A multi-ansatz variational quantum solver for compressible flows

TL;DR

This paper presents a hybrid quantum-classical variational solver with a multi-ansatz architecture for efficiently solving linear systems in compressible flow simulations, demonstrating accurate shock capturing on quantum virtual machines.

Contribution

It introduces a multi-ansatz variational quantum linear solver framework that enhances solution space and stability for CFD problems, suitable for NISQ and future quantum hardware.

Findings

Accurately captures shock and discontinuities in 1D simulations.

Increasing ansatz branches improves convergence and stability.

Effective on quantum virtual machines with limited qubits.

Abstract

Simulating nonlinear partial differential equations (PDEs) such as the Navier--Stokes (NS) equations remains computationally intensive, especially when implicit time integration is used to capture multiscale flow dynamics. This work introduces a hybrid quantum--classical framework for solving the linear systems arising from such implicit schemes in compressible flow simulations. At its core is a variational quantum linear solver (VQLS) enhanced by a multi-ansatz tree architecture, designed to expand the accessible solution space and alleviate training issues such as barren plateaus. The proposed method is evaluated through one-dimensional shock tube simulations implemented on a quantum virtual machine. Results demonstrate that the solver accurately captures shock, rarefaction, and contact discontinuities across a range of test cases. Parametric studies further show that increasing the number of ansatz branches and applying domain decomposition improves convergence and stability, even under limited qubit resources. These findings suggest that multi-ansatz VQLS architectures offer a promising pathway for incorporating quantum computing into computational fluid dynamics (CFD), with compatibility for both current noisy intermediate-scale quantum (NISQ) hardware and future fault-tolerant devices.