FET: Small: Collaborative Research: Efficient and Robust Characterization of Quantum Systems

FET：小型：协作研究：量子系统的高效且稳健的表征

• 批准号: 1909141
• 负责人: Amir Kalev
• 金额: $ 3万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909141&HistoricalAwards=false
• 关键词: FET; Small; Collaborative; Research; Efficient

Advances in quantum information and algorithms enable solutions that are beyond the reach of conventional technologies, with applications in many-body quantum physics, chemistry, cryptography, communication, and machine learning. While prototypes for quantum computers are being built, these are still prone to errors: reducing the noise to a tolerable and controllable level faces technical hurdles. To be able to scale-up to a full-fledged quantum computer, it is imperative to characterize, verify, and rigorously certify the behavior of current and near-future prototypes. The latter task incurs a heavy burden in data acquisition, processing and storage that leads to a pressing need for efficient and noise-robust characterization and verification protocols to test current quantum devices. The work done by members of this research team on novel optimization theory and algorithms, compressed sensing, and verification techniques have direct applications to this problem. The project will investigate and propose novel, highly-efficient, and robust methodologies to characterize, verify, and certify the behavior of quantum systems implementations, for better acquisition and processing of quantum information. The research will affect positively on how non-convex algorithms could be used in modern data science applications. Key broader outcomes of this proposal will be the establishment of an academic-industry collaboration (with the mentorship of two students in the process), and the introduction of quantum computing courses to Rice University.This project focuses on benchmarking and testing quantum states and processes, through efficient, noise-robust and provable quantum state tomography, as well as novel validation and certification tools for quantum computing. This research proposes to investigate new theoretical and practical approaches, via the following three paths: i) By using provable methods for distributing non-convex computations and optimization for the task of large-scale quantum state tomography. The proposed method complements the setting of compressed sensing quantum state tomography, where only a few measurements --compared to full tomography-- are available from a low-rank (highly-pure) quantum state and will be used in settings that are beyond the reach of state-of-the-art approaches. ii) By robustifying state-of-the-art validation methods using noise-robust optimization and techniques. The proposed research involves new, customized non-convex algorithms for the case where measurements are contaminated with non-homogeneous noise. iii) By designing efficient and noise-robust schemes for validating and certifying experimentally-relevant quantum operations. This project will study schemes to validate gates and error models behavior, which in turn will help certify the quality of the basic physical operations. The results of this proposal will be publicly available as an integrated part of an open-source software framework, in order to enhance reproducibility on available quantum information processors.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.

量子信息和算法的进步使得解决方案超出了传统技术的范围，并在多体量子物理、化学、密码学、通信和机器学习中得到应用。虽然量子计算机的原型正在建造中，但它们仍然容易出错：将噪声降低到可容忍和可控的水平面临着技术障碍。为了能够扩展到成熟的量子计算机，必须表征、验证和严格证明当前和近期原型的行为。后一项任务在数据采集、处理和存储方面带来了沉重的负担，从而迫切需要高效且抗噪声的表征和验证协议来测试当前的量子器件。该研究团队成员在新颖的优化理论和算法、压缩感知和验证技术方面所做的工作可直接应用于该问题。该项目将研究并提出新颖、高效且​​稳健的方法来表征、验证和证明量子系统实现的行为，以更好地获取和处理量子信息。该研究将对非凸算法如何在现代数据科学应用中使用产生积极影响。该提案的更广泛的关键成果将是建立学术与工业合作（在此过程中由两名学生指导），以及在莱斯大学引入量子计算课程。该项目的重点是通过高效、抗噪声和可证明的量子态断层扫描以及用于量子计算的新颖验证和认证工具来对量子态和过程进行基准测试和测试。 本研究建议通过以下三个途径研究新的理论和实践方法：i）使用可证明的方法来分布式非凸计算和优化大规模量子态层析成像任务。所提出的方法补充了压缩感知量子态断层扫描的设置，与完整断层扫描相比，低阶（高纯）量子态只能进行少量测量，并且将用于超出最先进方法范围的设置。 ii) 通过使用抗噪声优化和技术来增强最先进的验证方法。拟议的研究涉及新的、定制的非凸算法，用于测量被非均匀噪声污染的情况。 iii) 通过设计高效且抗噪声的方案来验证和证明与实验相关的量子操作。该项目将研究验证门和错误模型行为的方案，这反过来将有助于证明基本物理操作的质量。该提案的结果将作为开源软件框架的集成部分公开，以提高可用量子信息处理器的可重复性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。