Rolls-Royce and partners slash jet engine simulations with quantum computing - Airport Technology

Rolls-Royce reduced simulation runtimes from weeks to under an hour. Credit: Sarmiko.id / Shutterstock Rolls-Royce, Xanadu, and Riverlane have reported a substantial reduction in the time required to simulate jet engine airflow by applying quantum computing methods. The collaboration combined Xanadu’s PennyLane software with Riverlane’s quantum algorithms, enabling Rolls-Royce to decrease simulation runtimes from several weeks to less than an hour. Go deeper with GlobalDataReports Innovation in Ship: Gas turbine-propelled vessels Reports Innovation in Ship: Vessel internal combusion engine Go deeper with GlobalData The gold standard of business intelligence. Find out more Discover B2B Marketing That Performs Combine business intelligence and editorial excellence to reach engaged professionals across 36 leading media platforms. Find out more This outcome is expected to affect prototyping and design processes in the aerospace sector. The project is said to address the computational demands of simulating airflow in jet engines, a process that involves solving large systems of linear equations. This typically results in extended runtimes when using classical supercomputers. Xanadu CEO and founder Christian Weedbrook said: “Our efforts here successfully reduced prototyping runtimes by up to 1000-fold in some instances, overcoming the unsustainable wait times of weeks for Rolls-Royce. “To make quantum simulations, and more generally quantum computing, practical for Rolls-Royce, we targeted their specific application and used our Catalyst compiler to optimise their PennyLane programmes.” GlobalData Strategic Intelligence US Tariffs are shifting - will you react or anticipate? Don’t let policy changes catch you off guard. Stay proactive with real-time data and expert analysis. By GlobalData Learn more about Strategic Intelligence Quantum computing offers an alternative approach, with the potential to accelerate these simulations and support more efficient engineering workflows. Joint funding from the governments of Canada and the UK supported the initiative, which aimed to advance quantum computing expertise across both countries. Rolls-Royce provided the industrial use case, Riverlane contributed quantum algorithms, and Xanadu optimised the quantum-classical programme through its PennyLane platform and Catalyst compiler. Rolls-Royce computational science fellow Leigh Lapworth said: “This has been a hugely successful collaboration, which has significantly advanced our quantum applications capability. “The single-minded focus on fault tolerant quantum algorithms has put us and our partners in a leading position as we enter the error-corrected era.” The collaboration drew on the technical capabilities of all three organisations to address the full prototyping process. The work has produced quantum algorithm components and software implementations that will allow Rolls-Royce to access relevant insights as soon as Xanadu’s fault-tolerant quantum hardware becomes available. The project also identified and addressed key bottlenecks in the simulation workflow. Riverlane staff quantum scientist Christoph Sünderhauf said: “To solve problems on a quantum computer, certain parameters of the quantum circuit need to be precomputed classically. “This process was traditionally very time-consuming. However, our research, along with that of academic groups, has significantly accelerated this step, enabling the classical preprocessing to keep pace with the quantum computation itself.” Sign up for our daily news round-up! Give your business an edge with our leading industry insights. Sign up Go deeper with GlobalData Reports Innovation in Ship: Gas turbine-propelled vessels Reports Innovation in Ship: Vessel internal combusion engine Go deeper with GlobalData The gold standard of business intelligence. Find out more Access deeper industry intelligence Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.