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Complete quantum-inspired framework for computational fluid dynamics

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abstract

Computational fluid dynamics is both a thriving research field and a key tool for advanced industry applications. The central challenge is to simulate turbulent flows in complex geometries, a compute-power intensive task due to the large vector dimensions required by discretized meshes. We present a full-stack method to solve for incompressible fluids with memory and runtime scaling poly-logarithmically in the mesh size. Our framework is based on matrix-product states, a powerful compressed representation of quantum states. It is complete in that it solves for flows around immersed objects of arbitrary geometries, with non-trivial boundary conditions, and self-consistent in that it can retrieve the solution directly from the compressed encoding, i.e. without ever passing through the expensive dense-vector representation. This machinery lays the foundations for a new generation of potentially radically more efficient solvers of real-life fluid problems.

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Inchworm tensor train hybridization expansion quantum impurity solver

cond-mat.str-el · 2025-05-22 · conditional · novelty 6.0

A tensor-train inchworm hybridization-expansion solver is benchmarked against exact solutions, but its multi-orbital results bypass the inchworm propagation step by substituting the exact diagonalization propagator.

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  • Inchworm tensor train hybridization expansion quantum impurity solver cond-mat.str-el · 2025-05-22 · conditional · none · ref 29 · internal anchor

    A tensor-train inchworm hybridization-expansion solver is benchmarked against exact solutions, but its multi-orbital results bypass the inchworm propagation step by substituting the exact diagonalization propagator.