AF: Small: Is the Simulation of Quantum Many-Body Systems Feasible on the Cloud?

对于包括超级计算机和服务器数量非常多的计算机云在内的经典计算系统来说，以其独特的叠加、干涉和纠缠等效应模拟量子力学是一个难题。要使用计算机云有效地模拟大型量子力学系统，必须克服主要障碍。本研究探讨了凝聚态物理研究中出现的张量收缩网络的优化算法。这些算法最大限度地减少了计算机云节点之间所需的通信，并利用了其层次化组织。这项工作的更广泛的研究目标是优化利用具有细粒度并行的大数据应用的层次组织系统的体系结构。该研究项目旨在找到新的高效方法来模拟对于量子信息处理、凝聚态物理、材料科学和化学具有重要意义的大型量子系统。它的目标是设计和实现针对云计算环境优化的新型并行和分布式模拟算法，例如亚马逊Web服务和国家科学基金会-S未来的科学计算云。该项目的最终动机是使世界各地的研究人员能够在合理的时间和合理的预算内，在他们可以可靠地模拟的量子系统的大小方面，显著地突破这一界限。虽然这项研究主要集中在研究凝聚态系统性质的有效算法及其实现上，但它也试图将通用策略派生到其他类别的应用中，例如在人工智能和机器学习中。

Simulating quantum mechanics with its unique effects such as superposition, interference, and entanglement is a hard problem for classical computing systems including supercomputers and computer clouds with a very large number of servers. To efficiently simulate large quantum-mechanical systems using a computer cloud one has to overcome major obstacles. This research investigates optimal algorithms for contracting tensor networks which arising in the study of condensed matter physics. These algorithms minimize the required communication between the nodes of the computer cloud and exploit its hierarchical organization. The broader research objective in this effort is to optimally exploit the architecture of hierarchically organized systems for big data applications that exhibit fine-grained parallelism.This research project aims to find new efficient methods for simulating large quantum systems that are important for quantum information processing, condensed matter physics, materials science, and chemistry. Its goals are to design and implement novel parallel and distributed simulation algorithms optimized for cloud computing environments such as Amazon Web Services and the National Science Foundation?s future cloud for scientific computing. The ultimate motivation of this project is to enable researchers world-wide to significantly push the boundary in terms of the size of quantum systems that they can simulate reliably and within a reasonable time and with a reasonable budget. While the research mainly concentrates on efficient algorithms and their implementation for the study of properties of condensed matter systems, it also attempts to derive generic strategies to other classes of applications, for instance, in artificial intelligence and in machine learning.