Technical report on a quantum-inspired solver for simulating compressible flows

TL;DR

This paper introduces a quantum-inspired tensor network solver for 2D Euler equations that significantly reduces computational complexity, offering a promising approach for simulating compressible flows efficiently.

Contribution

It presents a novel tensor network-based solver that scales polylogarithmically with mesh size, addressing the curse of dimensionality in computational fluid dynamics.

Findings

Solver scales polylogarithmically with mesh size in runtime

Reduces memory requirements for simulating compressible flows

Demonstrates potential for quantum-inspired CFD applications

Abstract

This document presents a quantum-inspired solver for 2D Euler equations, accepted at the final phase of the Airbus-BWM Group Quantum Computing Challenge (ABQCC) 2024. We tackle the case study of Quantum Solvers for Predictive Aeroacoustic and Aerodynamic modeling tasks. We propose a tensor network based solver that scales polylogarithmically with the mesh size, in both runtime and memory. This provides a promising avenue for tackling the curse of dimensionality that plagues the direct numerical simulations in the field of computational fluid dynamics.