Year: 2025
Type: article
Abstract: Computational fluid dynamics (CFD) is a specialized branch of fluid mechanics that utilizes numerical methods and algorithms to solve and analyze fluid-flow problems. One promising avenue to enhance CFD is the use of quantum computing, which has the potential to resolve nonlinear differential equations more efficiently than classical computers. Here, we try to answer the question of which regimes of nonlinear partial differential equations for fluid dynamics can have an efficient quantum algorithm. We propose a connection between the numerical parameter R , which guarantees efficiency in the truncation of the Carleman linearization, and the physical parameters that describe the fluid flow. This link can be made thanks to the Kolmogorov scale, which determines the minimum size of the grid needed to properly resolve the energy cascade induced by the nonlinear term. Additionally, we introduce the formalism for vector field simulation in different spatial dimensions, providing the discretization of the operators and the boundary conditions.
Source: Physical Review Research
Authors: Javier Gonzalez-Conde, Dylan Lewis, Sachin S. Bharadwaj, Mikel Sanz
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,625
Awards: G1622782223, G2262748287, EPSRC Centre for Doctoral Training in Delivering Quantum Technologies, G3371861857, G3480869486, G3496905377, G3681454997, G3987967833, G4042783231, G425668148, G4484594549, G451917667, Open Superconducting Quantum Computers (OpenSuperQPlus), G5967599077, G5987792135, G6033112984, G6351756048, G67347242, G7084143925, G7421034968, G8953647516
OpenAlex ID: W4404343031