Quantum Machine Learning for Next-Gen Wireless: Fundamentals and Path Ahead
L Shalini, Srividya Bhat, Dhanashree Kulkarni +3 more
Quantum computing is on the cusp of transforming the computational strength of “Intelligent Transportation Systems” (ITSs), but issues such as the scalability of qubits and error correction continue. With wireless networks shifting from 5G to 6G, technologies such as extended mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and the Internet of Things (IoT) will transform global connectivity. Though 5G offers improved speeds, it is prone to threats based on quantum computing, making quantum-safe cryptographic solutions for 6G networks essential. This paper discusses the revolutionary capability of quantum machine learning (QML) to meet the changing needs of future wireless systems. Through the use of quantum algorithms for data processing, QML can improve network performance, real-time decision-making, and increase security with quantum-safe cryptography. This paper sets out the challenges and prospects for the integration of quantum technologies into the design and deployment of secure, efficient, and scalable 6G wireless communication systems.
CRC Press eBooks•May 29, 2026•0 citations