def test_isdm():
# Check when matrix is non-semi-positive-definite
non_spd = jnp.array([[1, 1], [-1, 1]])
assert isdm(non_spd) == False
# Check standard density matrices
assert isdm(to_dm(basis(2, 0))) == True
# Check when matrix is non-hermitian
assert isdm(sigmax() * sigmay()) == False
# Check when trace is non-unity
assert isdm(jnp.eye(2) * 2) == False
def test_sigmax():
assert_array_equal(sigmax(), jnp.array([[0.0, 1.0], [1.0, 0.0]]))
def test_sigmax():
assert_array_equal(sigmax(), jnp.array([[0.0, 1.0], [1.0, 0.0]]))
def test_sigmay():
assert_array_equal(
sigmay(), jnp.array([[0.0 + 0.0j, 0.0 - 1.0j], [0.0 + 1.0j, 0.0 + 0.0j]])
)
def test_sigmaz():
assert_array_equal(sigmaz(), jnp.array([[1.0, 0.0], [0.0, -1.0]]))
@pytest.mark.parametrize("op", [sigmax(), sigmay(), sigmaz()])
@pytest.mark.parametrize("state", [basis(2, 0), basis(2, 1)])
def test_expect_sigmaxyz(op, state):
"""Tests the `expect` function on Pauli-X, Pauli-Y and Pauli-Z."""
# The stacked pytest decorators check all the argument combinations like a Cartesian product
if jnp.all(op != sigmaz()):
assert expect(op, state) == 0.0
elif jnp.all(state == basis(2, 0)):
assert expect(op, state) == 1.0
else:
assert expect(op, state) == -1.0
@pytest.mark.parametrize(
"oper, state",
[