Tensor Networks Machine Learning for High-Energy Physics

by Alberto Coppi

Thursday 2 Apr 2026, 17:00 → 17:15 Europe/Rome

1/1-3 - Aula B (Dipartimento di Fisica e Astronomia - Edificio Marzolo)

Tensor Networks (TN), originally developed in the context of quantum many-body physics, have recently emerged as powerful and interpretable Machine Learning (ML) architectures. The objective of this talk is to provide an intuitive and practical perspective on TNML methods, starting from the seminal work that initially established a connection between TNs and supervised learning. From this introduction, methods for optimization are discussed, presenting intuitive ideas to improve convergence. Finally, an application in High Energy Physics is showcased. In this context, we compare TNML against classical ML in the jet tagging task for fast inference at the trigger level. We show that unique characteristics of TNs, such as computational efficiency and interpretability, play a crucial role.