Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Communications in computer and information science

Quantum Computing and Its Potential for Turbulence Simulations

Shervin Sammak, Arash G. Nouri, Naseem Ansari, Peyman Givi

Published Jan 1, 2015• 7 citations
Publisher
DOI
10.1007/978-3-319-25058-8_13

Related papers

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.

•Sep 6, 2026•0 citations

Towards quantum computing of turbulence

Koji Fukagata

Nature Computational Science•Feb 15, 2022•13 citations

Quantum computing meets compressible aerodynamics - The challenging path to the next generation of aerodynamic simulation

Langer, Stefan, Stefan Görtz

Accurately simulating turbulent, compressible fluids remains a major computational challenge for the aviation industry and a key to achieving future climate goals. Despite advances in computational fluid dynamics (CFD), limitations in turbulence models and the immense cost of high-fidelity simulations make scale-resolving full-aircraft simulations unrealistic. Quantum computing could overcome these barriers, as even few qubits can represent vast degrees of freedom. However, due to numerous obstacles such as the restriction to unitary operators, the read-out problem, and the limited availability of actual quantum hardware, the development of efficient algorithms for solving fluid mechanics equations is a real scientific challenge. This presentation highlights current efforts within the DLR QCI project ToQuaFlics to explore the potential of quantum computing for aerodynamic simulations.

•Dec 2, 2025•0 citations

Quantum computing for nonlinear differential equations and turbulence

Felix Tennie, Sylvain Laizet, Seth Lloyd +1 more

Nature Reviews Physics•Jan 22, 2025•46 citations

Numerical simulation of quantum turbulence

Makoto Tsubota

Bulletin of the American Physical Society•Mar 16, 2016•0 citations

Quantum lattice Boltzmann method for simulating nonlinear fluid dynamics

Boyuan Wang, Zhaoyuan Meng, Yaomin Zhao +1 more

Quantum computing holds transformative potential for simulating nonlinear physical systems, such as fluid turbulence. However, mapping nonlinear dynamics to the linear operations required by quantum hardware remains a fundamental challenge. Here, we bridge this gap by introducing a novel node-level ensemble description of a lattice gas, which enables the simulation of nonlinear fluid dynamics on quantum computers. This approach combines the advantages of the lattice Boltzmann method with low-dimensional representation (computational cost) and lattice gas cellular automata with linear collision treatment (quantum compatibility). Building on this framework, we propose a quantum lattice Boltzmann method that relies on linear operations with medium dimensionality, offering the potential for quantum speedup. We validated the algorithm through simulations of vortex-pair merging and decaying turbulence on up to 16.8 million computational grid points. The results demonstrate remarkable agreement with direct numerical simulation, effectively capturing the essential nonlinear mechanisms of fluid dynamics. This work potentially advances the development of quantum algorithms for other nonlinear problems across various transport phenomena in engineering.

arXiv:2502.16568•npj Quantum Information•Dec 13, 2025•18 citations
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.
Accurately simulating turbulent, compressible fluids remains a major computational challenge for the aviation industry and a key to achieving future climate goals. Despite advances in computational fluid dynamics (CFD), limitations in turbulence models and the immense cost of high-fidelity simulations make scale-resolving full-aircraft simulations unrealistic. Quantum computing could overcome these barriers, as even few qubits can represent vast degrees of freedom. However, due to numerous obstacles such as the restriction to unitary operators, the read-out problem, and the limited availability of actual quantum hardware, the development of efficient algorithms for solving fluid mechanics equations is a real scientific challenge. This presentation highlights current efforts within the DLR QCI project ToQuaFlics to explore the potential of quantum computing for aerodynamic simulations.
Quantum computing holds transformative potential for simulating nonlinear physical systems, such as fluid turbulence. However, mapping nonlinear dynamics to the linear operations required by quantum hardware remains a fundamental challenge. Here, we bridge this gap by introducing a novel node-level ensemble description of a lattice gas, which enables the simulation of nonlinear fluid dynamics on quantum computers. This approach combines the advantages of the lattice Boltzmann method with low-dimensional representation (computational cost) and lattice gas cellular automata with linear collision treatment (quantum compatibility). Building on this framework, we propose a quantum lattice Boltzmann method that relies on linear operations with medium dimensionality, offering the potential for quantum speedup. We validated the algorithm through simulations of vortex-pair merging and decaying turbulence on up to 16.8 million computational grid points. The results demonstrate remarkable agreement with direct numerical simulation, effectively capturing the essential nonlinear mechanisms of fluid dynamics. This work potentially advances the development of quantum algorithms for other nonlinear problems across various transport phenomena in engineering.