CAREER: On the Foundations of End-to-End Quantum Applications

职业：端到端量子应用的基础

• 批准号: 1942837
• 负责人: Xiaodi Wu
• 金额: $ 50.93万
• 依托单位: University of Maryland, College Park
• 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
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942837&HistoricalAwards=false
• 关键词: CAREER; Foundations; End; Quantum; Applications

This project sets a comprehensive research agenda focusing on the foundation of the design and implementation of end-to-end quantum applications, as an effort to bridge the gap between the theoretical foundation of quantum computing and the limitation of realistic quantum machines. In particular, it aims to make fundamental contributions to research in quantum algorithms, quantum machine learning, and quantum software foundation. This project will help facilitate end-to-end quantum applications in all related fields, such as physics, chemistry, and material science, and hence contribute to the establishment of major milestones of quantum computing. The results obtained will be disseminated through a variety of venues, including conferences, new course materials, expository writings, and high school open days aimed at exposing young computer scientists to the frontiers of quantum information research.The PI will integrate methods from the fields of theoretical computer science, (theoretical) machine learning, formal methods and programming languages to address the unique research challenges in achieving end-to-end quantum applications. In particular, this project includes (1) designing quantum algorithms for sub-modular optimization and log-concave sampling, (2) developing new functionality for variational quantum methods (or the so-called quantum neural networks) and their training methods, (3) leveraging techniques from formal methods and programming languages to certify the correctness of the implementation of quantum applications as well as to obtain concrete (rather than asymptotic) resource/error analysis. This research agenda will enable a more complete assessment of end-to-end quantum applications and their near-term feasibility.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.

该项目设置了一个全面的研究议程，重点是端到端量子应用的设计和实现的基础，努力弥合量子计算的理论基础和现实量子机器的限制之间的差距。 特别是，它旨在为量子算法，量子机器学习和量子软件基础的研究做出基础性贡献。 该项目将有助于促进物理、化学和材料科学等所有相关领域的端到端量子应用，从而有助于建立量子计算的重要里程碑。 所获得的成果将通过各种渠道传播，包括会议、新课程材料、临时著作和高中开放日，旨在使年轻的计算机科学家接触量子信息研究的前沿。PI将整合理论计算机科学领域的方法，（理论）机器学习，形式化方法和编程语言，以解决实现端到端量子应用的独特研究挑战。 特别是，这个项目包括（1）设计子模优化和对数凹采样的量子算法，（2）开发变分量子方法的新功能（或所谓的量子神经网络）及其训练方法，（3）利用形式方法和编程语言的技术来证明量子应用实现的正确性，并获得具体的（而不是渐近）资源/误差分析。 该研究议程将使端到端量子应用及其近期可行性的评估更加全面。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

ICCAD Special Session Paper: Quantum Variational Methods for Quantum Applications

ICCAD 特别会议论文：量子应用的量子变分方法

• DOI: 10.1109/iccad51958.2021.9643519
• 发表时间: 2021
• 期刊: 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD
• 作者: Chakrabarti, Shouvanik;You, Xuchen;Wu, Xiaodi
• 通讯作者: Wu, Xiaodi

Differentiable Analog Quantum Computing for Optimization and Control

• DOI: 10.48550/arxiv.2210.15812
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Jiaqi Leng;Yuxiang Peng;Yi-Ling Qiao;Ming-Chyuan Lin;Xiaodi Wu

A Verified Optimizer for Quantum Circuits

• DOI: 10.1145/3434318
• 发表时间: 2021-01-01
• 期刊: PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL
• 影响因子: 1.8
• 作者: Hietala, Kesha;Rand, Robert;Hicks, Michael
• 通讯作者: Hicks, Michael

Fixing and Mechanizing the Security Proof of Fiat-Shamir with Aborts and Dilithium

通过中止和 Dilithium 修复和机械化 Fiat-Shamir 的安全证明

• 发表时间: 2023
• 期刊: 43rd International Cryptology Conference (CRYPTO 2023
• 作者: Manuel Barbosa;Gilles Barthe;Christian Doczkal;Jelle Don;Serge Fehr;Benjamin Gr ́egoire;Yu-Hsuan Huang;Andreas Hu ̈lsing;Yi Lee;Xiaodi Wu
• 通讯作者: Xiaodi Wu

Exponentially Many Local Minima in Quantum Neural Networks

• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: Xuchen You;Xiaodi Wu