Quantum Machine Learning and Blockchain Integration for Enhanced Security and Efficiency in UGU, UAV, and Drone Networks: A Comprehensive Review

Pranto Halder; Md. Soyaeb Islam

doi:10.5455/EJCSI.20251115051912

Abstract
The rapid evolution of autonomous systems—such as Unmanned Ground Units (UGUs), Unmanned Aerial Vehicles (UAVs), and drone networks—has generated unprecedented demands for secure, intelligent, and efficient data processing. This study presents a comprehensive review of recent advancements integrating Quantum Machine Learning (QML) and blockchain technologies to address these challenges. Following a systematic review approach, relevant studies published between 2020 and 2024 were analyzed to evaluate their contributions to enhancing decision-making, communication reliability, and data protection in autonomous networks. The findings reveal that QML enables adaptive learning and optimization in dynamic environments, while blockchain ensures data immutability, decentralized trust, and robust security. Together, these technologies offer a promising foundation for next-generation autonomous systems characterized by transparency, resilience, and computational efficiency. The paper concludes by outlining open research issues, including scalability, interoperability, and quantum resource constraints, and highlights future research opportunities for developing unified QML–blockchain frameworks in autonomous networks.

Key words: Keywords: Quantum Machine Learning (QML), Blockchain, Unmanned Ground Units (UGUs), Unmanned Aerial Vehicles (UAVs), Drones, Autonomous Networks, Data Security, Decentralized Systems, Artificial Intelligence, Quantum Computing.