Muon/Pion Identification at BESIII based on Variational Quantum Classifier

Zhipeng Yao, Xingtao Huang, Teng Li, Weidong Li, Tao Lin, Jiaheng Zou
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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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基于变分量子分类器的 BESIII μ介子/离子识别
在对撞机物理实验中,粒子识别(PID),即识别探测器中的带电粒子种类通常是数据分析中最关键的工具之一。近十年来,机器学习技术逐渐成为粒子识别的主流方法之一,与经典算法相比,机器学习技术通常具有更强的识别能力。近年来,量子机器学习(QML)在传统机器学习和量子计算技术之间架起了一座桥梁,为传统机器学习模型提供了进一步改进的潜力。在这项工作中,针对$\mu^{\pm} /\pi^{\pm/pi^\{pm}$的辨别问题,我们开发了一种具有九个量子比特的变分量子分类器(VQC)。利用 IBM 量子模拟器,我们研究了各种编码电路和变分算法,以探索它们的性能。经典优化器能够有效地最小化量子经典混合模型中的损失函数。我们将 VQC 与传统的多层感知神经网络进行了比较,发现它们在相同数据集上的表现相似,这说明未来将量子机器学习应用于对撞机物理实验的数据分析是可行的。
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