]],
dtype=np.complex))
sim_object.add_noise_object(charge_noise)
return sim_object
def test_noise(noise_type):
'''
function that test the noise types implemented.
noise types can be:
Static Gaussian
White noise
1/f^alpha voltage noise
'''
# define hamiltonian
db = double_dot_hamiltonian(18.4e9, 19.7e9, 850e9, 840e9, 0 * 0.250e9)
# # # J = 6MHZ @ epsilon 835e9, t = 210e6
chargingE = 850e9 * np.pi * 2
detuningE = 828.6e9 * np.pi * 2
# Add noise to qubit 1
B_noise_1 = me.noise_py()
if noise_type == 'pink':
B_noise_1.init_pink(db.H_B_field1, 1e7, 1)
if noise_type == 'white':
B_noise_1.init_white(db.H_B_field1, 4e3)
if noise_type == 'static_gauss':
B_noise_1.init_gauss(db.H_B_field1, 30e-9 * np.sqrt(2))
# Add noise object to simulation object.
db.add_noise_object(B_noise_1)
# Init wavefuntion
psi0 = np.array(
list(
basis(6, 0) * basis(6, 0).dag() + basis(6, 2) * basis(6, 0).dag() +
basis(6, 0) * basis(6, 2).dag() +
basis(6, 2) * basis(6, 2).dag()))[:, 0] / 2
# 5000 simulations to average.
db.number_of_sim_for_static_noise(5000)
db.calc_time_evolution(psi0, 0e-9, 100e-9, 500)
db.plot_expect()
x = np.linspace(0, 100e-9, 500)
if noise_type == 'white':
y = np.exp(-x / 31e-9)
plt.plot(x * 1e9, y, label='fit')
plt.savefig('White_noise.png')
if noise_type == 'static_gauss':
y = np.exp(-(x / 31e-9)**2)
plt.plot(x * 1e9, y, label='fit')
plt.savefig('Static_gauss_noise.png')
if noise_type == 'pink':
y = np.exp(-(x / 27e-9)**2)
plt.plot(x * 1e9, y, label='fit')
plt.savefig('Pink_noise.png')
plt.show()
lindmid = time.time()
s.calculate_evolution(Z0, 0, total_t, evo_steps)
lindend = time.time()
s.plot_expectation(*pop, 1)
# VonNeumann calculation and plotting
vnstart = time.time()
v = me.VonNeumann(2)
v.add_H0(Z)
white = me.noise_py()
white.init_white(X, noise_ampl)
v.add_noise_obj(white)
v.set_number_of_evalutions(vn_samples)
vnmid = time.time()
v.calculate_evolution(Z0, 0, total_t, evo_steps)
vnend = time.time()
v.plot_expectation(*pop, 2)
print('\n Measuring the time of the script:')
print(
f'Lindblad approach -- total time = {(lindend-lindstart):.5}s of which {(lindend-lindmid):.5}s of core operations.'
pink_ampl = 0.5 total_t = 10 Z = np.array([[1,0],[0,-1]], dtype=complex) X = np.array([[0,1],[1, 0]], dtype=complex) Z0 = np.array([[0,0],[0,1]], dtype=complex) Z1 = Z+Z0 pop = (np.array([Z0, Z1]), ['0','1']) # Lindblad calculation and plotting lindstart = time.time() s = me.Lindblad(2) # this is a copy of VonNeumann with added support for lindblad operators s.add_H0(Z) # works exactly like for VonNeumann s.add_lindbladian(X, white_ampl**2) # produces the same results of a white noise, but takes as second input the noise variance instead of the amplitude (var = ampl^2) pink = me.noise_py() pink.init_pink(X, pink_ampl, 1) s.add_noise_obj(pink) s.set_number_of_evalutions(samples) lindmid = time.time() s.calculate_evolution(Z0, 0, total_t, evo_steps) lindend = time.time() s.plot_expectation(*pop, 1) # VonNeumann calculation and plotting vnstart = time.time()
