Abstract
In collider physics experiments, particle identification (PID), i.e., the identification of the charged particle species in the detector is usually one of the most crucial tools in data analysis. In the past decade, machine learning techniques have gradually become one of the mainstream methods in PID, usually providing superior discrimination power compared to classical algorithms. In recent years, quantum machine learning (QML) has bridged the traditional machine learning and the quantum computing techniques, providing further improvement potential for traditional machine learning models. In this work, targeting at the \(\mu ^{\pm } /\pi ^{\pm }\) discrimination problem at the BESIII experiment, we developed a variational quantum classifier (VQC) with nine qubits. Using the IBM quantum simulator, we studied various encoding circuits and variational ansatzes to explore their performance. Classical optimizers are able to minimize the loss function in quantum-classical hybrid models effectively. A comparison of VQC with the traditional multiple layer perception neural network reveals they perform similarly on the same datasets. This illustrates the feasibility to apply quantum machine learning to data analysis in collider physics experiments in the future.
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The data used in the paper is within the BESIII collaboration, which is not available in public repositories. However, all data used in the paper can be made available from the corresponding author on reasonable request.
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This work was supported by National Natural Science Foundation of China (NSFC) under Contracts Nos. 12025502, 12105158, 12188102.
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Yao, Z., Huang, X., Li, T. et al. Muon/pion identification at BESIII based on variational quantum classifier. Eur. Phys. J. Plus 139, 356 (2024). https://doi.org/10.1140/epjp/s13360-024-05144-9
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DOI: https://doi.org/10.1140/epjp/s13360-024-05144-9