🤖 AI Summary
This study addresses the reliance of fingerprint recognition on extensive labeled data and the unclear attribution of benefits in quantum self-supervised learning. For the first time, a quantum feature extraction module (QuFeX) is embedded into three self-supervised frameworks—SimCLR, MoCo v2, and BYOL—using an equal-width controlled variable approach to construct hybrid models, which are systematically evaluated via KNN classification. Results indicate that these hybrid models significantly outperform classical baselines exclusively within contrastive learning settings, while exhibiting no reliable improvement in non-contrastive scenarios. This work reveals that quantum-enhanced gains are inherently dependent on the specific self-supervised objective employed, providing critical empirical evidence for understanding the true sources of advantage in quantum machine learning.
📝 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.