def test_self_loop_raises_error(self):
"""Test graphs with self loop raises ValueError"""
g = nx.complete_graph(3).to_directed()
edge_weight_data = {edge: (i + 1) * 0.5 for i, edge in enumerate(g.edges)}
for k, v in edge_weight_data.items():
g[k[0]][k[1]]["weight"] = v
g.add_edge(1, 1) # add self loop
with pytest.raises(ValueError, match="Graph contains self-loops"):
loss_hamiltonian(g)
def test_loss_hamiltonian_complete(self):
"""Test if the loss_hamiltonian function returns the expected result on a
manually-calculated example of a 3-node complete digraph"""
g = nx.complete_graph(3).to_directed()
edge_weight_data = {
edge: (i + 1) * 0.5
for i, edge in enumerate(g.edges)
}
for k, v in edge_weight_data.items():
g[k[0]][k[1]]["weight"] = v
h = loss_hamiltonian(g)
expected_ops = [
qml.PauliZ(0),
qml.PauliZ(1),
qml.PauliZ(2),
qml.PauliZ(3),
qml.PauliZ(4),
qml.PauliZ(5),
]
expected_coeffs = [
np.log(0.5),
np.log(1),
np.log(1.5),
np.log(2),
np.log(2.5),
np.log(3)
]
assert expected_coeffs == h.coeffs
assert all(
[op.wires == exp.wires for op, exp in zip(h.ops, expected_ops)])
assert all(
[type(op) is type(exp) for op, exp in zip(h.ops, expected_ops)])
def test_missing_edge_weight_data_raises_error(self):
"""Test graphs with no edge weight data raises `KeyError`"""
g = nx.complete_graph(3).to_directed()
with pytest.raises(KeyError, match="does not contain weight data"):
loss_hamiltonian(g)