权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

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

项目摘要

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得到化学系化学理论、模型和计算方法项目的支持，通过优化轨道基组进行量子计算映射中的资源减少项目。该提案是为了回应量子算法挑战亲爱的同事信，NSF 20-056。 Jianfeng Lu和他的研究小组正在寻求新的算法，以减少在量子计算机上进行量子化学计算的资源需求。该研究旨在提供最先进的计算方法，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，为新一代计算科学家创造了一个理想的培训平台，他们将能够理解量子计算和跨科学和工程的相关领域并做出贡献。量子化学是量子计算应用的一个自然有前途的领域，近年来已经看到了许多令人兴奋的发展，以及实际量子设备上小规模问题的实验演示。量子计算化学能力的当前瓶颈在于当前和不久的将来NISQ硬件提供的有限资源。这个瓶颈为新算法的进步和突破提供了令人兴奋和前所未有的机会。拟议的研究旨在通过优化将量子化学问题映射到量子计算机的基组来减少资源，使用系统和有效的变分方法，该方法扩展了当前的变分量子本征解算器框架。最佳基组将采用新的优化技术，地面和激发态。将开发有效的经典-量子混合算法，以在NISQ预算限制下获得具有大基组的大分子的精确量子化学计算。这些创新与量子计算化学的其他进展相结合，将有助于提升量子计算机解决经典算法无法解决的量子化学问题的能力。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。