Quantum Track Reconstruction Algorithms for non-HEP applications

Kristiane Novotny, Cenk Tüysüz, C. Rieger, D. Dobos, K. Potamianos, B. Demirkoz, F. Carminati, S. Vallecorsa, J. Vlimant, Fabio Fracas
{"title":"Quantum Track Reconstruction Algorithms for non-HEP applications","authors":"Kristiane Novotny, Cenk Tüysüz, C. Rieger, D. Dobos, K. Potamianos, B. Demirkoz, F. Carminati, S. Vallecorsa, J. Vlimant, Fabio Fracas","doi":"10.3929/ETHZ-B-000484722","DOIUrl":null,"url":null,"abstract":"The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. High occupancy, track density, complexity and fast growth therefore exponentially increase the demand of algorithms in terms of time, memory and computing resources. While traditionally Kalman filter (or even simpler algorithms) are used, they are expected to scale worse than quadratic and thus strongly increasing the total processing time. Graph Neural Networks (GNN) are currently explored for HEP, but also non HEP trajectory reconstruction applications. Quantum Computers with their feature of evaluating a very large number of states simultaneously are therefore good candidates for such complex searches in large parameter and graph spaces. In this paper we present our work on implementing a quantum-based graph tracking machine learning algorithm to evaluate Traffic collision avoidance system (TCAS) probabilities of commercial flights.","PeriodicalId":20428,"journal":{"name":"Proceedings of 40th International Conference on High Energy physics — PoS(ICHEP2020)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 40th International Conference on High Energy physics — PoS(ICHEP2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3929/ETHZ-B-000484722","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. High occupancy, track density, complexity and fast growth therefore exponentially increase the demand of algorithms in terms of time, memory and computing resources. While traditionally Kalman filter (or even simpler algorithms) are used, they are expected to scale worse than quadratic and thus strongly increasing the total processing time. Graph Neural Networks (GNN) are currently explored for HEP, but also non HEP trajectory reconstruction applications. Quantum Computers with their feature of evaluating a very large number of states simultaneously are therefore good candidates for such complex searches in large parameter and graph spaces. In this paper we present our work on implementing a quantum-based graph tracking machine learning algorithm to evaluate Traffic collision avoidance system (TCAS) probabilities of commercial flights.
非hep应用的量子航迹重建算法
同时碰撞的预期增加给高亮度LHC实验中精确的粒子轨迹重建带来了挑战。在非hep轨迹重建用例中也可以看到类似的挑战,其中使用了跟踪和跟踪评估算法。因此,高占用率、轨道密度、复杂性和快速增长使得算法在时间、内存和计算资源方面的需求呈指数级增长。虽然使用传统的卡尔曼滤波(或更简单的算法),但它们的缩放预期比二次型更差,因此大大增加了总处理时间。图神经网络(GNN)目前正在探索用于HEP和非HEP的轨迹重建应用。量子计算机具有同时评估大量状态的特性,因此是在大参数和图空间中进行复杂搜索的良好候选者。在本文中,我们介绍了我们在实现基于量子的图跟踪机器学习算法来评估商业航班的交通碰撞避免系统(TCAS)概率方面的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信