| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 12008 | |
| Number of page(s) | 4 | |
| Section | Quantum Computing | |
| DOI | https://doi.org/10.1051/epjconf/202429512008 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429512008
Zc(3900) observation at BESIII with QSVM method
1 University of Jinan
2 Institute of High Energy Physics
3 Shandong University
* e-mail: dingb@ihep.ac.cn
Published online: 6 May 2024
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.
© The Authors, published by EDP Sciences, 2024
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