Cargando…
Performance of Particle Tracking Using a Quantum Graph Neural Network
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), an...
Autores principales: | Tüysüz, Cenk, Novotny, Kristiane, Rieger, Carla, Carminati, Federico, Demirköz, Bilge, Dobos, Daniel, Fracas, Fabio, Potamianos, Karolos, Vallecorsa, Sofia, Vlimant, Jean-Roch |
---|---|
Lenguaje: | eng |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2863873 |
Ejemplares similares
-
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
por: Tüysüz, Cenk, et al.
Publicado: (2020) -
Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
por: Tüysüz, Cenk, et al.
Publicado: (2021) -
Particle Track Reconstruction with Quantum Algorithms
por: Tüysüz, Cenk, et al.
Publicado: (2020) -
Quantum Track Reconstruction Algorithms for non-HEP applications
por: Novotny, Kristiane Sylvia, et al.
Publicado: (2021) -
Embedding of particle tracking data using hybrid quantum-classical neural networks
por: Rieger, Carla, et al.
Publicado: (2021)