粒子径迹重建的量子图神经网络方法
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
摘要：
在高亮度大型强子对撞机（HL-LHC）实验中，跟踪探测器的计算复杂度和数据规模将空前增加。虽然目前使用的基于卡尔曼滤波器的算法在来自同时碰撞、占用和可扩展性（比二次更差）的增加的数量的模糊性方面达到了它们的极限，但是探索了用于粒子轨道重建的各种机器学习方法。HEP.TrkX之前已经使用TrackML数据集证明，图形神经网络通过将事件处理为连接跟踪测量的图形，可以通过将组合背景减少到可管理的量并扩展到计算上合理的大小来提供有希望的解决方案。在之前的工作中，我们已经展示了量子计算首次尝试将神经网络图形化以进行粒子的轨迹重建。我们的目标是利用量子计算的能力，同时评估非常大量的状态，从而有效地搜索一个大的参数空间。作为本文的下一步，我们提出了一个改进的模型与迭代方法，以克服初始过度简化的树张量网络（TTN）模型的低精度收敛。
英文摘要：
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, by processing events as a graph connecting track measurements can provide a promising solution by reducing the combinatorial background to a manageable amount and are scaling to a computationally reasonable size. In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. We aim to leverage the capability of quantum computing to evaluate a very large number of states simultaneously and thus to effectively search a large parameter space. As the next step in this paper, we present an improved model with an iterative approach to overcome the low accuracy convergence of the initial oversimplified Tree Tensor Network (TTN) model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
