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.
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.