Speaker
Description
Understanding the substructure of jets is a fundamental challenge in high-energy physics due to its inherent complexity and multi-scale dynamics. While classical methods such as Monte Carlo simulation serve as powerful tools for reproducing the phenomenological properties of jets, such methods struggle to accurately capture the intricate correlations and stochastic processes governing jet formation and evolution. Quantum Generative Adversarial Networks (QGANs) offer a novel and complementary approach to this problem by leveraging the ability of quantum computing to model high-dimensional correlations and entanglement in a data-driven way. In this work, we employ a QGAN framework to model the kinematics of leading hadrons within jets. Our study investigates whether quantum machine learning can provide new insights into jet substructure modelling, particularly in regions where classical methods encounter limitations. The results demonstrate that QGANs can effectively capture key features of jet substructure, paving the way for quantum-assisted approaches to explore the mechanisms driving jet formation and evolution in high-energy physics.
Email Address of submitter
Yacine.haddad@cern.ch, contact@yacinehaddad.com
