EAGER-QAC-QSA: Variational Quantum Algorithms for Nonequilibrium Quantum Many-Body Systems

EAGER-QAC-QSA：非平衡量子多体系统的变分量子算法

基本信息

批准号：
2038010
负责人：
Thomas Iadecola
金额：
$ 30万
依托单位：
Iowa State 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：
2020-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038010&HistoricalAwards=false
关键词：
EAGER QAC QSA Variational Quantum

项目摘要

Nontechnical SummaryThis award is made on an EAGER proposal invited through the Quantum Algorithm Challenge Dear Colleague Letter. It supports research and education to develop and implement algorithms to run on quantum computers that currently exist or will exist in the near future. A perfect quantum computer is expected to solve certain problems of practical interest much faster than any conventional computer, also called a classical computer. Examples include factoring large numbers into primes with Shor's algorithm and using Lloyd's algorithm to predict the time evolution of an interacting quantum system away from equilibrium. The latter could help in optimization and design of new biomaterials, drugs, and functional quantum materials. While the past few years have brought tremendous progress in efforts to build a perfect quantum computer, the current noisy intermediate-scale quantum (NISQ) technology is potentially decades away from being able to run Shor's and Lloyd's algorithms at practically relevant scales. The underlying reason is that NISQ computers possess of the order of hundreds of qubits as opposed to the millions required to fully correct for errors that arise from noisy gate operations. This severely limits the complexity of quantum algorithms compatible with NISQ hardware and exposes a critical need for further algorithmic advancement to achieve practical quantum advantage.In this project, the PIs develop algorithms tailored to NISQ quantum processing units (QPUs) that can address fundamental challenges in nonequilibrium physics. Specifically, the research team proposes new hybrid quantum-classical variational algorithms to simulate nonequilibrium dynamics and highly excited states in novel materials such as disordered quantum magnets. The algorithms will be implemented on QPUs from IBM and Rigetti and carefully benchmarked against existing state-of-the-art quantum and classical algorithms. In addition to finding opportunities for near-term quantum advantage, this project will provide new insights into fundamental questions of how systems of interacting particles relax to thermal equilibrium and how they respond to strong external fields.The educational component of this project addresses the national priorities of educating a quantum-enabled workforce and enhancing the participation and inclusion of traditionally underrepresented groups in STEM areas. Within this project, the PIs will educate and train one graduate student, one undergraduate student, and one postdoctoral scholar in applying quantum computing algorithms to challenging open scientific questions. The PIs will also design a hands-on outreach workshop on quantum computing and present it at "Go Further," a STEM career conference aimed at female middle- and high-school students. Finally, the PIs will create and maintain an online blog for the general public that highlights career options in quantum information science and informs readers about recent trends in quantum algorithms.Technical SummaryThis award is made on an EAGER proposal invited through the Quantum Algorithm Challenge Dear Colleague Letter. It supports research and education to develop and implement algorithms to run on quantum computers that currently exist or will exist in the near future. Noisy intermediate-scale quantum (NISQ) computing devices have computing capabilities that are beyond the reach of any classical supercomputer, as has recently been demonstrated by the Google team. Whether they also offer a practical quantum advantage for calculations of interest in physics, chemistry or materials science is an open question. Simulations of nonequilibrium dynamics and highly excited states in quantum many-body systems are a promising yet challenging target, since classical algorithms for such simulations suffer from the exponential complexity and highly entangled nature of generic excited states. The development of resource-efficient quantum algorithms for current NISQ hardware can unlock their potential to address important challenges in nonequilibrium many-body physics.Here, the PIs propose two novel variational quantum algorithms that are based on the variational quantum eigensolver (VQE) algorithm, which has been demonstrated on NISQ hardware. Instead of targeting ground states as in previous works, the PIs propose to use different cost functions to explore the manifold of highly excited and time-evolved quantum states. After careful benchmarking against known quantum and classical algorithms, both algorithms will be used to address fundamental open scientific questions in the fields of many-body localization, nonlinear response and nonequilibrium dynamics in strongly disordered and interacting quantum systems.The project will advance the capabilities of variational algorithms beyond exploring properties of low-energy states. Results of this research will provide insights into the properties of highly excited states in strongly disordered interacting spin chains, and address the existence of many-body localized phases and mobility emulsions, which violate the eigenstate thermalization hypothesis. The research team will also shed light on the correlation and entanglement properties of prethermal quantum states that emerge in post-quench dynamics and test the hypothesis that their temporal stability is related to a small energy variance, making them approximate eigenstates. Finally, by implementing efficient NISQ quantum algorithms for the computation of higher-order and out-of-time-ordered correlation functions, the PIs will determine whether these functions can disentangle effects of disorder and interactions on quasiparticle properties and explore the onset of quantum chaos in spin models. By addressing fundamental challenges in condensed matter physics, the research team will contribute to the open question of whether NISQ technology offers a practical quantum advantage.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.

