Quantum Algorithms, Quantum Hardware

Quantum fluid dynamics codes tested on QuTech’s Tuna-9 and Tuna-17 chips

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November 6, 2026 by Ivy Delaney

Researchers at TU Delft have bridged algorithm development with quantum hardware by running computational fluid dynamics (CFD) algorithms on the locally developed Tuna-9 and Tuna-17 quantum processors. The Quantum Computational Fluid Dynamics (QCFD) Lab, led by Associate Professor Matthias Möller, used TU Delft’s Quantum Inspire platform to test their methods on actual quantum hardware without cost. This work explores potential quantum applications in sectors including aerospace, automotive, energy and transportation.

Quantum CFD Algorithms Tested on Delft’s Tuna Processors

Quantum Inspire, TU Delft’s cloud-based platform, provided the QCFD Lab with a zero-cost environment to test their algorithms on actual quantum hardware, which was beneficial given the current limitations of quantum computing for complex simulations. The team ran a quantum lattice Boltzmann simulation on a one-dimensional grid with eight grid cells, initializing fluid velocity to the right and allowing it to stream for four timesteps, then compared the results to a perfect state-vector quantum simulator and other commercially available quantum processing units.

Compiled circuits for the simulation exceeded one thousand native gates, approaching the maximum capacity of current noisy intermediate-scale quantum (NISQ) machines. The experiments used QuTech’s Tuna-9 and Tuna-17 processors.

Associate Professor Matthias Möller, leading the QCFD Lab, noted that “It is still a long way before we can run meaningful CFD simulations on a quantum computer, but the opportunity to benchmark our codes at zero cost on QuTech’s on-premises quantum computers is very helpful to evaluate where we stand.” Results after five timesteps demonstrated a significant degradation in accuracy, highlighting the challenges of maintaining coherence in complex quantum computations. This work extends beyond the immediate scope of quantum hardware development at QuTech, aligning with the institution’s broader goal of supporting quantum computing accessibility for diverse research groups.

QuTech’s collaboration with the QCFD Lab exemplifies this approach, combining expertise in quantum computing and computational fluid dynamics to test algorithms, identify limitations and contribute to the advancement of future quantum applications, the company says. The team’s focus remains on algorithm development, using the hardware as a benchmarking tool to assess progress and guide future research directions, even as practical CFD simulations on quantum computers remain a long-term goal.

It is still a long way before we can run meaningful CFD simulations on a quantum computer, but the opportunity to benchmark our codes at zero costs on QuTech’s on-premises quantum computers is very helpful to evaluate where we stand, even if our focus as a team is on advancing algorithms and methodologies.

Matthias Möller, Associate Professor at TU Delft
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Computational fluid dynamics quantum algorithms Quantum Computing QuTech TU Delft
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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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