粒子轨迹重建的量子算法
Particle Track Reconstruction with Quantum Algorithms
摘要：
高亮度大型强子对撞机（HL-LHC）实验中粒子径迹重建参数的精确确定是一个重大挑战。预计在HL-LHC的同时碰撞的数量增加和由此产生的高探测器占用率将使轨道重建算法在时间和计算资源方面的要求非常高。命中次数的增加将增加航迹重建算法的复杂度。此外，由于探测器的有限分辨率和命中的物理接近性，将命中分配给粒子轨迹的模糊性将增加。因此，带电粒子径迹的重建将是正确解释HL-LHC数据的一个重大挑战。目前使用的大多数方法都是基于卡尔曼滤波器，这被证明是强大的，并提供良好的物理性能。然而，预计它们的扩展性比二次扩展差。设计一种能够在命中水平上减少组合背景的算法，将为卡尔曼滤波器提供更干净的初始种子，大大减少总处理时间。量子计算机的一个显著特征是能够同时评估大量的状态，这使它们成为在大参数空间中搜索的理想工具。事实上，不同的研发计划正在探索量子跟踪算法如何利用这些功能。在本文中，我们提出了我们的工作，实现了一个基于量子的跟踪查找算法，旨在减少组合背景在初始播种阶段。我们使用为kaggle TrackML挑战设计的公开数据集。
英文摘要：
Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical closeness of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much cleaner initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R\&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge.
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