def test_grouped_pauli_expectation(self):
""" grouped pauli expectation test """
two_qubit_H2 = (-1.052373245772859 * I ^ I) + \
(0.39793742484318045 * I ^ Z) + \
(-0.39793742484318045 * Z ^ I) + \
(-0.01128010425623538 * Z ^ Z) + \
(0.18093119978423156 * X ^ X)
wf = CX @ (H ^ I) @ Zero
expect_op = PauliExpectation(group_paulis=False).convert(~StateFn(two_qubit_H2) @ wf)
self.sampler._extract_circuitstatefns(expect_op)
num_circuits_ungrouped = len(self.sampler._circuit_ops_cache)
self.assertEqual(num_circuits_ungrouped, 5)
expect_op_grouped = PauliExpectation(group_paulis=True).convert(~StateFn(two_qubit_H2) @ wf)
q_instance = QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=self.seed, seed_transpiler=self.seed)
sampler = CircuitSampler(q_instance)
sampler._extract_circuitstatefns(expect_op_grouped)
num_circuits_grouped = len(sampler._circuit_ops_cache)
self.assertEqual(num_circuits_grouped, 2)
def test_vqe_caching_direct(self, batch_mode):
backend = get_aer_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op,
var_form,
optimizer,
'matrix',
batch_mode=batch_mode)
circuit_cache = CircuitCache(skip_qobj_deepcopy=True)
quantum_instance_caching = QuantumInstance(backend,
circuit_cache=circuit_cache,
skip_qobj_validation=True)
result_caching = algo.run(quantum_instance_caching)
self.assertLessEqual(circuit_cache.misses, 1)
self.assertAlmostEqual(
self.reference_vqe_result['statevector_simulator']['energy'],
result_caching['energy'])
def test_graph_partition_vqe(self):
""" Graph Partition VQE test """
aqua_globals.random_seed = 10213
wavefunction = RealAmplitudes(self.qubit_op.num_qubits, insert_barriers=True,
reps=5, entanglement='linear')
result = VQE(self.qubit_op,
wavefunction,
SPSA(maxiter=300),
max_evals_grouped=2).run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
x = sample_most_likely(result.eigenstate)
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
self.assertEqual(graph_partition.objective_value(np.array([0, 1, 0, 1]), self.w),
graph_partition.objective_value(ising_sol, self.w))
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_graph_partition_vqe(self):
""" Graph Partition VQE test """
aqua_globals.random_seed = 10598
result = VQE(self.qubit_op,
RY(self.qubit_op.num_qubits,
depth=5,
entanglement='linear'),
SPSA(max_trials=300),
max_evals_grouped=2).run(
QuantumInstance(
BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
x = sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_uccsd_hf_aer_qasm(self):
""" uccsd hf test with Aer qasm_simulator. """
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest("Aer doesn't appear to be installed. Error: '{}'".format(str(ex)))
return
backend = Aer.get_backend('qasm_simulator')
optimizer = SPSA(maxiter=200, last_avg=5)
solver = VQE(var_form=self.var_form, optimizer=optimizer,
expectation=PauliExpectation(),
quantum_instance=QuantumInstance(backend=backend,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
gsc = GroundStateEigensolver(self.fermionic_transformation, solver)
result = gsc.solve(self.driver)
self.assertAlmostEqual(result.total_energies[0], -1.138, places=2)
def setUp(self):
super().setUp()
aqua_globals.random_seed = 8
try:
self.driver = PySCFDriver(atom='H .0 .0 .0; H .0 .0 0.75',
unit=UnitsType.ANGSTROM,
charge=0,
spin=0,
basis='sto3g')
except QiskitChemistryError:
self.skipTest('PYSCF driver does not appear to be installed')
self.reference_energies = [-1.8427016, -1.8427016 + 0.5943372, -1.8427016 + 0.95788352,
