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Fluid simulations, especially at high Reynolds numbers, are computationally expensive on classical computers, making them promising application targets for quantum computing. Recent studies have combined the lattice Boltzmann method (LBM)…

量子物理 · 物理学 2026-05-28 Kazumasa Ueno , Keita Kanno , Yasunori Lee

We present a quantum computing algorithm for fluid flows based on the Carleman-linearization of the Lattice Boltzmann (LB) method. First, we demonstrate the convergence of the classical Carleman procedure at moderate Reynolds numbers,…

量子物理 · 物理学 2024-05-21 Claudio Sanavio , Sauro Succi

We apply Carleman linearization of the Lattice Boltzmann (CLB) representation of fluid flows to quantum emulate the dynamics of a 2D Kolmogorov-like flow. We assess the accuracy of the result and find a relative error of the order of…

量子物理 · 物理学 2025-05-27 Claudio Sanavio , William A. Simon , Alexis Ralli , Peter Love , Sauro Succi

Quantum computing holds great promise to accelerate scientific computations in fluid dynamics and other classical physical systems. While various quantum algorithms have been proposed for linear flows, developing quantum algorithms for…

流体动力学 · 物理学 2025-02-25 Boyuan Wang , Zhaoyuan Meng , Yaomin Zhao , Yue Yang

This work proposes a multi-circuit quantum lattice Boltzmann method (QLBM) algorithm that leverages parallel quantum computing to reduce quantum resource requirements. Computational fluid dynamics (CFD) simulations often entail a large…

Quantum algorithms have been identified as a potential means to accelerate computational fluid dynamics (CFD) simulations, with the lattice Boltzmann method (LBM) being a promising candidate for realizing quantum speedups. Here, we extend…

The Lattice Boltzmann method (LBM) is a well-established mesoscopic approach for simulating fluid dynamics by evolving particle distribution functions on discrete lattices. While the LBM is highly parallelizable on classical hardware, its…

量子物理 · 物理学 2026-04-14 Trong Duong , Matthias Möller , Norbert Hosters

We present a quantum algorithm for computational fluid dynamics based on the Lattice-Boltzmann method. Our approach involves a novel encoding strategy and a modified collision operator, assuming full relaxation to the local equilibrium…

We propose a quantum algorithm to tackle the quadratic nonlinearity in the Lattice Boltzmann (LB) collision operator. The key idea is to build the quantum gates based on the particle distribution functions (PDF) within the coherence time…

量子物理 · 物理学 2024-10-30 Dinesh Kumar E , Steven H. Frankel

The Quantum Lattice Boltzmann Method (QLBM) is one of the most promising approaches for realizing the potential of quantum computing in simulating computational fluid dynamics. Many recent works mostly focus on classical simulation, and…

量子物理 · 物理学 2025-04-23 Apurva Tiwari , Jason Iaconis , Jezer Jojo , Sayonee Ray , Martin Roetteler , Chris Hill , Jay Pathak

Fluid flow simulations marshal our most powerful computational resources. In many cases, even this is not enough. Quantum computers provide an opportunity to speed up traditional algorithms for flow simulations. We show that lattice-based…

We attempt the use of a unitary operator to approximate the lattice Boltzmann collision operator. We use a modified amplitude encoding to bypass the renormalization that would have required classical processing at every step (thus eroding…

量子物理 · 物理学 2026-01-08 Wael Itani , Katepalli R. Sreenivasan

We present a new 3D lattice Boltzmann (LB) algorithm based on central moments for the D3Q27 lattice using a cuboid grid, which is parameterized by two grid aspect ratios that are related to the ratios of the particle speeds with respect to…

计算工程、金融与科学 · 计算机科学 2021-08-31 Eman Yahia , William Schupbach , Kannan Premnath

The Lattice Boltzmann Method (LBM) is widely recognized as an efficient algorithm for simulating fluid flows in both single-phase and multi-phase scenarios. In this research, a quantum Carleman Linearization formulation of the Lattice…

量子物理 · 物理学 2024-03-05 Bastien Bakker , Thomas W. Watts

This paper presents the first application of quantum generative models to learned latent space representations of computational fluid dynamics (CFD) data. While recent work has explored quantum models for learning statistical properties of…

机器学习 · 计算机科学 2025-12-30 Achraf Hsain , Fouad Mohammed Abbou

Lattice Boltzmann method (LBM) is particularly well-suited for implementation on quantum circuits owing to its simple algebraic operations and natural parallelism. However, most quantum LBMs fix $\tau$ = 1 to avoid nonlinear collision,…

量子物理 · 物理学 2025-05-19 Yang Xiao , Liming Yang , Chang Shu , Yinjie Du , Hao Dong , Jie Wu

We propose a single-step simplified lattice Boltzmann algorithm capable of performing magnetohydrodynamic (MHD) flow simulations in pipes for very small values of magnetic Reynolds numbers $R_m$. In some previous works, most lattice…

流体动力学 · 物理学 2023-01-19 Hugo S. Tavares , Bruno Magacho , Luca Moriconi , Juliana B. R. Loureiro

We propose a quantum algorithm for solving physical problems represented by the lattice Boltzmann formulation. Specifically, we deal with the case of a single phase, incompressible fluid obeying the Bhatnagar-Gross-Krook model. We use the…

量子物理 · 物理学 2023-04-13 Wael Itani , Katepalli R. Sreenivasan , Sauro Succi

Computational Fluid Dynamics simulations are crucial in industrial applications but require extensive computational resources, particularly for extreme turbulent regimes. While classical digital approaches remain the standard, quantum…

量子物理 · 物理学 2025-04-18 Francesco Turro , Alessandra Lignarolo , Daniele Dragoni

We introduce a novel quantum algorithm for the lattice Boltzmann method (LBM) based on the one-step simplified LBM. The structure of the algorithm allows for more flexibility in modelling different physics in contrast to earlier quantum…

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