ExpandQISE: Track 1: Reimagining Adaptive Quantum Algorithms

ExpandQISE：轨道 1：重新构想自适应量子算法

• 批准号: 2231328
• 负责人: Christian Arenz
• 金额: $ 80万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231328&HistoricalAwards=false
• 关键词: ExpandQISE; Track; Reimagining; Adaptive; Quantum

Non-technical Description:This research focuses on new methods for solving important problems in science using quantum computers, broadly addressing a wide range of problems for various practical applications. In particular, the project will develop a family of quantum algorithms that are able to solve important problems of societal impact, including in health, agriculture, and climate. The education and outreach activities play a critical role in the growth and diversification of a strong quantum information science and engineering (QISE) workforce. The developed textbook from this project will make QISE accessible to all students and may also influence how linear algebra is introduced. Technical Description:Quantum simulation algorithms face several challenges associated with ansatz selection, noise, rugged optimization landscapes, and applicability to large-scale problems. The project aims to overcome these challenges by developing a family of quantum algorithms that adaptively create the quantum circuit needed to carry out ground state quantum simulation. This is achieved through a broad family of adaptive algorithms that leverage deterministic as well as random elements and variational and non-variational approaches, to efficiently prepare ground states in a noise-robust and reliable manner. Randomized adaptive strategies that do not involve classical optimization routines are expected to provide convergence guarantees, while additionally adding classical optimization enhances the speed of convergence. Performance is benchmarked and tested under realistic conditions using IBM’s largest quantum devices. The insights gained are leveraged to design the most efficient adaptive quantum algorithms that are noise-robust and yield high convergence guarantees. While the research activities are expected to have a profound effect across the sciences by providing solution strategies that use existing and near-term, noisy quantum hardware to solve a wide range of problems, the research also establishes the much-needed algorithms for reliable and efficient quantum state preparation in the fault-tolerant era. The project includes educational and outreach activities that make quantum science accessible to students from all backgrounds and that could play a critical role in the growth and diversification of a strong QISE workforce. The research is tightly integrated into the workforce development program by engaging students in research early on. The cornerstone of the workforce development program is a pictorial formalism that exposes students to quantum computing without the need for elaborate mathematical concepts. This formalism is developed in a textbook, which simultaneously serves as a guide to designing a freshman quantum information science and engineering course and training high-school teachers and K12 students in QISE summer programs.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.

非技术描述：本研究的重点是使用量子计算机解决科学中重要问题的新方法，广泛解决各种实际应用中的各种问题。特别是，该项目将开发一系列量子算法，这些算法能够解决健康、农业和气候等重要的社会影响问题。教育和推广活动在强大的量子信息科学与工程（QISE）劳动力的增长和多样化方面发挥着关键作用。该项目开发的教科书将使所有学生都能使用QISE，也可能影响线性代数的介绍方式。技术描述：量子模拟算法面临着与样本选择、噪声、崎岖的优化环境以及对大规模问题的适用性相关的几个挑战。该项目旨在通过开发一系列量子算法来克服这些挑战，这些算法可以自适应地创建进行基态量子模拟所需的量子电路。这是通过广泛的自适应算法家族来实现的，这些算法利用确定性和随机元素以及变分和非变分方法，以噪声鲁棒性和可靠的方式有效地准备基态。不涉及经典优化例程的随机自适应策略预计将提供收敛保证，而另外添加经典优化提高了收敛速度。性能是基准测试和测试在现实条件下使用IBM的最大的量子设备。所获得的见解被用来设计最有效的自适应量子算法，这些算法具有噪声鲁棒性并具有高收敛性保证。虽然研究活动预计将通过提供使用现有和近期噪声量子硬件来解决各种问题的解决方案策略，对整个科学产生深远的影响，但该研究还建立了急需的算法，用于可靠和有效的量子状态准备在容错时代。该项目包括教育和推广活动，使来自各种背景的学生都能接触到量子科学，并可能在强大的QISE劳动力的增长和多样化方面发挥关键作用。通过让学生尽早参与研究，该研究与劳动力发展计划紧密结合。劳动力发展计划的基石是一种图形形式主义，让学生接触量子计算，而无需详细的数学概念。该奖项体现了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。

项目成果

Robustness of random-control quantum-state tomography

• DOI: 10.1103/physreva.108.022408
• 发表时间: 2023-02
• 期刊: Physical Review A
• 影响因子: 2.9
• 作者: Jingcheng Wang;Shaoliang Zhang;J. Cai;Zhenyu Liao;C. Arenz;R. Betzholz