EAGER: Collaborative Research: Tensor Network Methods for Quantum Simulations

EAGER：协作研究：量子模拟的张量网络方法

• 批准号: 1844434
• 负责人: Eduardo Mucciolo
• 金额: $ 10.92万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844434&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Tensor; Network

Recent advances in quantum technologies have made small, high-quality quantum computers with tens of qubits finally available. While these machines are still too small to make an impact on areas such as cryptography, they are big enough to study physical and chemical systems of relevance to materials science and chemistry, thus helping us better understand the origins of certain materials properties and how certain important chemical reactions take place. This project aims at developing novel simulation techniques to help benchmark these quantum machines. These simulations will run on ordinary computers, but will make use of cloud computing services to scale up the system sizes as far as possible. The simulation techniques will also be used to investigate other quantum systems of relevance to physics and materials science that are not yet accessible to quantum hardware. Advancing knowledge in those areas is essential for developing better and stronger materials, as well as faster and smaller electronics. These projects will have the additional societal benefit of training graduate students in a very interdisciplinary area of research, at the interface between computer science and physics, thus helping bring highly-sought skills into the workforce.The project consists of developing and deploying a novel method to simulate quantum many-body systems using tensor networks. The method is based on the state history representation of the quantum dynamical evolution, as expressed in the Keldysh-Schwinger formalism. Thus, rather than using the tensor network to represent the evolution of the probability amplitude of a state vector over time, the method uses the tensor network to represent the evolution itself, such that the full contraction of the network directly calculates quantities such as the expectation value of an observable or a two-point correlation function. In this approach, entanglement is kept low, resulting in low bond dimensions on the network links, making contractions more amenable to exact computations. For the contraction, a two-step contraction-decimation scheme is used to collapse the network. More specifically, the scheme consists of the removal of local entanglement by compressing the information via singular value decomposition, followed by the decimation of the network by selectively removing rows or columns of the network. This contraction scheme will be coded to optimally utilize the resources available on the largest instances of commercial cloud computing services. The codes developed during the project will be made available through public repositories.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.

量子技术的最新进展使得具有数十个量子位的小型高质量量子计算机终于可用。 虽然这些机器仍然太小，无法对密码学等领域产生影响，但它们足够大，可以研究与材料科学和化学相关的物理和化学系统，从而帮助我们更好地理解某些材料特性的起源以及某些特性的重要性。发生化学反应。该项目旨在开发新颖的模拟技术来帮助对这些量子机器进行基准测试。 这些模拟将在普通计算机上运行，​​但将利用云计算服务来尽可能扩大系统规模。模拟技术还将用于研究与物理和材料科学相关的其他量子系统，这些系统尚无法通过量子硬件实现。推进这些领域的知识对于开发更好、更强的材料以及更快、更小的电子产品至关重要。这些项目将带来额外的社会效益，即在计算机科学和物理学之间的跨学科研究领域培训研究生，从而帮助将备受追捧的技能带入劳动力队伍。该项目包括开发和部署一种新颖的方法使用张量网络模拟量子多体系统。 该方法基于量子动力学演化的状态历史表示，如凯尔迪什-施温格形式主义所示。 因此，该方法不是使用张量网络来表示状态向量的概率幅随时间的演化，而是使用张量网络来表示演化本身，使得网络的完全收缩直接计算诸如期望之类的量可观测函数或两点相关函数的值。 在这种方法中，纠缠保持在较低水平，从而导致网络链接上的键维数较低，从而使收缩更适合精确计算。 对于收缩，使用两步收缩抽取方案来崩溃网络。 更具体地说，该方案包括通过奇异值分解压缩信息来去除局部纠缠，然后通过选择性地去除网络的行或列来抽取网络。 这种收缩方案将被编码以最佳地利用最大的商业云计算服务实例上的可用资源。 该项目期间开发的代码将通过公共存储库提供。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

Nonuniversal entanglement level statistics in projection-driven quantum circuits

投影驱动量子电路中的非普适纠缠级统计

• DOI: 10.1103/physrevb.101.235104
• 发表时间: 2020-06
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Zhang, Lei;Reyes, Justin A.;Kourtis, Stefanos;Chamon, Claudio;Mucciolo, Eduardo R.;Ruckenstein, Andrei E.
• 通讯作者: Ruckenstein, Andrei E.

Quantum coherence in ergodic and many-body localized systems

遍历和多体局域系统中的量子相干性

• DOI: 10.1103/physrevb.102.045140
• 发表时间: 2020-02-21
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: S. Dhara;A. Hamma;E. Mucciolo
• 通讯作者: E. Mucciolo