EAGER-QAC-QSA: Quantum Algorithms for Correlated Electron-Phonon System

EAGER-QAC-QSA：相关电子声子系统的量子算法

• 批准号: 2038011
• 负责人: Yao Wang
• 金额: $ 29.98万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038011&HistoricalAwards=false
• 关键词: EAGER; QAC; QSA; Quantum; Algorithms

Non-Technical SummaryIn the past few decades, many unconventional quantum phenomena have been discovered in materials and molecules. These quantum many-body phenomena are expected to have revolutionary applications in functional materials, quantum information, drug discovery, and catalyst design. However, due to the complexity originating from interacting particles, a comprehensive theoretical understanding of the physics behind these phenomena is impractical even with the help of state-of-the-art supercomputers. That lack of profound understanding, in turn, hinders the design and application of these phenomena.This EAGER award supports research and education on developing algorithms to address these quantum phenomena, with a focus on materials with strong interactions between the electrons and the vibrations of the atoms in the solid. Motivated by recent theoretical progress on this type of interaction, this project aims to develop a hybrid algorithm that takes advantage of both classical computers and existing quantum computers. In addition, the research team will also apply this new algorithm to address several specific open questions in quantum materials, including superconductivity and nonequilibrium states. This project will provide both a new class of hybrid algorithms extensible for various quantum many-body phenomena and a theoretical guideline for designing functional materials.This project will contribute to the education and professional development of a broad pipeline of students and scholars. As a subject related to physics, computer science, chemistry, and materials science, the research outcomes will be incorporated into interdisciplinary courses. The collaboration between Clemson University and Harvard University will allow for the exchange of educational experiences with cultural and geographical diversity. Undergraduate students will be involved in the research project through summer internships or workshops, with the particular involvement of underrepresented minorities.Technical SummaryThe quantitative understanding of quantum many-body systems, especially systems with both strong electron-electron and electron-phonon interactions, is the key to many areas of science and technology. Due to the exponential growth of their Hilbert space sizes with the number of particles, a satisfactory solution for correlated systems is not accessible in classical computers and requires quantum computing techniques. Recent progress in hybrid quantum-classical algorithms constitutes a promising new direction, but the existing framework restricts their application to quantum magnets or pure fermionic systems. Therefore, the demands and difficulties motivate the development of new quantum algorithms.This EAGER award supports research and education on developing a hybrid quantum-classical algorithm applicable to correlated electron-phonon systems, based on recent progress in the variational quantum eigensolver and the variational non-Gaussian approach. This project includes two specific goals: (i) to develop a high-accuracy quantum algorithm suitable for the ground-state calculation of electron-phonon systems; (ii) to extend the algorithm for the evaluation of dynamics and excitation spectrum. In addition to algorithm development, both goals include applications for solving cutting-edge problems in condensed matter physics, such as superconductivity and nonequilibrium states of matter.This research will advance quantum algorithms and enable applications for systems with infinitely large Hilbert spaces. It will provide a unique tool to simulate the equilibrium and nonequilibrium properties of relevant quantum many-body systems. Moreover, the simulations based on the new algorithm will provide physical insights into understanding a few experimental phenomena, including high-Tc superconductivity and photoinduced emergent phases. These insights are crucial for the engineering and design of functional materials.This collaborative research will provide a novel educational experience for undergraduate and graduate students at Clemson University and Harvard University. By incorporating the latest research into courses and seminars, the impact will also extend to students who are not directly involved in this project. The postdoc partially supported by this grant will receive career training in both scientific and practical skills. Through summer research and workshop activities, this project will improve science education among diverse students, particularly underrepresented minorities.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.

