Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, greatly motivate the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, one can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their AUC scores, the quantum networks were shown to outperform the classical networks. However, seeing the computational advantage of the quantum networks in practice may have to wait for the further development of quantum technology and its associated APIs.

翻译：机器学习算法在理解欧洲核子研究中心大型强子对撞机（LHC）产生的高能粒子碰撞海量数据中发挥着关键作用。此类碰撞事件的数据可自然地以图结构表示。因此，深度几何方法（如图神经网络GNN）已被广泛用于高能物理中的各类数据分析任务。典型任务之一是喷注标记，即将喷注视为点云，其包含不同特征及组分粒子间的边连接关系。随着LHC粒子数据集规模与复杂度的提升，以及用于分析的计算模型日益庞大，亟需开发量子计算等新型高效计算范式。此外，为增强深度网络的有效性与鲁棒性，可利用数据中存在的根本对称性，通过引入不变输入与等变层实现。本文对经典图神经网络（GNN）与等变图神经网络（EGNN）及其量子对应模型——量子图神经网络（QGNN）与等变量子图神经网络（EQGNN）进行了公平全面的比较。四种架构在二元分类任务（识别引发喷注的部分子级粒子）上进行了基准测试。基于AUC得分，量子网络表现优于经典网络。然而，在实践中观察到量子网络的计算优势，仍需等待量子技术及其相关应用程序接口的进一步发展。