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EAGER: Collaborative: Tensor Networks Methods for Quantum Simulations

EAGER：协作：量子模拟的张量网络方法

基本信息

批准号：
1844190
负责人：
Claudio Chamon
金额：
$ 18.88万
依托单位：
Trustees of Boston University
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=1844190&HistoricalAwards=false
关键词：
EAGER Collaborative Tensor Networks Methods

项目摘要

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拥有数十个量子比特的小型高质量量子计算机终于问世。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了解某些材料性质的起源以及某些重要的化学反应如何发生。该项目旨在开发新的模拟技术，以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尽可能地扩大系统规模。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，这些量子系统尚未被量子硬件所访问。推进这些领域的知识对于开发更好、更坚固的材料，以及更快、更小的电子产品至关重要。这些项目将有额外的社会效益，在一个非常跨学科的研究领域培养研究生，在计算机科学和物理学之间的界面，从而帮助将高需求的技能带入劳动力市场。该项目包括开发和部署一种使用张量网络模拟量子多体系统的新方法。该方法基于量子动力学演化的状态历史表示，用Keldysh-Schwinger形式主义表示。因此，该方法不是使用张量网络来表示状态向量的概率幅度随时间的演变，而是使用张量网络来表示演变本身，这样网络的完全收缩直接计算诸如可观测值或两点相关函数的期望值等量。在这种方法中，纠缠保持在较低的水平，导致网络链路上的键维较低，使收缩更适合精确计算。对于收缩，采用两步收缩抽取方案对网络进行收缩。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对网络进行抽取。将对这一收缩方案进行编码，以最佳地利用最大的商业云计算服务实例上的可用资源。项目期间开发的代码将通过公共存储库提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。