Is data-efficient learning feasible with quantum models? (2508.19437v1)

Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive. In this work, we concentrate on the size of the dataset as an indicator of its complexity and explores the potential for QML models to demonstrate superior data-efficiency compared to classical models, particularly through the lens of quantum kernel methods (QKMs). We provide a method for generating semi-artificial fully classical datasets, on which we show one of the first evidence of the existence of classical datasets where QKMs require less data during training. Additionally, our study introduces a new analytical tool to the QML domain, derived for classical kernel methods, which can be aimed at investigating the classical-quantum gap. Our empirical results reveal that QKMs can achieve low error rates with less training data compared to classical counterparts. Furthermore, our method allows for the generation of datasets with varying properties, facilitating further investigation into the characteristics of real-world datasets that may be particularly advantageous for QKMs. We also show that the predicted performance from the analytical tool we propose - a generalization metric from classical domain - show great alignment empirical evidence, which fills the gap previously existing in the field. We pave a way to a comprehensive exploration of dataset complexities, providing insights into how these complexities influence QML performance relative to traditional methods. This research contributes to a deeper understanding of the generalization benefits of QKM models and potentially a broader family of QML models, setting the stage for future advancements in the field.