A Quantum Inspired Approach to Exploit Turbulence Structures

TL;DR

This paper introduces a quantum-inspired method to analyze turbulence by quantifying interscale correlations, leading to a more efficient simulation algorithm that reduces computational complexity and paves the way for quantum computing applications in fluid dynamics.

Contribution

It presents a novel quantum-inspired framework for turbulence analysis and a structure-resolving algorithm that significantly reduces computational requirements for simulating turbulent flows.

Findings

Accurately captures interscale correlations in turbulence

Reduces computational space by over an order of magnitude

Enables potential quantum computing applications in fluid dynamics

Abstract

Understanding turbulence is the key to our comprehension of many natural and technological flow processes. At the heart of this phenomenon lies its intricate multi-scale nature, describing the coupling between different-sized eddies in space and time. Here we introduce a new paradigm for analyzing the structure of turbulent flows by quantifying correlations between different length scales using methods inspired from quantum many-body physics. We present 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 within a computational space reduced by over an order of magnitude compared to direct numerical simulation. Our quantum-inspired approach provides a pathway towards conducting computational fluid dynamics on quantum computers.