-1.8427016 + 1.5969296]
self.transformation = \
FermionicTransformation(qubit_mapping=FermionicQubitMappingType.JORDAN_WIGNER)
solver = NumPyEigensolver()
self.ref = solver
self.quantum_instance = QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_transpiler=90, seed_simulator=12)
def test_saving_and_loading_one_circ(self):
""" Saving and Loading one Circ test """
with tempfile.NamedTemporaryFile(suffix='.inp',
delete=True) as cache_tmp_file:
cache_tmp_file_name = cache_tmp_file.name
var_form = RYRZ(num_qubits=4, depth=5)
backend = BasicAer.get_backend('statevector_simulator')
params0 = aqua_globals.random.random_sample(
var_form.num_parameters)
circ0 = var_form.construct_circuit(params0)
qi0 = QuantumInstance(backend,
circuit_caching=True,
cache_file=cache_tmp_file_name,
skip_qobj_deepcopy=True,
skip_qobj_validation=True,
seed_simulator=self.seed,
seed_transpiler=self.seed)
_ = qi0.execute([circ0])
with open(cache_tmp_file_name, "rb") as cache_handler:
saved_cache = pickle.load(cache_handler, encoding="ASCII")
self.assertIn('qobjs', saved_cache)
self.assertIn('mappings', saved_cache)
qobjs = [Qobj.from_dict(qob) for qob in saved_cache['qobjs']]
self.assertTrue(isinstance(qobjs[0], Qobj))
self.assertGreaterEqual(len(saved_cache['mappings'][0][0]), 50)
qi1 = QuantumInstance(backend,
circuit_caching=True,
cache_file=cache_tmp_file_name,
skip_qobj_deepcopy=True,
skip_qobj_validation=True,
seed_simulator=self.seed,
seed_transpiler=self.seed)
params1 = aqua_globals.random.random_sample(
var_form.num_parameters)
circ1 = var_form.construct_circuit(params1)
qobj1 = qi1.circuit_cache.load_qobj_from_cache(
[circ1], 0, run_config=qi1.run_config)
self.assertTrue(isinstance(qobj1, Qobj))
_ = qi1.execute([circ1])
self.assertEqual(qi0.circuit_cache.mappings,
qi1.circuit_cache.mappings)
self.assertLessEqual(qi1.circuit_cache.misses, 0)
def test_vqc_minibatching_no_gradient_support(self):
""" vqc minibatching with no gradient support test """
n_dim = 2 # dimension of each data point
seed = 1024
aqua_globals.random_seed = seed
_, training_input, test_input, _ = _ad_hoc_data(training_size=6,
test_size=3,
n=n_dim, gap=0.3)
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = n_dim
optimizer = COBYLA(maxiter=40)
feature_map = SecondOrderExpansion(feature_dimension=num_qubits, depth=2)
var_form = RYRZ(num_qubits=num_qubits, depth=3)
vqc = VQC(optimizer, feature_map, var_form, training_input, test_input, minibatch_size=2)
quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed,
optimization_level=0)
result = vqc.run(quantum_instance)
vqc_accuracy = 0.666
self.log.debug(result['testing_accuracy'])
self.assertGreaterEqual(result['testing_accuracy'], vqc_accuracy)
def test_grover(self, input_test, sol, oracle_cls, mct_mode,
simulator, optimization, lam, rotation_counts):
""" grover test """
groundtruth = sol
oracle = oracle_cls(input_test, optimization=optimization)
grover = Grover(oracle, incremental=True, lam=lam,
rotation_counts=rotation_counts, mct_mode=mct_mode)
backend = BasicAer.get_backend(simulator)
quantum_instance = QuantumInstance(backend, shots=1000)
ret = grover.run(quantum_instance)
self.log.debug('Ground-truth Solutions: %s.', groundtruth)
self.log.debug('Top measurement: %s.', ret.top_measurement)
if ret.oracle_evaluation:
self.assertIn(ret.top_measurement, groundtruth)
self.log.debug('Search Result: %s.', ret.assignment)
else:
self.assertEqual(groundtruth, [])
self.log.debug('Nothing found.')
