发表机构
National Dong Hwa University; National Chiayi University（国立东华大学; 国立嘉义大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在匹配宽度下比较混合量子-经典SSL与经典SSL在指纹识别中的表现，发现对比目标下量子特征提取有优势，但该优势依赖SSL目标且非量子电路本身所致。
AI 中文摘要
指纹识别是一种广泛部署的生物识别技术，但监督训练需要大量带标签的注册集。自监督学习（SSL）消除了这一需求，而混合量子-经典模型已被提出用于丰富学习到的表征。先前的量子SSL研究仅考虑单一对比目标，因此尚不清楚所报告的优势是否依赖于该目标，还是可归因于量子电路。我们将QuFeX量子特征提取模块插入到三个SSL框架中，即对比性的SimCLR和MoCo v2以及非对比性的BYOL，并在匹配表征宽度（8个特征，等于8个量子比特）下，在SOCOFing指纹数据集上，以CIFAR-10作为对照，使用编码器特征上的k近邻识别，将每个混合模型与其经典对应模型进行比较。在单次运行实验中，混合模型在两种对比目标下得分明显更高，而对于BYOL，多种子分析显示没有可靠差异，这表明任何优势都取决于SSL目标。硬件高效电路（QNet）未显示出同样的增益。我们考察了这些增益是否可归因于量子电路，考虑了电路架构、可训练参数数量、非线性以及8量子比特电路的经典可模拟性。
英文摘要
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.
