Classical Communications and Networks in the Quantum Era

Abstract

The advent of quantum information technologies disrupts the existing evolution of communication systems in at least two ways.

First, the type of data exchanged via communication networks is extended from classical to quantum data. Second, a number of fundamental operations in distributed quantum systems critically depend on classical communication, such as teleportation or Quantum Key Distribution. This tutorial will explain the basics of communication and networking for quantum applications, with emphasis on classical communication. The tutorial will briefly introduce quantum information and its differences with classical information.

Next, it will introduce the role of classical communication for various quantum applications, such as QKD, and, quantum systems, such as the Quantum Internet. This will be followed by network architectures, protocols, and algorithms that enable reliable communication and distributed computation on top of unreliable classical and quantum elements. Specifically, several examples of protocols will be analyzed in detail, such as protocols for joint communication and entanglement distribution, as well as satellite-aided entanglement distribution.

Bio

Petar Popovski is a Professor at Aalborg University, where he heads the section on Connectivity and a Visiting Excellence Chair at the University of Bremen. From February 2025, he is a Director of CLASSIQUE, a large center at Aalborg University for classical communication in quantum systems. He received his Dipl.-Ing (1997) and M. Sc. (2000) degrees in communication engineering from the University of Sts. Cyril and Methodius in Skopje and the Ph.D. degree (2005) from Aalborg University.

He is a Fellow of the IEEE. He received an ERC Consolidator Grant (2015), the Danish Elite Researcher award (2016), IEEE Fred W. Ellersick prize (2016), IEEE Stephen O. Rice prize (2018), Technical Achievement Award from the IEEE Technical Committee on Smart Grid Communications (2019), the Danish Telecommunication Prize (2020) and Villum Investigator Grant (2021). He authored the book “Wireless Connectivity: An Intuitive and Fundamental Guide”, published by Wiley in 2020. He is currently an Editor-in-Chief of IEEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS.

He was the Chair of the IEEE Communication Theory Technical Committee, a Member at Large at the Board of Governors in IEEE Communication Society, as well as the General Chair for IEEE SmartGridComm 2018 and IEEE Communication Theory Workshop 2019. His research interests are in communication theory.

Diffusions in computational statistics and machine learning

Abstract

Many cutting-edge algorithms in computational statistics and machine learning draw inspiration from natural physical processes, utilising diffusions simulated on digital computers. I will give a gentle and accessible introduction to the mathematical background and core algorithms, focusing on applications in Bayesian inference and generative modelling.

Bio

Nikolas Nüsken obtained his PhD from Imperial College London in 2018 under the supervision of Professor Greg Pavliotis. After a short stay at the Alan Turing Institute, he worked as a postdoctoral researcher at the University of Potsdam within the Collaborative Research Centre “Scaling Cascades in Complex Systems’’. He joined King’s College London in 2022 as a Lecturer in Mathematical Data Science, working primarily on the mathematical foundations of interacting particle systems and their applications in machine learning and computational statistics.

From Intelligent Surfaces to Analog Computing for Communications and Signal Processing

Abstract

This tutorial explores new architectures for analog-domain beamforming, processing, and computing.

First, we introduce Beyond Diagonal RIS (BD-RIS), a general framework of reconfigurable intelligent surfaces where elements are inter-connected via tunable impedances. This enables engineered coupling, allowing waves to propagate between elements, enhancing RIS-aided communications with greater signal manipulation flexibility.

Next, we perform computation, e.g. matrix inversion with low complexity, directly in the analog domain and enable ultra-massive MIMO communications with 10⁴× lower computational complexity than digital beamforming.

Bio

Bruno Clerckx is a Professor, the Head of the Communications and Signal Processing Group, and the Head of the Wireless Communications and Signal Processing Lab, within the Electrical and Electronic Engineering Department, Imperial College London, London, U.K.

He received the MSc and Ph.D. degrees in Electrical Engineering from Université Catholique de Louvain, Belgium, and the Doctor of Science (DSc) degree from Imperial College London, U.K. He spent many years in industry with Silicon Austria Labs (SAL), Austria, where he was the Chief Technology Officer (CTO) responsible for all research areas of Austria’s top research center for electronic based systems and with Samsung Electronics, South Korea, where he actively contributed to 4G (3GPP LTE/LTE-A and IEEE 802.16m). He has authored two books on “MIMO Wireless Communications” and “MIMO Wireless Networks”, over 300 peer-reviewed international research papers, and 150 standards contributions, and is the inventor of 80 issued or pending patents among which several have been adopted in the specifications of 4G standards and are used by billions of devices worldwide.

His research spans the general area of wireless communications and signal processing for wireless networks. He received the prestigious Blondel Medal 2021 from France for exceptional work contributing to the progress of Science and Electrical and Electronic Industries, the 2021 Adolphe Wetrems Prize in mathematical and physical sciences from Royal Academy of Belgium, multiple awards from Samsung, IEEE best student paper award, and the EURASIP (European Association for Signal Processing) best paper award 2022. He is a Fellow of the IEEE and the IET.

Topological Signal Processing and Learning

Abstract

As data becomes increasingly complex and interconnected, traditional graph-based methods struggle to capture higher-order relationships inherent in many real-world datasets. This tutorial introduces Topological Signal Processing (TSP) and Topological Machine Learning (TML)—emerging paradigms that extend classical and graph signal processing to topological domains such as simplicial and cell complexes. These structures allow modeling of multi-way interactions, enabling a richer representation and analysis of data residing on higher-order elements like edges, faces, or volumes.

Central to this framework is Hodge theory, which provides a mathematical foundation for defining signal operators, spectral decompositions, and topological analogs of convolutional filters. The tutorial covers the theoretical underpinnings of TSP and TML, practical design of topological filters and neural networks, and advanced learning tasks such as topology inference and spatiotemporal modeling. Designed for signal processing and machine learning researchers, this tutorial bridges fundamental concepts with cutting-edge applications in network analysis, wireless communications, and beyond.

Bio

Paolo Di Lorenzo (Senior Member, IEEE) received the M.Sc. and Ph.D. degrees in telecommunication engineering from Sapienza University of Rome, Italy, in 2008 and 2012, respectively. Currently, he is an Associate Professor with the Department of Information Engineering, Electronics, and Telecommunications at Sapienza University of Rome, Rome, Italy. He is the technical manager of the SNS-JU European Project 6G-GOALS, and was the Principal Investigator of CNIT-Sapienza Research Unit in the H2020 European Project RISE 6G. His research interests include topological signal processing, goal-oriented and semantic communications, distributed optimization, and federated learning.

He held a visiting research appointment with the Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA, USA. He is the recipient of the 2022 EURASIP Early Career Award, and of three best student paper awards at IEEE SPAWC10, EURASIP EUSIPCO11, and IEEE CAMSAP11, respectively. He is also the recipient of the 2012 GTTI (Italian National Group on Telecommunications and Information Theory) Award for the Best Ph.D. Thesis in communication engineering. He served as an Associate Editor for the IEEE Transactions on Signal Processing, for the IEEE Transactions on Signal and Information Processing over Networks, and for the EURASIP Journal on Advances in Signal Processing. He is currently a Senior Area Editor of the IEEE Transactions on Signal Processing.