def test_grover(self, input, sol, oracle_cls, mct_mode, simulator, optimization='off'):
self.groundtruth = sol
if optimization == 'off':
oracle = oracle_cls(input, optimization='off')
else:
oracle = oracle_cls(input, optimization='qm-dlx' if oracle_cls == TTO else 'espresso')
grover = Grover(oracle, incremental=True, mct_mode=mct_mode)
backend = BasicAer.get_backend(simulator)
quantum_instance = QuantumInstance(backend, shots=1000, circuit_caching=False)
ret = grover.run(quantum_instance)
self.log.debug('Ground-truth Solutions: {}.'.format(self.groundtruth))
self.log.debug('Top measurement: {}.'.format(ret['top_measurement']))
if ret['oracle_evaluation']:
self.assertIn(ret['top_measurement'], self.groundtruth)
self.log.debug('Search Result: {}.'.format(ret['result']))
else:
self.assertEqual(self.groundtruth, [])
self.log.debug('Nothing found.')
def test_vqc_minibatching_with_gradient_support(self):
""" vqc minibatching with gradient support test """
n_dim = 2 # dimension of each data point
seed = 1024
np.random.seed(seed)
_, training_input, test_input, _ = _ad_hoc_data(training_size=8,
test_size=4,
n=n_dim, gap=0.3)
aqua_globals.random_seed = seed
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = n_dim
optimizer = L_BFGS_B(maxfun=100)
feature_map = SecondOrderExpansion(feature_dimension=num_qubits, depth=2)
var_form = RYRZ(num_qubits=num_qubits, depth=2)
vqc = VQC(optimizer, feature_map, var_form, training_input, test_input, minibatch_size=2)
quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)
result = vqc.run(quantum_instance)
vqc_accuracy_threshold = 0.8
self.log.debug(result['testing_accuracy'])
self.assertGreater(result['testing_accuracy'], vqc_accuracy_threshold)
def test_custom_excitation_pool(self):
""" Test custom excitation pool """
class CustomFactory(VQEUCCSDFactory):
"""A custom MES factory."""
def get_solver(self, transformation):
solver = super().get_solver(transformation)
# Here, we can create essentially any custom excitation pool.
# For testing purposes only, we simply select some hopping operator already
# available in the variational form object.
# pylint: disable=no-member
custom_excitation_pool = [solver.var_form._hopping_ops[2]]
solver.var_form.excitation_pool = custom_excitation_pool
return solver
solver = CustomFactory(QuantumInstance(BasicAer.get_backend('statevector_simulator')))
calc = AdaptVQE(self.transformation, solver)
res = calc.solve(self.driver)
self.assertAlmostEqual(res.electronic_energies[0], self.expected, places=6)
def test_qpe(self, qubit_op, simulator, num_time_slices, n_ancillae):
""" QPE test """
self.log.debug('Testing QPE')
exact_eigensolver = ExactEigensolver(qubit_op, k=1)
results = exact_eigensolver.run()
ref_eigenval = results['eigvals'][0]
ref_eigenvec = results['eigvecs'][0]
self.log.debug('The exact eigenvalue is: %s', ref_eigenval)
self.log.debug('The corresponding eigenvector: %s', ref_eigenvec)
state_in = Custom(qubit_op.num_qubits, state_vector=ref_eigenvec)
iqft = Standard(n_ancillae)
qpe = QPE(qubit_op, state_in, iqft, num_time_slices, n_ancillae,
expansion_mode='suzuki', expansion_order=2,
shallow_circuit_concat=True)
backend = BasicAer.get_backend(simulator)
quantum_instance = QuantumInstance(backend, shots=100)
# run qpe
result = qpe.run(quantum_instance)
# report result
self.log.debug('top result str label: %s', result['top_measurement_label'])
self.log.debug('top result in decimal: %s', result['top_measurement_decimal'])
self.log.debug('stretch: %s', result['stretch'])
self.log.debug('translation: %s', result['translation'])
self.log.debug('final eigenvalue from QPE: %s', result['energy'])
self.log.debug('reference eigenvalue: %s', ref_eigenval)
self.log.debug('ref eigenvalue (transformed): %s',
(ref_eigenval + result['translation']) * result['stretch'])
