Performance of Particle Tracking Using a Quantum Graph Neural Network

Date

2020-12-02

Author

Tüysüz, Cenk
Novotny, Kristiane
Rieger, Carla
Carminati, Federico
Demirköz, Melahat Bilge
Dobos, Daniel
Fracas, Fabio
Potamianos, Karolos
Vallecorsa, Sofia
Vlimant, Jeanroch

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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.

URI

https://arxiv.org/pdf/2012.01379.pdf
https://hdl.handle.net/11511/81273
https://www.researchgate.net/publication/346578764_Performance_of_Particle_Tracking_Using_a_Quantum_Graph_Neural_Network

Conference Name

BAŞARIM 2020 - HIGH PERFORMANCE COMPUTING CONFERENCE, 8 - 09 Ekim 2020

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Department of Physics, Conference / Seminar

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Citation Formats

C. Tüysüz et al., “Performance of Particle Tracking Using a Quantum Graph Neural Network,” presented at the BAŞARIM 2020 - HIGH PERFORMANCE COMPUTING CONFERENCE, 8 - 09 Ekim 2020, Ankara, Türkiye, 2020, Accessed: 00, 2021. [Online]. Available: https://arxiv.org/pdf/2012.01379.pdf.