arXiv：cs.LG· Maria S. Edwards, Kidwell Dlamini, Pin-An Lin, Wen-Hsien Hsu, Wen-Chieh Fang·· 5 小时前AI 评分28
混合量子-经典自监督学习用于指纹识别：宽度匹配对比研究
A Width-Matched Comparison of Hybrid Quantum-Classical Self-Supervised Learning for Fingerprint Recognition
AI 导读
研究将 QuFeX 量子特征提取模块插入 SimCLR、MoCo v2 和 BYOL 三种自监督学习框架，在 SOCOFing 指纹数据集上与经典版本进行等宽度（8 特征，等同 8 qubits）对比。单次实验显示两种对比目标下混合模型得分更高，但 BYOL 多种子分析未发现可靠差异，表明增益取决于自监督目标。硬件高效电路 QNet 未表现出相同增益。
正文
Abstract:Fingerprint recognition is a widely deployed biometric, but supervised training requires large labeled enrollment sets. Self-supervised learning (SSL) removes this requirement, and hybrid quantum-classical models have been proposed to enrich the learned representations. Prior quantum SSL studies consider a single contrastive objective, so it is unclear whether reported benefits depend on the objective or can be attributed to the quantum circuit. We insert the QuFeX quantum feature-extraction module into three SSL frameworks, the contrastive SimCLR and MoCo v2 and the non-contrastive BYOL, and compare each hybrid with its classical counterpart at matched representation width (8 features, equal to 8 qubits) on the SOCOFing fingerprint dataset, with a CIFAR-10 control, using k-nearest-neighbor identification on encoder features. In single-run experiments the hybrid scores clearly higher for both contrastive objectives, whereas for BYOL a multi-seed analysis shows no reliable difference, suggesting that any benefit depends on the SSL objective. A hardware-efficient circuit (QNet) does not show the same gain. We examine whether the gains can be attributed to the quantum circuit, considering circuit architecture, trainable parameter count, nonlinearity, and the classical simulability of 8-qubit circuits.
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.39172 [quant-ph] |
| (or arXiv:2609.39172v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2609.39172 arXiv-issued DOI via DataCite |
Submission history
From: Wen-Chieh Fang [view email]
[v1]
Wed, 30 Sep 2026 07:31:42 UTC (387 KB)
[v2]
Fri, 2 Oct 2026 15:07:31 UTC (418 KB)
来源：arXiv：cs.LG · arxiv.org