def test_manhattan_distance():
assert mcpe.manhattan_dist(cirq.GridQubit(0, 0), cirq.GridQubit(0, 0)) == 0
assert mcpe.manhattan_dist(cirq.GridQubit(1, 2), cirq.GridQubit(1, 2)) == 0
assert mcpe.manhattan_dist(cirq.GridQubit(1, 2), cirq.GridQubit(3, 4)) == 4
assert mcpe.manhattan_dist(cirq.GridQubit(3, 4), cirq.GridQubit(1, 2)) == 4
assert mcpe.manhattan_dist(cirq.GridQubit(-1, 2), cirq.GridQubit(3,
-4)) == 10
def test_distance_fn():
a1, a2, a3, b1, b2, b3 = cirq.GridQubit.rect(2, 3)
# A gate operating on (a1, a3) will be improved by swapping a1 and a2, but
# by how much depends on the distance function used.
assert mcpe.effect_of_swap((a1, a2), (a1, a3), mcpe.manhattan_dist) == 1
double_manhattan = lambda q1, q2: 2 * mcpe.manhattan_dist(q1, q2)
assert mcpe.effect_of_swap((a1, a2), (a1, a3), double_manhattan) == 2
def euclidean_dist(q1, q2):
return ((q1.row - q2.row) ** 2 + (q1.col - q2.col) ** 2) ** 0.5
# Before, the gate qubits (a1, b2) are sqrt(2) units apart from each other
# by euclidean distance.
# After swapping a1 and a2, they'd only be 1 unit apart, so things improve
# by sqrt(2) - 1.
effect = mcpe.effect_of_swap((a1, a2), (a1, b2), euclidean_dist)
np.testing.assert_allclose(effect, 2 ** 0.5 - 1)