def test_invalid_n_mean(self):
"""Test if function raises a ``ValueError`` when the mean photon number is specified to
be negative."""
g = nx.complete_graph(5)
with pytest.raises(ValueError,
match="Mean photon number must be non-negative"):
similarity.prob_orbit_mc(g, [1, 1, 1, 1], n_mean=-1)
def test_invalid_samples(self):
"""Test if function raises a ``ValueError`` when a number of samples less than one is
requested."""
g = nx.complete_graph(5)
with pytest.raises(ValueError,
match="Number of samples must be at least one"):
similarity.prob_orbit_mc(g, [1, 1, 1, 1], samples=0)
def test_known_result(self):
"""Tests if the probability for known cases is correctly
reproduced."""
g = nx.complete_graph(3)
assert np.allclose(similarity.prob_orbit_mc(g, [1, 1], 2),
0.2422152682538481)
assert np.allclose(similarity.prob_orbit_mc(g, [4], 1), 0)
def test_invalid_loss(self):
"""Test if function raises a ``ValueError`` when the loss parameter is specified outside
of range."""
g = nx.complete_graph(5)
with pytest.raises(
ValueError,
match="Loss parameter must take a value between zero and one"):
similarity.prob_orbit_mc(g, [1, 1, 1, 1], loss=2)
def test_no_loss(self, monkeypatch):
"""Test if function correctly creates the SF program for GBS without loss."""
graph = nx.complete_graph(5)
mock_eng_run = mock.MagicMock()
with monkeypatch.context() as m:
m.setattr(sf.LocalEngine, "run", mock_eng_run)
similarity.prob_orbit_mc(graph, [1, 1, 1, 1], samples=1)
p_func = mock_eng_run.call_args[0][0]
assert not all([isinstance(op, sf.ops.LossChannel) for op in p_func.circuit])
def test_all_loss(self, monkeypatch):
"""Test if function samples from the vacuum when maximum loss is applied."""
dim = 5
graph = nx.complete_graph(dim)
mock_eng_run = mock.MagicMock()
with monkeypatch.context() as m:
m.setattr(sf.LocalEngine, "run", mock_eng_run)
similarity.prob_orbit_mc(graph, [1, 1, 1, 1], samples=1, loss=1)
p_func = mock_eng_run.call_args[0][0]
eng = sf.LocalEngine(backend="gaussian")
state = eng.run(p_func).state
cov = state.cov()
disp = state.displacement()
assert np.allclose(cov, 0.5 * state.hbar * np.eye(2 * dim))
assert np.allclose(disp, np.zeros(dim))
def test_mean_computation_orbit(self, monkeypatch):
"""Tests if the calculation of the sample mean is performed correctly. The test
monkeypatches the fock_prob function so that the probability is the same for each sample and
is equal to 1/5, i.e., one over the number of samples in the orbit [1,1,1,1] for 5 modes."""
graph = nx.complete_graph(5)
with monkeypatch.context() as m:
m.setattr(
"strawberryfields.backends.gaussianbackend.GaussianState.fock_prob",
lambda *args, **kwargs: 0.2,
)
assert np.allclose(similarity.prob_orbit_mc(graph, [1, 1, 1, 1]), 1.0)
def test_prob_vacuum_orbit(self):
"""Tests if the function gives the right probability for the empty orbit when the GBS
device has been configured to have zero mean photon number."""
graph = nx.complete_graph(10)
assert similarity.prob_orbit_mc(graph, [], 0) == 1.0
def test_max_loss(self):
"""Test if function samples from the vacuum when maximum loss is applied."""
graph = nx.complete_graph(5)
assert similarity.prob_orbit_mc(graph, [1, 1, 1, 1], samples=1,
loss=1) == 0.0
def test_odd_photon_numbers(self, k):
"""Test if function returns zero probability for odd number of total photons."""
graph = nx.complete_graph(10)
assert similarity.prob_orbit_mc(graph, [k]) == 0.0
