Events for April 24, 2026

Abstract:Artificial intelligence and combinatorial optimization problems—such as drug discovery and prime factorization—remain challenging even for advanced computers. We are attempting to address these limitations by building photonic processors inspired by the brain—photonic neural networks—which utilize light for faster and more energy-efficient processing . We will discuss photonic networks, including Ising machines enabled by thin-film lithium niobate photonics , highlighting their applications in number partitioning, protein folding, wireless communications, and deep learning. Time permitting, we will briefly introduce a quantum photonic neural network that can learn to act as near-perfect components of quantum technologies and discuss the role of weak nonlinearities . Shastri, B.J. et al. Photonics for artificial intelligence and neuromorphic computing. Nature Photonics 15 (2021) Al-Kayed, N. et al. Programmable 200 GOPS Hopfield-inspired photonic Ising machine. Nature 648 (2025) Ewaniuk, J et al. Imperfect quantum photonic neural networks. Advanced Quantum Technologies (2023) .Co-sponsored by: Prof. Nicolas QuesadaSpeaker(s): Bhavin J. ShastriJ. Armand Bombardier J-1035, Polytechnique Montréal, Montréal, Quebec, Canada, H3T 1J4

[]Abstract: Future 6G wireless communication systems will require high spectral and energy efficiencies for both economic and environmental reasons. Current amplifiers can have very low amplification efficiency, especially when used with variable-envelope broadband signals like the OFDM-based schemes and single-carrier schemes with compact spectrum (both widely employed in broadband wireless land and satellite communications). In fact, the maximum amplification efficiency for quasi-linear amplifiers (like class-A amplifiers) is 50%. This value drops to 5-10% when high-PAPR signals are employed. By using strongly nonlinear, switched amplifiers (like class D or F amplifiers), we can increase the maximum theoretical amplification to 100%, but the strong nonlinear distortion levels preclude its use with variable-envelope <a href="http://signals.In" target="_blank" title="signals.In">signals.In this presentation, we make an overview on block transmission techniques for broadband wireless communications, as well as current power amplification schemes, with their advantages and limitations when employed with variable-envelope signals. We also present an innovative and highly disruptive amplification scheme named quantized digital amplification (QDA), which can overcome those limitations. It is shown that the QDA allows a quasi-linear amplification of variable-envelope signals like OFDM ones, while maintaining very high energy efficiency, being able to fulfill the spectral masks and EVM (Error Vector Magnitude) requirements of the most demanding wireless systems, including OFDM-based MIMO systems employing large QAM constellations. The power efficiency gains of the QDA allow significant improvements in bit rates and coverage for wireless systems in <a href="http://general.Room:" target="_blank" title="general.Room:">general.Room: MCLD 3038, Bldg: Hector J. MacLeod Building - MCLD, 2356 Main Mall, Vancouver, BC V6T 1Z4, Vancouver, British Columbia, Canada

Zoom Link: <a href="https://ulaval.zoom.us/j/65778451409?pwd=B1j19PbbWPhyXWjxkTf9PjOfIekUCY.1Talk Abstract:Digital twins of the wireless environments offer new capabilities to the communication network design and operation. They could be utilized offline to build site-specific datasets for pre-training and evaluation machine learning models, or online to provide real-time or near real-time priors that aid the various communication system decisions on precoding, channel estimation, spectrum sharing, resource allocation, among many interesting applications. In this talk, I will present key aspects and considerations for modeling, building, calibrating, and utilizing these digital twins to maximize their gains while balancing constraints on cost, latency, and computational overhead. I will also introduce DeepVerse 6G, the world’s first large-scale digital-twin research platform, which provides high-fidelity multi-modal sensing and communication “true” digital twin datasets to accelerate research and development across a wide range of <a href="http://applications.Speaker" target="_blank" title="applications.Speaker">applications.Speaker Biography:Ahmed Alkhateeb received his B.S. and M.S. degrees in Electrical Engineering from Cairo University, Egypt, in 2008 and 2012, and his Ph.D. degree in Electrical and Computer Engineering from The University of Texas at Austin, USA, in 2016. After the Ph.D., he spent some time as a Wireless Communications Researcher at the Connectivity Lab, Facebook, before joining Arizona State University (ASU) in the Spring of 2018, where he is currently an Associate Professor in the School of Electrical, Computer, and Energy Engineering. His research interests are in the broad areas of wireless communications, signal processing, machine learning, and applied math. Dr. Alkhateeb is the recipient of the 2012 MCD Fellowship from The University of Texas at Austin, the 2016 IEEE Signal Processing Society Young Author Best Paper Award for his work on hybrid precoding and channel estimation in millimeter-wave communication systems, and the NSF CAREER Award 2021 to support his research on leveraging machine learning for large-scale MIMO <a href="http://systems.Meeting" target="_blank" title="systems.Meeting">systems.Meeting Link: https://ulaval.zoom.us/j/65778451409?pwd=B1j19PbbWPhyXWjxkTf9PjOfIekUCY.1, Québec City, Quebec, Canada, G1X 4C5