self.log.debug('reference binary str label: %s', decimal_to_binary(
(ref_eigenval.real + result['translation']) * result['stretch'],
max_num_digits=n_ancillae + 3,
fractional_part_only=True
))
np.testing.assert_approx_equal(result['energy'], ref_eigenval.real, significant=2)
def test_qsvm_setup_data(self, data_preparation_type):
""" QSVM Setup Data test """
ref_kernel_testing = np.array(
[[0.1443953, 0.18170069, 0.47479649, 0.14691763],
[0.33041779, 0.37663733, 0.02115561, 0.16106199]])
ref_support_vectors = np.array([[2.95309709, 2.51327412],
[3.14159265, 4.08407045],
[4.08407045, 2.26194671],
[4.46106157, 2.38761042]])
backend = BasicAer.get_backend('statevector_simulator')
data_preparation = self.data_preparation[data_preparation_type]
try:
if data_preparation_type == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
svm = QSVM(data_preparation)
if data_preparation_type == 'wrapped':
warnings.filterwarnings('always', category=DeprecationWarning)
svm.setup_training_data(self.training_data)
svm.setup_test_data(self.testing_data)
quantum_instance = QuantumInstance(
backend,
seed_transpiler=self.random_seed,
seed_simulator=self.random_seed)
result = svm.run(quantum_instance)
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=4)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'],
ref_support_vectors,
decimal=4)
self.assertEqual(result['testing_accuracy'], 0.5)
except NameError as ex:
self.skipTest(str(ex))
def test_uccsd_hf_qpUCCD(self):
""" paired uccd test """
optimizer = SLSQP(maxiter=100)
initial_state = HartreeFock(
self.fermionic_transformation.molecule_info['num_orbitals'],
self.fermionic_transformation.molecule_info['num_particles'],
qubit_mapping=self.fermionic_transformation._qubit_mapping,
two_qubit_reduction=self.fermionic_transformation.
_two_qubit_reduction)
var_form = UCCSD(
num_orbitals=self.fermionic_transformation.
molecule_info['num_orbitals'],
num_particles=self.fermionic_transformation.
molecule_info['num_particles'],
active_occupied=None,
active_unoccupied=None,
initial_state=initial_state,
qubit_mapping=self.fermionic_transformation._qubit_mapping,
two_qubit_reduction=self.fermionic_transformation.
_two_qubit_reduction,
num_time_slices=1,
shallow_circuit_concat=False,
method_doubles='pucc',
excitation_type='d')
solver = VQE(
var_form=var_form,
optimizer=optimizer,
quantum_instance=QuantumInstance(
backend=BasicAer.get_backend('statevector_simulator')))
gsc = GroundStateEigensolver(self.fermionic_transformation, solver)
result = gsc.solve(self.driver)
self.assertAlmostEqual(result.energy,
self.reference_energy_pUCCD,
places=6)
def test_qpe(self, qubit_op, simulator, num_time_slices, n_ancillae):
"""Test the QPE algorithm."""
self.log.debug('Testing QPE')
qubit_op = self._dict[qubit_op]
exact_eigensolver = NumPyMinimumEigensolver(qubit_op)
results = exact_eigensolver.run()
ref_eigenval = results.eigenvalue
ref_eigenvec = results.eigenstate
self.log.debug('The exact eigenvalue is: %s', ref_eigenval)
self.log.debug('The corresponding eigenvector: %s', ref_eigenvec)
state_in = Custom(qubit_op.num_qubits, state_vector=ref_eigenvec)
iqft = QFT(n_ancillae, do_swaps=False).inverse().reverse_bits()
qpe = QPE(qubit_op, state_in, iqft, num_time_slices, n_ancillae,
expansion_mode='suzuki', expansion_order=2,
shallow_circuit_concat=True)
backend = BasicAer.get_backend(simulator)
quantum_instance = QuantumInstance(backend, shots=100, seed_transpiler=1, seed_simulator=1)
# run qpe
result = qpe.run(quantum_instance)
# report result
self.log.debug('top result str label: %s', result.top_measurement_label)
self.log.debug('top result in decimal: %s', result.top_measurement_decimal)
self.log.debug('stretch: %s', result.stretch)
self.log.debug('translation: %s', result.translation)
self.log.debug('final eigenvalue from QPE: %s', result.eigenvalue)
