Researchers from Quanscient, a leader in cloud-based multiphysics simulation technology and quantum algorithms, and Haiqu, a leading developer of quantum middleware, today announced a new algorithm that can significantly advance the use of quantum computing in real-world engineering applications. The teams conducted a 15-step nonlinear fluid benchmark with an obstacle, making this the most physically complex, publicly documented variant of a Quantum Lattice Boltzmann Method (QLBM) hardware demonstration to date. Developed and tested on IBMs largest-available quantum computer, the IBM Heron R3, the algorithm reduces the number of qubits required to run complex simulations in computational fluid dynamics (CFD) on quantum computers, demonstrating a viable path toward future industrial-scale solutions. CFD is widely used to model how air, water, and other fluids behave around objects, such as airflow over an aircraft wing. It plays a critical role in product development and testing across industries, including aerospace, automotive, and energy. However, these simulations are extremely demanding for even today’s most powerful supercomputers, often taking days or even weeks to complete, if possible at all. The new algorithm addresses one of the key challenges in applying quantum computing to CFD: high resource requirements. By significantly reducing the number of qubits and computational operations needed, this approach makes it more practical to run complex simulations on quantum computers. It demonstrates a more efficient path toward using quantum systems for real-world applications, and ultimately could help companies design better products and optimize complex systems more quickly. “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