def test_decomposition_interferometer_with_zero(self, tol):
"""Test that an graph is correctly decomposed when the interferometer
has one zero somewhere in the unitary matrix, which is the case for the
adjacency matrix below"""
n = 6
prog = sf.Program(n)
A = np.array(
[
[0, 1, 0, 0, 1, 1],
[1, 0, 1, 0, 1, 1],
[0, 1, 0, 1, 1, 0],
[0, 0, 1, 0, 1, 0],
[1, 1, 1, 1, 0, 1],
[1, 1, 0, 0, 1, 0],
]
)
_, U = dec.graph_embed(A)
assert not np.allclose(U, np.identity(n))
G = ops.GraphEmbed(A)
cmds = G.decompose(prog.register)
last_op = cmds[-1].op
param_val = last_op.p[0]
assert isinstance(last_op, ops.Interferometer)
assert last_op.ns == n
assert np.allclose(param_val, U, atol=tol, rtol=0)
def test_decomposition(self, tol):
"""Test that an graph is correctly decomposed"""
n = 3
prog = sf.Program(n)
A = np.random.random([n, n]) + 1j * np.random.random([n, n])
A += A.T
A -= np.trace(A) * np.identity(n) / n
sq, U = dec.graph_embed(A)
G = ops.GraphEmbed(A)
cmds = G.decompose(prog.register)
assert np.all(sq == G.sq)
assert np.all(U == G.U)
S = np.identity(2 * n)
# calculating the resulting decomposed symplectic
for cmd in cmds:
# all operations should be BSgates, Rgates, or Sgates
assert isinstance(
cmd.op, (ops.Interferometer, ops.BSgate, ops.Rgate, ops.Sgate)
)
# build up the symplectic transform
modes = [i.ind for i in cmd.reg]
if isinstance(cmd.op, ops.Sgate):
S = _squeezing(cmd.op.p[0], cmd.op.p[1], modes, n) @ S
if isinstance(cmd.op, ops.Rgate):
S = _rotation(cmd.op.p[0], modes, n) @ S
if isinstance(cmd.op, ops.BSgate):
S = _beamsplitter(cmd.op.p[0], cmd.op.p[1], modes, n) @ S
if isinstance(cmd.op, ops.Interferometer):
U1 = cmd.op.p[0]
S_U = np.vstack(
[np.hstack([U1.real, -U1.imag]), np.hstack([U1.imag, U1.real])]
)
S = S_U @ S
# the resulting covariance state
cov = S @ S.T
# calculate Hamilton's A matrix: A = X.(I-Q^{-1})*
A_res = np.real_if_close(Amat(cov))
# The bottom right corner of A_res should be identical to A,
# up to some constant scaling factor. Check if the ratio
# of all elements is one
ratio = np.real_if_close(A_res[n:, n:] / A)
ratio /= ratio[0, 0]
assert np.allclose(ratio, np.ones([n, n]), atol=tol, rtol=0)
def test_mean_photon(self, tol):
"""Test that the mean photon number is correct in graph_embed"""
num_modes = 6
A = np.random.random([num_modes, num_modes]) + 1j * np.random.random([num_modes, num_modes])
A += A.T
n_mean = 10.0 / num_modes
sc, _ = dec.graph_embed(A, mean_photon_per_mode=n_mean)
n_mean_calc = np.mean(np.sinh(sc) ** 2)
assert np.allclose(n_mean, n_mean_calc, atol=tol, rtol=0)
def test_mean_photon(self, tol):
"""This test verifies that the maximum amount of squeezing used to encode
the graph is indeed capped by the parameter max_mean_photon"""
max_mean_photon = 2
A = np.random.random([6, 6]) + 1j * np.random.random([6, 6])
A += A.T
sc, _ = dec.graph_embed(A, max_mean_photon=max_mean_photon)
res_mean_photon = np.sinh(np.max(np.abs(sc)))**2
assert np.allclose(res_mean_photon, max_mean_photon, atol=tol, rtol=0)
def test_takagi_fixed_random_symm(self):
