Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Tools of the Trade
  • Published:

Machine learning toolbox for quantum many body physics

Nature Reviews Physics volume 3, page 156 (2021)Cite this article

Subjects

The past decade has seen a hectic development in the field of machine learning (ML), with applications touching every sector from healthcare and energy grids to manufacturing and autonomous driving. ML represents a major change in the way we devise algorithms: instead of hard-coding a specific set of rules, only the high-level structure of the solution needs to be defined while the rest is obtained by learning the underlying patterns in data. This approach allowed the development of powerful algorithms with a compact representation in memory for certain types of problems.

A new approach has emerged bridging the gap between quantum physics and ML. The central idea is to encode the quantum state into a compact function approximator that can capture the underlying structure: neural networks. Their strength comes from their ability to capture long range correlations in arbitrary dimensions with few parameters, and from their flexibility, as it is relatively easy to enforce symmetries or constraints of the Hilbert space within the approximation itself. This dense representation is then complemented by Monte Carlo sampling of the configuration space in order to address the exponential blow-up of both memory and runtime.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Author information

Authors and Affiliations

  1. Institute of Physics, EPFL, Lausanne, Switzerland

    Filippo Vicentini

Authors
  1. Filippo Vicentini
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Filippo Vicentini.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vicentini, F. Machine learning toolbox for quantum many body physics. Nat Rev Phys 3, 156 (2021). https://doi.org/10.1038/s42254-021-00285-7

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s42254-021-00285-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing