EPJ Web of Conferences (Jan 2024)

Zc(3900) observation at BESIII with QSVM method

  • Ding Biao,
  • Meng Zhaoxia,
  • Zou Jiaheng,
  • Li Teng,
  • Lin Tao

DOI
https://doi.org/10.1051/epjconf/202429512008
Journal volume & issue
Vol. 295
p. 12008

Abstract

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In recent years, quantum computing shows significant potentials in many areas. In this proceeding, we revisit the observation of the Zc(3900) resonance with quantum machine learning techniques, specifically quantum support vector machine (QSVM). Meanwhile, the outcomes are compared with classical support vector machine (SVM) method. With the IBM Qiskit toolkit, the QSVM method achieves a competitive signal and background classification accuracy compared to classical methods. This study emphasizes the potential of quantum machine learning in high-energy physics research, and it reveals the feasibility of applying quantum computing in future physics data analysis.