def time_evolution_RF(tfinal, qtype='hybrid-SOSS'):
from qubitsim.qubit import HybridQubit as hybrid
if qtype == 'hybrid-SOSS':
indices = [0, 1]
match_freq = 10.0
operating_point = 3.5
qubit = hybrid.SOSSHybrid(operating_point, match_freq)
H0 = qubit.hamiltonian_lab()
ChoiSimulation = CJFidelities.CJ(indices, H0, np.zeros((3,3)))
if tfinal == 0:
return ChoiSimulation.chi0
else:
return ChoiSimulation.chi_final_RF(tfinal)
else:
indices = [0, 1]
match_freq = 10.0
operating_point = 1.0
qubit = hybrid.HybridQubit(operating_point * match_freq, match_freq, 0.7 * match_freq, 0.7*match_freq)
H0 = qubit.hamiltonian_lab()
ChoiSimulation = CJFidelities.CJ(indices, H0, np.zeros((3,3)))
if tfinal == 0:
return ChoiSimulation.chi0
else:
return ChoiSimulation.chi_final_RF(tfinal)
def test_dimension_chi0():
H0 = np.identity(3)
Hnoise = np.zeros((3, 3))
indices = [0, 1]
cj_test = CJ.CJ(indices, H0, Hnoise)
chi_test = cj_test.chi0
assert chi_test.shape == (9, 9)
def noise_sample(qubit, ded, time):
"""
Calculate the resulting process matrix at for the input qubit
subjected to noise ded at time, time.
Parameters
----------
qubit: HybridQubit object
The qubit class to use for operational parameters
ded: float
the specific value of quasistatic noise
Units: GHz
time: float
final time for this simulation
Units: ns
Returns
-------
(9, 9) complex array
"""
indices = [0, 1]
H0 = np.diag(qubit.energies())
noise = qubit.detuning_noise_qubit(ded)
ChoiSimulation = CJ.CJ(indices, H0, noise)
if time == 0:
return ChoiSimulation.chi0
else:
return ChoiSimulation.chi_final(time)
def test_kernel_basic():
H0 = np.array([[0, 1], [1, 0]])
Hnoise = np.zeros((2, 2))
indices = [0, 1]
cj_test = CJ.CJ(indices, H0, Hnoise)
kernel_from_class = cj_test.kernel
test_kernel = np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1],
[0, 0, 1, 0]])
assert np.array_equal(kernel_from_class, test_kernel)
def noise_sample(qubit, tfinal, ded):
H0 = np.diag(qubit.energies())
noise = qubit.detuning_noise_qubit(ded)
indices = [0, 1]
ChoiSimulation = cj.CJ(indices, H0, noise)
if tfinal == 0:
return ChoiSimulation.chi0
else:
return ChoiSimulation.chi_final_RF(tfinal)
def test_basic_evolution():
H0 = np.array([[0, 1], [1, 0]])
Hnoise = np.zeros((2, 2))
indices = [0, 1]
tfinal = 0.5 * math.pi
cj_test = CJ.CJ(indices, H0, Hnoise)
chi_final_from_class = cj_test.chi_final(tfinal)
actual_chi_final = 0.5 * np.array(
[[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]],
dtype=complex)
assert np.array_equal(chi_final_from_class, actual_chi_final)
def noise_sample(qubit, ded, time):
indices = [0, 1]
H0 = np.diag(qubit.energies())
noise = qubit.detuning_noise_qubit(ded)
# noise = np.zeros((3,3))
ChoiSimulation = CJ.CJ(indices, H0, noise)
if time == 0:
return ChoiSimulation.chi0
else:
return ChoiSimulation.chi_final_RF(time)
def test_chi0_initiliazation():
H0 = np.identity(3)
Hnoise = np.zeros((3, 3))
indices = [0, 1]
test_matrix = np.zeros((9, 9), dtype=complex)
test_matrix[0, 0] += 0.5
test_matrix[0, 4] += 0.5
test_matrix[4, 0] += 0.5
test_matrix[4, 4] += 0.5
cj_test = CJ.CJ(indices, H0, Hnoise)
chi_test = cj_test.chi0
assert np.array_equal(test_matrix, chi_test)
def noise_sample_run(ded, tfinal):
"""Run a single noise sample at noise point ded (GHz)
and at time tfinal"""
operating_point = 3.0
match_freq = 10.0
indices = [0, 1]
qubit = hybrid.SOSSHybrid(operating_point, match_freq)
H0 = np.diag(qubit.energies())
noise = qubit.detuning_noise_qubit(ded)
# noise = np.zeros((3,3))
ChoiSimulation = cj.CJ(indices, H0, noise)
if tfinal == 0:
return ChoiSimulation.chi0
else:
return ChoiSimulation.chi_final_RF(tfinal)
def noise_testing(input_params):
ed = input_params['ed']
stsplitting = input_params['stsplitting']
delta1 = input_params['delta1']
delta2 = input_params['delta2']
tstep = input_params['tstep']
noise_samples = input_params['noise_samples']
indices = [0, 1]
qubit = hybrid.HybridQubit(ed, stsplitting, delta1, delta2)
H0 = np.diag(qubit.energies())
cj_array = np.zeros((9, 9, noise_samples.shape[0]), dtype=complex)
for i in range(noise_samples.shape[0]):
ded = noise_samples[i]
noise = qubit.detuning_noise_qubit(ded)
ChoiSimulation = cj.CJ(indices, H0, noise)
if tstep == 0:
cj_array[:, :, i] = ChoiSimulation.chi0
else:
cj_array[:, :, i] = ChoiSimulation.chi_final_RF(tstep)
return cj_array
def test_fidelity_normalization():
actual_chi = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0],
[1, 0, 0, 0]])
assert CJ.fidelity(actual_chi, actual_chi) == 1.0