提出量子机器学习的边界理论，提升泛化能力评估可靠性。
Understanding Generalization in Quantum Machine Learning with Margins
- 基于边界构建量子机器学习泛化上界
- 边界指标预测性能优于参数量等传统指标
- 结合量子信息理论实现经典-量子混合优化
理解并提升泛化能力对经典和量子机器学习（QML）都至关重要。近期研究揭示了现有泛化理论（尤其依赖一致界的方法）在经典与量子场景中的局限性。本文提出一种针对QML模型的边界基泛化上界，提供更可靠的泛化评估框架。在量子相识别（QPR）数据集上的实验表明，边界基度量能有效预测泛化性能，优于传统指标如参数数量。通过将该边界度量与量子信息理论关联，我们展示了在处理经典数据时，如何利用经典-量子混合方法提升QML的泛化表现。
原文摘要 · Abstract (English)
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.
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