Quantum Leap: Exploring the Potential of Quantum Machine Learning for Communication Networks

Quantum Leap: Exploring the Potential of Quantum Machine Learning for Communication Networks

연구 분야: Cryptography

학회: MSWiM '23: Proceedings of the Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems

Future 6G networks are expected to surpass the advances made in 5G, by providing the faster speeds, lower latency and extended coverage needed for emerging transformative applications, while at the same time achieving greater energy and spectral efficiency, as well as enhanced security and reliability. While 6G is currently in its initial phases of development and standardization, it is already foreseen to incorporate not just incremental technical enhacements but also pioneering innovations compared to its forerunner, 5G. Indeed, quantum technologies are expected to play an important role in 6G. This goes beyond mechanisms such as quantum key distribution for secure communications; it includes the integration of quantum computing for advanced data processing within 6G networks. As 6G networks are set to integrate artificial intelligence and machine learning even more intrinsically into their operation, the concept of quantum machine learning (QML) emerges as a promising opportunity to enable swift data processing, network optimization, and increased security and privacy. In this presentation, we will look at the fundamentals of quantum computing and quantum machine learning, explore the possibilities they offer for future 6G networks, and the potential for revolutionary advances they offer, while presenting some the important challenges associated with their integration into 6G networks.