Quandela’s Photonic Tech to Accelerate Safran’s Engine Design Cycles

Safran Tech is partnering with Quandela to leverage photonic quantum technology, using light to process quantum information, for simulating airflow around aircraft. The newly launched AQeFLU project aims to increase the number of engine design configurations tested, improving accuracy and supplementing traditional wind tunnel testing and numerical calculations. This collaboration intends to accelerate the development cycles of future engines and energy systems, potentially leading to significant improvements in aircraft performance and fuel efficiency. “In the long term, quantum computing could make industrial design phases more efficient, and enable advances such as improved aerodynamic performance and reduced fuel consumption,” explains Frédéric Feyel, Director of the Digital Platform at Safran Tech. The research is supported by the Île-de-France Region’s PAQ Quantique programme, indicating regional investment in industrial quantum applications.

Quandela Photonic Technology and Safran Fluid Dynamics Expertise

The pursuit of more efficient aircraft designs is now aided by light itself. Safran Tech, the research and technology centre of Safran, is collaborating with Quandela to apply photonic quantum technology to the complex problem of modelling airflow, a different approach than more common quantum computing methods based on superconductivity or trapped ions. This partnership, formalized in the AQeFLU research project, seeks to improve the accuracy and speed of aerodynamic simulations, potentially reducing reliance on expensive and time-consuming wind tunnel testing. This innovation combines Quandela’s expertise in photonic quantum computing, which utilizes light to process quantum information, with Safran Tech’s established knowledge of fluid mechanics and computational methods. Teams will initially validate algorithms using standardized academic models before applying them to advanced industrial challenges, specifically in combustion and propulsion systems.

The potential for reduced fuel consumption highlights the broader implications for sustainability within the aerospace industry. Valérian Giesz, co-founder and COO of Quandela, emphasizes the significance of this collaboration, stating, “We are delighted with this new partnership with Safran, an industrial group recognised worldwide for its expertise in the fields of aerospace, space and defence.” Quandela’s systems are designed for practical deployment, operating at room temperature and integrating with existing data centre infrastructure, positioning them as a viable solution for real-world industrial challenges.

AQeFLU Project Aims to Accelerate Aerospace Design Cycles

The aerospace industry currently relies heavily on extensive physical testing and computationally intensive simulations to refine aerodynamic designs; these processes are time-consuming and expensive, limiting the speed of innovation in engine and aircraft development. Safran Tech and Quandela are attempting to overcome these limitations through the AQeFLU project, focusing on the application of photonic quantum computing to model airflow with increased precision. Unlike many quantum computing initiatives centered on superconductivity or trapped ions, this collaboration leverages light to process quantum information, an approach that Quandela has specialized in developing for practical, real-world applications. The core objective isn’t simply to replicate existing simulations faster, but to dramatically expand the scope of design exploration; researchers aim to multiply the number of engine configurations they can accurately test, potentially identifying more efficient and higher-performing designs than currently feasible. This research benefits from regional support through the Île-de-France Region’s PAQ Quantique programme, demonstrating a commitment to fostering quantum technology within the French industrial sector.

In the long term, the use of quantum computing could make industrial design phases more efficient, whilst paving the way for tangible advances, such as improved aerodynamic performance and reduced fuel consumption.

Frédéric Feyel, Director of the Digital Platform at Safran Tech
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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