Quantum CFD algorithm cuts qubit needs for fluid modelling

Quanscient and Haiqu have unveiled a new algorithm for computational fluid dynamics simulations on quantum computers that reduces the number of qubits needed for complex fluid modelling.

The research focuses on a quantum approach to computational fluid dynamics, or CFD, which engineers use to model how fluids such as air and water move around objects. The teams carried out a 15-step nonlinear fluid benchmark with an obstacle, describing it as the most physically complex publicly documented variant of a Quantum Lattice Boltzmann Method hardware demonstration to date.

The algorithm was developed and tested on IBM's Heron R3 quantum computer. According to Quanscient and Haiqu, the results suggest a path to more demanding engineering simulations on quantum systems by lowering qubit requirements and reducing the number of computational operations.

CFD is used across aerospace, automotive and energy, helping model airflow, heat transfer and other physical effects during product design and testing. These simulations are often expensive to run on conventional high-performance computing systems, especially when they involve nonlinear flows or complex geometries.

Technical shift

The teams developed a One-Step Simplified LBM based on a quantum Lattice Boltzmann Method algorithm. In practical terms, this allowed them to run a multi-step nonlinear fluid-flow simulation involving an obstacle on quantum hardware.

Haiqu said its software layer helped reduce circuit depth, improve algorithmic subroutines and apply error-reduction techniques, making the multi-step workflow possible on current devices. Together, these advances enabled the quantum system to run a simulation that would otherwise be beyond the reach of present-day hardware.

Independent academic reaction centred on the importance of applying the method to more realistic flow problems rather than simple demonstrations. "This is an interesting and timely contribution to quantum CFD," said Oleksandr Kyriienko, Professor and Chair in Quantum Technologies at the University of Sheffield. "It proposes a more flexible quantum LBM framework while keeping the core algorithm efficient, and it strengthens the case with applications ranging from linear acoustics to IBM-QPU-assisted nonlinear flow simulations. We need more works like this to achieve industrially relevant quantum solutions."

Industrial aim

The announcement reflects a broader push across the quantum sector to demonstrate practical use cases on existing machines despite hardware limits. Here, the target is one of engineering's most computationally demanding workloads, where even established supercomputers can struggle with scale and runtime.

Mykola Maksymenko, Chief Technology Officer of Haiqu, said the result marks a step toward industrially useful simulations on quantum hardware. "This is one of the most realistic CFD simulations ever executed on a quantum computer. It is an important signal that quantum CFD research is moving toward simulating how fluids interact with real-world shapes and obstacles on quantum hardware," Maksymenko said. "This is the direction that any industrially meaningful workflow would have to take to reach commercial viability," Maksymenko added.

For Quanscient, the work aligns with its focus on multiphysics simulation software and the use of quantum methods in engineering research and development. It said the hybrid quantum-classical structure of the new approach means it can run on current hardware, offering a practical route beyond basic linear examples.

Valtteri Lahtinen, Chief Scientist of Quanscient, linked the work to the longer-term prospect of tackling simulations that exceed what classical systems can handle economically or within reasonable time limits. "CFD is one of the most computationally difficult branches of simulation with some of the largest impact on the world's biggest sectors," Lahtinen said. "Quantum computers offer a future path to simulations that are far more complex than what classical computers can handle, which may allow for the design of more efficient vehicles and aircraft, better energy systems and more. Our work with Haiqu is a critical step toward making this a reality," Lahtinen added.