非技术摘要这一奖项是根据通过量子算法挑战挑战的渴望提出的提议颁发的。它支持研究和教育，以开发和实施算法，以在不久的将来存在或将存在的量子计算机上运行。预计一台完美的量子计算机可以比任何传统的计算机（也称为经典计算机）更快地解决实践兴趣的问题。示例包括将大量分解为使用Shor算法的素数，并使用Lloyd的算法来预测相互作用的量子系统的时间演变，从而远离平衡。后者可以帮助优化和设计新的生物材料，药物和功能量子材料。尽管过去几年在制造完美的量子计算机方面取得了巨大的进步，但当前的嘈杂的中间量子量子（NISQ）技术可能几十年来就可以在实际相关尺度上运行Shor和Lloyd的算法。根本的原因是NISQ计算机拥有数百吨数的顺序，而不是完全纠正嘈杂的门操作发生的错误所需的数百万美元。这严重限制了与NISQ硬件兼容的量子算法的复杂性，并暴露了进一步的算法进步以实现实用量子优势的批判性需求。在该项目中，PIS开发了针对NISQ量子量子处理单元（QPU）量身定制的算法，可以解决无水平物理学中的基础挑战。具体而言，研究团队提出了新的杂种量子型变分变量算法，以模拟新型材料（例如无序量子磁铁）中的非平衡动力学和高度激发的状态。该算法将在IBM和Rigetti的QPU上实施，并针对现有的最新量子和经典算法进行精心测试。除了找到近期量子优势的机会外，该项目还将提供有关相互作用粒子系统如何放松对热平衡系统的基本问题的新见解，以及它们如何对强大的外部领域做出反应。该项目的教育组成部分解决了量子支持量子的劳动力的国家优先事项，并增强了传统属于替代品的人的参与和增强型不足的人的参与和包容。在该项目中，PI将教育并培训一名研究生，一名本科生和一位博士后学者，以将量子计算算法应用于挑战开放的科学问题。 PIS还将设计一个有关量子计算的动手外展研讨会，并在“进一步”的“走”中介绍该研讨会，这是针对女性中学和高中生的STEM职业会议。最后，PI将为公众创建并维护一个在线博客，该博客重点介绍了量子信息科学中的职业选择，并向读者介绍了量子算法的最新趋势。技术摘要颁奖典礼是根据量子算法挑战的渴望提出的奖项颁发的。它支持研究和教育，以开发和实施算法，以在不久的将来存在或将存在的量子计算机上运行。正如Google团队最近所证明的那样，嘈杂的中间量子量子（NISQ）计算设备具有超出任何经典超级计算机的计算功能。他们是否还为对物理学的兴趣，化学或材料科学的兴趣提供了实用的量子优势是一个悬而未决的问题。量子多体系统中非平衡动力学和高度激发态的模拟是一个有前途但挑战的目标，因为用于此类模拟的经典算法遭受了通用激发态的指数复杂性和高度纠缠的性质。当前NISQ硬件的资源效率量子算法的开发可以解锁其潜力，以应对非平衡多体物理学中的重要挑战。这里，PIS提出了基于变异量子量子量化算法（VQE）Algorithm的两种新颖的变性量子算法，这些算法已证明了NISQ硬件。 PIS建议使用不同的成本函数来探索高度激发和随时间发展的量子状态的多种作用，而不是像以前的工作那样瞄准基态。在针对已知的量子和经典算法进行了仔细的基准测试之后，这两种算法都将用于解决多体定位，非线性响应和非平衡动态和相互作用量子系统的基本开放科学问题。这项研究的结果将提供有关强烈混乱的相互作用旋转链中高度激发态的特性的见解，并解决了多体局部相位和迁移率乳液的存在，这违反了本征态热假设。研究团队还将阐明prethermal量子的相关性和纠缠特性，这些量子量子出现在淬火后动力学中，并检验了其时间稳定性与较小的能量差异有关的假设，使它们近似具有特征性。最后，通过实施有效的NISQ量子算法，以计算高阶和超级订购的相关函数，PIS将确定这些功能是否可以解散疾病的影响和对准粒子性质的相互作用，并探索自旋模型中量子混乱的发作。通过解决凝聚态物理学的基本挑战，研究团队将为NISQ技术是否提供实用的量子优势做出贡献。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影响审查标准来评估的支持。