Simulation of Quantum Many-Body Systems on Amazon Cloud

TL;DR

This paper demonstrates that cloud computing, specifically AWS EC2, can effectively simulate quantum many-body systems using tensor network contractions, offering a scalable alternative to supercomputers.

Contribution

It introduces a heuristic method for tensor network contraction that improves efficiency and demonstrates its application on AWS EC2 for quantum system simulations.

Findings

Cloud computing is a viable alternative to supercomputers for quantum simulations.

The heuristic tensor network contraction reduces computation time and memory usage.

Successful simulation of the Ising model on large instances using AWS EC2.

Abstract

Quantum many-body systems (QMBs) are some of the most challenging physical systems to simulate numerically. Methods involving approximations for tensor network (TN) contractions have proven to be viable alternatives to algorithms such as quantum Monte Carlo or simulated annealing. However, these methods are cumbersome, difficult to implement, and often have significant limitations in their accuracy and efficiency when considering systems in more than one dimension. In this paper, we explore the exact computation of TN contractions on two-dimensional geometries and present a heuristic improvement of TN contraction that reduces the computing time, the amount of memory, and the communication time. We run our algorithm for the Ising model using memory optimized x1.32x large instances on Amazon Web Services (AWS) Elastic Compute Cloud (EC2). Our results show that cloud computing is a viable alternative to supercomputers for this class of scientific applications.