We develop simulation and design methods for high-performance, long-lasting fuel cell systems. In doing so, we are paving the way for hydrogen-based propulsion systems for climate-neutral aviation and accelerating their industrial deployment.

With Q2Fly, we are closing an important technological gap in the field of sustainable aviation: Until now, there has been a lack of powerful methods for efficiently simulating charge transport mechanisms and catalytic processes in fuel cells under realistic operating conditions and for identifying suitable materials based on these simulations. Q2Fly combines hardware-optimized quantum simulation with AI-driven quantum optimization, thereby laying the foundation for systematic, faster, and more resource-efficient material development for long-lasting, high-performance fuel cells in aviation.

Motivation

With Q2Fly, we aim to advance the development of high-performance fuel cell materials for a decarbonized aviation industry. To this end, we use quantum algorithms to simulate relevant microscopic transport processes under electrochemical conditions that closely resemble those encountered during operation. This allows us to better understand the processes occurring within the material and develop new materials in a more targeted manner. We deliberately use the noise inherent in today’s quantum hardware to model interactions with the material’s environment—thereby turning a technical limitation into a resource.

We combine these simulations with data-driven methods of inverse design and optimization. In this way, we simultaneously improve the properties of the materials under consideration in terms of activity, transport, stability, cost, and availability. This marks the first time we have combined quantum simulations with data-driven material design. As a result, we enable realistic, multi-criteria material optimization on quantum hardware.

Challenge

With Q2Fly, we aim to develop new fuel cell materials more quickly and with greater precision. To achieve this, we combine quantum simulation, AI-driven active learning, and quantum optimization. The goal is to gain a better understanding of complex charge transport processes and to identify promising candidates from large material design spaces in a targeted manner. In this way, we aim to complement experimental trial-and-error with data- and physics-based material design.

In doing so, we combine DLR’s technical expertise in the fields of fuel cells and quantum simulation with H2Fly’s practical experience in developing and testing hydrogen-powered aircraft systems. Through the DLR QCI, we implement the methods we’ve developed on high-performance quantum hardware.

Impact

With Q2Fly, we are bridging the gap between available quantum hardware and an application of industrial relevance: material development for hydrogen-based fuel cells. This provides the DLR QCI with a concrete use case that demonstrates the advantages of quantum computing on real hardware and the transfer of technology to industry. At the same time, transferable methods for material and molecular simulation as well as for quantum optimization are being developed, thereby generating expertise for a European quantum computing ecosystem.