Muon/Pion Identification at BESIII based on Variational Quantum Classifier

TL;DR

This paper explores the application of a variational quantum classifier for particle identification at BESIII, demonstrating comparable performance to classical neural networks and highlighting the potential of quantum machine learning in collider physics.

Contribution

It introduces a variational quantum classifier for muon/pion discrimination and compares its performance with classical neural networks, showing feasibility for quantum ML in collider experiments.

Findings

VQC achieves similar accuracy to neural networks on the dataset.

Quantum encoding circuits and ansatzes influence classifier performance.

Quantum-classical hybrid models can effectively optimize quantum classifiers.

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.