Quanscient and Haiqu conducted a nonlinear quantum fluid simulation consisting of 15 steps.

      A new quantum algorithm has successfully executed a 15-step nonlinear fluid simulation around a solid obstacle on actual quantum hardware, marking the most physically complex publicly documented example of its type. This technique lessens the number of required qubits and circuit depth, making industrial computational fluid dynamics (CFD) applications more achievable.

      Finnish simulation firm Quanscient and quantum middleware developer Haiqu have shown what they label as the most physically intricate quantum CFD simulation carried out on real hardware to date. The two organizations conducted a 15-step nonlinear fluid simulation around a solid obstacle, where the fluid flows around a shape, a problem pertinent to aircraft wing design and vehicle aerodynamics, on IBM’s Heron R3 quantum computer using a new algorithm they co-developed called the One-Step Simplified Lattice Boltzmann Method (OSSLBM).

      Computational fluid dynamics is one of the most resource-demanding fields in engineering simulation. Modeling fluid behavior around complex shapes necessitates substantial classical computing resources, and as simulations become more detailed, the demand increases non-linearly.

      Quantum computing has been theorized as a possible avenue for simulations beyond classical limits; however, realizing this potential has been hampered by the high number of qubits and circuit depth required to execute even moderately complex scenarios without the calculations being overwhelmed by errors.

      The OSSLBM algorithm directly addresses this issue. Based on the quantum Lattice Boltzmann Method (QLBM), a well-established approach for mapping classical fluid equations to quantum computation, the new framework diminishes the computational overhead of each step, allowing for a longer multi-step simulation that remains within the reliability limits of current quantum hardware.

      Haiqu’s middleware was crucial in this process: it reduced circuit depth, created new algorithmic subroutines, and applied specific techniques for error reduction that enabled the system to complete a workflow that would otherwise have been unattainable with today's devices.

      The importance of this achievement lies in the obstacle. Previous quantum CFD demonstrations have mainly focused on simpler linear scenarios, where fluid behavior is not complicated by interactions with a solid boundary. Modeling how fluid flows around an object is essential for any industrially relevant application. Professor Oleksandr Kyriienko, Chair in Quantum Technologies at the University of Sheffield, referred to the work as “an interesting and timely contribution to quantum CFD,” emphasizing the need for further research of this nature to develop industrially relevant quantum solutions.

      Quanscient and Haiqu have been working together on quantum CFD since at least 2024 when they became finalists in the Airbus and BMW Quantum Mobility Challenge and have previously showcased their work on IonQ hardware through Amazon Braket. Though industrial applications are still years away, this research represents a milestone that confirms the feasibility of this approach on current hardware at this level of complexity.