Performance of Particle Tracking Using a Quantum Graph Neural Network

• URL: http://arxiv.org/abs/2012.01379v2
• Date: Mon, 25 Jan 2021 10:24:04 GMT
• Title: Performance of Particle Tracking Using a Quantum Graph Neural Network
• Authors: Cenk T\"uys\"uz, Kristiane Novotny, Carla Rieger, Federico Carminati, Bilge Demirk\"oz, Daniel Dobos, Fabio Fracas, Karolos Potamianos, Sofia Vallecorsa, Jean-Roch Vlimant
• Abstract summary: The Large Hadron Collider (LHC) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid Graph Neural Network in order to benefit the exponentially growing Hilbert Space.
• Score: 1.0480625205078853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded 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. This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid Graph Neural Network in order to benefit the exponentially growing Hilbert Space. Several Parametrized Quantum Circuits (PQC) are tested and their performance against the classical approach is compared. We show that the hybrid model can perform similar to the classical approach. We also present a future road map to further increase the performance of the current hybrid model.

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