Towards quantum efficient training for radio frequency fingerprint identification

Radio frequency fingerprint identification (RFFI) is an emerging physical-layer method for authenticating devices through their unique hardware impairments. Deep learning (DL) is widely used to identify devices based on their signal transmissions. However, training DL models is resource-intensive because it requires repeated updates to numerous parameters, which becomes particularly problematic in resource-constrained environments. In this paper, we propose quantum-assisted training (QAST), a framework that addresses training inefficiencies in RFFI systems. QAST integrates a quantum neural network with a classical neural network to generate parameters for a DL model. Compared to traditional training methods, this indirect training strategy substantially decreases the number of trainable parameters, thereby mitigating the overall computational and resource demands. Experimental results show that QAST enables training an RFFI model with 90% fewer trainable parameters than traditional training approaches, while maintaining comparable classification accuracy.