揭示量子机器学习中鲁棒性与泛化性的关系，提出可训练编码的优化方法。
The interplay of robustness and generalization in quantum machine learning
- 用利普希茨约束量化模型鲁棒性与泛化能力，依赖参数设计正则化训练。
- 理论结果在时间序列分析任务中验证，提升模型稳定性和预测性能。
- 适合关注量子模型可靠性与实际应用的研究者参考。
尽管对抗鲁棒性和泛化能力在近期量子机器学习研究中分别受到广泛关注，但两者之间的相互作用仍较少被探讨。本文针对变分量子模型（variational quantum models），这些模型最近被提出作为监督学习中的函数逼近器，系统分析了其鲁棒性与泛化能力的关联。我们综述了利用利普希茨界量化这两者的最新成果，这些边界显式依赖于模型参数，从而引出基于正则化的训练方法，以提升量子模型的鲁棒性与泛化性，强调了可训练数据编码策略的重要性。理论结果通过时间序列分析的实际应用得到验证，展示了其实际意义。
原文摘要 · Abstract (English)
While adversarial robustness and generalization have individually received substantial attention in the recent literature on quantum machine learning, their interplay is much less explored. In this chapter, we address this interplay for variational quantum models, which were recently proposed as function approximators in supervised learning. We discuss recent results quantifying both robustness and generalization via Lipschitz bounds, which explicitly depend on model parameters. Thus, they give rise to a regularization-based training approach for robust and generalizable quantum models, highlighting the importance of trainable data encoding strategies. The practical implications of the theoretical results are demonstrated with an application to time series analysis.
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