Most famous quantum supervised machine learning algorithm【Qiskit】

Basis

Import packages

import matplotlib.pyplot as plt
import numpy as np

from qiskit import BasicAer
from qiskit.circuit.library import ZZFeatureMap
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms import QSVM
from qiskit.aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name

seed = 10599
aqua_globals.random_seed = seed

Dataset

(ad-hoc data)

from qiskit.ml.datasets import ad_hoc_data, sample_ad_hoc_data

feature_dim = 2
sample_total, training_input, test_input, class_labels = ad_hoc_data(
    training_size=20,
    test_size=10,
    n=feature_dim,
    gap=0.3,
    plot_data=True
)
extra_test_data = sample_ad_hoc_data(sample_total, 10, n=feature_dim)
datapoints, class_to_label = split_dataset_to_data_and_labels(extra_test_data)
print(class_to_label)

Success Ratio

feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement='linear')
qsvm = QSVM(feature_map, training_input, test_input, datapoints[0])

backend = BasicAer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)

result = qsvm.run(quantum_instance)

print(f'Testig success ratio: {result["testing_accuracy"]}')
print()
print('prediction from datapoints set:')
print(f' ground truth: {map_label_to_class_name(datapoints[1], qsvm.label_to_class)}')
print(f' prediction: {result["predicted_classes"]}')
predicted_labels = result["predicted_labels"]
print(f' success rate: {100*np.count_nonzero(predicted_labels == datapoints[1])/len(predicted_labels)}%')

Kernel matrix that was built from the training sample of the dataset

kernel_matrix = result['kernel_matrix_training']
plt.imshow(
    np.asmatrix(kernel_matrix),
    interpolation='nearest',
    origin="upper",
    cmap='bone_r'
)

Classical SVM implementation that takes the same input data for classification

from qiskit.aqua.algorithms import SklearnSVM

result = SklearnSVM(training_input, test_input, datapoints[0]).run()

print(f'Testing success ratio: {result["testing_accuracy"]}')
print()
print('Prediction from datapoints set:')
print(f' ground truth: {result["predicted_classes"]}')
predicted_labels = result["predicted_labels"]
print(f' success rate: {100*np.count_nonzero(predicted_labels == datapoints[1])/len(predicted_labels)}%')

kernel_matrix = result['kernel_matrix_training']
plt.imshow(
    np.asmatrix(kernel_matrix),
    interpolation='nearest',
    origin='upper',
    cmap="bone_r"
);

Practice by breast cancer data

from qiskit.ml.datasets import breast_cancer

feature_dim = 2
sample_total, training_input, test_input, class_labels = breast_cancer(
    training_size=20,
    test_size=10,
    n=feature_dim,
    plot_data=True
)

feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement='linear')
qsvm = QSVM(feature_map, training_input, test_input)

backend = BasicAer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)

result = qsvm.run(quantum_instance)

print(f'Testing success ratio: {result["testing_accuracy"]}')

kernel_matrix = result['kernel_matrix_training']
img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r')

result = SklearnSVM(training_input, test_input).run()

print(f'Testing success ratio: {result["testing_accuracy"]}')

kernel_matrix = result['kernel_matrix_training']
plt.imshow(np.asmatrix(kernel_matrix), interpolation='nearest', origin='upper', cmap='bone_r');