Keynote Speakers

 

Prof. Trung Q. Duong (IEEE Fellow, IET Fellow, CAE Fellow, EIC Fellow, AAIA Fellow)
Memorial University of Newfoundland, Canada

Dr. Trung Q. Duong (IEEE Fellow, IET Fellow, CAE Fellow, EIC Fellow, and AAIA Fellow) is a Canada Excellence Research Chair and Full Professor at Memorial University of Newfoundland, Canada. He is also an adjunct professor at Queen’s University Belfast, UK and a visiting professor at Kyung Hee University, Republic of Korea, and Edinburgh Napier University, UK. His current research interests include quantum optimisation and quantum machine learning for wireless communications. He is an author/co-author of 410+ journal articles (mostly in IEEE) with 28,000+ citations and h-index 89. He has served as an Editor for many reputable IEEE journals (IEEE Trans on Wireless Communications, IEEE Trans on Communications, IEEE Trans on Vehicular Technology, IEEE Communications Surveys & Tutorials, IEEE Communications Letters, and IEEE Wireless Communications Letters) and has been awarded best paper awards in many flagship conferences including IEEE ICC 2014, IEEE GLOBECOM 2016, 2019, and 2022. He was the only UK-based researcher awarded both the Research Fellowship and Research Chair from the Royal Academy of Engineering. In 2017, he was awarded the Newton Prize from the UK government. He is currently the Editor-in-Chief of IEEE Communications Surveys & Tutorials and an IEEE ComSoc Distinguished Lecturer. He is a fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Institution of Engineering and Technology (IET), the Canadian Academy of Engineering (CAE), the Engineering Institute of Canada (EIC), and the Asia-Pacific Artificial Intelligence Association (AAIA). He is the Founding Director of Quantum Communications and Computing Center (QC3).

Speech Title: Hybrid Classical-Quantum-Inspired Algorithms for Next Generation Communications

Abstract: Quantum computing uses the concept of quantum mechanics to offer a massive leap forward in relations to solving complex computation problems. Hybrid quantum-classical machine learning (QML) and quantum optimization (QOpt) algorithms can significantly enhance the processing efficiency and exponentially computational speed-up, highly capable of guaranteeing high QoS requirements of 6G networks. This talk presents the state-of-the-art in QML and QOpt and provide a comprehensive overview of its potential to next generation communications networks. Furthermore, this talk introduces some recent results of applying QML and QOpt in RIS channel estimation, RF fingerprinting, ISTNs etc. Finally, some dominating research issues, potential challenges, and future research directions for QML and QOpt in 6G networks are elaborated.