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

Date

2020-10-22

Author

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

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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.

URI

https://02336fea-f9bc-4ac5-9481-17869b5c8547.filesusr.com/ugd/e7502c_f1a3b33c54c34d09a2495edde12a6aa9.pdf
https://hdl.handle.net/11511/76587

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Citation Formats

C. Tüysüz et al., “Quantum Graph Neural Networks for Track Reconstruction in Particle Physics and Beyond,” 2020, Accessed: 00, 2021. [Online]. Available: https://02336fea-f9bc-4ac5-9481-17869b5c8547.filesusr.com/ugd/e7502c_f1a3b33c54c34d09a2495edde12a6aa9.pdf.