用量子退火优化机器学习中的特征、实例和聚类问题，效果优于传统方法。
Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering
- 将特征/实例选择与聚类转为QUBO问题，用量子与经典退火求解
- 量子退火在特征选择中计算更高效，聚类的紧凑性和检索指标提升
- 适合需要离散优化的机器学习任务，尤其关注效率的场景
本文研究量子退火（QA）与经典模拟退火（SA）在机器学习组合优化问题中的应用，包括特征选择、实例选择和聚类。我们将每项任务建模为无约束二次二值优化（QUBO）问题，并实现量子与经典求解器进行对比。在特征选择中，提出多种平衡特征重要性与冗余的QUBO配置，表明量子退火可获得计算更高效的解；在实例选择中，设计若干新颖的实例级重要性度量启发式方法，扩展了现有技术；在聚类任务中，构建从经典聚类到量子退火的流程，通过QUBO优化中心点（medoid），显著提升聚类紧凑性与检索性能。结果表明，即使受限于当前量子硬件，量子退火仍可成为离散机器学习优化的有力工具。
原文摘要 · Abstract (English)
This paper explores the applications of quantum annealing (QA) and classical simulated annealing (SA) to a suite of combinatorial optimization problems in machine learning, namely feature selection, instance selection, and clustering. We formulate each task as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implement both quantum and classical solvers to compare their effectiveness. For feature selection, we propose several QUBO configurations that balance feature importance and redundancy, showing that quantum annealing (QA) produces solutions that are computationally more efficient. In instance selection, we propose a few novel heuristics for instance-level importance measures that extend existing methods. For clustering, we embed a classical-to-quantum pipeline, using classical clustering followed by QUBO-based medoid refinement, and demonstrate consistent improvements in cluster compactness and retrieval metrics. Our results suggest that QA can be a competitive and efficient tool for discrete machine learning optimization, even within the constraints of current quantum hardware.
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