"""This test verifies that the maximum amount of squeezing used to encode the graph is indeed capped by the parameter max_mean_photon"""
self.logTestName()
error = np.empty(nsamples)
max_mean_photon = 2
for i in range(nsamples):
X = random_degenerate_symmetric()
sc, U = dec.graph_embed(X, max_mean_photon=max_mean_photon)
error[i] = np.sinh(np.max(np.abs(sc)))**2 - max_mean_photon
self.assertAlmostEqual(error.mean(), 0, delta=self.tol)
def test_make_traceless(self, monkeypatch, tol):
"""Test that A is properly made traceless"""
A = np.random.random([6, 6]) + 1j * np.random.random([6, 6])
A += A.T
assert not np.allclose(np.trace(A), 0, atol=tol, rtol=0)
with monkeypatch.context() as m:
# monkeypatch the takagi function to simply return A,
# so that we can inspect it and make sure it is now traceless
m.setattr(dec, "takagi", lambda A, tol: (np.ones([6]), A))
_, A_out = dec.graph_embed(A, make_traceless=True)
assert np.allclose(np.trace(A_out), 0, atol=tol, rtol=0)
def test_decomposition(self, hbar, tol):
"""Test that an graph is correctly decomposed"""
n = 3
prog = sf.Program(n)
A = np.random.random([n, n]) + 1j * np.random.random([n, n])
A += A.T
A -= np.trace(A) * np.identity(n) / 3
sq, U = dec.graph_embed(A)
G = ops.GraphEmbed(A)
cmds = G.decompose(prog.register)
assert np.all(sq == G.sq)
assert np.all(U == G.U)
S = np.identity(2 * n)
# calculating the resulting decomposed symplectic
for cmd in cmds:
# all operations should be BSgates, Rgates, or Sgates
assert isinstance(
cmd.op, (ops.Interferometer, ops.BSgate, ops.Rgate, ops.Sgate))
# build up the symplectic transform
modes = [i.ind for i in cmd.reg]
if isinstance(cmd.op, ops.Sgate):
S = _squeezing(cmd.op.p[0].x, cmd.op.p[1].x, modes, n) @ S
if isinstance(cmd.op, ops.Rgate):
S = _rotation(cmd.op.p[0].x, modes, n) @ S
if isinstance(cmd.op, ops.BSgate):
S = _beamsplitter(cmd.op.p[0].x, cmd.op.p[1].x, modes, n) @ S
if isinstance(cmd.op, ops.Interferometer):
U1 = cmd.op.p[0].x
S_U = np.vstack([
np.hstack([U1.real, -U1.imag]),
np.hstack([U1.imag, U1.real])
])
S = S_U @ S
# the resulting covariance state
cov = S @ S.T
# calculate Hamilton's A matrix: A = X.(I-Q^{-1})*
I = np.identity(n)
O = np.zeros_like(I)
X = np.block([[O, I], [I, O]])
x = cov[:n, :n]
xp = cov[:n, n:]
p = cov[n:, n:]
aidaj = (x + p + 1j * (xp - xp.T) - 2 * I) / 4
aiaj = (x - p + 1j * (xp + xp.T)) / 4
Q = np.block([[aidaj, aiaj.conj()], [aiaj, aidaj.conj()]
]) + np.identity(2 * n)
A_res = X @ (np.identity(2 * n) - np.linalg.inv(Q)).conj()
# The bottom right corner of A_res should be identical to A,
# up to some constant scaling factor. Check if the ratio
# of all elements is one
ratio = np.real_if_close(A_res[n:, n:] / A)
ratio /= ratio[0, 0]
assert np.allclose(ratio, np.ones([n, n]), atol=tol, rtol=0)
def test_symmetric_validation(self):
"""Test that the graph_embed decomposition raises exception if not symmetric"""
A = np.random.random([5, 5]) + 1j * np.random.random([5, 5])
with pytest.raises(ValueError, match="matrix is not symmetric"):
dec.graph_embed(A)
