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CaloQVAE:使用混合量子-经典生成模型模拟高能粒子量热计相互作用。

CaloQVAE: Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models.

作者信息

Hoque Sehmimul, Jia Hao, Abhishek Abhishek, Fadaie Mojde, Toledo-Marín J Quetzalcoatl, Vale Tiago, Melko Roger G, Swiatlowski Maximilian, Fedorko Wojciech T

机构信息

Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5 Canada.

Faculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1 Canada.

出版信息

Eur Phys J C Part Fields. 2024;84(12):1244. doi: 10.1140/epjc/s10052-024-13576-x. Epub 2024 Dec 2.

DOI:10.1140/epjc/s10052-024-13576-x
PMID:39634031
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11611925/
Abstract

The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy particles propagating through the calorimeter section of the detector is the most computationally intensive MC simulation task. We introduce a technique combining recent advancements in generative models and quantum annealing for fast and efficient simulation of high-energy particle-calorimeter interactions.

摘要

大型强子对撞机的高亮度时代在碰撞事件分析中带来了重大计算挑战。需要大量的蒙特卡罗(MC)模拟来将模拟数据集的统计不确定性限制在实验数据之下。对高能粒子在探测器量热计部分传播进行建模是计算量最大的MC模拟任务。我们引入了一种结合生成模型和量子退火最新进展的技术,用于快速有效地模拟高能粒子与量热计的相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1232/11611925/8357f2ea1b39/10052_2024_13576_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1232/11611925/60a8d5e4719a/10052_2024_13576_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1232/11611925/c03f33b6a015/10052_2024_13576_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1232/11611925/8357f2ea1b39/10052_2024_13576_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1232/11611925/60a8d5e4719a/10052_2024_13576_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1232/11611925/c03f33b6a015/10052_2024_13576_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1232/11611925/8357f2ea1b39/10052_2024_13576_Fig3_HTML.jpg

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本文引用的文献

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Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters.利用生成对抗网络加速科学研究:在多层量热器中的 3D 粒子簇射中的应用。
Phys Rev Lett. 2018 Jan 26;120(4):042003. doi: 10.1103/PhysRevLett.120.042003.
2
Determination and correction of persistent biases in quantum annealers.量子退火器中持续偏差的确定与校正。
Sci Rep. 2016 Jan 19;6:18628. doi: 10.1038/srep18628.