The Nonequilibrium Many-Body Problem as a paradigm for extreme data science

TL;DR

This paper discusses the nonequilibrium quantum many-body problem as a model for extreme data challenges, highlighting computational and computer science approaches to manage exponentially large data sets in physics.

Contribution

It reviews how methods from physics and computer science can address extreme data problems exemplified by nonequilibrium quantum many-body systems.

Findings

Progress made with computational methods on the problem

New approaches from computer science improve accuracy and simulation times

The problem serves as a paradigm for extreme data science challenges

Abstract

Generating big data pervades much of physics. But some problems, which we call extreme data problems, are too large to be treated within big data science. The nonequilibrium quantum many-body problem on a lattice is just such a problem, where the Hilbert space grows exponentially with system size and rapidly becomes too large to fit on any computer (and can be effectively thought of as an infinite-sized data set). Nevertheless, much progress has been made with computational methods on this problem, which serve as a paradigm for how one can approach and attack extreme data problems. In addition, viewing these physics problems from a computer-science perspective leads to new approaches that can be tried to solve them more accurately and for longer times. We review a number of these different ideas here.