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Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware / Wu, Sau Lan (Wisconsin U., Madison) ; Chan, Jay (Wisconsin U., Madison) ; Cheng, Alkaid (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Wang, Alex (Wisconsin U., Madison) ; Zhang, Rui (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Livny, Miron (U. Wisconsin, Madison (main)) ; Di Meglio, Alberto (CERN) et al.
Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. [...]
FERMILAB-CONF-22-331-DI-QIS.- 2022 - 8 p. - Published in : PoS EPS-HEP2021 (2022) 842 Fulltext: 9e81af30dcb178c482ac8c56f379040d - PDF; document - PDF; External link: Fermilab Library Server
In : European Physics Society conference on High Energy Physics 2021, Online, Online, 26 - 30 Jul 2021, pp.842

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