Hybrid quantum computing and deep learning approaches for enhaving wireless communication security

Abstract

The rapid expansion of wireless communication networks has introduced significant security challenges, particularly with the rise of sophisticated cyberattacks. Traditional cryptographic methods often struggle to balance security and computational efficiency, necessitating innovative approaches. In this study, we propose a hybrid quantum computing and deep learning framework to enhance wireless communication security. The quantum computing component leverages quantum key distribution (QKD) to establish theoretically unbreakable encryption, while deep learning models—trained on adversarial attack datasets—enhance intrusion detection and anomaly recognition. Experimental results demonstrate that our hybrid approach improves attack detection accuracy to 98.7%, surpassing conventional machine learning models by 5.2%. The quantum-enhanced cryptographic layer reduces key exchange vulnerabilities, leading to a 40% improvement in resistance against man-in-the-middle attacks compared to classical schemes. Our findings underscore the potential of integrating quantum computing with deep learning for robust, scalable, and future-proof wireless security solutions