The Foundations of AI Seminar Series is dedicated to topics of interest in artificial intelligence, machine learning, both empirically and theoretically, as well as related areas. Our goal is for these meetings to serve as a forum for discussions and quick dissemination of results. We invite anyone interested in the latest advancements in AI/ML to join us!
Speaker: Dr. Eric Anschuetz Date: 22-01-2026 11am-12pm (BST) Location: Department of Computer Science CS1.04, University of Warwick, Coventry, UK
Quantum computers use the principles of quantum mechanics to perform computations in a way fundamentally different from traditional computational methods. This new computing paradigm exhibits great promise in–among other things–performing linear algebra and sampling extremely efficiently, naturally making the development of new machine learning models with an eye toward quantum implementations an exciting prospect. In this talk, I will lay out the important differences between quantum and traditional machine learning models, share why much of our intuition from machine learning fails for these new models, and show that structure is a necessary ingredient for building efficiently-trainable quantum neural networks.
Eric is a Burke Fellow at Caltech who completed his PhD at MIT under the joint supervision of Aram Harrow and Misha Lukin. Much of his research involves studying the limitations of quantum algorithms through the lens of statistical physics and quantum foundations theory. These insights have led to the development of novel quantum algorithms for optimization and learning, and a new, physically-motivated approach to quantum computational complexity.
Jan 22, 2026 at 11:00AM 
Department of Computer Science CS1.04, University of Warwick, Coventry, UK 
TBD University of Warwick, Coventry, UK 
Assistant Professor, CS Department, University of Warwick
Assistant Professor, CS Department, University of Warwick
Professor, CS Department, University of Warwick