PoS - Proceedings of Science
Volume 364 - European Physical Society Conference on High Energy Physics (EPS-HEP2019) - Detector R&D and Data Handling
Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
C. Zhou*, J. Chan, W. Guan, S. Sun, A.Z. Wang, S.L. Wu, M. Livny, F. Carminati and A. Di Meglio
Full text: pdf
Pre-published on: October 09, 2020
Published on: November 12, 2020
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.
DOI: https://doi.org/10.22323/1.364.0116
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