def init_params(self, params, algo_input):
"""
Initialize via parameters dictionary and algorithm input instance
Args:
params (dict): parameters dictionary
algo_input (EnergyInput): EnergyInput instance
"""
if algo_input is None:
raise AlgorithmError("EnergyInput instance is required.")
operator = algo_input.qubit_op
qaoa_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
operator_mode = qaoa_params.get(QAOA.PROP_OPERATOR_MODE)
p = qaoa_params.get(QAOA.PROP_P)
initial_point = qaoa_params.get(QAOA.PROP_INIT_POINT)
# Set up optimizer
opt_params = params.get(QuantumAlgorithm.SECTION_KEY_OPTIMIZER)
optimizer = get_optimizer_instance(opt_params['name'])
optimizer.init_params(opt_params)
self.init_args(operator, operator_mode, p, optimizer,
opt_init_point=initial_point, aux_operators=algo_input.aux_ops)
def init_params(self, params, algo_input):
# Set up optimizer
opt_params = params.get(QuantumAlgorithm.SECTION_KEY_OPTIMIZER)
optimizer = get_optimizer_instance(opt_params['name'])
# hard-coded params if SPSA is used.
if opt_params['name'] == 'SPSA' and opt_params['parameters'] is None:
opt_params['parameters'] = np.asarray(
[4.0, 0.1, 0.602, 0.101, 0.0])
optimizer.init_params(opt_params)
# Set up variational form
fea_map_params = params.get(QuantumAlgorithm.SECTION_KEY_FEATURE_MAP)
num_qubits = self._auto_detect_qubitnum(algo_input.training_dataset)
fea_map_params['num_qubits'] = num_qubits
feature_map = get_feature_map_instance(fea_map_params['name'])
feature_map.init_params(fea_map_params)
# Set up variational form
var_form_params = params.get(QuantumAlgorithm.SECTION_KEY_VAR_FORM)
var_form_params['num_qubits'] = num_qubits
var_form = get_variational_form_instance(var_form_params['name'])
var_form.init_params(var_form_params)
self.init_args(algo_input.training_dataset, algo_input.test_dataset,
algo_input.datapoints, optimizer, feature_map, var_form)
def init_params(self, params, algo_input):
"""
Initialize via parameters dictionary and algorithm input instance
Args:
params (dict): parameters dictionary
algo_input (EnergyInput): EnergyInput instance
"""
if algo_input is None:
raise AlgorithmError("EnergyInput instance is required.")
operator = algo_input.qubit_op
qaoa_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
operator_mode = qaoa_params.get(QAOA.PROP_OPERATOR_MODE)
p = qaoa_params.get(QAOA.PROP_P)
initial_point = qaoa_params.get(QAOA.PROP_INIT_POINT)
# Set up optimizer
opt_params = params.get(QuantumAlgorithm.SECTION_KEY_OPTIMIZER)
optimizer = get_optimizer_instance(opt_params['name'])
optimizer.init_params(opt_params)
if 'statevector' not in self._backend and operator_mode == 'matrix':
logger.debug('Qasm simulation does not work on {} mode, changing \
the operator_mode to paulis'.format(operator_mode))
operator_mode = 'paulis'
self.init_args(operator,
operator_mode,
p,
optimizer,
opt_init_point=initial_point,
aux_operators=algo_input.aux_ops)
def test_svm_variational_directly(self):
np.random.seed(self.random_seed)
svm = get_algorithm_instance("QSVM.Variational")
svm.random_seed = self.random_seed
svm.setup_quantum_backend(backend='local_qasm_simulator', shots=1024)
optimizer = get_optimizer_instance('SPSA')
optimizer.init_args(max_trials=10, c0=4.0, skip_calibration=True)
optimizer.set_options(save_steps=1)
num_qubits = 2
feature_map = get_feature_map_instance('SecondOrderExpansion')
feature_map.init_args(num_qubits=num_qubits, depth=2)
var_form = get_variational_form_instance('RYRZ')
var_form.init_args(num_qubits=num_qubits, depth=3)
svm.init_args(self.training_data, self.testing_data, None, optimizer,
feature_map, var_form)
result = svm.run()
np.testing.assert_array_almost_equal(result['opt_params'],
self.ref_opt_params,
decimal=4)
np.testing.assert_array_almost_equal(result['training_loss'],
self.ref_train_loss,
decimal=8)
self.assertEqual(result['testing_accuracy'], 0.5)
def init_params(self, params, algo_input):
algo_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
override_spsa_params = algo_params.get('override_SPSA_params')
# Set up optimizer
opt_params = params.get(QuantumAlgorithm.SECTION_KEY_OPTIMIZER)
optimizer = get_optimizer_instance(opt_params['name'])
# If SPSA then override SPSA params as reqd to our predetermined values
if opt_params['name'] == 'SPSA' and override_spsa_params:
opt_params['c0'] = 4.0
