论文标题
$ b $ -JET收费标识的量子机业学习
Quantum Machine Learning for $b$-jet charge identification
论文作者
论文摘要
机器学习算法在HADRONIC JET分类问题中发挥了重要作用。应用于大型强子撞机数据的各种模型表明，仍然有改进的余地。在这种情况下，Quantum机器学习是一种新的且几乎没有开发的方法，其中量子计算的内在属性可用于利用粒子相关性以改善喷射分类性能。在本文中，我们提出了一种全新的方法，以确定生产时在生产时在生产时由$ b $或$ \ bar {b} $ quark形成的强子，基于应用于LHCB实验的模拟数据的变异量子分类器。使用LHCB模拟对量子模型进行训练和评估。将JET识别性能与深神网络模型进行比较，以评估哪种方法具有更好的性能。
Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context Quantum Machine Learning is a new and almost unexplored methodology, where the intrinsic properties of quantum computation could be used to exploit particles correlations for improving the jet classification performance. In this paper, we present a brand new approach to identify if a jet contains a hadron formed by a $b$ or $\bar{b}$ quark at the moment of production, based on a Variational Quantum Classifier applied to simulated data of the LHCb experiment. Quantum models are trained and evaluated using LHCb simulation. The jet identification performance is compared with a Deep Neural Network model to assess which method gives the better performance.
