def test_loss(self, monkeypatch):
"""Test if function correctly creates the SF program for lossy GBS."""
graph = nx.complete_graph(5)
mock_eng_run = mock.MagicMock()
with monkeypatch.context() as m:
m.setattr(sf.LocalEngine, "run", mock_eng_run)
similarity._get_state(graph, loss=0.5)
p_func = mock_eng_run.call_args[0][0]
assert isinstance(p_func.circuit[1].op, sf.ops.LossChannel)
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._get_state(graph)
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_max_loss(self):
"""Test if function samples from the vacuum when maximum loss is applied."""
dim = 5
graph = nx.complete_graph(dim)
state = similarity._get_state(graph, 0, 1)
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_known_result(self):
"""Tests if the probability for known cases is correctly
reproduced."""
g = nx.complete_graph(3)
p = similarity.prob_orbit_exact(g, [1, 1], 2)
assert np.allclose(p, 0.24221526825385403)
s = similarity._get_state(g, 2)
temp = s.fock_prob([1, 1, 0]) + s.fock_prob([1, 0, 1]) + s.fock_prob(
[0, 1, 1])
assert np.allclose(p, temp)
assert np.allclose(similarity.prob_orbit_exact(g, [4], 1), 0)