CAREER: Efficient and Effective Quantum Program Optimization in the High-Dimensional Space

职业:高维空间中高效且有效的量子程序优化

基本信息

  • 批准号:
    2338773
  • 负责人:
  • 金额:
    $ 75.63万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-10-01 至 2029-09-30
  • 项目状态:
    未结题

项目摘要

Quantum computing holds immense potential for revolutionizing technology in many domains, such as physics, chemistry, material science, optimization, etc. Currently, quantum hardware advancements outpace the corresponding software capabilities, and this project aims to bridge the gap between quantum hardware capabilities and software compiler optimizations. Existing quantum compiler optimizations mostly only perform local small-scale transformations and a large optimization space remains untouched. This project aims to reshape the quantum compiler infrastructure with novel programming languages and optimization methods, which will systematically enable more effective and efficient large-scale program optimizations across high-dimensional quantum spaces and compiler support for more diverse quantum hardware platforms beyond traditional quantum bits. The project's interdisciplinary nature stimulates collaboration across various domains and its technical advancements lay the foundation for unlocking the full potential of quantum computing. This project also supports education initiatives and training opportunities for both academic and industrial participants, nurturing a skilled quantum computing workforce for societal advancement.This project will significantly enhance the performance of quantum computing software by greatly improving the capability, applicability, and reliability of quantum compiler optimizations. Specifically, this project takes a three-pronged approach. First, this project will pioneer quantum algorithmic optimizations with high-level intermediate representations and corresponding compilation algorithms, enabling comprehensive quantum program optimizations across extensive qubit and operation ranges, thus drastically improving compiler efficiency and program performance. Second, this project seeks to expand compiler support beyond qubit-based systems to encompass diverse quantum hardware paradigms like continuous-variable quantum computing and analog quantum simulators. These efforts will broaden the quantum compiler infrastructure's adaptability, unlocking the potential of diverse quantum hardware landscapes. Third, this project will develop innovative testing and verification tools that can ensure the accuracy and scalability of high-dimensional quantum program optimizations, fostering the creation of dependable quantum software. The success of this research agenda will greatly enhance quantum compiler infrastructure and contribute to the realization of more powerful quantum computing technologies and applications.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.
量子计算在物理、化学、材料科学、优化等许多领域具有巨大的技术革命潜力。目前,量子硬件的进步超过了相应的软件能力,该项目旨在弥合量子硬件能力和软件之间的差距编译器优化。现有的量子编译器优化大多仅执行局部小规模转换,而大的优化空间保持不变。该项目旨在通过新颖的编程语言和优化方法重塑量子编译器基础设施,这将系统地实现跨高维量子空间的更有效和高效的大规模程序优化,并为超越传统量子比特的更多样化的量子硬件平台提供编译器支持。该项目的跨学科性质促进了跨领域的合作,其技术进步为释放量子计算的全部潜力奠定了基础。该项目还为学术界和工业界参与者提供教育计划和培训机会,为社会进步培养一支熟练的量子计算人才队伍。该项目将通过大幅提高量子编译器的能力、适用性和可靠性来显着提高量子计算软件的性能优化。具体来说,该项目采取了三管齐下的方式。首先,该项目将开创量子算法优化的高级中间表示和相应的编译算法,实现跨广泛量子位和操作范围的全面量子程序优化,从而大幅提高编译器效率和程序性能。其次,该项目旨在将编译器支持扩展到基于量子位的系统之外,以涵盖不同的量子硬件范例,例如连续变量量子计算和模拟量子模拟器。这些努力将扩大量子编译器基础设施的适应性,释放不同量子硬件领域的潜力。第三,该项目将开发创新的测试和验证工具,确保高维量子程序优化的准确性和可扩展性,促进可靠量子软件的创建。该研究议程的成功将极大地增强量子编译器基础设施,并有助于实现更强大的量子计算技术和应用。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优势和更广泛的影响审查进行评估,被认为值得支持标准。

项目成果

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Gushu Li其他文献

GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
GNNAdvisor:用于 GPU 上 GNN 加速的自适应且高效的运行时系统
  • DOI:
    10.1112/blms/bdp090
  • 发表时间:
    2020-06-11
  • 期刊:
  • 影响因子:
    0.9
  • 作者:
    Yuke Wang;Boyuan Feng;Gushu Li;Shuangchen Li;Lei Deng;Yuan Xie;Yufei Ding
  • 通讯作者:
    Yufei Ding
Palleon: A Runtime System for Efficient Video Processing toward Dynamic Class Skew
Palleon:针对动态类偏差进行高效视频处理的运行时系统
GraphIA: an in-situ accelerator for large-scale graph processing
GraphIA:大规模图形处理的原位加速器
Software-Hardware Co-Optimization for Computational Chemistry on Superconducting Quantum Processors
超导量子处理器计算化学的软硬件协同优化
QASMTrans: A QASM Quantum Transpiler Framework for NISQ Devices
QASMTrans:适用于 NISQ 设备的 QASM 量子转译器框架
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Fei Hua;Meng Wang;Gushu Li;B. Peng;Chenxu Liu;Muqing Zheng;S. Stein;Yufei Ding;Eddy Z. Zhang;Travis Humble;Ang Li
  • 通讯作者:
    Ang Li

Gushu Li的其他文献

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