51ÂÜÀò

Quantum technologies are maturing by the day and making exciting advances across computing, communications, networking, sensing, and beyond. The critical path to scaling these technologies for a practical advantage over classical systems involves the implementation of fault tolerant procedures. The most established fault tolerance framework uses quantum error correcting codes and decoders. The theory of quantum error correction has recently produced codes with optimal parameters that could potentially reduce the resource overhead of fault tolerance. However, several challenges remain to be addressed before these theoretical advances lead to scalable, fault tolerant, practical quantum systems. Besides computing, error correction techniques are necessary for other applications as well.

For future wireless communications, higher data rate, reliability, and traffic demands will lead to the development of novel communication frameworks that fully exploit the physics of electromagnetic waves. These emerging technologies include holographic MIMO, super-directive antenna array, extremely large antenna arrays, reconfigurable intelligent surfaces, orbital angular momentum (OAM) multiplexing, etc. To explore both potentials and limitations of these technologies, research into electromagnetic and information theory (EIT) is actively underway in both academia and industry. EIT is an interdisciplinary framework integrating electromagnetic wave (EM) theory and information theory (IT) for the analysis of physical systems for the communication, processing, and storage of information. It has been shown that physically large antenna arrays, large intelligent surfaces, RF lens antenna arrays, holographic MIMO, and/or continuous-aperture MIMO can be analyzed more effectively within an EIT framework. Furthermore, it is expected that the physical properties of the OAM, the non-diffraction properties of the Bessel beam, and/or the acceleration properties of the Airy beam will open new opportunities under the EIT framework

This special issue of the 51ÂÜÀò Journal on Selected Areas in Information Theory is dedicated to the memory of Toby Berger, one of the most important information theorists of our time, who passed away in 2022 at the age of 81. He made foundational contributions to a wide range of areas in information theory, including rate-distortion theory, network information theory, quantum information theory, and bio-information theory. He also left a deep imprint on diverse fields in applied mathematics and theoretical engineering, such as Markov random fields, group testing, multiple access theory, and detection and estimation. Well known for his technical brilliance, he tackled many challenging problems, but above all, it is his pursuit of elegance in research and writing that shines throughout his work. The goal of this special issue is to celebrate Toby Berger’s lasting legacy and his impact on information theory and beyond.

Over the past decade, machine learning (ML), that is the process of enabling computing systems to take data and churn out decisions, has been enabling tremendously exciting technologies. Such technologies can assist humans in making a variety of decisions by processing complex data to identify patterns, detect anomalies, and make inferences. At the same time, these automated decision-making systems raise questions about security and privacy of user data that drive ML, fairness of the decisions, and reliability of automated systems to make complex decisions that can affect humans in significant ways. In short, how can ML models be deployed in a responsible and trustworthy manner that ensures fair and reliable decision-making? This requires ensuring that the entire ML pipeline assures security, reliability, robustness, fairness, and privacy. Information theory can shed light on each of these challenges by providing a rigorous framework to not only quantify these desirata but also rigorously evaluate and provide assurances. From its beginnings, information theory has been devoted to a theoretical understanding of the limits of engineered systems. As such, it is a vital tool in guiding machine learning advances.