self.log.debug('reference eigenvalue: %s', ref_eigenval)
self.log.debug('ref eigenvalue (transformed): %s',
(ref_eigenval + result.translation) * result.stretch)
self.log.debug('reference binary str label: %s', decimal_to_binary(
(ref_eigenval.real + result.translation) * result.stretch,
max_num_digits=n_ancillae + 3,
fractional_part_only=True
))
self.assertAlmostEqual(result.eigenvalue.real, ref_eigenval.real, delta=2e-2)
def test_qsvm_multiclass_error_correcting_code(self, use_circuits):
""" QSVM Multiclass error Correcting Code test """
training_input = {'A': np.asarray([[0.6560706, 0.17605998], [0.25776033, 0.47628296],
[0.8690704, 0.70847635]]),
'B': np.asarray([[0.38857596, -0.33775802], [0.49946978, -0.48727951],
[0.49156185, -0.3660534]]),
'C': np.asarray([[-0.68088231, 0.46824423], [-0.56167659, 0.65270294],
[-0.82139073, 0.29941512]])}
test_input = {'A': np.asarray([[0.57483139, 0.47120732], [0.48372348, 0.25438544],
[0.48142649, 0.15931707]]),
'B': np.asarray([[-0.06048935, -0.48345293], [-0.01065613, -0.33910828],
[0.06183066, -0.53376975]]),
'C': np.asarray([[-0.74561108, 0.27047295], [-0.69942965, 0.11885162],
[-0.66489165, 0.1181712]])}
total_array = np.concatenate((test_input['A'], test_input['B'], test_input['C']))
aqua_globals.random_seed = self.random_seed
feature_map = SecondOrderExpansion(feature_dimension=get_feature_dimension(training_input),
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
try:
svm = QSVM(feature_map, training_input, test_input, total_array,
multiclass_extension=ErrorCorrectingCode(code_size=5))
quantum_instance = QuantumInstance(BasicAer.get_backend('qasm_simulator'),
shots=self.shots,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = svm.run(quantum_instance)
self.assertAlmostEqual(result['testing_accuracy'], 0.444444444, places=4)
self.assertEqual(result['predicted_classes'], ['A', 'A', 'C', 'A',
'A', 'A', 'A', 'C', 'C'])
except NameError as ex:
self.skipTest(str(ex))
def test_tapered_op(self):
# set_qiskit_chemistry_logging(logging.DEBUG)
tapered_ops = []
for coeff in itertools.product([1, -1], repeat=len(self.sq_list)):
tapered_op = Operator.qubit_tapering(self.qubit_op, self.cliffords, self.sq_list, list(coeff))
tapered_ops.append((list(coeff), tapered_op))
smallest_idx = 0 # Prior knowledge of which tapered_op has ground state
the_tapered_op = tapered_ops[smallest_idx][1]
the_coeff = tapered_ops[smallest_idx][0]
optimizer = SLSQP(maxiter=1000)
init_state = HartreeFock(num_qubits=the_tapered_op.num_qubits,
num_orbitals=self.core._molecule_info['num_orbitals'],
qubit_mapping=self.core._qubit_mapping,
two_qubit_reduction=self.core._two_qubit_reduction,
num_particles=self.core._molecule_info['num_particles'],
sq_list=self.sq_list)
var_form = UCCSD(num_qubits=the_tapered_op.num_qubits, depth=1,
num_orbitals=self.core._molecule_info['num_orbitals'],
num_particles=self.core._molecule_info['num_particles'],
active_occupied=None, active_unoccupied=None,
initial_state=init_state,
qubit_mapping=self.core._qubit_mapping,
two_qubit_reduction=self.core._two_qubit_reduction,
num_time_slices=1,
cliffords=self.cliffords, sq_list=self.sq_list,
tapering_values=the_coeff, symmetries=self.symmetries)
algo = VQE(the_tapered_op, var_form, optimizer, 'matrix')
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend=backend)
algo_result = algo.run(quantum_instance)
lines, result = self.core.process_algorithm_result(algo_result)
self.assertAlmostEqual(result['energy'], self.reference_energy, places=6)
def test_hhl_non_hermitian(self):
""" hhl non hermitian test """
self.log.debug('Testing HHL with simple non-hermitian matrix')
matrix = [[1, 1], [2, 1]]
vector = [1, 0]
# run ExactLSsolver
ref_result = ExactLSsolver(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, 6, True)
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)
