In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, consequently, greater complexity in reconstructing charged particle tracks, motivating frontier research in new technologies. Quantum machine learning models are being investigated as potential new approaches to high energy physics (HEP) tasks. We characterize and upgrade a quantum graph neural network (QGNN) architecture for charged particle track reconstruction on a simulated high luminosity dataset. The model operates on a set of event graphs, each built from the hits generated in tracking detector layers by particles produced in proton collisions, performing a classification of the possible hit connections between adjacent layers. In this approach the QGNN is designed as a hybrid architecture, interleaving classical feedforward networks with parametrized quantum circuits. We characterize the interplay between the classical and quantum components. We report on the principal upgrades to the original design, and present new evidence of improved training behavior, specifically in terms of convergence toward the final trained configuration.

翻译：在未来几年，LHC实验装置将进行升级以利用LHC瞬时亮度的大幅提升，这将导致更大、更密集的碰撞事件，进而使带电粒子径迹重建面临更复杂的挑战，从而推动前沿技术研究。量子机器学习模型正作为高能物理（HEP）任务的新型潜在方法被广泛研究。本文基于模拟高亮度数据集，对用于带电粒子径迹重建的量子图神经网络（QGNN）架构进行了特性分析与升级。该模型对一组事件图进行操作，每个事件图由质子碰撞产生的粒子在径迹探测器各层中生成的击中点构建而成，可对相邻层间可能的击中点连接进行分类。在此方法中，QGNN被设计为混合架构，将经典前馈网络与参数化量子电路交错组合。我们分析了经典与量子组件间的相互作用机制，报告了原始设计的主要升级方案，并提供了训练行为改善的新证据，特别是在向最终训练配置收敛方面的改进表现。