Quantum Computing Applications in Future Colliders

被引:5
|
作者
Gray, Heather M. [1 ,2 ]
Terashi, Koji [3 ]
机构
[1] Univ Calif, Dept Phys, Berkeley, CA 94720 USA
[2] Lawrence Berkeley Natl Lab, Phys Div, Berkeley, CA 94720 USA
[3] Univ Tokyo, Int Ctr Elementary Particle Phys ICEPP, Tokyo, Japan
来源
FRONTIERS IN PHYSICS | 2022年 / 10卷
关键词
quantum computing; future collider experiments; quantum machine learning; quantum annealers; digital quantum computer; pattern recognition; TRACK;
D O I
10.3389/fphy.2022.864823
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
High-energy physics is facing a daunting computing challenge with the large amount of data expected from the HL-LHC and other future colliders. In addition, the landscape of computation has been expanding dramatically with technologies beyond the standard x86 CPU architecture becoming increasingly available. Both of these factors necessitate an extensive and broad-ranging research and development campaign. As quantum computation has been evolving rapidly over the past few years, it is important to evaluate how quantum computation could be one potential avenue for development for future collider experiments. A wide variety of applications have been considered by different authors. We review here selected applications of quantum computing to high-energy physics, including topics in simulation, reconstruction, and the use of machine learning, and their challenges. In addition, recent advances in quantum computing technology to enhance such applications are briefly highlighted. Finally, we will discuss how such applications might transform the workflows of future collider experiments and highlight other potential applications.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] On the impact of quantum computing technology on future developments in high-performance scientific computing
    Moller, Matthias
    Vuik, Cornelis
    ETHICS AND INFORMATION TECHNOLOGY, 2017, 19 (04) : 253 - 269
  • [32] On the impact of quantum computing technology on future developments in high-performance scientific computing
    Matthias Möller
    Cornelis Vuik
    Ethics and Information Technology, 2017, 19 : 253 - 269
  • [33] A review on quantum computing and deep learning algorithms and their applications
    Fevrier Valdez
    Patricia Melin
    Soft Computing, 2023, 27 : 13217 - 13236
  • [34] A review on quantum computing and deep learning algorithms and their applications
    Valdez, Fevrier
    Melin, Patricia
    SOFT COMPUTING, 2023, 27 (18) : 13217 - 13236
  • [35] Cryo-CMOS Electronics for Quantum Computing Applications
    Charbon, Edoardo
    49TH EUROPEAN SOLID-STATE DEVICE RESEARCH CONFERENCE (ESSDERC 2019), 2019, : 1 - 6
  • [36] Hybrid Quantum-Classical Computing for Future Network Optimization
    Fan, Lei
    Han, Zhu
    IEEE NETWORK, 2022, 36 (05): : 72 - 76
  • [37] Shaping the future of the application of quantum computing in intelligent transportation system
    Wang S.
    Pei Z.
    Wang C.
    Wu J.
    Intelligent and Converged Networks, 2021, 2 (04): : 259 - 276
  • [38] Planar Optical Quantum Computing: Current Status and Future Challenges
    Cincotti, Gabriella
    ICTON: 2009 11TH INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS, VOLS 1 AND 2, 2009, : 374 - 377
  • [39] Role of quantum computing in shaping the future of 6 G technology
    Akbar, Muhammad Azeem
    Khan, Arif Ali
    Hyrynsalmi, Sami
    INFORMATION AND SOFTWARE TECHNOLOGY, 2024, 170
  • [40] Quantum Computing for Finance: State-of-the-Art and Future Prospects
    Egger D.J.
    Gambella C.
    Marecek J.
    McFaddin S.
    Mevissen M.
    Raymond R.
    Simonetto A.
    Woerner S.
    Yndurain E.
    Simonetto, Andrea (andrea.simonetto@ibm.com), 1600, Institute of Electrical and Electronics Engineers Inc. (01):