Performance Analysis of Parameterized Quantum Kernel Methods on a Selection of Machine Learning Datasets

This work explores Quantum Kernel Estimation (QKE) and Quantum Kernel Training (QKT) in the context of classification tasks. Two quantum feature maps are analyzed for this purpose, in comparison to classical Support Vector Machine counterparts. Classification performance is analyzed on a selection of both ad-hoc and classical datasets, with QKT applied to optimize kernel parameters in QKE. Experimental data shows that quantum methods outperform classical ones in ad-hoc data. However, when confronting classical datasets, they frequently encounter difficulties in generalization, despite achieving high accuracy on the training set. We conclude that the choice of the feature mapping and the optimization of kernel parameters are critical for maximizing the effectiveness of the quantum methods.