ABSTRACT: In the current Near-Term Intermediate-Scale Quantum (NISQ) computing era the number of quantum bits handled by quantum computers is still limited, yet, as we will discuss, there are already a suite of sophisticated solutions for harnessing Quantum Processing Units (QPUs) in support of wireless communications. At the same time, ultimately secure quantum key distribution (QKD) has reached commercial reality and has found its way into high-security banking, defence and governmental applications. Despite this relative maturity, numerous limitations exist in quantum communications. Hence a wide community-effort is required for solving the suite of open problems and circumventing quantum communications calamities. Based on “Quantum Information Processing, Sensing and Communications: Their Myths, Realities and Futures” by Hanzo et al., Proceedings of the IEEE, 2025, the marriage of classical and quantum communications is discussed, outlining their intricate symbiosis.

BIO: Lajos Hanzo is a Fellow of the Royal Academy of Engineering, FIEEE, FIET, Fellow of EURASIP and a Foreign Member of the Hungarian Academy of Sciences. He has published widely, including 20 Wiley-IEEE Press research monographs. He was bestowed upon the IEEE ComSoc Edwin Howard Armstrong Award and the Eric Sumner Technical Field Award. More information about Lajos Hanzo can be found at: https://www-mobile.ecs.soton.ac.uk/ and https://en.wikipedia.org/wiki/Lajos_Hanzo.

ABSTRACT: Artificial intelligence is rapidly moving beyond centralized models and controlled environments. It is becoming distributed across clouds, edge devices, wireless networks, sensors, robots, and other cyber-physical systems. Although advances in foundation models have greatly expanded what AI can do, capability alone does not guarantee dependable operation. In mission-critical and resource-constrained environments, intelligent systems must also remain reliable, efficient, secure, fair, and accountable despite heterogeneous resources, uncertain conditions, intermittent connectivity, and changing participants.

This talk examines the emerging need for assured distributed intelligence. This approach treats assurance as a fundamental systems requirement rather than as an evaluation performed after deployment. The talk will explore how computation, communication, data, and learning can be orchestrated across the cloud–edge continuum. It will discuss the tension between performance and assurance, including the need to balance accuracy, latency, energy consumption, resilience, privacy, and fairness. The talk will conclude by outlining a research agenda for intelligent systems that can monitor their own operating conditions, adapt safely at runtime, and provide meaningful evidence about their behaviour. The next phase of intelligent computing will not be defined only by larger models or greater autonomy, but by our ability to make distributed intelligence dependable in the physical world.

BIO: Albert Zomaya is the Peter Nicol Russell Chair Professor of Computer Science at the University of Sydney and Director of the Parallel and Distributed Computing Research Lab. A global leader in parallel and distributed systems, he has authored more than 800 publications and 30 books. He is a Fellow of the IEEE, the Australian Academy of Science, and the Royal Society of New South Wales, and an elected member of Academia Europaea and the European Academy of Sciences and Arts. Professor Zomaya has been recognized as a Clarivate Highly Cited Researcher in multiple years and previously served as Editor-in-Chief of IEEE Transactions on Computers, IEEE Transactions on Sustainable Computing, and ACM Computing Surveys. He is the recipient of many awards, and his work spans parallel and distributed computing, networking, and complex systems, with a lasting influence on both theory and practice.

ABSTRACT: The rapid evolution of wireless technologies, together with their ongoing convergence with AI and diverse vertical applications are profoundly reshaping future 6G enabled systems. To effectively support diverse and dynamic applications, future 6G networks must evolve beyond connectivity by intelligently orchestrating heterogeneous resources, services, machines and application requirements for eventual goal and value realization. This presentation begins by examining the fundamental challenges in 6G. A layered intelligence-native 6G architectural paradigm is introduced to elaborate how intelligence can be embedded across different levels of networked system operation. At the resource and service layers, a multi-dimensional multiple access (MDMA) mechanism is introduced as a heterogeneous service provisioning platform that intelligently coordinates multi-dimensional radio resources for value-oriented service provisioning. To achieve diverse operation goals at system level, this talk will also explore the critical aspects of trusted machine collaboration in 6G-enabled networked systems for effective task completion and value-oriented system operation.

