用量子聚类做袋装学习，抗标签噪声更强。
A Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets
- 用量子K均值聚类作基学习器，无监督处理数据
- 在噪声标签下表现优于传统监督袋装方法
- 适合标签不可靠的量子机器学习场景
在嘈杂中等规模量子（NISQ）时代，构建抗噪声的量子机器学习算法至关重要。本文提出一种基于量子袋装的框架，以量子K均值（QMeans）作为基学习器，降低预测方差并增强对标签噪声的鲁棒性。与依赖监督学习器的传统袋装不同，该方法利用QMeans的无监督特性，结合基于QRAM的量子重采样和多数投票的袋装聚合。在多个带噪声的分类与回归任务上进行大量仿真表明，该量子袋装算法性能可比经典KMeans方法，且对标签污染的容忍度更高，凸显了无监督量子袋装在处理不可靠数据中的潜力。
原文摘要 · Abstract (English)
The development of noise-resilient quantum machine learning (QML) algorithms is critical in the noisy intermediate-scale quantum (NISQ) era. In this work, we propose a quantum bagging framework that uses QMeans clustering as the base learner to reduce prediction variance and enhance robustness to label noise. Unlike bagging frameworks built on supervised learners, our method leverages the unsupervised nature of QMeans, combined with quantum bootstrapping via QRAM-based sampling and bagging aggregation through majority voting. Through extensive simulations on both noisy classification and regression tasks, we demonstrate that the proposed quantum bagging algorithm performs comparably to its classical counterpart using KMeans while exhibiting greater resilience to label corruption than supervised bagging methods. This highlights the potential of unsupervised quantum bagging in learning from unreliable data.
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