论文标题
使用量子图神经网络的粒子跟踪性能
Performance of Particle Tracking Using a Quantum Graph Neural Network
论文作者
论文摘要
欧洲核研究组织（CERN）的大型强子对撞机（LHC）将被升级，以进一步提高粒子碰撞（光度）的瞬时速率并成为高光度LHC。亮度的增加将产生更多的检测器命中（占用率），因此测量结果将对跟踪重建算法的挑战构成挑战，负责从这些命中中确定粒子轨迹。这项工作探讨了转换新的图形神经网络模型的可能性，该模型证明了轨道重建任务，将其证明为混合图神经网络，以使成倍增长的希尔伯特空间受益。测试了几个参数化的量子电路（PQC），并比较了它们针对经典方法的性能。我们表明，混合模型可以执行类似于经典方法。我们还提供了未来的路线图，以进一步提高当前混合模型的性能。
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in luminosity, will yield many more detector hits (occupancy), and thus measurements will pose a challenge to track reconstruction algorithms being responsible to determine particle trajectories from those hits. This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid Graph Neural Network in order to benefit the exponentially growing Hilbert Space. Several Parametrized Quantum Circuits (PQC) are tested and their performance against the classical approach is compared. We show that the hybrid model can perform similar to the classical approach. We also present a future road map to further increase the performance of the current hybrid model.
