def test_bad_input(self):
"""Test bad input."""
A = numpy.random.randn(3, 3)
v = numpy.random.randn(2)
G = givens_matrix_elements(v[0], v[1])
with self.assertRaises(ValueError):
double_givens_rotate(A, G, 0, 1, which='a')
def test_odd_dimension(self):
"""Test that it raises an error for odd-dimensional input."""
A = numpy.random.randn(3, 3)
v = numpy.random.randn(2)
G = givens_matrix_elements(v[0], v[1])
with self.assertRaises(ValueError):
double_givens_rotate(A, G, 0, 1, which='row')
with self.assertRaises(ValueError):
double_givens_rotate(A, G, 0, 1, which='col')
def test_main_procedure(self):
for n in self.test_dimensions:
# Obtain a random quadratic Hamiltonian
quadratic_hamiltonian = random_quadratic_hamiltonian(n)
# Get the diagonalizing transformation
transformation_matrix = (
quadratic_hamiltonian.diagonalizing_bogoliubov_transform())
left_block = transformation_matrix[:, :n]
right_block = transformation_matrix[:, n:]
lower_unitary = numpy.empty((n, 2 * n), dtype=complex)
lower_unitary[:, :n] = numpy.conjugate(right_block)
lower_unitary[:, n:] = numpy.conjugate(left_block)
# Get fermionic Gaussian decomposition of lower_unitary
decomposition, left_decomposition, diagonal, left_diagonal = (
fermionic_gaussian_decomposition(lower_unitary))
# Compute left_unitary
left_unitary = numpy.eye(n, dtype=complex)
for parallel_set in left_decomposition:
combined_op = numpy.eye(n, dtype=complex)
for op in reversed(parallel_set):
i, j, theta, phi = op
c = numpy.cos(theta)
s = numpy.sin(theta)
phase = numpy.exp(1.j * phi)
givens_rotation = numpy.array(
[[c, -phase * s],
[s, phase * c]], dtype=complex)
givens_rotate(combined_op, givens_rotation, i, j)
left_unitary = combined_op.dot(left_unitary)
for i in range(n):
left_unitary[i] *= left_diagonal[i]
left_unitary = left_unitary.T
for i in range(n):
left_unitary[i] *= diagonal[i]
# Check that left_unitary zeroes out the correct entries of
# lower_unitary
product = left_unitary.dot(lower_unitary)
for i in range(n - 1):
for j in range(n - 1 - i):
self.assertAlmostEqual(product[i, j], 0.)
# Compute right_unitary
right_unitary = numpy.eye(2 * n, dtype=complex)
for parallel_set in decomposition:
combined_op = numpy.eye(2 * n, dtype=complex)
for op in reversed(parallel_set):
if op == 'pht':
swap_rows(combined_op, n - 1, 2 * n - 1)
else:
i, j, theta, phi = op
c = numpy.cos(theta)
s = numpy.sin(theta)
phase = numpy.exp(1.j * phi)
givens_rotation = numpy.array(
[[c, -phase * s],
[s, phase * c]], dtype=complex)
double_givens_rotate(combined_op, givens_rotation,
i, j)
right_unitary = combined_op.dot(right_unitary)
# Compute left_unitary * lower_unitary * right_unitary^\dagger
product = left_unitary.dot(lower_unitary.dot(
right_unitary.T.conj()))
# Construct the diagonal matrix
diag = numpy.zeros((n, 2 * n), dtype=complex)
diag[range(n), range(n, 2 * n)] = diagonal
# Assert that W and D are the same
for i in numpy.ndindex((n, 2 * n)):
self.assertAlmostEqual(diag[i], product[i])