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Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

The ambitious HL-LHC program will require enormous computing resources in the next two decades. A burning question is whether quantum computer can solve the ever growing demand of computing resources in High Energy Physics in general and physics at LHC in particular.Using IBM Quantum Computer Simula...

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Detalles Bibliográficos
Autores principales: Chan, Jay, Guan, Wen, Sun, Shaojun, Wang, Alex, Wu, Sau Lan, Zhou, Chen, Livny, Miron, Carminati, Federico, Di Meglio, Alberto 
Lenguaje:eng
Publicado: SISSA 2020
Materias:
Acceso en línea:https://dx.doi.org/10.22323/1.364.0116
http://cds.cern.ch/record/2771012
Descripción
Sumario:The ambitious HL-LHC program will require enormous computing resources in the next two decades. A burning question is whether quantum computer can solve the ever growing demand of computing resources in High Energy Physics in general and physics at LHC in particular.Using IBM Quantum Computer Simulators and Quantum Computer Hardware, we have successfully employed the Quantum Support Vector Machine Method (QSVM) in applying quantum machine learning for a ttH (H to two photons), Higgs coupling to top quarks analysis at LHC.