BIO: Dr. Xianbin Wang is a Distinguished University Professor and a Tier-1 Canada Research Chair in Trusted Communications and Computing at Western University, Canada. His current research interests include 5G/6G technologies, communications security, physical AI and networked systems. He has over 800 highly cited journals and conference papers, in addition to over 30 granted and pending patents and several standard contributions. Dr. Wang is a Fellow of IEEE, a Fellow of Royal Society of Canada, a Fellow of the Canadian Academy of Engineering and a Fellow of the Engineering Institute of Canada. He has received many prestigious awards and recognitions, including the IEEE Canada R. A. Fessenden Award, Canada Research Chair, Engineering Research Excellence Award at Western University, Canadian Federal Government Public Service Award, Ontario Early Researcher Award, and 12 Best Paper Awards. He is currently a member of the Senate, Senate Committee on Academic Policy and Senate Committee on University Planning at Western. He has been involved in many flagship conferences, including ICC, GLOBECOM, VTC, PIMRC, WCNC, CCECE, and ICNC, in different roles, such as General Chair, TPC Chair, Symposium Chair, Tutorial Instructor, Track Chair, Session Chair, and Keynote Speaker. He has served as the Editor-in-Chief, Associate EiC, area editor, associate editor and guest editor for over ten journals. He was nominated as an IEEE Distinguished Lecturer multiple times by different IEEE technical societies. He has served on the Fellow Committees of IEEE and IEEE Communications Society. He was the Chair of the IEEE ComSoc Signal Processing and Computing for Communications (SPCC) Technical Committee and the Central Area Chair of IEEE Canada.

ABSTRACT: The RAN intelligent controller (RIC) is cloud native and a central component of a virtualized RAN network. The RIC enables to deployment of machine learning and AI techniques to optimize resources and services in the RAN. Thus, it is an important component that brings intelligence, agility, and programmability to the radio access network. On the other hand, there is a future trend to explore the concurrent use of converged computer-and-communications infrastructure to run RAN and AI and Generative AI workloads, enhancing platform utilization and creating new monetization opportunities. Lastly, it is crucial to understand the radio interface requirements for running AI and Generative AI applications across consumer, enterprise, and government sectors. In this talk, we will share about AI-RAN Alliance and how it plans to transform future networks with AI. Furthermore, we will also share some of our work in AI-RAN through Singapore’s Future Communications Research and Development Programme (FCP).

BIO: Tony Q.S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, respectively. At Massachusetts Institute of Technology, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Associate Provost (AI & Digital Innovation) and Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD). He also serves as the Director of the Future Communications R&D Programme, and the ST Engineering Distinguished Professor. His current research topics include wireless communications and networking, network intelligence, non-terrestrial networks, open radio access network, and 6G. His current research topics include wireless communications and networking, AI-RAN, non-terrestrial networks, open radio access network, and 6G.

Dr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, the 2020 Nokia Visiting Professorship, the 2022 IEEE Signal Processing Society Best Paper Award, the 2024 IIT Bombay International Award For Excellence in Research in Engineering and Technology, and the IEEE Communications Society WTC Recognition Award 2024. He is an IEEE Fellow, a WWRF Fellow, and a Fellow of the Academy of Engineering Singapore.

ABSTRACT: Intelligent computing is emerging as a new quality productive force in the digital economy era. With intelligent applications represented by large models evolving rapidly, cloud computing systems face entirely new requirements. Traditional cloud architectures encounter three major challenges: at the "cloud" level, it is difficult to adapt to diverse and heterogeneous computing power; at the "compute" level, balancing high throughput with high compute utilization remains challenging; and at the "intelligence" level, coordinating differentiated application objectives proves difficult. This talk shares insights and practical experiences regarding intelligent computing cloud systems across three dimensions—"efficient pooling," "maximizing compute utilization," and "perception-driven scheduling"—alongside their large-scale deployment in leading enterprises and open-source communities. It further looks ahead to building adaptive cloud computing systems through a "perception–decision–evaluation" closed loop, providing a scalable and inclusive computing foundation for the era of intelligent computing.

BIO: Dr. Minyi Guo is the Zhiyuan Chair Professor of Shanghai Jiao Tong University. His present research interests include parallel/distributed computing, compiler optimizations, big data and cloud computing. He has more than 700 publications in major journals and international conferences in these areas. He received 10 best/highlight paper awards from international conferences including ISCA2024 and ISCA2026. Dr. Guo also got the national science and technology achievement award in 2019. He was Editor-in-Chief of IEEE Transactions on Sustainable Computing and now is the Transactions Operation Chair of IEEE Computer Society. He won 2023 IEEE Edward J. McCluskey Technical Achievement Award for his contributions to the design and realization of resource-efficient cloud computing. He is Fellow of IEEE and CCF.

