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Published Articles
Title	Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
Author(s)	Chan, Jay (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Wang, Alex (Wisconsin U., Madison) ; Wu, Sau Lan (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Livny, Miron (Wisconsin U., Madison) ; Carminati, Federico (CERN) ; Di Meglio, Alberto  (CERN)
Publication	SISSA, 2020
Number of pages	7
In:	PoS EPS-HEP2019 (2020) 116
In:	European Physical Society Conference on High Energy Physics (EPS-HEP) 2019, Ghent, Belgium, 10 - 17 Jul 2019, pp.116
DOI	10.22323/1.364.0116
Subject category	Detectors and Experimental Techniques ; Particle Physics - Experiment ; Computing and Computers
Abstract	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.
Copyright/License	© The Authors (License: CC-BY-NC-ND-4.0)

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