opt_params['c1'] = 0.1
opt_params['c2'] = 0.602
opt_params['c3'] = 0.101
opt_params['c4'] = 0.0
opt_params['skip_calibration'] = True
optimizer.init_params(opt_params)
# Set up variational form
fea_map_params = params.get(QuantumAlgorithm.SECTION_KEY_FEATURE_MAP)
num_qubits = get_feature_dimension(algo_input.training_dataset)
fea_map_params['num_qubits'] = num_qubits
feature_map = get_feature_map_instance(fea_map_params['name'])
feature_map.init_params(fea_map_params)
# Set up variational form
var_form_params = params.get(QuantumAlgorithm.SECTION_KEY_VAR_FORM)
var_form_params['num_qubits'] = num_qubits
var_form = get_variational_form_instance(var_form_params['name'])
var_form.init_params(var_form_params)
self.init_args(algo_input.training_dataset, algo_input.test_dataset,
algo_input.datapoints, optimizer, feature_map, var_form)
def test_vqe_direct(self):
num_qbits = self.algo_input.qubit_op.num_qubits
init_state = get_initial_state_instance('ZERO')
init_state.init_args(num_qbits)
var_form = get_variational_form_instance('RY')
var_form.init_args(num_qbits, 3, initial_state=init_state)
optimizer = get_optimizer_instance('L_BFGS_B')
optimizer.init_args()
algo = get_algorithm_instance('VQE')
algo.setup_quantum_backend(backend='local_statevector_simulator')
algo.init_args(self.algo_input.qubit_op, 'matrix', var_form, optimizer)
result = algo.run()
self.assertAlmostEqual(result['energy'], -1.85727503)
def init_params(self, params, algo_input):
"""
Initialize via parameters dictionary and algorithm input instance
Args:
params (dict): parameters dictionary
algo_input (EnergyInput): EnergyInput instance
"""
if algo_input is None:
raise AlgorithmError("EnergyInput instance is required.")
operator = algo_input.qubit_op
vqe_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
operator_mode = vqe_params.get('operator_mode')
initial_point = vqe_params.get('initial_point')
# Set up initial state, we need to add computed num qubits to params
init_state_params = params.get(
QuantumAlgorithm.SECTION_KEY_INITIAL_STATE)
init_state_params['num_qubits'] = operator.num_qubits
init_state = get_initial_state_instance(init_state_params['name'])
init_state.init_params(init_state_params)
# Set up variational form, we need to add computed num qubits, and initial state to params
var_form_params = params.get(QuantumAlgorithm.SECTION_KEY_VAR_FORM)
var_form_params['num_qubits'] = operator.num_qubits
var_form_params['initial_state'] = init_state
var_form = get_variational_form_instance(var_form_params['name'])
var_form.init_params(var_form_params)
# Set up optimizer
opt_params = params.get(QuantumAlgorithm.SECTION_KEY_OPTIMIZER)
optimizer = get_optimizer_instance(opt_params['name'])
optimizer.init_params(opt_params)
if 'statevector' not in self._backend and operator_mode == 'matrix':
logger.debug('Qasm simulation does not work on {} mode, changing \
the operator_mode to paulis'.format(operator_mode))
operator_mode = 'paulis'
self.init_args(operator,
operator_mode,
var_form,
optimizer,
opt_init_point=initial_point,
aux_operators=algo_input.aux_ops)
logger.info(self.print_setting())
def test_qaoa(self, w, p, solutions):
self.log.debug('Testing {}-step QAOA with MaxCut on graph\n{}'.format(p, w))
np.random.seed(0)
optimizer = get_optimizer_instance('COBYLA')
qubitOp, offset = maxcut.get_maxcut_qubitops(w)
qaoa = get_algorithm_instance('QAOA')
qaoa.init_args(qubitOp, 'matrix', p, optimizer)
qaoa.setup_quantum_backend(backend='local_statevector_simulator', shots=100)
result = qaoa.run()
x = maxcut.sample_most_likely(result['eigvecs'][0])
graph_solution = maxcut.get_graph_solution(x)
self.log.debug('energy: {}'.format(result['energy']))
self.log.debug('time: {}'.format(result['eval_time']))
self.log.debug('maxcut objective: {}'.format(result['energy'] + offset))
self.log.debug('solution: {}'.format(graph_solution))
self.log.debug('solution objective: {}'.format(maxcut.maxcut_value(x, w)))
self.assertIn(''.join([str(int(i)) for i in graph_solution]), solutions)
def init_params(self, params, algo_input):
SVMQK_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
circuit_depth = SVMQK_params.get('circuit_depth')
print_info = SVMQK_params.get('print_info')
# Set up optimizer
opt_params = params.get(QuantumAlgorithm.SECTION_KEY_OPTIMIZER)
optimizer = get_optimizer_instance(opt_params['name'])
