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.

非技术概要过去几十年来，人们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方面具有革命性的应用。然而，由于相互作用粒子产生的复杂性，即使在最先进的超级计算机的帮助下，对这些现象背后的物理原理进行全面的理论理解也是不切实际的。缺乏深刻的理解反过来又阻碍了这些现象的设计和应用。EAGER 奖支持开发算法来解决这些量子现象的研究和教育，重点关注电子和振动之间具有强相互作用的材料。固体中的原子。在此类相互作用的最新理论进展的推动下，该项目旨在开发一种利用经典计算机和现有量子计算机的混合算法。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具体的悬而未决的问题，包括超导和非平衡态。该项目将提供一类可扩展用于各种量子多体现象的新型混合算法，并为设计功能材料提供理论指南。该项目将为广大学生和学者的教育和专业发展做出贡献。作为与物理、计算机科学、化学、材料科学相关的学科，研究成果将纳入跨学科课程。克莱姆森大学和哈佛大学之间的合作将允许交流具有文化和地理多样性的教育经验。本科生将通过暑期实习或研讨会参与研究项目，特别是代表性不足的少数群体。技术摘要对量子多体系统，特别是具有强电子-电子和电子-声子相互作用的系统的定量理解是许多科学技术领域的关键。由于希尔伯特空间大小随着粒子数量呈指数增长，相关系统的令人满意的解决方案在经典计算机中无法获得，并且需要量子计算技术。混合量子经典算法的最新进展构成了一个有前途的新方向，但现有框架限制了它们在量子磁体或纯费米子系统中的应用。因此，这些需求和困难激励了新量子算法的发展。该 EAGER 奖支持基于变分量子本征解算器和变分非-高斯方法。该项目包括两个具体目标：（i）开发适合电子声子系统基态计算的高精度量子算法； (ii) 扩展用于评估动力学和激励谱的算法。除了算法开发之外，这两个目标还包括解决凝聚态物理中的前沿问题的应用，例如超导性和物质的非平衡态。这项研究将推进量子算法的发展，并为具有无限大希尔伯特空间的系统提供应用。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特性。此外，基于新算法的模拟将为理解一些实验现象提供物理见解，包括高温超导和光诱导涌现相。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体验。通过将最新研究纳入课程和研讨会，影响也将延伸到没有直接参与该项目的学生。获得这笔补助金部分支持的博士后将接受科学和实践技能方面的职业培训。通过夏季研究和研讨会活动，该项目将改善不同学生，特别是代表性不足的少数群体的科学教育。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审查标准进行评估，被认为值得支持。

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