论文标题
成像大气中使用量子分类器的伽马 - 戴隆分离
Gamma-hadron Separation in Imaging Atmospheric Cherenkov Telescopes using Quantum Classifiers
论文作者
论文摘要
在本文中，我们在使用量子机学习中引入了一种新颖的方法，用于成像大气Cherenkov望远镜（IACT）。 IACTS捕获了由非常高的能量伽玛射线产生的广泛空气淋浴（EAS）的图像。我们使用图像参数使用了QML算法，量子支持向量分类器（QSVC）和变分量子分类器（VQC）将事件分为信号（GAMMA）和背景（HADRON）的二进制分类。魔术伽玛望远镜数据集用于该研究，该研究是由Monte Carlo Software Coriska生成的。这些量子算法达到的性能与标准多元分类技术相当，可用于解决各种现实世界中的问题。通过超级参数调整提高了分类精度。我们为在大型数据集上有效地使用QSVC提出了一种新的体系结构，发现聚类可以增强整体性能。
In this paper we have introduced a novel method for gamma hadron separation in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine Learning. IACTs captures images of Extensive Air Showers (EAS) produced from very high energy gamma rays. We have used the QML Algorithms, Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) for binary classification of the events into signals (Gamma) and background(hadron) using the image parameters. MAGIC Gamma Telescope dataset is used for this study which was generated from Monte Carlo Software Coriska. These quantum algorithms achieve performance comparable to standard multivariate classification techniques and can be used to solve variety of real-world problems. The classification accuracy is improved by hyper parameter tuning. We propose a new architecture for using QSVC efficiently on large datasets and found that clustering enhance the overall performance.
