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CAREER: FET: A Top-down Compilation Infrastructure for Optimization and Debugging in the Noisy Intermediate Scale Quantum (NISQ) era

CAREER: FET: A Top-down Compilation Infrastructure for Optimization and Debugging in the Noisy Intermediate Scale Quantum (NISQ) era
职业:FET:用于噪声中级量子 (NISQ) 时代优化和调试的自上而下的编译基础设施
批准号:
2048144
负责人:
Yufei Ding
金额:
$49.95万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-12-15 至 2024-03-31

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中文摘要
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英文摘要
This project sets a comprehensive research agenda focusing on the construction of a top-down compilation infrastructure for quantum computing. It will develop theoretical and practical methodologies for enabling high-level algorithmic optimization, low-level pulse optimizations, as well as whole-system debugging and analysis. The compilation stack would facilitate automatic resource-efficient quantum algorithm designs and high-fidelity program executions on near-term quantum devices. It will benefit major quantum computing applications such as quantum chemistry and combinatorial optimization and contribute to the leadership of the United States in quantum information science and technology. The research findings will be widely disseminated through open-source software packages, publications in premier conferences and journals, tutorials at teaching workshops, as well as specialized K-12 programs for exposing the young generation to the frontiers of quantum information research.The project will integrate methods from the classical computing fields such as code generation and optimization, programming language design and implementation, and compilation techniques, to address the unique research challenges in quantum programming, optimization, and debugging. Specific targets include 1) A language and an autotuning compiler to automate resource-efficient algorithm development in the quantum computing domain; 2) A pulse-level compilation infrastructure to exploit the abundant freedom in pulse variation and scheduling towards more effective optimization and error mitigation; 3) Advanced testing and debugging support via systematic projection-based runtime assertion and invariant generation for quantum programming. The success of this research agenda will enable a more complete and efficient software stack for improving the feasibility of supporting end-to-end quantum applications on near-term devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(6)
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会议论文
DOI: 10.1145/3579371.3589043
发表时间: 2022-07
期刊: Proceedings of the 50th Annual International Symposium on Computer Architecture
影响因子: --
作者: [S. Stein;N. Wiebe;Yufei Ding;James Ang;A. Li]
通讯作者: S. Stein;N. Wiebe;Yufei Ding;James Ang;A. Li
DOI: 10.1109/micro56248.2022.00074
发表时间: 2022-07
期刊: 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)
影响因子: --
作者: [Anbang Wu;Hezi Zhang;Gushu Li;A. Shabani;Yuan Xie;Yufei Ding]
通讯作者: Anbang Wu;Hezi Zhang;Gushu Li;A. Shabani;Yuan Xie;Yufei Ding
A synthesis framework for stitching surface code with superconducting quantum devices
一种用超导量子器件拼接表面代码的综合框架
DOI: 10.1145/3470496.3527381
发表时间: 2022
期刊: Proceedings of the 49th Annual International Symposium on Computer Architecture
影响因子: --
作者: [Wu, Anbang, Li, Gushu, Zhang, Hezi, Guerreschi, Gian Giacomo, Ding, Yufei, Xie, Yuan]
通讯作者: Xie, Yuan
DOI: 10.1145/3579371.3589047
发表时间: 2022-09
期刊: Proceedings of the 50th Annual International Symposium on Computer Architecture
影响因子: --
作者: [Hezi Zhang;Anbang Wu;Yuke Wang;Gushu Li;Hassan Shapourian;A. Shabani;Yufei Ding]
通讯作者: Hezi Zhang;Anbang Wu;Yuke Wang;Gushu Li;Hassan Shapourian;A. Shabani;Yufei Ding
CAREER: FET: A Top-down Compilation Infrastructure for Optimization and Debugging in the Noisy Intermediate Scale Quantum (NISQ) era
  • 批准号:
    2421059
  • 项目类别:
    Continuing Grant
  • 资助金额:
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  • 财政年份:
    2024
  • 负责人:
    Yufei Ding
  • 依托单位:
FET: NSF Workshop on Software-Hardware Co-design for Quantum Computing
SHF:SMALL: MECAR: Memory-Centric Architecture to Bridge the Gap Between Computing and Memory
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