Quantum Integration of Data and Emergence at Atomic Scales (Qu-IDEAS)

原子尺度数据的量子整合和出现 (Qu-IDEAS)

• 批准号: 2118310
• 负责人: Eun-Ah Kim
• 金额: $ 240.21万
• 依托单位: Cornell 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: 2022-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118310&HistoricalAwards=false
• 关键词: Quantum; Integration; Data; Emergence; Atomic

An intensely computational and data-intensive theme of modern quantum materials research is to search for new materials platforms with desired properties and understand them. Quantum materials have key properties and exhibit phenomena deeply rooted in the laws of quantum physics and the collective behavior of their constituent electrons. These materials are foundational for innovative future quantum-based technologies. A successful search requires connecting data to theoretical insight. Rapid advances in research on quantum materials presents an opportunity for significant progress; however, the volume and novelty of the ever-growing datasets present a problem for analysis and search. Rapid advances in quantum technologies have led to development of Noisy Intermediate-Scale Quantum (NISQ) devices, the current quantum computer technology. NISQ devices present new opportunities to compare the complex reality of the material world and idealized theoretical models that contain the essential physics but are notoriously difficult to compute with conventional computers. The need for optimal control of NISQ devices and understanding image-like data that results from their use places quantum materials research and quantum simulation on NISQ devices at a crossroads of opportunity that can benefit from new solutions to data-driven challenges and optimization. This project addresses the challenges using machine learning tools based on artificial intelligence. New insights will be fed into synthesizing new quantum materials and optimizing use of the NISQ devices. In the process, new software infrastructure for conventional computers and those using NISQ technology will be developed and made available to the broader community. Internship opportunities will help train students in machine learning, quantum computation, and materials discovery for the next-generation quantum workforce. The team will develop ML tools guided by the structure of the data, scientific meaning, and objectives. The tools will then be used to gain new theoretical insights, and feed the insight back into material synthesis and optimal use of NISQ devices. Specifically, data from the Inorganic Crystal Structure Database and Materials Project, guided by quantum chemical reasoning, will be used to discover new descriptors, and predict new topological materials. Quantum-classical hybrid approaches for NISQ computing will be developed, exploiting their advantage in encoding sampling problems. This project plans to deliver new topological materials, suites of ML tools for solving the data problems, and new AI algorithms for quantum-classical hybrid optimization. The effort will help build the next-generation quantum workforce through internship opportunities. The suit of classical ML tools and quantum-classical hybrid AI tools will be openly available in a user-friendly formats.The Office of Advanced Cyberinfrastructure in the Computer and Information Science and Engineering Directorate and the Division of Materials Research in the Mathematical and Physical Sciences Directorate jointly supported this award.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.

现代量子材料研究的一个计算和数据密集型主题是寻找具有所需特性的新材料平台并了解它们。量子材料具有关键特性，并表现出深深植根于量子物理定律及其组成电子的集体行为的现象。这些材料是未来创新量子技术的基础。成功的搜索需要将数据与理论见解联系起来。量子材料研究的快速发展为取得重大进展提供了机会;然而，不断增长的数据集的数量和新奇给分析和搜索带来了问题。量子技术的快速发展导致了噪声中间尺度量子（NISQ）设备的发展，这是当前的量子计算机技术。NISQ设备提供了新的机会，可以比较物质世界的复杂现实和理想化的理论模型，这些模型包含基本的物理学，但众所周知，用传统计算机很难计算。对NISQ器件的最佳控制和理解其使用产生的图像数据的需求将NISQ器件的量子材料研究和量子模拟置于机遇的十字路口，可以从数据驱动的挑战和优化的新解决方案中受益。该项目使用基于人工智能的机器学习工具解决挑战。新的见解将用于合成新的量子材料和优化NISQ设备的使用。在此过程中，将为传统计算机和使用NISQ技术的计算机开发新的软件基础设施，并提供给更广泛的社区。实习机会将有助于培养学生在机器学习，量子计算和材料发现为下一代量子劳动力。该团队将在数据结构、科学意义和目标的指导下开发ML工具。然后，这些工具将用于获得新的理论见解，并将这些见解反馈到材料合成和NISQ器件的最佳使用中。具体来说，来自无机晶体结构数据库和材料项目的数据，在量子化学推理的指导下，将用于发现新的描述符，并预测新的拓扑材料。量子经典的混合方法NISQ计算将开发，利用其优势，在编码采样问题。该项目计划提供新的拓扑材料，用于解决数据问题的ML工具套件，以及用于量子经典混合优化的新AI算法。这项工作将有助于通过实习机会建立下一代量子劳动力。经典ML工具和量子经典混合AI工具的套装将在用户中公开提供-该奖项由计算机和信息科学与工程理事会高级网络基础设施办公室以及数学和物理科学理事会材料研究部共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

Graph gauge theory of mobile non-Abelian anyons in a qubit stabilizer code

• DOI: 10.1016/j.aop.2023.169286
• 发表时间: 2022-10
• 期刊: Annals of Physics
• 影响因子: 3
• 作者: Y. Lensky;K. Kechedzhi;I. Aleiner;Eun-Ah Kim

Probing the onset of quantum avalanches in a many-body localized system

探测多体局域系统中量子雪崩的发生

• DOI: 10.1038/s41567-022-01887-3
• 发表时间: 2023
• 期刊: Nature Physics
• 影响因子: 19.6
• 作者: Léonard, Julian;Kim, Sooshin;Rispoli, Matthew;Lukin, Alexander;Schittko, Robert;Kwan, Joyce;Demler, Eugene;Sels, Dries;Greiner, Markus
• 通讯作者: Greiner, Markus

Latent Diffusion for Language Generation

• DOI: 10.48550/arxiv.2212.09462
• 发表时间: 2022-12
• 期刊: ArXiv
• 作者: Justin Lovelace;Varsha Kishore;Chao-gang Wan;Eliot Shekhtman;Kilian Q. Weinberger

Frustration- and doping-induced magnetism in a Fermi–Hubbard simulator

• DOI: 10.1038/s41586-023-06280-5
• 发表时间: 2022-12
• 期刊: Nature
• 影响因子: 64.8
• 作者: Muqing Xu;L. Kendrick;Anant Kale;You-Na Gang;G. Ji;R. Scalettar;M. Lebrat;M. Greiner

Verification of the area law of mutual information in a quantum field simulator

• DOI: 10.1038/s41567-023-02027-1
• 发表时间: 2022-06
• 期刊: Nature Physics
• 影响因子: 19.6
• 作者: Mohammadamin Tajik;I. Kukuljan;S. Sotiriadis;B. Rauer;T. Schweigler;Federica Cataldini;João Sabino;F. Møller;Philipp Schuttelkopf;S. Ji;Dries Sels;E. Demler;J. Schmiedmayer