Record-Scale Quantum CFD: 64×64 Grid Simulation on IonQ Hardware

People want safe, affordable, and comfortable transportation. The economics of delivering quality to end customers are set early in the value chain: aircraft manufacturers make efficiency decisions that airlines convert into lower operating costs, which people then experience as lower fares and improved comfort. The introduction of Airbus A320 sharklets to reduce wingtip vortex drag and cut fuel burn is a good example. Airlines recovered retrofit costs in as little as two years per aircraft, achieved fleet-wide savings in the multi-billion-dollar, and supported lower operating cost per seat.

Producing innovations like the Airbus A320 sharklets is slow and expensive. Development spans years and depends on repeated wind-tunnel testing and flight campaigns to validate performance and safety. Each cycle consumes capital, engineering effort, and limited testing capacity. Errors discovered late in development are especially costly, often triggering redesigns and delays.

Computational Fluid Dynamics (CFD) shortens this cycle by predicting how fluid flow affects aerodynamic forces, moments, pressure distributions, and thermal loads before physical testing begins. Accurate simulations reduce reliance on experiments and avoid costly late-stage changes. However, in complex flow conditions—most notably turbulence—classical CFD struggles because flows span large swirling regions down to very small eddies. 

Quantum Computational Fluid Dynamics (Q-CFD) aims to model the full dynamics of turbulent flows by encoding large grids using only logarithmically many qubits—changing the resource scaling relative to classical solvers. Unfortunately, today’s quantum processors are noisy. Current devices only support on the order of ~20 high-quality qubits (as measured by Quantum Volume metrics) and only reliably sustain algorithms  with a depth of ~300 two-qubit operations.

Haiqu, in partnership with Quanscient, overcame this barrier as finalists in the BMW–Airbus Quantum Computing Challenge. Using Haiqu’s middleware, the team executed the largest quantum CFD simulation to date on real hardware, running a 64×64 computational grid over multiple time steps on IonQ’s Aria 1 processor. By compressing circuits, optimizing execution, and mitigating noise, Haiqu enabled deep simulations that were previously impractical on today’s devices.

For decision makers, the implications are clear and immediate. Haiqu increases the performance of CFD workloads, transforming quantum computing from a long-term theoretical research bet into a near-term empirical piloting program on real quantum hardware. 

Crucially, this is not a promise for the next decade. Haiqu makes quantum workloads executable today, allowing enterprises to capture competitive advantage years earlier.

Quantum for business. Run more with Haiqu.

Read more about this result on the AWS Blog.

Book a Demo 

Quanscient's new quantum lattice Boltzmann algorithm and Haiqu's execution stack produced the first nonlinear Navier-Stokes flow with geometry on a quantum processor. 

A well-designed quantum algorithm is one thing. Getting it to run on hardware is another. Haiqu closed the engineering gap and enabled on-hardware execution thanks to the compilation, state preparation, tomography, and noise-mitigation stack that made the nonlinear obstacle benchmark practical on IBM's superconducting hardware.

Read a technical perspective from Haiqu in the recent blog.