提出分类边界度量，揭示量子机器学习中数据随机性对准确率的限制。
The role of data-induced randomness in quantum machine learning classification tasks
- 引入类间距指标，融合平均随机性与分类边界分析。
- 实证表明数据诱导随机性会制约分类性能上限。
- 适合研究量子模型嵌入策略的评估与优化者阅读。
量子机器学习（QML）作为前沿研究领域，旨在突破经典机器学习的性能瓶颈。数据嵌入过程直接影响模型表现，但现有研究对嵌入策略的影响缺乏系统分析。本文针对二分类任务，提出新度量‘类间距’，结合平均随机性与分类边界概念，建立数据诱导随机性与分类准确率之间的解析关联。通过该指标评估多种数据嵌入方法，验证了数据诱导随机性对分类性能存在理论上限。本工作为评估量子机器学习模型的数据嵌入过程提供了新工具，弥补了现有分析方法的不足。
原文摘要 · Abstract (English)
Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond the capabilities of classical machine learning models. A critical aspect of any learning task is the process of data embedding, which directly impacts model performance. Poorly designed data-embedding strategies can significantly impact the success of a learning task. Despite its importance, rigorous analyses of data-embedding effects are limited, leaving many cases without effective assessment methods. In this work, we introduce a metric for binary classification tasks, the class margin, by merging the concepts of average randomness and classification margin. This metric analytically connects data-induced randomness with classification accuracy for a given data-embedding map. We benchmark a range of data-embedding strategies through class margin, demonstrating that data-induced randomness imposes a limit on classification performance. We expect this work to provide a new approach to evaluate QML models by their data-embedding processes, addressing gaps left by existing analytical tools.
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