APS : Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC

Wu, Sau Lan; Chan, Jay; Zhang, Rui; Tavernelli, Ivano; Spentzouris, Panagiotis; Sun, Shaojun; Livny, Miron; Cheng, Chi Lung; Qian, Yan; Barkoutsos, Panagiotis Kl.; Wei, Tzu-Chieh; Lykken, Joseph; Yoo, Shinjae; Wang, Alex Zeng; Guan, Wen; Li, Andy C.Y.; Chen, Samuel Yen-Chi; Zhou, Chen; Pham, Tuan; Glick, Jennifer; Carminati, Federico; Di Meglio, Alberto; Woerner, Stefan

doi:10.1103/PhysRevResearch.3.033221

Article
Report number	arXiv:2104.05059 ; FERMILAB-PUB-21-552-DI-QIS
Title	Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
Author(s)	Wu, Sau Lan (Wisconsin U., Madison) ; Sun, Shaojun (Wisconsin U., Madison) ; Guan, Wen (Wisconsin U., Madison) ; Zhou, Chen (Wisconsin U., Madison) ; Chan, Jay (Wisconsin U., Madison) ; Cheng, Chi Lung (Wisconsin U., Madison) ; Pham, Tuan (Wisconsin U., Madison) ; Qian, Yan (Wisconsin U., Madison) ; Wang, Alex Zeng (Wisconsin U., Madison) ; Zhang, Rui (Wisconsin U., Madison) ; Livny, Miron (Wisconsin U., Madison) ; Glick, Jennifer (IBM Watson Res. Ctr.) ; Barkoutsos, Panagiotis Kl. (IBM, Zurich) ; Woerner, Stefan (IBM, Zurich) ; Tavernelli, Ivano (IBM, Zurich) ; Carminati, Federico (CERN) ; Di Meglio, Alberto (CERN) ; Li, Andy C.Y. (Fermilab) ; Lykken, Joseph (Fermilab) ; Spentzouris, Panagiotis (Fermilab) ; Chen, Samuel Yen-Chi (Brookhaven) ; Yoo, Shinjae (Brookhaven) ; Wei, Tzu-Chieh (YITP, Stony Brook)
Publication	2021-09-08
Imprint	2021-04-11
Number of pages	9
In:	Phys. Rev. Res. 3 (2021) 033221
DOI	10.1103/PhysRevResearch.3.033221 (publication)
Subject category	hep-ex ; Particle Physics - Experiment ; quant-ph ; General Theoretical Physics
Abstract	Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In this study, we employ a support vector machine with a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship physics analysis: $t\bar{t}H$ (Higgs boson production in association with a top quark pair). In our quantum simulation study using up to 20 qubits and up to 50000 events, the QSVM-Kernel method performs as well as its classical counterparts in three different platforms from Google Tensorflow Quantum, IBM Quantum and Amazon Braket. Additionally, using 15 qubits and 100 events, the application of the QSVM-Kernel method on the IBM superconducting quantum hardware approaches the performance of a noiseless quantum simulator. Our study confirms that the QSVM-Kernel method can use the large dimensionality of the quantum Hilbert space to replace the classical feature space in realistic physics datasets.
Copyright/License	preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2021-2024 authors (License: CC BY 4.0)

$Representative Feynman diagrams for (a) \ttH\ production, (b) \Hgamgam\ decay, and (c) non-resonant two-photon production. In these diagrams, $H$ denotes a Higgs boson, $g$ denotes a gluon, $q$ denotes a quark, $t$ denotes a top quark, $b$ denotes a bottom quark, $W$ denotes a W boson, and $\gamma$ denotes a photon.$ $Representative Feynman diagrams for (a) \ttH\ production, (b) \Hgamgam\ decay, and (c) non-resonant two-photon production. In these diagrams, $H$ denotes a Higgs boson, $g$ denotes a gluon, $q$ denotes a quark, $t$ denotes a top quark, $b$ denotes a bottom quark, $W$ denotes a W boson, and $\gamma$ denotes a photon.$ $Representative Feynman diagrams for (a) \ttH\ production, (b) \Hgamgam\ decay, and (c) non-resonant two-photon production. In these diagrams, $H$ denotes a Higgs boson, $g$ denotes a gluon, $q$ denotes a quark, $t$ denotes a top quark, $b$ denotes a bottom quark, $W$ denotes a W boson, and $\gamma$ denotes a photon.$ Show more plots

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