def test_hhl_random_hermitian(self):
self.log.debug('Testing HHL with random hermitian matrix')
hermitian_params = self.params
hermitian_params['eigs']['num_ancillae'] = 4
n = 2
np.random.seed(0)
matrix = rmg.random_hermitian(n, eigrange=[0, 1])
vector = random(n)
algo_input = LinearSystemInput()
algo_input.matrix = matrix
algo_input.vector = vector
# run ExactLSsolver
ref_result = run_algorithm(self.els_params, algo_input)
ref_solution = ref_result['solution']
ref_normed = ref_solution/np.linalg.norm(ref_solution)
# run hhl
hhl_result = run_algorithm(hermitian_params, algo_input)
hhl_solution = hhl_result['solution']
hhl_normed = hhl_solution/np.linalg.norm(hhl_solution)
# compare result
fidelity = state_fidelity(ref_normed, hhl_normed)
np.testing.assert_approx_equal(fidelity, 1, significant=2)
self.log.debug('HHL solution vector: {}'.format(hhl_solution))
self.log.debug('algebraic solution vector: {}'.format(ref_normed))
self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
self.log.debug('probability of result: {}'.format(hhl_result["probability_result"]))
def test_hhl_random_hermitian_sv(self):
self.log.debug('Testing HHL with random hermitian matrix')
hermitian_params = self.params
hermitian_params['eigs']['num_ancillae'] = 4
n = 2
matrix = rmg.random_hermitian(n, eigrange=[0, 1])
vector = random(2)
algo_input = LinearSystemInput()
algo_input.matrix = matrix
algo_input.vector = vector
# run hhl
result = run_algorithm(hermitian_params, algo_input)
hhl_solution = result["solution_hhl"]
hhl_normed = hhl_solution / np.linalg.norm(hhl_solution)
# linear algebra solution
linalg_solution = np.linalg.solve(matrix, vector)
linalg_normed = linalg_solution / np.linalg.norm(linalg_solution)
# compare result
fidelity = abs(linalg_normed.dot(hhl_normed.conj()))**2
np.testing.assert_approx_equal(fidelity, 1, significant=2)
self.log.debug('HHL solution vector: {}'.format(hhl_solution))
self.log.debug('algebraic solution vector: {}'.format(linalg_solution))
self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
self.log.debug('probability of result: {}'.format(
result["probability_result"]))
def test_hhl_random_hermitian(self):
""" hhl random hermitian test """
self.log.debug('Testing HHL with random hermitian matrix')
n = 2
matrix = rmg.random_hermitian(n, eigrange=[0, 1])
vector = aqua_globals.random.random(n)
# run NumPyLSsolver
ref_result = NumPyLSsolver(matrix, vector).run()
ref_solution = ref_result['solution']
ref_normed = ref_solution / np.linalg.norm(ref_solution)
# run hhl
orig_size = len(vector)
matrix, vector, truncate_powerdim, truncate_hermitian = HHL.matrix_resize(
matrix, vector)
# Initialize eigenvalue finding module
eigs = TestHHL._create_eigs(matrix, 4, False)
num_q, num_a = eigs.get_register_sizes()
# Initialize initial state module
init_state = Custom(num_q, state_vector=vector)
# Initialize reciprocal rotation module
reci = LookupRotation(negative_evals=eigs._negative_evals,
evo_time=eigs._evo_time)
algo = HHL(matrix, vector, truncate_powerdim, truncate_hermitian, eigs,
init_state, reci, num_q, num_a, orig_size)
warnings.filterwarnings('ignore', category=DeprecationWarning)
hhl_result = algo.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
warnings.filterwarnings('always', category=DeprecationWarning)
hhl_solution = hhl_result['solution']
hhl_normed = hhl_solution / np.linalg.norm(hhl_solution)
# compare result
fidelity = state_fidelity(ref_normed, hhl_normed)
np.testing.assert_approx_equal(fidelity, 1, significant=1)
self.log.debug('HHL solution vector: %s', hhl_solution)
self.log.debug('algebraic solution vector: %s', ref_normed)
self.log.debug('fidelity HHL to algebraic: %s', fidelity)
self.log.debug('probability of result: %s',
hhl_result["probability_result"])
def test_random_hermitian(self, N):
a = random_hermitian(N)
distance = abs(np.sum(a - a.T.conj()))
self.assertAlmostEqual(distance, 0, places=10)
def test_random_hermitian(self, m_v):
""" random hermitian test """
r_a = random_hermitian(m_v)
distance = abs(np.sum(r_a - r_a.T.conj()))
self.assertAlmostEqual(distance, 0, places=10)
