ML-Vs-QML

It will be shown how to train a quantum machine learning model to overcome a classification problem.

As an example of a quantum model, we’ll train a variational quantum classifier (VQC). The VQC is the simplest classifier available in Qiskit Machine Learning and is a good starting point for newcomers to quantum machine learning who have a background in classical machine learning.

Conclusion

print(f"Model                           | Test Score | Train Score")
print(f"SVC, 4 features                 | {train_score_c4:10.2f} | {test_score_c4:10.2f}")
print(f"VQC, 4 features, RealAmplitudes | {train_score_q4:10.2f} | {test_score_q4:10.2f}")
print(f"----------------------------------------------------------")
print(f"SVC, 2 features                 | {train_score_c2:10.2f} | {test_score_c2:10.2f}")
print(f"VQC, 2 features, RealAmplitudes | {train_score_q2_ra:10.2f} | {test_score_q2_ra:10.2f}")
print(f"VQC, 2 features, EfficientSU2   | {train_score_q2_eff:10.2f} | {test_score_q2_eff:10.2f}")