Quantum Graph Neural Networks for Track Reconstruction in Particle Physics and Beyond

Cenk Tüysüz, Bilge Melahat Demirköz, D. Dobos, Kristiane Novotny, Karolos Jozef Potamianos, Federico Carminati, Sofia Vallecorsa, Fabio Fracas, Jean-Roch Vlimant

Published Oct 22, 2020• 0 citations

OpenAlex

Abstract

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will undergo an upgrade to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in luminosity, will yield many more detector hits (occupancy), and thus measurements will pose a challenge to track reconstruction algorithms being responsible to determine particle trajectories from those hits. Similar challenges exist in non-high energy physics (HEP) trajectory reconstruction use-cases. High occupancy, track density, complexity and fast growth exponentially increase the demand of algorithms in terms of time, memory and computing resources.

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