NSF Convergence Accelerator Track C: A Toolkit for Solving Practical Materials Science Problems on Near-Term

NSF 融合加速器轨道 C：解决近期实际材料科学问题的工具包

• 批准号: 2040549
• 负责人: Andrew Potter
• 金额: $ 99.91万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040549&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Toolkit

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This project seeks to address the gap between the theoretical advantages of quantum simulations and the capabilities of existing quantum hardware. By bringing together a cross-sector team, the project will develop quantum tensor network simulation techniques for materials and chemistry problems. The project aims to deploy and demonstrate these techniques on trapped ion quantum computing systems being developed at Honeywell Quantum Solutions. Deliverables include a comprehensive software development toolkit that will enable its use by a multi-disciplinary and cross-sector user base. The project team includes academic researchers in quantum information theory computational materials and chemistry techniques along with industry scientists at Honeywell Quantum Solutions who are developing large-scale high-performance trapped-ion quantum computing systems. The cross-sector team will aim to pioneer a new suite of quantum algorithm methods and software tools for “holographic” quantum-algorithms. These tools will exploit efficient compression of physically important states afforded by tensor-network state representations. Mid-circuit measurement and reset (MCMR) of selected qubits will be enabled by Honeywell’s trapped ion quantum computers in order to apply qubits as efficiently as possible towards the classically hard aspect of materials simulation: representing electronic correlations and entanglement. These techniques aim to reduce the number of qubits and accuracy of gates required to tackle large-scale realistic materials and chemistry simulations. This project seeks to narrow the gap between real-world problems and the capabilities of near-term quantum hardware. Deliverables include a comprehensive MCMR algorithm development toolkit that is tightly integrated with existing material science, chemistry simulation packages, and quantum programming frameworks. This toolkit will engage a broad user- and researcher- base to aid in the further development of techniques and innovations in quantum computing. In-person workshops and conferences, and online education and training materials will be developed to disseminate this work to a broad audience of industry and academic engineers, chemists, materials scientists, and software developers.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.

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，以应对国家重要性的挑战，并将在不久的将来为社会提供有价值的成果。该项目旨在解决量子模拟的理论优势与现有量子硬件能力之间的差距。通过组建跨部门团队，该项目将开发用于材料和化学问题的量子张量网络模拟技术。该项目旨在将这些技术部署和演示在霍尼韦尔量子解决方案正在开发的捕获离子量子计算系统上。该软件包包括一个全面的软件开发工具包，将使其能够被多学科和跨部门的用户群使用。该项目团队包括量子信息理论、计算材料和化学技术方面的学术研究人员，沿着还有霍尼韦尔量子解决方案公司的行业科学家，他们正在开发大规模高性能的捕获离子量子计算系统。跨部门团队的目标是开创一套新的量子算法方法和“全息”量子算法的软件工具。这些工具将利用张量网络状态表示提供的物理重要状态的有效压缩。选定量子位的中间电路测量和重置（MCMR）将由霍尼韦尔的捕获离子量子计算机实现，以便尽可能有效地将量子位应用于材料模拟的经典困难方面：表示电子相关性和纠缠。这些技术旨在减少处理大规模现实材料和化学模拟所需的量子比特数量和门的精度。该项目旨在缩小现实世界问题与近期量子硬件能力之间的差距。可扩展性包括一个全面的MCMR算法开发工具包，该工具包与现有的材料科学，化学模拟包和量子编程框架紧密集成。该工具包将吸引广泛的用户和研究人员，以帮助量子计算技术和创新的进一步发展。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

Holographic Simulation of Correlated Electrons on a Trapped-Ion Quantum Processor

• DOI: 10.1103/prxquantum.3.030317
• 发表时间: 2021-12
• 期刊: PRX Quantum
• 影响因子: 9.7
• 作者: Daoheng Niu;R. Haghshenas;Yuxuan Zhang;M. Foss-Feig;Garnet Kin-Lic Chan;Andrew C. Potter

Holographic dynamics simulations with a trapped-ion quantum computer

• DOI: 10.1038/s41567-022-01689-7
• 发表时间: 2022-08-04
• 期刊: NATURE PHYSICS
• 影响因子: 19.6
• 作者: Chertkov, Eli;Bohnet, Justin;Foss-Feig, Michael
• 通讯作者: Foss-Feig, Michael

Variational Power of Quantum Circuit Tensor Networks

• DOI: 10.1103/physrevx.12.011047
• 发表时间: 2022-03-11
• 期刊: PHYSICAL REVIEW X
• 影响因子: 12.5
• 作者: Haghshenas, Reza;Gray, Johnnie;Chan, Garnet Kin-Lic
• 通讯作者: Chan, Garnet Kin-Lic