Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers

Abstract

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science.

31 pages, 10 figures

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Cited by in corpus (4)

• A hybrid quantum-classical framework for computational fluid dynamics
• Quantum homotopy analysis method with quantum-compatible linearization for nonlinear partial differential equations
• Quantum implicit representation of vortex filaments in turbulence
• Nonlinear path-following via the asymptotic numerical method on a quantum processor