hhl_result = algo.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
hhl_solution = hhl_result['solution']
hhl_normed = hhl_solution/np.linalg.norm(hhl_solution)
# compare result
fidelity = state_fidelity(ref_normed, hhl_normed)
self.assertGreater(fidelity, 0.8)
self.log.debug('HHL solution vector: %s', hhl_solution)
self.log.debug('algebraic solution vector: %s', ref_solution)
self.log.debug('fidelity HHL to algebraic: %s', fidelity)
self.log.debug('probability of result: %s', hhl_result["probability_result"])
def test_hhl_diagonal_longdivison(self, vector):
""" hhl diagonal long division test """
self.log.debug('Testing HHL simple test in mode LongDivision and statevector simulator')
matrix = [[1, 0], [0, 1]]
# run ExactLSsolver
ref_result = ExactLSsolver(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, 3, False)
num_q, num_a = eigs.get_register_sizes()
# Initialize initial state module
init_state = Custom(num_q, state_vector=vector)
# Initialize reciprocal
reci = LongDivision(scale=1.0, 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)
hhl_result = algo.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
hhl_solution = hhl_result['solution']
hhl_normed = hhl_solution/np.linalg.norm(hhl_solution)
# compare results
fidelity = state_fidelity(ref_normed, hhl_normed)
np.testing.assert_approx_equal(fidelity, 1, significant=5)
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_application(self):
"""Test an end-to-end application."""
a_n = np.eye(2)
b = np.zeros(2)
num_qubits = [2, 2]
# specify the lower and upper bounds for the different dimension
bounds = [(0, 0.12), (0, 0.24)]
mu = [0.12, 0.24]
sigma = 0.01 * np.eye(2)
# construct corresponding distribution
dist = NormalDistribution(num_qubits, mu, sigma, bounds=bounds)
# specify cash flow
c_f = [1.0, 2.0]
# specify approximation factor
rescaling_factor = 0.125
# get fixed income circuit appfactory
fixed_income = FixedIncomeExpectedValue(num_qubits, a_n, b, c_f,
rescaling_factor, bounds)
# build state preparation operator
state_preparation = fixed_income.compose(dist, front=True)
# run simulation
iae = IterativeAmplitudeEstimation(
epsilon=0.01,
alpha=0.05,
state_preparation=state_preparation,
post_processing=fixed_income.post_processing)
backend = QuantumInstance(Aer.get_backend('qasm_simulator'),
seed_simulator=2,
seed_transpiler=2)
result = iae.run(backend)
# compare to precomputed solution
self.assertAlmostEqual(result.estimation, 2.3389012822103044)
def test_vqc_on_wine(self, mode):
"""Test VQE on the wine test using circuits as feature map and variational form."""
aqua_globals.random_seed = 27521 if mode == 'wrapped' else 2752
feature_dim = 4 # dimension of each data point
training_dataset_size = 6
testing_dataset_size = 3
_, training_input, test_input, _ = wine(
training_size=training_dataset_size,
test_size=testing_dataset_size,
n=feature_dim,
plot_data=False)
ref_accuracy = 0.2
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
data_preparation = SecondOrderExpansion(feature_dim)
wavefunction = RYRZ(feature_dim, depth=1)
else:
data_preparation = ZZFeatureMap(feature_dim)
wavefunction = TwoLocal(feature_dim, ['ry', 'rz'],
'cz',
reps=1,
insert_barriers=True)
if mode == 'circuit':
data_preparation = QuantumCircuit(feature_dim).compose(
data_preparation)
wavefunction = QuantumCircuit(feature_dim).compose(
wavefunction)
vqc = VQC(COBYLA(maxiter=100), data_preparation, wavefunction,
training_input, test_input)
if mode == 'wrapped':
warnings.filterwarnings('always', category=DeprecationWarning)
result = vqc.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
shots=1024,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.assertGreater(result['testing_accuracy'], ref_accuracy)
def test_h2_two_qubits_statevector(self):
"""Test H2 with parity mapping and statevector backend."""