非技术摘要在过去的几十年中，在材料和分子中发现了许多非常规量子现象。这些量子多体现象有望在功能材料，量子信息，药物发现和催化剂设计中具有革命性的应用。但是，由于相互作用粒子的复杂性，即使在最先进的超级计算机的帮助下，对这些现象背后物理学的全面理论理解也是不切实际的。反过来，缺乏深刻的理解阻碍了这些现象的设计和应用。该渴望的奖项支持研究和教育开发算法来解决这些量子现象，重点是电子与固体原子的振动之间具有强烈相互作用的材料。该项目旨在开发一种利用古典计算机和现有量子计算机的混合算法的促进，旨在开发一种混合算法。此外，研究团队还将应用这种新算法来解决量子材料中的几个特定开放问题，包括超导性和非平衡状态。该项目将为各种量子多体现象提供可扩展的新型混合算法，也可以为设计功能材料提供理论指南。该项目将有助于学生和学者的广泛渠道的教育和专业发展。作为与物理，计算机科学，化学和材料科学有关的主题，研究成果将纳入跨学科课程中。克莱姆森大学和哈佛大学之间的合作将允许与文化和地理多样性的教育经验交换。本科生将通过暑期实习或研讨会参与研究项目，并特别参与了代表性不足的少数群体。技术总结对量子多体系统的定量了解，尤其是具有强大的电子电子和电子互动互动的系统，是科学和技术许多领域的关键。由于其Hilbert空间大小的指数增长与粒子数量，因此在古典计算机中无法访问相关系统的令人满意的解决方案，并且需要量子计算技术。混合量子古典算法的最新进展构成了一个有希望的新方向，但是现有的框架限制了其应用于量子磁铁或纯费米子系统。因此，需求和困难激发了新的量子算法的开发。该急切的奖项基于变异量子eigensolver和Variational非高斯方法的最新进展，支持适用于相关的电子phonon系统的混合量子量子古典算法的研究和教育。该项目包括两个具体的目标：（i）开发适合电子 - phonon系统基础计算的高准确量子算法； （ii）扩展算法以评估动力学和激发光谱。除算法开发外，这两个目标还包括解决凝结物理学的最先进问题的应用，例如超导性和物质的非平衡状态。这项研究将推进量子算法，并促进无限大希尔伯特空间的系统。它将提供一个独特的工具来模拟相关量子多体系统的平衡和非平衡性能。此外，基于新算法的仿真将提供理解一些实验现象的物理见解，包括高-TC超导性和光诱导的新兴阶段。这些见解对于功能材料的工程和设计至关重要。这项合作研究将为克莱姆森大学和哈佛大学的本科生和研究生提供新颖的教育经验。通过将最新研究纳入课程和研讨会，影响还将扩展到不直接参与该项目的学生。该赠款部分支持的博士后将接受科学和实践技能的职业培训。通过夏季研究和研讨会活动，该项目将改善不同学生的科学教育，特别是代表性不足的少数群体。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。

项目成果

Microscopic evolution of doped Mott insulators from polaronic metal to Fermi liquid

• DOI: 10.1126/science.abe7165
• 发表时间: 2020-09
• 期刊: Science
• 影响因子: 56.9
• 作者: J. Koepsell;Dominik Bourgund;P. Sompet;Sarah Hirthe;A. Bohrdt;Yao Wang;F. Grusdt;E. Demler;G. Salomon;C. Gross;I. Bloch

Machine learning on neutron and x-ray scattering and spectroscopies

• DOI: 10.1063/5.0049111
• 发表时间: 2021-09-01
• 期刊: CHEMICAL PHYSICS REVIEWS
• 作者: Chen, Zhantao;Andrejevic, Nina;Li, Mingda
• 通讯作者: Li, Mingda

A hybrid quantum-classical method for electron-phonon systems

电子声子系统的混合量子经典方法

• DOI: 10.1038/s42005-023-01353-3
• 发表时间: 2023
• 期刊: Communications Physics
• 影响因子: 5.5
• 作者: Denner, M. Michael;Miessen, Alexander;Yan, Haoran;Tavernelli, Ivano;Neupert, Titus;Demler, Eugene;Wang, Yao
• 通讯作者: Wang, Yao

Anomalously strong near-neighbor attraction in doped 1D cuprate chains

• DOI: 10.1126/science.abf5174
• 发表时间: 2021-09-10
• 期刊: SCIENCE
• 影响因子: 56.9
• 作者: Chen, Zhuoyu;Wang, Yao;Shen, Zhi-Xun
• 通讯作者: Shen, Zhi-Xun

One-dimensional Holstein model revisited

重温一维荷斯坦模型

• DOI: 10.1103/physrevb.107.075142
• 发表时间: 2023
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Zhao, Sijia;Han, Zhaoyu;Kivelson, Steven A.;Esterlis, Ilya
• 通讯作者: Esterlis, Ilya