def test_move_vac_modes(self, N, crop, expected):
"""Test the `move_vac_modes` function"""
samples = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]],
[[9, 10], [11, 12]]])
res = move_vac_modes(samples, N, crop=crop)
assert np.all(res == expected)
def test_ghz(self):
"""Generates a GHZ state and checks that the correct correlations (noise reductions) are observed
from the samples
See Eq. 5 of https://advances.sciencemag.org/content/5/5/eaaw4530
"""
# Set up the circuit
np.random.seed(42)
vac_modes = 1
n = 4
shots = 100
sq_r = 5
alpha = [
np.arccos(np.sqrt(1 / (n - i + 1))) if i != n + 1 else 0
for i in range(n)
]
alpha[0] = 0.0
phi = [0] * n
phi[0] = np.pi / 2
timebins_per_shot = len(alpha)
# Measuring X nullifier
theta = [0] * n
samples_X = singleloop(sq_r, alpha, phi, theta, shots)
reshaped_samples_X = move_vac_modes(samples_X, 2, crop=True)
# Measuring P nullifier
theta = [np.pi / 2] * n
samples_P = singleloop(sq_r, alpha, phi, theta, shots)
reshaped_samples_P = move_vac_modes(samples_P, 2, crop=True)
# We will check that the x of all the modes equal the x of the last one
nullifier_X = lambda sample: (sample - sample[-1])[:-1]
val_nullifier_X = np.var(
[nullifier_X(x[0]) for x in reshaped_samples_X], axis=0)
assert np.allclose(val_nullifier_X,
sf.hbar * np.exp(-2 * sq_r),
rtol=5 / np.sqrt(shots))
# We will check that the sum of all the p is equal to zero
val_nullifier_P = np.var([np.sum(p[0]) for p in reshaped_samples_P],
axis=0)
assert np.allclose(val_nullifier_P,
0.5 * sf.hbar * n * np.exp(-2 * sq_r),
rtol=5 / np.sqrt(shots))
def test_epr(self):
"""Generates an EPR state and checks that the correct correlations (noise reductions) are observed
from the samples"""
np.random.seed(42)
vac_modes = 1
sq_r = 5.0
c = 2
shots = 100
# This will generate c EPRstates per copy. I chose c = 4 because it allows us to make 4 EPR pairs per copy that can each be measured in different basis permutations.
alpha = [0, np.pi / 4] * c
phi = [np.pi / 2, 0] * c
# Measurement of 2 subsequent EPR states in XX, PP to investigate nearest-neighbour correlations in all basis permutations
theta = [np.pi / 2, 0, 0, np.pi / 2]
timebins_per_shot = len(alpha)
samples = singleloop(sq_r, alpha, phi, theta, shots)
reshaped_samples = move_vac_modes(samples, 2, crop=True)
X0 = reshaped_samples[:, 0, 0]
X1 = reshaped_samples[:, 0, 1]
P2 = reshaped_samples[:, 0, 2]
P3 = reshaped_samples[:, 0, 3]
rtol = 5 / np.sqrt(shots)
minusstdX1X0 = (X1 - X0).var()
plusstdX1X0 = (X1 + X0).var()
squeezed_std = np.exp(-2 * sq_r)
expected_minus = sf.hbar * squeezed_std
expected_plus = sf.hbar / squeezed_std
assert np.allclose(minusstdX1X0, expected_minus, rtol=rtol)
assert np.allclose(plusstdX1X0, expected_plus, rtol=rtol)
minusstdP2P3 = (P2 - P3).var()
plusstdP2P3 = (P2 + P3).var()
assert np.allclose(minusstdP2P3, expected_plus, rtol=rtol)
assert np.allclose(plusstdP2P3, expected_minus, rtol=rtol)