Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC

2021/04/30 by Sau Lan Wu, Shaojun Sun, Wen Guan +20 · 86 citations
Computer Science · Physics and Astronomy · #High-Energy Particle Collisions Research #Kernel (algebra) #Kernel method #Large Hadron Collider #Particle physics theoretical and experimental studies #Quantum #Quantum Computing Algorithms and Architecture #Quantum algorithm #Quantum computer #Quantum information #Quantum machine learning #Quantum technology #Qubit #hep-ex #quant-ph

paper · pdf · doi:10.1103/physrevresearch.3.033221

published in Physical Review Research 3(3) (American Physical Society)

openalex created_date 2021/04/26 · openalex publication_date 2021/09/08 · arxiv created 2021/09/09 · arxiv updated 2021/09/10 · openalex updated_date 2026/08/05

Abstract

Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in high energy physics by offering computational speedups. In this study, we employ a support vector machine with a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship physics analysis: ttH (Higgs boson production in association with a top quark pair). In our quantum simulation study using up to 20 qubits and up to 50\phantom\rule0.16em0ex000 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 data sets.

Citations

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• Stochastic gradient boosting
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