CAREER: Uncertainty Quantification for Quantum Computing Algorithms

职业：量子计算算法的不确定性量化

• 批准号: 2143915
• 负责人: Xiu Yang
• 金额: $ 40万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143915&HistoricalAwards=false
• 关键词: CAREER; Uncertainty; Quantification; Quantum; Computing

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Quantum computing harnesses properties of quantum states to enable computations that would be intractable using classical computing. It is widely established that, in the future, quantum computing can revolutionize the way one performs and thinks about computation and serve as the backbone of groundbreaking new technologies for scientific discovery, engineering design, national security, and business development, to name a few. Currently, the key barrier in the development of quantum computing is the error induced by the noise in the hardware. The research goal of this project is to develop methods to model the error propagation in quantum computing algorithms and filter the resulting noise in the outcomes. This study will help enhance the performance of general quantum computing algorithms in terms of accuracy and efficiency. The educational goals of this effort are to prepare students for interdisciplinary research and promote STEM participation and equity among underrepresented groups. Outreach activities also involve K-12 students. The investigator will develop new uncertainty quantification methods in the following four directions to understand and alleviate the effect of noise on quantum computing algorithms: (1) describing propagation of gate error and readout error using epistemic uncertainty models; (2) mitigating errors using constrained optimization methods and Bayesian approaches; (3) analyzing asymptotic behavior of the propagation of the error; (4) developing an open-source software package to implement the uncertainty quantification algorithms. These new methods will leverage Bayesian inference approaches, tensor decomposition techniques, asymptotic analysis tools for stochastic differential equations, and high-performance computing packages to build the foundation of a "quantum numerical analysis" framework from a probabilistic perspective. This framework is general, and it can be used to assess the performance of real-world quantum processors and evaluate the suitability of specific quantum computing hardware architectures for a wide range of applications. This project is jointly supported by the Division of Mathematical Sciences: Computational Mathematics Program and the Division of Computing and Communication Foundations: Foundations of Emerging Technologies Program.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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。量子计算利用量子状态的属性启用使用经典计算会棘手的计算。众所周知，将来，量子计算可以彻底改变人们的性能和思考计算的方式，并成为开创性的科学发现，工程设计，国家安全和业务发展的开创性新技术的骨干。当前，量子计算开发的关键障碍是硬件中的噪声引起的错误。该项目的研究目标是开发方法来对量子计算算法中的误差传播建模并过滤结果中产生的噪声。这项研究将有助于从准确性和效率方面提高通用量子计算算法的性能。这项努力的教育目标是为学生做好跨学科研究的准备，并促进代表性不足的群体中的STEM参与和公平。外展活动还涉及K-12学生。研究者将在以下四个方向上开发新的不确定性定量方法，以了解和减轻噪声对量子计算算法的影响：（1）使用认知不确定性模型描述栅极误差和读数错误的传播； （2）使用约束优化方法和贝叶斯方法缓解错误； （3）分析误差传播的渐近行为； （4）开发一个开源软件包，以实现不确定性量化算法。这些新方法将利用贝叶斯推理方法，张量分解技术，用于随机微分方程的渐近分析工具以及高性能计算套件，从概率的角度来建立“量子数值分析”框架的基础。该框架是一般的，可以用来评估现实世界量子处理器的性能，并评估特定量子计算硬件架构对广泛应用的适用性。该项目由数学科学划分共同支持：计算数学计划和计算和通信基础的部门：新兴技术计划的基础。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。

项目成果

A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors

用于表征和减轻门和测量误差的贝叶斯方法

• DOI: 10.1145/3563397
• 发表时间: 2023
• 期刊: ACM Transactions on Quantum Computing
• 作者: Zheng, Muqing;Li, Ang;Terlaky, Tamás;Yang, Xiu
• 通讯作者: Yang, Xiu