ABSTRACT: Recently, the rapid development of information and communications technologies has widely promote the applications of artificial intelligence (AI). This has driven the fast expansion of the scale of large language models (LLMs), making their training not be able to be handled by single GPUs. Therefore, distributed training has to be adopted for LLMs, and nowadays, distributed LLM training has become the fastest-growing application in data-center networks (DCNs). Compared with legacy DCN applications, LLM training jobs rely more on the collective communication that organizes a cluster of training processes to take part in synchronized computing and communication phases iteratively, and thus generate traffic patterns that are more skewed in the spatial and temporal domains and can shift repeatedly. This motivates DCN operators to push harder for the network reconfigurability in inter-rack/pod segments, resulting in the introduction of optical circuit switching (OCS) into DCNs. Specifically, in addition to traffic engineering (TE), OCS brings the flexibility of topology engineering (TPE) to offer extra reconfigurability, i.e., since the switching matrix of an OCS switch can be reconfigured in run-time, TPE can be realized to steer inter-pod connectivity to adapt to the dynamic and skewed inter-pod traffic matrix. In this talk, we will discuss our recent progress on how to realize adaptive topology engineering (TPE) for accelerating the training of LLMs in optically-interconnected AI clusters.

BIO: Zuqing Zhu obtained his PhD degree from the University of California, Davis, in 2007. He is currently a Full Professor at the School of Information Science and Technology, University of Science and Technology of China (USTC). Prior to that, he worked in the Service Provider Technology Group of Cisco, San Jose. Dr. Zhu has been the Chair of the Technical Committee on Optical Networking (ONTC) of the IEEE Communications Society (ComSoc) (2022-2023). He is currently the Lead Series Editor of the Optical Communications and Networks Series in IEEE Communications Magazine, the Area Editor of Optical Communications and Networking of IEEE Open Journal of the Communications Society, and on the editorial boards of IEEE Transactions on Network and Service Management, Optical Switching and Networking (Elsevier), etc. Dr. Zhu is the Steering Committee Chair of the IEEE International Conference on High Performance Switching and Routing (HPSR), the Chair of the GLOBECOM/ICC Technical Content (GITC) Committee (one of the two steering committees of GLOBECOM/ICC), a Member of the Conferences Council of ComSoc, and a Member of the Fellow Evaluation Committee of IEEE ComSoc, etc. His research focuses on optical networks, data-center networks, and artificial intelligence, and he has published 390+ papers in referred journals and conferences of IEEE, ACM and Optica. He has received the 2023 Distinguished Technical Achievement Award from the Communications Switching and Routing Technical Committee (CSR-TC) of ComSoc, and the 2023 Malathi Veeraraghavan Service Award from the ONTC of ComSoc. He is a Fellow of IEEE.

ABSTRACT: Agentic AI systems that can reason, plan, and act with tools are becoming a promising paradigm for real world applications, especially at the edge where low latency, privacy, and resilience are critical. However, edge environments impose strict constraints on compute, memory, and reliability, making it challenging to achieve high quality outcomes. In this talk, I will present a perspective on enabling agentic AI at the edge by integrating insights from recent benchmarking and prior work on model selection and routing. Our study shows that agentic performance does not scale simply with model size and instead depends on the interaction between models, tools, and task domains, with non steady scaling trends and distinct failure patterns. Building on this, I will discuss approaches such as TMO for adaptive inference across both local and cloud nodes and MESS+ for cost optimal model routing with service guarantees, which together help balance quality, latency, and cost. I will conclude by outlining key challenges and opportunities in moving toward robust agentic systems through workload aware model selection and system level co-design.

BIO: Shiqiang Wang is a Professor of Artificial Intelligence in the Department of Computer Science, University of Exeter, United Kingdom. He was a researcher at IBM T. J. Watson Research Center, NY, United States until Oct. 2025. He received his Ph.D. from Imperial College London, United Kingdom, in 2015. His research focuses on the intersection of artificial intelligence (AI), distributed computing, and optimization, with a broad range of applications including large language models (LLMs), agentic AI, efficient model training and inference, and AI in distributed systems. He has made foundational contributions to edge computing and federated learning that generated both academic and industrial impact. Dr. Wang served as an associate editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, and IEEE Transactions on Computational Social Systems. He also served as an area chair of major AI and machine learning conferences, including AAAI, ICLR, ICML, and NeurIPS. He received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize in 2021, IEEE ComSoc Best Young Professional Award in Industry in 2021, Best Paper Runner-Up of ACM MobiHoc 2025, IBM Outstanding Technical Achievement Awards (OTAA) in 2019, 2021, 2022, and 2023, multiple Invention Achievement Awards from IBM since 2016, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015. He is an IEEE Fellow and a member of ACM and ELLIS. More details are available on his webpage: https://shiqiang.wang.