论文标题
LHC处的量子聚类和喷气重建
Quantum clustering and jet reconstruction at the LHC
论文作者
论文摘要
聚类是许多领域中最常见的问题之一，尤其是在实验分析中，JET重建是核心的粒子物理学中。 CERN大型强子对撞机（LHC）的喷气聚集在计算上很昂贵，并且随着即将到来的高亮度LHC（HL-LHC）的难度将增加此任务的困难。在本文中，我们研究了量子计算算法可以通过考虑两种新型量子算法来改善可能加快经典喷气聚类算法的量子聚类的情况。第一个是一个量子子例程，用于计算两个数据点之间的基于Minkowski的距离，而第二个数据点是由量子电路组成的，将最大值跟踪到未分类数据列表中。后一种算法可能超出粒子物理学，例如在统计中。当将这些算法中的一种或两种实现为众所周知的聚类算法的经典版本（K-均值，亲和力传播和$ k_t $ -JET）时，我们会获得与其经典对应物相当的效率。更重要的是，当应用距离算法或最大搜索算法时，可以在前两种算法中实现指数加速。
Clustering is one of the most frequent problems in many domains, in particular, in particle physics where jet reconstruction is central in experimental analyses. Jet clustering at the CERN's Large Hadron Collider (LHC) is computationally expensive and the difficulty of this task will increase with the upcoming High-Luminosity LHC (HL-LHC). In this paper, we study the case in which quantum computing algorithms might improve jet clustering by considering two novel quantum algorithms which may speed up the classical jet clustering algorithms. The first one is a quantum subroutine to compute a Minkowski-based distance between two data points, whereas the second one consists of a quantum circuit to track the maximum into a list of unsorted data. The latter algorithm could be of value beyond particle physics, for instance in statistics. When one or both of these algorithms are implemented into the classical versions of well-known clustering algorithms (K-means, Affinity Propagation and $k_T$-jet) we obtain efficiencies comparable to those of their classical counterparts. Even more, exponential speed-up could be achieved, in the first two algorithms, in data dimensionality and data length when the distance algorithm or the maximum searching algorithm are applied.
