A quantum-inspired approach to exploit turbulence structures

Nikita Gourianov, Michael Lubasch, Sergey Dolgov, Quincy Y. van den Berg, Hessam Babaee, Peyman Givi, Martin Kiffner, Dieter Jaksch

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Understanding turbulence is key to our comprehension of many natural and technological flow processes. At the heart of this phenomenon lies its intricate multiscale nature, describing the coupling between different-sized eddies in space and time. Here we analyze the structure of turbulent flows by quantifying correlations between different length scales using methods inspired from quantum many-body physics. We present the results for interscale correlations of two paradigmatic flow examples, and use these insights along with tensor network theory to design a structure-resolving algorithm for simulating turbulent flows. With this algorithm, we find that the incompressible Navier–Stokes equations can be accurately solved even when reducing the number of parameters required to represent the velocity field by more than one order of magnitude compared to direct numerical simulation. Our quantum-inspired approach provides a pathway towards conducting computational fluid dynamics on quantum computers.

Original languageEnglish
Pages (from-to)30-37
Number of pages8
JournalNature Computational Science
Issue number1
Early online date13 Jan 2022
Publication statusPublished - 31 Jan 2022

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Science (miscellaneous)


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