arXiv：cs.LG· Jingyan Xu, Yuanhong Wu, Samuel Yen-Chi Chen, Sean Patten, Christina Schweikert, D. Frank Hsu·· 13 小时前
量子-经典模型多样性感知融合：CFA 在四类二分类基准上最高提升 6.03% 准确率
Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion
AI 导读
研究者将组合融合分析（CFA）用于 29 个架构各异的经典模型与变分量子电路（VQC），通过 rank-score 函数与认知多样性做多样性感知的模型选择，并以分数和排名两种方式组合。在四个不同领域的二分类基准上，该经典-量子混合融合方案在每个数据集上均优于最佳单模型，准确率最高提升 6.03%，与相关文献相比也在四个数据集上全部取得更优表现。
正文
Abstract:Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best design and discard the remaining candidates. This paper addresses whether the structural diversity across quantum circuit search spaces can be used to produce stronger predictions through model fusion. We apply Combinatorial Fusion Analysis (CFA), a model fusion framework that selects and combines multiple scoring systems using rank-score function (RSF) and cognitive diversity (CD), to a pool of 29 architecturally diverse classical models and quantum circuits. Using a diversity-aware model selection method, we identify subsets of maximally diverse models and combine them with both score and rank combination. Experiments on four binary classification benchmarks covering different domains demonstrate that CFA-based hybrid classical-quantum fusion consistently outperform the best individual model on every dataset considered with improvements up to 6.03% in accuracy. When comparing with relevant literatures, the CFA framework further achieves superior performance across all four datasets.
| Comments: | 12 pages, 4 tables, 6 figures; Accepted to the 2026 IEEE Conference on Quantum Computing and Engineering (IEEE Quantum Week 2026/QCE26) |
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.13808 [quant-ph] |
| (or arXiv:2610.13808v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13808 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jingyan Xu [view email]
[v1]
Fri, 9 Oct 2026 16:20:02 UTC (1,681 KB)
来源：arXiv：cs.LG · arxiv.org