Application of the Quantum Kernel Algorithm on the Particle Identification at the BESIII Experiment. Issue 1 (1st February 2023)

• Record Type: Journal Article
• Authors: Li, Teng; Yao, Zhipeng; Huang, Xingtao; Zou, Jiaheng; Lin, Tao; Li, Weidong
• Is Part Of: Journal of physics. Volume 2438:Issue 1(2023)
• Publication Date: 2023-02-01
• Subjects: Particle Identification -- Quantum Computing -- Quantum Machine Learning -- Quantum Support Vector Machine; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2438/1/012071
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Particle identification is one of most fundamental tools in various particle physics experiments. For the BESIII experiment on the BEPCII, the realization of numerous physical goals heavily relies on advanced particle identification algorithms. In recent years, the emerging of quantum machine learning could potentially arm particle physics experiments with a powerful new toolbox. In this work, targeting at the muon/pion discrimination problem at BESIII, we have developed a quantum SVM classifier for the Noisy Intermediate-Scale Quantum (NISQ) device. By studying and optimizing various encoding circuits, the quantum SVM trained with the BESIII MC data shows comparable discrimination power than other traditional machine learning models. This has demonstrated the potential of using quantum machine learning techniques to form a new approach for particle identification in particle physics experiments. In this talk, we present the application of the quantum SVM for particle identification at BESIII, and demonstrate how to construct and optimize the quantum kernel. Furthermore, we present results obtained from the noisy simulator as well as the small-scale hardware to show the potential advantage of the quantum SVM algorithm.