def test_bounded(self, n_qubits):
tol = 1e-7
operator = rand_super_bcsz(2**n_qubits)
assert -tol <= unitarity(operator) <= 1 + tol
ax = f.add_subplot(121)
ax2 = f.add_subplot(122)
ax2.axis('off')
# ~~~~~~~~~~ Set the noise channel here ~~~~~~~~~~~
found_a_good_channel = False
limit = 0.4
while found_a_good_channel == False:
# two_qubit_noise = unitary_channel(4,0.9,0.01) # A unitary
# two_qubit_noise = qt.to_super(qt.cnot()) # CNOT
# two_qubit_noise = qt.rand_super_bcsz(4, dims=[[[2, 2], [2, 2]], [[2, 2], [2, 2]]], rank=2)
# two_qubit_noise = qt.rand_super_bcsz(4, dims=[[[2, 2], [2, 2]], [[2, 2], [2, 2]]])
two_qubit_noise = qt.rand_super(4, dims=[[[2, 2], [2, 2]], [[2, 2], [2, 2]]])
# two_qubit_noise = qt.super_tensor(qt.rand_super(2),qt.rand_super(2))
if qt.unitarity(two_qubit_noise) > limit:
found_a_good_channel = True
ax2.text(0.1,0.30, 'Noise is mix of product and unitary: rand_super & rand_unitary')
two_qubit_noise.dims = [[[2, 2], [2, 2]], [[2, 2], [2, 2]]]
# Prints information about the noise (this is helpful to see if the channel is suitable)
print "Unitarity: ", qt.unitarity(two_qubit_noise)
#Put those values on the plot
ax2.text(0.0,0.9, 'Calculated information about the noise channel:')
ax2.text(0.1,0.85, 'Unitarity: %f' % (qt.unitarity(two_qubit_noise)))
ax2.text(0.1,0.80, 'Correlated Unitarity: %f' % (correlated_unitarity(two_qubit_noise)))
eigenvaluesforchannel=eigens_of_A(two_qubit_noise)
def test_known_cases(self, operator, expected):
assert unitarity(operator) == pytest.approx(expected, abs=1e-7)
num_of_data_points = 20
for i in range(num_of_data_points):
print "Run: ", i
# ~~~~~~~~~~ Set the noise channel here ~~~~~~~~~~~
found_a_good_channel = False
limit = 0.3
while found_a_good_channel == False:
# two_qubit_noise = unitary_channel(4,0.9,0.01) # A unitary
# two_qubit_noise = qt.to_super(qt.cnot()) # CNOT
# two_qubit_noise = qt.rand_super_bcsz(4, dims=[[[2, 2], [2, 2]], [[2, 2], [2, 2]]], rank=2)
# two_qubit_noise = qt.rand_super_bcsz(4, dims=[[[2, 2], [2, 2]], [[2, 2], [2, 2]]])
two_qubit_noise = pauli_channel_2_qubit(4)
# two_qubit_noise = qt.super_tensor(qt.rand_super(2),qt.rand_super(2))
# same=qt.rand_super(2)
# two_qubit_noise = qt.super_tensor(same,same)
if qt.unitarity(two_qubit_noise) > limit:
found_a_good_channel = True
two_qubit_noise.dims = [[[2, 2], [2, 2]], [[2, 2], [2, 2]]]
# Prints information about the noise (this is helpful to see if the channel is suitable)
print "Unitarity: ", qt.unitarity(two_qubit_noise)
print "Correlated Unitarity: ", correlated_unitarity(two_qubit_noise)
print "True sub-unitarities:"
print " AB->AB: ", sub_unitarity_AB_to_AB(
two_qubit_noise), "A->A: ", sub_unitarity_A_to_A(
two_qubit_noise), " B->B: ", sub_unitarity_B_to_B(two_qubit_noise)
# Makes the gates noisy
noisy_cliffords = [
two_qubit_noise * qt.to_super(qt.tensor(x, y))