论文标题
量子机学习及其在高能量物理学中的至高无上
Quantum Machine Learning and its Supremacy in High Energy Physics
论文作者
论文摘要
本文揭示了高能物理学（HEP）中量子算法的未来前景。在实验性HEP中，了解其特性和特征是一个具有挑战性的问题。解决这些问题的关键技术是模式识别，这是机器学习的重要应用，无条件用于HEP问题。为了执行跟踪和顶点重建的模式识别任务，粒子物理社区大大使用了统计机器学习方法。这些方法因实验中使用的检测器几何形状和磁场而异。在本介绍性的文章中，我们为在HEP中的量子计算和量子机学习的清晰应用提供了未来的可能性，而不是关注该领域中的技术的深度数学结构。
This article reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector to detector geometry and magnetic field used in the experiment. Here in the present introductory article, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arise in this domain.
