Muon/Pion Identification at BESIII based on Variational Quantum
  Classifier
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.
Zhipeng Yao Xingtao Huang Teng Li Weidong Li Tao Lin Jiaheng Zou