Workshop on Tensor Networks and (Quantum) Machine Learning for High-Energy Physics

Session

Applications of Tensor Networks (TN) and Quantum Machine Learning (QML) to High-Energy Physics

4 Nov 2024, 09:00

4/3-006 - TH Conference Room (CERN)

12. Welcome and introduction

Michelangelo Mangano (CERN)

04/11/2024, 10:00

2. Quantum Machine Learning Integration in the High Energy Physics Pipeline

Dr Michele Grossi (CERN)

04/11/2024, 10:05

The integration of Quantum Machine Learning (QML) into the High Energy Physics (HEP) pipeline represents a transformative approach to addressing computational challenges in the analysis of vast and complex datasets. This talk will explore the synergy between quantum computing and machine learning, demonstrating how QML algorithms can handle HEP data sets and where QML has been applied to HEP...

1. TNS and the simulation of Lattice Gauge Theories

Mari Carmen Bañuls

04/11/2024, 11:05

Tensor networks have demonstrated their suitability to describe equilibrium states of LGT in small spatial dimensions, where it has been possible to realize continuum limit extrapolations with record precision. And they are also a most adequate tool to design and benchmark quantum simulation protocols.
Real time evolution poses a more challenging scenario, which escapes the reach of the most...

3. Discussion: Utilizing QML Algorithms within Tensor Networks

04/11/2024, 12:05