用于高能物理中多喷注聚类的绝热量子算法
Adiabatic Quantum Algorithm for Multijet Clustering in High Energy Physics
AI总结：
针对高亮度大型强子对撞机喷注聚类计算需求高的问题，提出新型量子退火二值聚类算法，效率达约96%，优于现有量子最优方法，并可推广目标函数以处理多喷注聚类。
摘要：
当前预测显示，即将到来的高亮度大型强子对撞机（HL-LHC）的事件重建，尤其是喷注聚类，计算需求将不断增长，这势必会挑战现有的计算资源，使其成为一个更为复杂的组合问题。在本文中，我们提出了一种新颖的量子退火二值聚类算法，证明量子退火能够处理双喷注事件聚类。经基准测试，该算法效率约为96%，相比当前量子领域的最优水平有显著提升。此外，我们还展示了如何将所提出的目标函数推广为更通用的形式，使其能够解决多喷注事件中的聚类问题。
英文摘要：
The currently predicted increase in computational demand for the upcoming High-Luminosity Large Hadron Collider (HL-LHC) event reconstruction, and in particular jet clustering, is bound to challenge present day computing resources, becoming an even more complex combinatorial problem. In this paper, we show that quantum annealing can tackle dijet event clustering by introducing a novel quantum annealing binary clustering algorithm. The benchmarked efficiency is of the order of $96\%$, thus yielding substantial improvements over the current quantum state-of-the-art. Additionally, we also show how to generalize the proposed objective function into a more versatile form, capable of solving the clustering problem in multijet events.
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。
