Fabio A. GONZÁLEZ - Université Nationale de la Colombie (UNAL) (Colombie) will give a seminar on “Beyond quantum computing: what quantum mechanics could bring to machine learning”

Thursday 16 April 2026, 11h00 – 12h15, UT Site Rangueil, IRIT, Auditorium J. Herbrand

Asbtract: This talk discusses the intersection of quantum mechanics (QM) and machine learning (ML), exploring the potential contributions of QM to enhance ML algorithms. With the rapid development of quantum computing (QC) in recent years, ML has emerged as a promising application that can benefit from QC's potential accelerated capabilities. Additionally, QM provides a robust mathematical foundation that offers a set of tools to model both classical and quantum probability distributions, which can serve as a novel basis for probabilistic deep learning. A concrete illustration of this idea is the framework of kernel density matrices (KDMs), which extends the quantum mechanical density matrix formalism to reproducing kernel Hilbert spaces, yielding a differentiable and unified representation of probability distributions that integrates naturally into deep neural architectures and supports a wide range of learning tasks. The talk highlights these and other promising avenues for incorporating QM principles into ML and the opportunities they present for the future of the field.

Short-bio: Fabio A. González is a full professor in the Department of Systems and Industrial Engineering at the National University of Colombia, where he directs the Machine Learning, Perception and Discovery Lab (MindLab). He holds a Bachelor's degree in Systems Engineering and a Master's degree in Mathematics from the National University of Colombia, as well as a Master's degree and a PhD in Computer Science from the University of Memphis in the United States. His research focuses on artificial intelligence, machine learning, and quantum computing, with a particular emphasis on the representation, indexing, and automatic analysis of multimodal data and, more recently, on algorithms and applications of quantum machine learning.