Strongly Interacting Atoms under Quantum Gas Microscope

量子气体显微镜下的强相互作用原子

基本信息

批准号：
2011386
负责人：
Erhai Zhao
金额：
$ 18万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Continuing 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=2011386&HistoricalAwards=false
关键词：
Strongly Interacting Atoms under Quantum

项目摘要

Comprehending quantum matter consisting of many strongly interacting quantum units, such as atoms, spins, or quantum bits, remains a great challenge. It underpins our capacity to design better materials or to solve hard problems beyond the reach of classical computers. A quantum simulator is a man-made system where the individual quantum units as well as their couplings are under precise control. Quantum gases of ultracold atoms confined in optical lattices formed by laser light have emerged as a leading platform for quantum simulation. The recent invention of the quantum gas microscope offers unprecedented precision readout of these simulators with single-atom and single-site resolution. It opens up new opportunities to probe the properties of strongly interacting ultracold atoms confined in two dimensions to solve long-standing open problems in strongly correlated quantum matter, for instance regarding the existence of d-wave superfluid in the Fermi-Hubbard model or quantum spin liquids in frustrated spin models. The ongoing experiments demand from theory quantitatively accurate predictions to boost the superfluid transition temperature or to scout out the locations of spin liquids in the parameter space. These tasks are challenging because strongly interacting quantum gases are marred with many closely competing orders. To treat them on equal footing, one is usually limited to small system sizes or low momentum resolution in order to keep the calculation tractable. The proposed research stimulates the cross-fertilization between quantum gases, quantum simulation and machine learning. Students involved in this project will be trained to acquire transferable skills in high performance computing and data analysis.This project develops new high-precision numerical algorithms to compute the properties of strongly interacting ultracold atoms confined in two-dimensional lattices. Two innovative many-body techniques are proposed to overcome the aforementioned technical challenges. First, functional renormalization group with full momentum resolution will be developed to accurately track the competing many-body instabilities for interacting fermionic atoms on optical lattices. It will be applied to optimize the optical lattice designs to promote d-wave superfluidity in repulsive Fermi-Hubbard gases. Second, frustrated quantum spin models of cold atoms localized in optical lattices are solved by neural network parametrization of the many-body wave function inspired by machine learning. Variational ansatz based on feed-forward neural networks will be developed to resolve the nature of their ground states. The proposed work expands the boundary of precision many-body algorithms for strongly interacting atoms and spins. It improves the number of running couplings in functional renormalization group from hundred thousands to tens of millions by solving the flow equations massively parallel on Graphics Processing Units. The resultant superior resolution will yield more accurate phase boundaries and estimations of the transition temperature to guide experiments. Large-scale neural network ansatz will help answer open questions regarding the existence and nature of spin liquids and other exotic phases in quantum spin systems. These many-body techniques developed are general and can be applied to correlated quantum materials or quantum spin models of interacting molecules and trapped ions.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.

理解由许多强相互作用的量子单元（例如原子、自旋或量子位）组成的量子物质仍然是一个巨大的挑战。它支撑着我们设计更好的材料或解决经典计算机无法解决的难题的能力。量子模拟器是一个人造系统，其中各个量子单元及其耦合都受到精确控制。限制在激光形成的光学晶格中的超冷原子的量子气体已成为量子模拟的领先平台。最近发明的量子气体显微镜为这些模拟器提供了前所未有的精确读数，具有单原子和单点分辨率。它为探测二维受限强相互作用超冷原子的特性提供了新的机会，以解决强相关量子物质中长期存在的开放性问题，例如费米-哈伯德模型或量子自旋中d波超流体的存在受挫旋转模型中的液体。正在进行的实验需要理论定量准确的预测，以提高超流体转变温度或在参数空间中寻找自旋液体的位置。这些任务具有挑战性，因为强相互作用的量子气体被许多紧密竞争的秩序所破坏。为了平等地对待它们，通常会限制系统尺寸较小或动量分辨率较低，以使计算易于处理。这项研究促进了量子气体、量子模拟和机器学习之间的交叉融合。参与该项目的学生将接受培训，获得高性能计算和数据分析方面的可转移技能。该项目开发新的高精度数值算法来计算限制在二维晶格中的强相互作用超冷原子的特性。提出了两种创新的多体技术来克服上述技术挑战。首先，将开发具有全动量分辨率的功能重正化群，以准确跟踪光学晶格上相互作用的费米子原子的竞争多体不稳定性。它将用于优化光学晶格设计，以促进排斥性费米-哈伯德气体中的 d 波超流性。其次，受机器学习启发，通过多体波函数的神经网络参数化来解决位于光学晶格中的冷原子的受挫量子自旋模型。将开发基于前馈神经网络的变分模拟来解析其基态的性质。所提出的工作扩展了强相互作用原子和自旋的精密多体算法的边界。它通过在图形处理单元上大规模并行求解流方程，将函数重正化群中的运行耦合数量从数十万提高到数千万。由此产生的卓越分辨率将产生更准确的相界和转变温度的估计以指导实验。大规模神经网络 ansatz 将有助于回答有关量子自旋系统中自旋液体和其他奇异相的存在和性质的悬而未决的问题。这些开发的多体技术是通用的，可应用于相关量子材料或相互作用分子和捕获离子的量子自旋模型。该奖项反映了 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进行评估，被认为值得支持审查标准。