EAGER: QAC-QSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets

EAGER：QAC-QSA：通过优化轨道基集减少量子计算化学绘图中的资源

• 批准号: 2037263
• 负责人: Jianfeng Lu
• 金额: $ 30万
• 依托单位: Duke University
• 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: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037263&HistoricalAwards=false
• 关键词: EAGER; QAC; QSA; Resource; Reduction

Jianfeng Lu of Duke University is supported by the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to conduct a project on resource reduction in quantum computational mapping by optimizing orbital basis sets. The proposal was submitted in response to the Quantum Algorithm Challenge Dear Colleague Letter, NSF 20-056. Jianfeng Lu and his research group are pursuing novel algorithms to reduce the resource requirements for carrying out quantum chemistry computations on a quantum computer. The research aims to provide state-of-the-art computational methods that push the current boundary of quantum computational chemistry. The project also includes new curriculum development that creates an ideal training platform for a new generation of computational scientists who will be able to understand and contribute to quantum computing and related fields across science and engineering. Quantum chemistry is a natural promising area of applications of quantum computing and has seen many exciting developments in recent years together with experimental demonstration of small-scale problems on actual quantum devices. The current bottleneck for capability of quantum computational chemistry lies in the limited resources provided by the current and near-future NISQ hardware. This bottleneck provides exciting and unprecedented opportunities for novel algorithmic advance and breakthroughs. The proposed research aims at resource reduction by optimizing basis sets for mapping quantum chemistry problem to quantum computer, using a systematic and efficient variational approach, which extends the current variational quantum eigensolver framework. The optimal basis set will be pursued using novel optimization techniques for ground and excited states. Efficient classical-quantum hybrid algorithms will be developed to obtain accurate quantum chemistry calculations for large molecules with large basis sets under NISQ budget constraints. Such innovations, combined with other advances in quantum computational chemistry, would help scale up the ability of quantum computer for quantum chemistry problems beyond the reach of classical algorithms.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.

杜克大学的Jianfeng Lu得到化学理论，模型和计算方法计划的支持，以通过优化轨道基集进行量子计算映射的资源减少项目。该提案是根据《量子算法挑战挑战赛》（NSF）20-056提交的。 Jianfeng Lu和他的研究小组正在追求新颖的算法，以减少在量子计算机上执行量子化学计算的资源要求。该研究旨在提供最先进的计算方法，以推动量子计算化学的当前边界。该项目还包括新的课程开发，为新一代的计算科学家创建理想的培训平台，他们将能够理解并为跨科学和工程的量子计算和相关领域做出贡献。量子化学是量子计算应用的自然有希望的领域，近年来已经看到了许多令人兴奋的发展，并在实际量子设备上进行了小规模问题的实验证明。量子计算化学能力的当前瓶颈在于当前和未实现的NISQ硬件提供的有限资源。这种瓶颈为新颖的算法进步和突破提供了令人兴奋且前所未有的机会。拟议的研究旨在通过使用系统有效的变分方法优化将量子化学问题映射到量子计算机的基础集来降低资源，从而扩展了当前的变异量子量化eigensolver框架。最佳基集将使用用于地面和激发态的新型优化技术来追求。将开发有效的经典量子杂种算法，以获得在NISQ预算约束下具有较大基集的大分子的准确量子化学计算。这种创新以及量子计算化学方面的其他进步将有助于扩展量子计算机对量子化学问题的能力，超出了古典算法的范围。

项目成果

Geometry of backflow transformation ansatze for quantum many-body fermonic wavefunctions

量子多体费米波函数回流变换模拟的几何结构

• 发表时间: 2023
• 期刊: Communications in mathematical sciences
• 影响因子: 1
• 作者: Huang, Hang;Landsberg, Joseph M.;Lu, Jianfeng
• 通讯作者: Lu, Jianfeng

On the Global Convergence of Randomized Coordinate Gradient Descent for Nonconvex Optimization

非凸优化的随机坐标梯度下降的全局收敛性

• DOI: 10.1137/21m1460375
• 发表时间: 2023
• 期刊: SIAM Journal on Optimization
• 影响因子: 3.1
• 作者: Chen, Ziang;Li, Yingzhou;Lu, Jianfeng
• 通讯作者: Lu, Jianfeng

Numerical analysis for inchworm Monte Carlo method: Sign problem and error growth

• DOI: 10.1090/mcom/3785
• 发表时间: 2020-06
• 期刊: Math. Comput.
• 作者: Zhenning Cai;Jianfeng Lu;Siyao Yang

Symmetry Breaking and the Generation of Spin Ordered Magnetic States in Density Functional Theory Due to Dirac Exchange for a Hydrogen Molecule

• DOI: 10.1007/s00332-022-09845-2
• 发表时间: 2022-09
• 期刊: Journal of Nonlinear Science
• 影响因子: 3
• 作者: M. Holst;Houdong Hu;Jianfeng Lu;J. Marzuola;D. Song;J. Weare

Improving the Accuracy of Variational Quantum Eigensolvers with Fewer Qubits Using Orbital Optimization

• DOI: 10.1021/acs.jctc.2c00895
• 发表时间: 2023-01-25
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Bierman, Joel;Li, Yingzhou;Lu, Jianfeng
• 通讯作者: Lu, Jianfeng