def test_exact_model_1d(n_modes, threshold, do_not_vary):
"""Test that the fitting is correct when the guess is exactly the correct answer"""
sq_n = 0.7 * (0.5**np.arange(n_modes))
noise = 0.1
eta = 0.7
params = {"sq_" + str(i): sq_n[i] for i in range(n_modes)}
params["n_modes"] = n_modes
params["eta"] = eta
params["noise"] = noise
if threshold:
probs = threshold_1d(degenerate_pmf(params), threshold)
else:
probs = degenerate_pmf(params)
fit = fit_1d(probs, params, threshold=threshold, do_not_vary=do_not_vary)
assert np.allclose(fit.chisqr, 0.0)
def test_marginal_calcs_1d():
"""Tests that marginal_calcs_1d returns the correct values as an array"""
nmean = 1.0
ps = degenerate_pmf({"n_modes": 1, "sq_0": nmean}, cutoff=100)
assert np.allclose(
marginal_calcs_1d(ps, as_dict=False), np.array([nmean, 3 + 1 / nmean, 15 + 9 / nmean])
)
def model_1d(params, pd_data):
ndim = pd_data.shape[0]
return (
degenerate_pmf(params, cutoff=cutoff, sq_label=sq_label, noise_label=noise_label)[
:ndim
]
- pd_data
)
def test_gen_hist_1d(sq_0):
"""Check that a histogram is constructed correctly for a degenerate squeezing source"""
nsamples = 1_000_000
q = 1.0 - np.tanh(np.arcsinh(np.sqrt(sq_0)))**2
r = 0.5
samples = 2 * np.random.negative_binomial(r, q, size=nsamples)
nmax = max(samples)
expected_pmf = degenerate_pmf({"sq_0": sq_0, "n_modes": 1}, cutoff=nmax)
pmf = gen_hist_1d(samples)
atol = 5 / np.sqrt(nsamples)
assert np.allclose(pmf, expected_pmf, atol=atol)
def test_two_schmidt_mode_guess_exact(eta, sq_0, sq_1):
"""Test that one can invert correctly when there are two Schmidt modes
and no dark counts
"""
pmf = degenerate_pmf({"sq_0": sq_0, "sq_1": sq_1, "n_modes": 2, "eta": eta})
guess = two_schmidt_mode_guess(pmf)
assert np.allclose(eta, guess["eta"], atol=1.0e-2)
sq_ns = [sq_0, sq_1] # We need to sort sq_n0 and sq_n1 so that sq_n0 >= sq_n1
sq_0 = np.max(sq_ns)
sq_1 = np.min(sq_ns)
assert np.allclose(sq_0, guess["sq_0"], atol=0.1)
assert np.allclose(sq_1, guess["sq_1"], atol=0.1)
def test_degenerate_correct_stats(eta, sq_0, sq_1, noise):
"""Test that the g2 of a single mode degenerate squeezer is 3+1/n regardless of the loss, where n is the mean photon number"""
params = {
"n_modes": 2,
"eta": eta,
"noise": noise,
"sq_0": sq_0,
"sq_1": sq_1
}
ps = degenerate_pmf(params)
vals = marginal_calcs_1d(ps)
K = (sq_0 + sq_1)**2 / (sq_0**2 + sq_1**2)
M = sq_0 + sq_1
g2noiseless = 1.0 + 1.0 / M + 2.0 / K
eps = noise / (eta * M)
g2 = (g2noiseless + 2 * eps + eps**2) / (1 + 2 * eps + eps**2)
assert np.allclose(vals["g2"], g2, atol=0.05)
assert np.allclose(vals["n"], eta * M + noise, atol=0.05)
def model_1d(params, pd_data):
ndim = pd_data.shape[0]
dpmf = degenerate_pmf(params, cutoff=cutoff)
return threshold_1d(dpmf, ndim) - pd_data