def test_vqc_with_raw_feature_vector_on_wine(self): """ vqc with raw features vector on wine test """ feature_dim = 4 # dimension of each data point training_dataset_size = 8 testing_dataset_size = 4 _, training_input, test_input, _ = _wine_data( training_size=training_dataset_size, test_size=testing_dataset_size, n=feature_dim ) aqua_globals.random_seed = self.seed feature_map = RawFeatureVector(feature_dimension=feature_dim) vqc = VQC(COBYLA(maxiter=100), feature_map, RYRZ(feature_map.num_qubits, depth=3), training_input, test_input) result = vqc.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'), shots=1024, seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed)) self.log.debug(result['testing_accuracy']) self.assertGreater(result['testing_accuracy'], 0.8)
def test_readme_sample(self): """ readme sample test """ # pylint: disable=import-outside-toplevel,redefined-builtin def print(*args): """ overloads print to log values """ if args: self.log.debug(args[0], *args[1:]) # --- Exact copy of sample code ---------------------------------------- from qiskit import BasicAer from qiskit.aqua import QuantumInstance, aqua_globals from qiskit.aqua.algorithms import VQC from qiskit.aqua.components.optimizers import COBYLA from qiskit.aqua.components.feature_maps import RawFeatureVector from qiskit.ml.datasets import wine from qiskit.circuit.library import TwoLocal seed = 1376 aqua_globals.random_seed = seed # Use Wine data set for training and test data feature_dim = 4 # dimension of each data point _, training_input, test_input, _ = wine(training_size=12, test_size=4, n=feature_dim) feature_map = RawFeatureVector(feature_dimension=feature_dim) vqc = VQC(COBYLA(maxiter=100), feature_map, TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3), training_input, test_input) result = vqc.run( QuantumInstance(BasicAer.get_backend('statevector_simulator'), shots=1024, seed_simulator=seed, seed_transpiler=seed)) print('Testing accuracy: {:0.2f}'.format(result['testing_accuracy'])) # ---------------------------------------------------------------------- self.assertGreater(result['testing_accuracy'], 0.8)
def test_raw_feature_vector_on_wine(self): """Test VQE on the wine dataset using the ``RawFeatureVector`` as data preparation.""" feature_dim = 4 # dimension of each data point training_dataset_size = 8 testing_dataset_size = 4 _, training_input, test_input, _ = wine( training_size=training_dataset_size, test_size=testing_dataset_size, n=feature_dim, plot_data=False) feature_map = RawFeatureVector(feature_dimension=feature_dim) vqc = VQC(COBYLA(maxiter=100), feature_map, TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3), training_input, test_input) result = vqc.run(self.statevector_simulator) self.log.debug(result['testing_accuracy']) self.assertGreater(result['testing_accuracy'], 0.8)
result = svm.run(quantum_instance) for k, v in result.items(): print(f'{k} : {v}') except Exception as e: print('QSVM 2 error', e) time0 = time.time() - start_time print("\nQSVM finished at: {0} seconds".format(str(round(time0, 5)))) training_input, test_input, total_array = traintest(df_enc, tonum=False) try: seed = 1376 aqua_globals.random_seed = seed feature_map = RawFeatureVector(feature_dimension=n) vqc = VQC(COBYLA(maxiter=10), feature_map, TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3), training_input, test_input) result = vqc.run( QuantumInstance(BasicAer.get_backend('statevector_simulator'), shots=1024, seed_simulator=seed, seed_transpiler=seed)) print('VQC:') print('Testing accuracy: {:0.2f}'.format(result['testing_accuracy'])) except Exception as e: print('VQC error', e) time0 = time.time() - start_time print("\nVQC finished at: {0} seconds".format(str(round(time0, 5))))
from qiskit.aqua.components.feature_maps import RawFeatureVector from qiskit.aqua.utils import get_feature_dimension feature_dim = 4 # dimension of each data point training_dataset_size = 20 testing_dataset_size = 10 random_seed = 10598 aqua_globals.random_seed = random_seed sample_Total, training_input, test_input, class_labels = wine( training_size=training_dataset_size, test_size=testing_dataset_size, n=feature_dim, plot_data=False) feature_map = RawFeatureVector( feature_dimension=get_feature_dimension(training_input)) vqc = VQC(COBYLA(maxiter=200), feature_map, TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3), training_input, test_input) result = vqc.run( QuantumInstance(BasicAer.get_backend('statevector_simulator'), seed_simulator=aqua_globals.random_seed, seed_transpiler=aqua_globals.random_seed)) print("VQC accuracy with RawFeatureVector: ", result['testing_accuracy']) feature_map = ZZFeatureMap(get_feature_dimension(training_input)) vqc = VQC(COBYLA(maxiter=200), feature_map, TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3), training_input, test_input) result = vqc.run( QuantumInstance(BasicAer.get_backend('statevector_simulator'),