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：机器学习算法被广泛依赖，用于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数据。此类碰撞事件的数据自然可以用图结构表示。因此，深度几何方法（如图神经网络（GNNs））已被用于高能物理中的各种数据分析任务。其中一项典型任务是喷注标记，即将喷注重视为具有不同特征及其组成粒子间边缘连接的点云。LHC粒子数据集规模与复杂性的日益增长，以及用于分析这些数据的计算模型，极大地推动了替代性快速高效计算范式（如量子计算）的发展。此外，为增强深度网络的有效性和鲁棒性，可通过使用不变输入和等变层来利用数据中存在的基本对称性。本文对经典图神经网络（GNNs）和等变图神经网络（EGNNs）及其量子对应物——量子图神经网络（QGNNs）和等变量子图神经网络（EQGNN）进行了公平而全面的比较。四种架构在二元分类任务上进行基准测试，以分类引发喷注的部分子级粒子。基于AUC得分，量子网络的表现优于经典网络。然而，在实践中实现量子网络的计算优势可能仍需等待量子技术及其相关应用程序接口（APIs）的进一步发展。