Quantum Computational Fluid Dynamics (QCFD)

QCFD:Quantum Computational Fluid Dynamics

Scientific and technological progress is broadly underpinned by the ability to accurately predict and optimise complex fluid flows which arise across the physical and life sciences including climate research, as well as in the energy, chemical, automotive, aircraft, and ship building industries. The wide separation of length and time scales that need to be covered when designing and optimising flows and a large number of design parameters make numerical simulations highly demanding. Current capabilities are thus insufficient to meet future demands of users in academia and industry.

We will tackle this challenge by developing a quantum software framework for solving a wide range of industrially relevant computational fluid dynamics problems. This will consist of platform-independent quantum algorithms and hardware optimised software for platforms in the European Quantum Technology Flagship Projects. Tensor-network simulations, gate-level classical simulations including realistic quantum noise models, and implementations on quantum hardware will provide detailed information on quantum hardware requirements, achievable quantum advantages, and provide feedback to hardware developers. The quantum software will be verified and benchmarked against standard computational fluid dynamics results. It will be developed in agile cycles to respond quickly to user demands and progress in the quality of quantum hardware.

We will demonstrate the feasibility and advantages of the quantum approach starting from a core set of highly scalable and industrially relevant design examples arising in the thermal management of battery-electric-vehicles aimed at increasing their efficiency. Subsequently, we will extend our approach to a wider class of fluid flows and industry partners. We will create an interface between the quantum software framework and the industry standard computational fluid dynamics software OpenFOAM to make it widely available and maximise its impact.

Website:

www.qcfd-h2020.eu

https://cordis.europa.eu/project/id/101080085

Joint Publications

 

  • "Operator Learning for efficient Quantum Computation", Over, Paul; Bengoechea, Sergio;   Busilacchi, Leonardo Borello ; Kiffner, Martin;  Rung, Thomas; Michailidis, Alexios. arXiv preprint (2026) Preprint DOI.
  • "Quantum-inspired tensor-network fractional-step method for incompressible flow in curvilinear coordinates", Hülst, Nis-Luca van; Siegl, Pia; Over, Paul; Bengoechea, Sergio; Hashizume, Tomohiro; Cecile, Mario Guillaume; Rung, Thomas; Jaksch, Dieter. Computer Physics Communications 325: 110169 (2026) Publisher DOI
  • "Quantum time‐marching algorithms for solving linear transport problems including boundary conditions", Bengoechea Lozano, Sergio; Over, Paul; Rung, Thomas. International Journal for Numerical Methods in Engineering 127 (8): e70326 (2026) Publisher DOI
  • "Toward variational quantum algorithms for generalized linear and nonlinear transport phenomena", Bengoechea Lozano, Sergio; Over, Paul; Jaksch, Dieter; Rung, Thomas. AIAA Journal 64 (2): 585-604 (2026) Publisher DOI
  • "Quantum algorithm for the advection-diffusion equation by direct block encoding of the time-marching operator", Over, Paul; Bengoechea, Sergio; Brearley, Peter; Laizet, Sylvain; Rung, Thomas. Physical Review A / Atomic, molecular, and optical physics 112 (1): L010401 (2025) Publisher DOI
  • "Boundary treatment for variational quantum simulations of partial differential equations on quantum computers", Over, Paul; Bengoechea, Sergio; Rung, Thomas; Clerici, Francesco; Scandurra, Leonardo; De Villiers Eugene; Jaksch, Dieter. Computers and Fluids 288: 106508 (2025) Publisher DOI
  • "Variational quantum algorithms for computational fluid dynamics", Jaksch, Dieter; Givi, Peyman; Daley, Andrew J.; Rung, Thomas. AIAA Journal 61 (5): 1885-1894 (2023) Publisher DOI

Metadata

Project AcronymQCFD
Project TitleQuantum Computational Fluid Dynamics
Duration2022-01-11 - 2027-04-30
Principal InvestigatorThomas Rung
ResearchersPaul Over, Sergio Bengoechea

Project partners

    Funding

    This project has received funding from the European Union´s Horizon Europe research and innovation programme (HORIZON-CL4-2021-DIGITAL-EMERGING-02-10) under grant agreement No. 101080085 QCFD.