two_qubit_reduction = True
qubit_mapping = 'parity'
warnings.filterwarnings('ignore', category=DeprecationWarning)
core = Hamiltonian(transformation=TransformationType.FULL,
qubit_mapping=QubitMappingType.PARITY,
two_qubit_reduction=two_qubit_reduction,
freeze_core=False,
orbital_reduction=[])
warnings.filterwarnings('always', category=DeprecationWarning)
qubit_op, _ = core.run(self.molecule)
num_orbitals = core.molecule_info['num_orbitals']
num_particles = core.molecule_info['num_particles']
initial_state = HartreeFock(num_orbitals=num_orbitals,
num_particles=num_particles,
qubit_mapping=qubit_mapping,
two_qubit_reduction=two_qubit_reduction)
var_form = UCCSD(num_orbitals=num_orbitals,
num_particles=num_particles,
initial_state=initial_state,
qubit_mapping=qubit_mapping,
two_qubit_reduction=two_qubit_reduction)
optimizer = COBYLA(maxiter=1000, tol=1e-8)
eom_vqe = QEomVQE(qubit_op,
var_form,
optimizer,
num_orbitals=num_orbitals,
num_particles=num_particles,
qubit_mapping=qubit_mapping,
two_qubit_reduction=two_qubit_reduction)
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend)
result = eom_vqe.run(quantum_instance)
np.testing.assert_array_almost_equal(self.reference,
result['energies'],
decimal=4)
def test_vqe(self):
"""Test VQE with gradients"""
method = 'lin_comb'
backend = 'qasm_simulator'
q_instance = QuantumInstance(BasicAer.get_backend(backend),
seed_simulator=79,
seed_transpiler=2)
# Define the Hamiltonian
h2_hamiltonian = -1.05 * (I ^ I) + 0.39 * (I ^ Z) - 0.39 * (Z ^ I) \
- 0.01 * (Z ^ Z) + 0.18 * (X ^ X)
h2_energy = -1.85727503
# Define the Ansatz
wavefunction = QuantumCircuit(2)
params = ParameterVector('theta', length=8)
itr = iter(params)
wavefunction.ry(next(itr), 0)
wavefunction.ry(next(itr), 1)
wavefunction.rz(next(itr), 0)
wavefunction.rz(next(itr), 1)
wavefunction.cx(0, 1)
wavefunction.ry(next(itr), 0)
wavefunction.ry(next(itr), 1)
wavefunction.rz(next(itr), 0)
wavefunction.rz(next(itr), 1)
# Conjugate Gradient algorithm
optimizer = CG(maxiter=10)
grad = Gradient(grad_method=method)
# Gradient callable
vqe = VQE(h2_hamiltonian,
wavefunction,
optimizer=optimizer,
gradient=grad)
result = vqe.run(q_instance)
np.testing.assert_almost_equal(result['optimal_value'],
h2_energy,
decimal=0)
def test_vqc(self, mode):
""" vqc test """
aqua_globals.random_seed = self.seed
optimizer = SPSA(max_trials=10,
save_steps=1,
c0=4.0,
c1=0.1,
c2=0.602,
c3=0.101,
c4=0.0,
skip_calibration=True)
data_preparation = self.data_preparation[mode]
wavefunction = self.ryrz_wavefunction[mode]
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
# set up algorithm
vqc = VQC(optimizer, data_preparation, wavefunction,
self.training_data, self.testing_data)
if mode == 'wrapped':
warnings.filterwarnings('always', category=DeprecationWarning)
quantum_instance = QuantumInstance(
BasicAer.get_backend('qasm_simulator'),
shots=1024,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = vqc.run(quantum_instance)
with self.subTest('opt params'):
np.testing.assert_array_almost_equal(result['opt_params'],
self.ref_opt_params[mode],
decimal=8)
with self.subTest('training loss'):
np.testing.assert_array_almost_equal(result['training_loss'],
self.ref_train_loss[mode],
decimal=8)
with self.subTest('accuracy'):
self.assertEqual(1.0 if mode == 'wrapped' else 0.5,
result['testing_accuracy'])
def test_expected_value(self, simulator):
# can be used in case a principal component analysis has been done to derive the uncertainty model, ignored in this example.
