AI for Quantum Mechanics: High Performance Quantum Many-Body Simulations via Deep Learning

Xuncheng ZhaoMingfan LiQian XiaoJunshi ChenFei WangLi ShenMeijia ZhaoWenhao Wu

量子计算方向论文：AI for Quantum Mechanics: High Performance Quantum Many-Body Simulations

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内容导读Reading note

分类与摘要Category & abstract

围绕量子计算实验与系统能力展开，适合作为量子方向的代表性成果入口。

Metadata

完整论文信息Full record

title: AI for Quantum Mechanics: High Performance Quantum Many-Body Simulations via Deep Learning
title_zh: 待补充
abstract: 待补充
abstract_zh: 待补充
keywords: Quantum, 学位认定 A, CCF A
year: 2022
published_date: 待补充
online_date: 待补充
paper_type: Conference
publication_status: Published
volume: 待补充
issue: 待补充
pages: 待补充
article_number: 待补充
publisher: 待补充
doi: 10.1109/sc41404.2022.00053
research_area: 量子计算
tags: Quantum, 学位认定 A, CCF A
category: 量子计算
summary: 围绕量子计算实验与系统能力展开，适合作为量子方向的代表性成果入口。
authors: Xuncheng Zhao, Mingfan Li, Qian Xiao, Junshi Chen, Fei Wang, Li Shen, Meijia Zhao, Wenhao Wu
corresponding_authors: 待补充
affiliations: 待补充
funding: 待补充

Research area

量子计算

Citation

Xuncheng Zhao, Mingfan Li, Qian Xiao, Junshi Chen, Fei Wang, Li Shen, Meijia Zhao, Wenhao Wu, Hong An, Lixin He, Xiao Liang，AI for Quantum Mechanics: High Performance Quantum Many-Body Simulations via Deep Learning，In the Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis，Dallas US, November 13–18, 2022. (CCF A)

BibTeX

@article{acsa2022_60,
  title = {AI for Quantum Mechanics: High Performance Quantum Many-Body Simulations via Deep Learning},
  year = {2022},
  doi = {10.1109/sc41404.2022.00053}
}