def test_cov_is_pure():
"""Tests space unrolling when going into the Gaussian backend"""
delays = [1, 6, 36]
modes = 216
angles = np.concatenate([
generate_valid_bs_sequence(delays, modes),
generate_valid_r_sequence(delays, modes)
])
net = modes + sum(delays)
d = len(delays)
n, N = get_mode_indices(delays)
prog = sf.TDMProgram([N])
vac_modes = sum(delays)
with prog.context(*angles) as (p, q):
Sgate(0.8) | q[n[0]]
for i in range(d):
Rgate(p[i + d]) | q[n[i]]
BSgate(p[i], np.pi / 2) | (q[n[i + 1]], q[n[i]])
prog.space_unroll()
eng = sf.Engine(backend="gaussian")
results = eng.run(prog)
cov = results.state.cov()
mu = np.zeros(len(cov))
mu_vac, cov_vac = reduced_state(mu, cov, list(range(vac_modes)))
mu_comp, cov_comp = reduced_state(mu, cov, list(range(vac_modes, net)))
assert np.allclose(cov_vac, 0.5 * (sf.hbar) * np.identity(2 * vac_modes))
assert is_pure_cov(cov_comp, hbar=sf.hbar)
def fock_prob(mu, cov, ocp):
"""
Calculates the probability of measuring the gaussian state s2 in the photon number
occupation pattern ocp"""
if is_pure_cov(cov):
return np.abs(pure_state_amplitude(mu, cov, ocp, check_purity=False)) ** 2
return density_matrix_element(mu, cov, list(ocp), list(ocp)).real
def fock_amplitudes_one_mode(alpha, cov, cutoff):
""" Returns the Fock space density matrix of gaussian state characterized
by a complex displacement alpha and a (symmetric) covariance matrix
The Fock ladder ladder goes from 0 to cutoff-1"""
r = 2 * np.array([alpha.real, alpha.imag])
if is_pure_cov(cov):
psi = state_vector(r, cov, normalize=True, cutoff=cutoff)
rho = np.outer(psi, psi.conj())
return rho
return density_matrix(r, cov, normalize=True, cutoff=cutoff)
def test_is_pure_cov_thermal(nbar):
""" Test if is_pure_cov for vacuum and thermal states"""
hbar = 2
dim = 10
cov = (2 * nbar + 1) * np.identity(dim)
val = is_pure_cov(cov, hbar=hbar)
if nbar == 0:
assert val
else:
assert not val
def test_is_pure_cov():
""" Test if is_pure_cov for a pure state"""
hbar = 2
val = is_pure_cov(V, hbar=hbar)
assert val