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))
