研究人员比较了用于指纹识别的混合量子-经典自监督学习（SSL）模型，发现量子集成的好处取决于具体的SSL目标。当QuFeX量子特征提取模块应用于SimCLR和MoCo v2等对比SSL框架时，混合模型与经典模型相比表现有所提高。然而，在非对比式BYOL框架中未观察到这种优势，这表明量子增强与对比学习方法有关。一种硬件高效的量子电路QNet并未带来类似的收益。 AI
影响 自监督学习中的量子集成可能为对比学习等特定目标在生物识别应用中提供性能提升。
排序理由 该集群包含一篇详细介绍量子-经典模型新应用研究结果的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2609.39172v1 Announce Type: cross 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 propose…
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 qua…