def setUp(self): super().setUp() self.current_level = get_qiskit_aqua_logging() set_qiskit_aqua_logging(logging.INFO)
def tearDown(self): set_qiskit_aqua_logging(self.current_level) super().tearDown()
import QKMC import matplotlib.pyplot as plt from qiskit.aqua.components.feature_maps.raw_feature_vector import RawFeatureVector from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit import BasicAer, execute from qiskit import Aer from qiskit.tools.visualization import plot_state_city from qiskit import IBMQ import logging from qiskit.aqua import set_qiskit_aqua_logging set_qiskit_aqua_logging(False) #X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]) X = np.array([1, 1]) #Y=X*10 #X = np.array([312, 523]) Y = [2, 0] #Y = np.array([ -0.35, 0.70]) #Y = np.array([1, 0]) #Y=1000*np.array([0.75, -0.5]) backend = IBMQ.get_provider().get_backend('ibmq_qasm_simulator') #print(QKMC.QKMC.quantum_calculate_squared_distance(backend, X, Y)) classifications = {'0': [array([11.81649276, 24.54856634]), array([13.42459402, 24.10990129]), array([11.55199032, 23.35616928]), array([14.85233046, 21.50430736]), array([14.32223019, 21.71420362]), array([14.93111274, 23.33765323]), array([16.76214486, 27.19700542]), array([13.51492985, 24.80276145]), array([15.0103456 , 26.63683521]), array([15.29714333, 27.07302925]), array([13.86292148, 19.30636735]), array([13.32833298, 22.3789185 ]), array([13.88470985, 22.29855478]), array([12.65330286, 26.66149104]), array([15.4309023 , 26.15439088]), array([14.03974876, 26.49109187]), array([12.63121823, 21.54360233])], '1': [array([22.89750891, 20.64559515]), array([25.31739056, 18.1450818 ]), array([26.3054508 , 20.16370253]), array([22.78651727, 23.64446433]), array([27.67264928, 20.69607017]), array([23.49041929, 18.17306578]), array([23.73633997, 19.88208634]), array([22.24457921, 18.40077177]), array([22.07618066, 19.26461151]), array([24.81843404, 20.6220368 ]), array([18.79502435, 17.21022472]), array([22.53576089, 15.97393167]), array([22.25872545, 17.18469893]), array([25.57427542, 19.5125783 ]), array([21.32256107, 17.58447166])], '2': [array([23.27533514, 18.90026478]), array([19.15622682, 17.11472094]), array([20.68448347, 16.45266443]), array([19.40108305, 10. ]), array([17.47444147, 14.32327414]), array([20.7508271 , 14.10558629]), array([18.2604467 , 15.13015395]), array([23.26617773, 15.99122675]), array([24.07378217, 13.97632705]), array([16.70443056, 19.30250409]), array([18.30338469, 19.23836884]), array([17.0087138 , 14.43424701]), array([17.79578683, 15.5432762 ]), array([18.59838382, 14.78733198]), array([21.03105278, 12.67621569]), array([17.9850352 , 14.31759193]), array([23.62191241, 14.37281409]), array([17.60012086, 13.93757481])]}
from qiskit.aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name from qiskit.aqua.input import ClassificationInput from qiskit.aqua import run_algorithm, QuantumInstance from qiskit.aqua.algorithms import SVM_Classical from qiskit.aqua.components.feature_maps import PauliExpansion from qiskit.aqua.algorithms import QSVM from qiskit.aqua.components.multiclass_extensions.one_against_rest import OneAgainstRest from qiskit.aqua.algorithms.classical.svm._rbf_svc_estimator import _RBF_SVC_Estimator from qiskit.aqua.algorithms.many_sample.qsvm._qsvm_estimator import _QSVM_Estimator # setup aqua logging import logging from qiskit.aqua import set_qiskit_aqua_logging set_qiskit_aqua_logging(False) # choose INFO, DEBUG to see the log #IBMQ.load_account() feature_dim=2 # we support feature_dim 2 or 3 sample_Total, training_input, test_input, class_labels = Iris( training_size=20, test_size=30, n=feature_dim, #gap=0.3, PLOT_DATA=False ) new_training_input = {} new_test_input = {}
import pandas as pd from sklearn.metrics import f1_score from qiskit import IBMQ from qiskit.aqua.components.optimizers import optimizer from qiskit.aqua.components.variational_forms.ryrz import RYRZ from qiskit.circuit.quantumcircuit import QuantumCircuit from qiskit.circuit import Parameter, QuantumRegister from qiskit.providers.aer import QasmSimulator from qiskit.circuit.library import ZZFeatureMap from qiskit.aqua.algorithms import VQC from qiskit.aqua.components.optimizers import SPSA import logging from qiskit.aqua import set_qiskit_aqua_logging set_qiskit_aqua_logging(logging.DEBUG) def run_exp( method='qrac', epochs=300, positive_factor=1 / 3, depth=4, seed=10598, reg=0., model_directory=None, result_directory=None, real_device=False, ): assert method in [ 'qrac', 'qrac_zz', 'te', 'te_zz', 'zz_dis', 'zz_dis_cont'
from sklearn import preprocessing from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score from qiskit import IBMQ, BasicAer from qiskit.circuit import QuantumCircuit, Parameter from qiskit.circuit.library import ZZFeatureMap, TwoLocal from qiskit.aqua import QuantumInstance from qiskit.aqua.algorithms import VQC from qiskit.aqua.components import variational_forms from qiskit.aqua.components.optimizers import COBYLA, ADAM, SPSA # setup aqua logging import logging from qiskit.aqua import set_qiskit_aqua_logging set_qiskit_aqua_logging(logging.DEBUG) # choose INFO, DEBUG to see the log from data_provider import load_titanic_pd from utils import record_test_result_for_kaggle from quantum_utils import select_features, encoder_3bits_1qubit def sampling_dataset(df_train, y_train, df_test, y_test=None, pos_sample=None, neg_sample=None): np.random.seed(777) if pos_sample and neg_sample:
# from qiskit.chemistry.aqua_extensions.components.initial_states import HartreeFock # from qiskit.chemistry.aqua_extensions.components.variational_forms import UCCSD from qiskit.chemistry.components.initial_states import HartreeFock from qiskit.chemistry.components.variational_forms import UCCSD from qiskit.aqua.components.optimizers import COBYLA from qiskit.aqua.components.variational_forms import RYRZ # from qiskit.aqua.algorithms.adaptive import VQE from qiskit.aqua.algorithms import VQE # from qiskit.aqua.adaptive import VQE from qiskit import BasicAer from qiskit.aqua import set_qiskit_aqua_logging, QuantumInstance np.set_printoptions(linewidth=230, suppress=True, precision=3, threshold=5000) from qiskit.aqua import set_qiskit_aqua_logging import logging set_qiskit_aqua_logging(logging.INFO) from qiskit.aqua.operators.legacy import * class r_mat_funcs(): def __init__(self, MoleculeFlag, check_r_matrix_flag, is_atomic): self.MoleculeFlag = MoleculeFlag [self.r_matrices, self.fer_op, self.num_particles] = mol_r_matrices(MoleculeFlag, check_r_matrix_flag, is_atomic) def sim_diag(self, r_matrices): r_qt_ob = [] for r_matrix in r_matrices: r_qt_ob.append(qt.Qobj(r_matrix))