def test_decompose_4x4_optimal_tensor_products():
Id = np.array([[1, 0], [0, 1]])
X = np.array([[0, 1], [1, 0]])
Y = np.array([[0, -1j], [1j, 0]])
Z = np.array([[1, 0], [0, -1]])
H = np.array([[1, 1], [1, -1]]) / np.sqrt(2)
ops = [Id, X, Y, Z, H]
for m1 in ops:
for m2 in ops:
A = np.kron(m1, m2)
check_decomp(A, decompose_4x4_optimal(A), tol=2e-9)
def test_decompose_4x4_optimal_corner_cases():
check_decomp(SWAP, decompose_4x4_optimal(SWAP))
check_decomp(CNOT, decompose_4x4_optimal(CNOT))
check_decomp(QFT_2, decompose_4x4_optimal(QFT_2))
w = np.exp((2j / 3) * np.pi)
A = w * np.array([[1, 1, 1, 0],
[1, w, w * w, 0],
[1, w * w, w, 0],
[0, 0, 0, np.sqrt(3)]]) / np.sqrt(3)
check_decomp(A, decompose_4x4_optimal(A), tol=3e-8)
Phi = np.sqrt(0.5) * np.array([[1, -1j, 0, 0],
[0, 0, -1j, 1],
[0, 0, -1j, -1],
[1, 1j, 0, 0]])
check_decomp(Phi, decompose_4x4_optimal(Phi))
def test_decompose_4x4_optimal_random_orthogonal():
np.random.seed(100)
for _ in range(10):
A = ortho_group.rvs(4)
check_decomp(A, decompose_4x4_optimal(A))
def test_decompose_4x4_optimal_random_unitary():
np.random.seed(100)
for _ in range(10):
A = unitary_group.rvs(4)
check_decomp(A, decompose_4x4_optimal(A))
def test_matrix_to_gates_random_orthogonal(self):
np.random.seed(100)
for matrix_size in [2, 4, 8]:
for _ in range(10):
A = np.array(ortho_group.rvs(matrix_size))
check_decomp(A, qd.matrix_to_gates(A))
def test_matrix_to_gates_random_unitary(self):
np.random.seed(100)
for matrix_size in [2, 4, 8, 16]:
for _ in range(10):
A = np.array(unitary_group.rvs(matrix_size))
check_decomp(A, qd.matrix_to_gates(A))