1. ABSTRACT

Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional approaches in data training and processing have exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly dynamic applications and services. In this regard, the fast developing quantum computing with machine learning and optimisation for 6G networks is investigated. Quantum-inspired optimal resource allocation algorithms can significantly enhance the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications. This talk will present the state-of-the-art in quantum computing and provide a comprehensive overview of its potential, via machine learning and optimisation approaches. Furthermore, this talk will also introduces quantum-inspired optimal resource allocation for 6G networks, considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning and optimisation in 6G networks are elaborated.