# hard-coded params if SPSA is used.
if opt_params['name'] == 'SPSA' and opt_params['parameters'] is None:
opt_params['parameters'] = np.asarray(
[4.0, 0.1, 0.602, 0.101, 0.0])
optimizer.init_params(opt_params)
optimizer.set_options(save_steps=10)
self.init_args(algo_input.training_dataset, algo_input.test_dataset,
algo_input.datapoints, optimizer, circuit_depth,
print_info)
def test_tnc(self):
optimizer = get_optimizer_instance('TNC')
optimizer.set_options(**{'maxiter': 1000})
optimizer.init_args(tol=1e-06)
res = self._optimize(optimizer)
self.assertLessEqual(res[2], 10000)
def test_spsa(self):
optimizer = get_optimizer_instance('SPSA')
optimizer.set_options(**{})
optimizer.init_args(max_trials=10000)
res = self._optimize(optimizer)
self.assertLessEqual(res[2], 100000)
def test_powell(self):
optimizer = get_optimizer_instance('POWELL')
optimizer.set_options(**{'maxfev': 10000})
optimizer.init_args(tol=1e-06)
res = self._optimize(optimizer)
self.assertLessEqual(res[2], 10000)
def test_nelder_mead(self):
optimizer = get_optimizer_instance('NELDER_MEAD')
optimizer.set_options(**{'maxfev': 10000})
optimizer.init_args(tol=1e-06)
res = self._optimize(optimizer)
self.assertLessEqual(res[2], 10000)
def test_nlopt(self, name):
optimizer = get_optimizer_instance(name)
optimizer.set_options(**{'max_evals': 50000})
optimizer.init_args()
res = self._optimize(optimizer)
self.assertLessEqual(res[2], 50000)
def configurar_COBYLA(configuracionaqua):
"""Esta función obtiene una instancia del optimizador COBYLA configurada"""
cobyla = get_optimizer_instance('COBYLA')
cobyla.set_options(maxiter=configuracionaqua["COBYLA"]["max_eval"])
return cobyla
def test_l_bfgs_b(self):
optimizer = get_optimizer_instance('L_BFGS_B')
optimizer.set_options(**{'maxfun': 1000})
optimizer.init_args()
res = self._optimize(optimizer)
self.assertLessEqual(res[2], 10000)
def test_vqe_2_iqpe(self):
num_qbits = self.algo_input.qubit_op.num_qubits
var_form = get_variational_form_instance('RYRZ')
var_form.init_args(num_qbits, 3)
optimizer = get_optimizer_instance('SPSA')
optimizer.init_args(max_trials=10)
# optimizer.set_options(**{'max_trials': 500})
algo = get_algorithm_instance('VQE')
algo.setup_quantum_backend(backend='qasm_simulator')
algo.init_args(self.algo_input.qubit_op, 'paulis', var_form, optimizer)
result = algo.run()
self.log.debug('VQE result: {}.'.format(result))
self.ref_eigenval = -1.85727503
num_time_slices = 50
num_iterations = 11
state_in = VarFormBased()
state_in.init_args(var_form, result['opt_params'])
iqpe = get_algorithm_instance('IQPE')
iqpe.setup_quantum_backend(backend='qasm_simulator',
shots=100,
skip_transpiler=True)
iqpe.init_args(
self.algo_input.qubit_op,
state_in,
num_time_slices,
num_iterations,
paulis_grouping='random',
expansion_mode='suzuki',
expansion_order=2,
)
result = iqpe.run()
self.log.debug('top result str label: {}'.format(
result['top_measurement_label']))
self.log.debug('top result in decimal: {}'.format(
result['top_measurement_decimal']))
self.log.debug('stretch: {}'.format(
result['stretch']))
self.log.debug('translation: {}'.format(
result['translation']))
self.log.debug('final eigenvalue from QPE: {}'.format(
result['energy']))
self.log.debug('reference eigenvalue: {}'.format(
self.ref_eigenval))
self.log.debug('ref eigenvalue (transformed): {}'.format(
(self.ref_eigenval + result['translation']) * result['stretch']))
self.log.debug('reference binary str label: {}'.format(
decimal_to_binary((self.ref_eigenval + result['translation']) *
result['stretch'],
max_num_digits=num_iterations + 3,
fractional_part_only=True)))
np.testing.assert_approx_equal(self.ref_eigenval,
result['energy'],
significant=2)
