def test_reduced_density_contraction(backend):
if backend == "pytorch":
pytest.skip("pytorch doesn't support complex numbers")
a = tn.Node(np.array([[0.0, 1.0j], [-1.0j, 0.0]], dtype=np.complex64),
backend=backend)
tn.reduced_density([a[0]])
result = tn.contractors.greedy(tn.reachable(a), ignore_edge_order=True)
np.testing.assert_allclose(result.tensor, np.eye(2))
def test_reduced_density_nondangling(backend):
a = tn.Node(np.random.rand(3, 3, 3), name="A", backend=backend)
b = tn.Node(np.random.rand(3, 3, 3), name="B", backend=backend)
c = tn.Node(np.random.rand(3, 3, 3), name="C", backend=backend)
a[0] ^ b[1]
b[2] ^ c[1]
err_msg = "traced_out_edges must only include dangling edges!"
with pytest.raises(ValueError, match=err_msg):
tn.reduced_density([a[0], b[1], c[1]])
def test_reduced_density(backend):
a = tn.Node(np.random.rand(3, 3, 3), name="A", backend=backend)
b = tn.Node(np.random.rand(3, 3, 3), name="B", backend=backend)
c = tn.Node(np.random.rand(3, 3, 3), name="C", backend=backend)
edges = tn.get_all_edges({a, b, c})
node_dict, edge_dict = tn.reduced_density([a[0], b[1], c[2]])
assert not a[0].is_dangling()
assert not b[1].is_dangling()
assert not c[2].is_dangling()
assert a[1].is_dangling() & a[2].is_dangling()
assert b[0].is_dangling() & b[2].is_dangling()
assert c[0].is_dangling() & c[1].is_dangling()
for node in {a, b, c}:
assert node_dict[node].name == node.name
for edge in edges:
assert edge_dict[edge].name == edge.name
