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Feasibility of Accelerating Incompressible Computational Fluid Dynamics Simulations With Fault-Tolerant Quantum Computers

Michael C. Garrett, Dmitriy Ponkratov, Wei Qiu

Marine Technology Society JournalJan 24, 20251 citation

The Quantum Lattice Boltzmann Methods: Towards Quantum Methods for Computational Fluid Dynamics

Merel A. Schalkers

Jan 13, 20260 citations

A Review of Quantum Machine Learning and Quantum-inspired Applied Methods to Computational Fluid Dynamics

Cesar A. Amaral, Vinícius L. Oliveira, Juan P. L. C. Salazar +1 more

arXiv:2510.14099Brazilian Journal of PhysicsDec 12, 20254 citations

Quantum Fluid Dynamics and Quantum Computational Fluid Dynamics

Chia‐Liang Lin, Jenn‐Kun Kuo, Tsu-Hsu Yen

Journal of Computational and Theoretical NanoscienceApr 1, 20092 citations

Quantum Finite Volume Method for Computational Fluid Dynamics with Classical Input and Output

Zhaoyun Chen, Cheng Xue, Siming Chen +5 more

Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical methods to solve fluid flows. The finite volume method (FVM) is an important one. In FVM, space is discretized to many grid cells. When the number of grid cells grows, massive computing resources are needed correspondingly. Recently, quantum computing has been proven to outperform a classical computer on specific computational tasks. However, the quantum CFD (QCFD) solver remains a challenge because the conversion between the classical and quantum data would become the bottleneck for the time complexity. Here we propose a QCFD solver with exponential speedup over classical counterparts and focus on how a quantum computer handles classical input and output. By utilizing quantum random access memory, the algorithm realizes sublinear time at every iteration step. The QCFD solver could allow new frontiers in the CFD area by allowing a finer mesh and faster calculation.Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical methods to solve fluid flows. The finite volume method (FVM) is an important one. In FVM, space is discretized to many grid cells. When the number of grid cells grows, massive computing resources are needed correspondingly. Recently, quantum computing has been proven to outperform a classical computer on specific computational tasks. However, the quantum CFD (QCFD) solver remains a challenge because the conversion between the classical and quantum data would become the bottleneck for the time complexity. Here we propose a QCFD solver with exponential speedup over classical counterparts and focus on how a quantum computer handles classical input and output. By utilizing quantum random access memory, the algorithm realizes sublinear time at every iteration step. The QCFD solver could allow new frontiers in the CFD area by allowing a finer mesh and faster calculation.

arXiv:2102.03557Feb 6, 20218 citations

Quantum-Inspired Simulation of Complex Fluid Dynamics

Jincheng Zhang

This paper presents a novel approach to simulating complex fluid dynamics, specifically turbulent flows, utilizing quantum-inspired algorithms. Traditional numerical methods, such as Navier-Stokes solvers, often struggle with the computational demands of accurately representing turbulence, leading to significant processing times and limitations in practical applications. We introduce a simulation strategy based on quantum amplitude estimation (QAE), a technique inspired by quantum computing principles, to accelerate the solution process. The core idea is to approximate the probability amplitude associated with different fluid states, thereby reducing the number of iterations required to reach a stable solution. We demonstrate, through theoretical analysis and a simplified numerical framework, that QAE offers a promising path toward more efficient and accurate simulations of turbulent flows, potentially bridging the gap between classical computational fluid dynamics (CFD) and the computational benefits anticipated from full-scale quantum simulation. This work lays the groundwork for future research exploring the full potential of quantum-inspired algorithms in tackling complex fluid dynamics problems.This paper presents a novel approach to simulating complex fluid dynamics, specifically turbulent flows, utilizing quantum-inspired algorithms. Traditional numerical methods, such as Navier-Stokes solvers, often struggle with the computational demands of accurately representing turbulence, leading to significant processing times and limitations in practical applications. We introduce a simulation strategy based on quantum amplitude estimation (QAE), a technique inspired by quantum computing principles, to accelerate the solution process. The core idea is to approximate the probability amplitude associated with different fluid states, thereby reducing the number of iterations required to reach a stable solution. We demonstrate, through theoretical analysis and a simplified numerical framework, that QAE offers a promising path toward more efficient and accurate simulations of turbulent flows, potentially bridging the gap between classical computational fluid dynamics (CFD) and the computational benefits anticipated from full-scale quantum simulation. This work lays the groundwork for future research exploring the full potential of quantum-inspired algorithms in tackling complex fluid dynamics problems.

Sep 6, 20260 citations

Quantum Approach to Accelerate Finite Volume Method on Steady Computational Fluid Dynamics Problems

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