Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, across both classical and quantum settings. In this work, we present a margin-based generalization bound for QML models, providing a more reliable framework for evaluating generalization. Our experimental studies on the quantum phase recognition (QPR) dataset demonstrate that margin-based metrics are strong predictors of generalization performance, outperforming traditional metrics like parameter count. By connecting this margin-based metric to quantum information theory, we demonstrate how to enhance the generalization performance of QML through a classical-quantum hybrid approach when applied to classical data.

翻译：理解和提升泛化能力对于经典与量子机器学习均至关重要。近期研究揭示了当前泛化理论（特别是依赖一致界的方法）在经典与量子场景中均存在局限。本文提出量子机器学习模型的基于间隔的泛化界，为评估泛化性能提供了更可靠的框架。我们在量子相识别数据集上的实验研究表明，基于间隔的度量指标是泛化性能的有效预测因子，其表现优于参数量等传统度量指标。通过将该间隔度量与量子信息理论建立联系，我们展示了在经典数据上应用经典-量子混合方法时如何提升量子机器学习的泛化性能。