A = np.eye(2)
b = np.zeros(2)
# specify the number of qubits that are used to represent the different dimenions of the uncertainty model
num_qubits = [2, 2]
# specify the lower and upper bounds for the different dimension
low = [0, 0]
high = [0.12, 0.24]
mu = [0.12, 0.24]
sigma = 0.01 * np.eye(2)
# construct corresponding distribution
u = MultivariateNormalDistribution(num_qubits, low, high, mu, sigma)
# specify cash flow
cf = [1.0, 2.0]
# specify approximation factor
c_approx = 0.125
# get fixed income circuit appfactory
fixed_income = FixedIncomeExpectedValue(u, A, b, cf, c_approx)
# set number of evaluation qubits (samples)
m = 5
# construct amplitude estimation
ae = AmplitudeEstimation(m, fixed_income)
# run simulation
quantum_instance = QuantumInstance(
BasicAer.get_backend('statevector_simulator'))
result = ae.run(quantum_instance=quantum_instance)
# compare to precomputed solution
self.assertEqual(0.0,
np.round(result['estimation'] - 2.4600, decimals=4))
def test_uccsd_hf_aer_statevector(self):
""" uccsd hf test with Aer statevector """
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
backend = Aer.get_backend('statevector_simulator')
solver = VQE(var_form=self.var_form,
optimizer=self.optimizer,
quantum_instance=QuantumInstance(backend=backend))
gsc = GroundStateEigensolver(self.fermionic_transformation, solver)
result = gsc.solve(self.driver)
self.assertAlmostEqual(result.total_energies[0],
self.reference_energy,
places=6)
def test_vqe_optimizer(self):
""" Test running same VQE twice to re-use optimizer, then switch optimizer """
vqe = VQE(self.h2_op,
optimizer=SLSQP(),
quantum_instance=QuantumInstance(
BasicAer.get_backend('statevector_simulator')))
def run_check():
result = vqe.compute_minimum_eigenvalue()
self.assertAlmostEqual(result.eigenvalue.real,
-1.85727503,
places=5)
run_check()
with self.subTest('Optimizer re-use'):
run_check()
with self.subTest('Optimizer replace'):
vqe.optimizer = L_BFGS_B()
run_check()
def main():
def tadaa(string):
print(string)
def incoming(string):
input(string)
API = "26f2e1b4b40ea8d23675f7c644437f38a7b16ba89d09963345b4549c37763401e9a00aa81041e9d3f560c71cb24c1d02a6c142f2cac3d6fa668f786c0e467e49"
IBMQ.enable_account(API)
provider = IBMQ.get_provider(hub="ibm-q")
backend = provider.get_backend("ibmq_qasm_simulator")
tadaa("\n Shor Algorithm")
tadaa("\n --------------")
tadaa("\n Executing.....")
factors = Shor(21)
result_dict = factors.run(
QuantumInstance(backend, shots=1, skip_qobj_validation=False))
result = result_dict['factors'] # Get factors from results
tadaa(result)
tadaa("\n Press any ket to close.")
incoming()
def test_end2end_h2(self, name, optimizer, backend, mode, shots):
if optimizer == 'COBYLA':
optimizer = COBYLA()
optimizer.set_options(maxiter=1000)
elif optimizer == 'SPSA':
optimizer = SPSA(max_trials=2000)
ryrz = RYRZ(self.algo_input.qubit_op.num_qubits,
depth=3,
entanglement='full')
vqe = VQE(self.algo_input.qubit_op,
ryrz,
optimizer,
mode,
aux_operators=self.algo_input.aux_ops)
quantum_instance = QuantumInstance(backend, shots=shots)
results = vqe.run(quantum_instance)
self.assertAlmostEqual(results['energy'],
self.reference_energy,
places=4)
