def test_singular_values_random4x4(self):
"""Tests computing the singular values for random 4 x 4 matrices."""
for _ in range(10):
matrix = np.random.randn(4, 4)
matrix += matrix.conj().T
qsve = QSVE(matrix)
print("Matrix:")
print(matrix)
sigmas = qsve.singular_values_classical()
print("Sigmas:", sigmas)
n = 6
qsigmas = qsve.top_singular_values(nprecision_bits=n,
init_state_row_and_col=None,
shots=50000,
ntop=4)
print("QSigmas:", qsigmas)
print("Max theory error:", qsve.max_error(n))
self.assertTrue(
qsve.has_value_close_to_singular_values(
qsigmas, qsve.max_error(n)))
print("Success!\n\n")
def test_non_square(self):
"""Tests QSVE on a simple non-square matrix."""
matrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], dtype=np.float64)
qsve = QSVE(matrix)
qsigmas = qsve.top_singular_values(nprecision_bits=2, ntop=2)
self.assertTrue(
qsve.has_value_close_to_singular_values(qsigmas,
qsve.max_error(2)))
def test_non_square_random(self):
"""Tests QSVE on a non-square random matrix."""
for _ in range(10):
matrix = np.random.randn(2, 4)
qsve = QSVE(matrix)
qsigmas = qsve.top_singular_values(nprecision_bits=4, ntop=-1)
self.assertTrue(
qsve.has_value_close_to_singular_values(
qsigmas, qsve.max_error(4)))
def test_singular_values_identity8(self):
"""Tests QSVE gets close to the correct singular values for the 8 x 8 identity matrix."""
qsve = QSVE(np.identity(8))
sigma = max(qsve.singular_values_classical())
for n in range(3, 7):
qsigma = qsve.top_singular_values(nprecision_bits=n,
init_state_row_and_col=None,
shots=50000,
ntop=3)
self.assertTrue(abs(sigma - qsigma[0]) < qsve.max_error(n))
