Q2Fly – Quantum effects and polarons in fuel cells

Project duration: 01.09.2026 – 31.12.2028

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 industrial scale-up.

With Q2Fly, we are addressing a key technological gap in sustainable aviation: to date, 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 using these simulations to identify suitable materials. Q2Fly combines hardware-optimised quantum simulation with AI-supported quantum optimisation, thereby laying the foundation for systematic, faster and more resource-efficient materials development for long-lasting, high-performance fuel cells in aviation.

Motivation

With Q2Fly, we aim to accelerate the development of high-performance fuel cell materials for a decarbonised aviation sector. To this end, we use quantum algorithms to simulate relevant microscopic transport processes under electrochemical conditions that closely resemble those encountered in operation. This enables us to gain a better understanding of the processes within the material and to develop new materials in a more targeted manner. We deliberately utilise 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 inverse design and optimisation methods. In this way, we simultaneously improve candidate materials in terms of activity, transport, stability, cost and availability. This marks the first time we have combined quantum simulation with data-driven materials design. In this way, we enable realistic, multi-criteria material optimisation on quantum hardware.

Challenge

With Q2Fly, we aim to develop new fuel cell materials more quickly and in a more targeted manner. To achieve this, we combine quantum simulation, AI-supported active learning and quantum optimisation. The aim 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 expertise in fuel cells and quantum simulation with H2Fly’s practical experience in the development and testing of hydrogen-powered aircraft systems. Through the DLR QCI, we implement the methods developed on high-performance quantum hardware.

Impact

With Q2Fly, we are building a bridge between available quantum hardware and an application of industrial relevance: materials development for hydrogen-based fuel cells. This provides the DLR QCI with a concrete use case that demonstrates the benefits 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 quantum optimisation are being developed, thereby generating expertise for a European quantum computing ecosystem.