GTRI, Georgia Tech Use Quantum Computing to Optimize CFD Applications

AI-generated graphic of complex CFD simulations

The ability for quantum computers to process a large amount of information simultaneously could significantly speed up complex CFD simulations and produce more accurate results (Credit: AI art generator

While quantum computing is still in its early stages, it has the power to unlock unprecedented speed and efficiency in solving complex computational fluid dynamics (CFD) problems that could revolutionize several industries, including the defense space. 

The Georgia Tech Research Institute (GTRI) and Georgia Institute of Technology (Georgia Tech) are exploring how the powerful processing capabilities of quantum computers can expedite CFD’s resource-intensive simulations used in aircraft design, weather prediction, nuclear weapons testing and more.  

“Through a collaboration between GTRI and Georgia Tech, we are developing an application of quantum computing to solve proof-of-principle problems in computational fluid dynamics that could streamline efficiencies and reduce costs across numerous industries,” said Bryan Gard, a GTRI senior research scientist who is leading this project.

Quantum computing offers a new way of doing computations using the principles of quantum mechanics, a science that explores the behavior of tiny particles such as atoms and photons. Computers and software that are built on the theories of quantum mechanics can process a large amount of information simultaneously and much faster than classical computers. That is because unlike classical computers, which use bits that are either 0 or 1, quantum computers use quantum bits or qubits. 

Classical bits are similar to regular on/off switches, which can only exist in one state at a time. Qubits, meanwhile, can exist in multiple states at once thanks to a property in quantum mechanics known as superposition.  

Because CFD involves complex simulations of how fluids, such as air or water, move and interact with different surfaces, classical computers often struggle with the immense number of calculations needed for such detailed simulations. The ability for quantum computers to process information in parallel could significantly speed up these simulations and produce more accurate results. 

“Say you are examining how air flows over a plane wing and you want to identify the large- and small-scale dynamics of that interaction,” explained Gard. “This type of problem would be very hard for a classical computer to handle because it wouldn’t be able to examine those large- and small-scale aspects simultaneously.” 

The team has split its research into two parts. The parts that involve linear differential equations are solved on a quantum computer and the other, non-linear parts are handled conventionally on a classical machine. 

The reason for this division is that as the problem scales up on classical supercomputers, the communication between nodes becomes inefficient, creating a bottleneck. Even though quantum computers are not yet large-scale, they can handle certain parts of the problem without facing the same communication challenges, Gard explained. 

These principles could help organizations strategically allocate resources and avoid costs associated with manufacturing and testing potentially flawed designs. In the defense realm, an example of this can be seen with designing aircraft. 

Instead of the conventional methods of building and testing structures in a wind tunnel, quantum-enhanced CFD would allow engineers to analyze stresses, assess designs and predict performance more efficiently and cost effectively. This becomes particularly relevant at high speeds, where factors such as air flows and turbulence pose additional challenges for running accurate simulations. 

“It all comes down to money, as with everything else,” said Gard. “If you could save yourself a lot of time and money by running this simulation, which you couldn't do before, then it would allow you to allocate your resources more effectively.” 

For this project, GTRI is collaborating with Spencer Bryngelson, an assistant professor in the School of Computational Science and Engineering who has expertise in computational physics, numerical methods, fluid dynamics and high-performance computing. Zhixin Song, a graduate student at Georgia Tech who is researching quantum algorithms for CFD, has also contributed.   

“This project is particularly interesting because although it is challenging, it could have outsize performance gains if one can find the right tools for the job, meaning the right quantum algorithm to solve the right fluid dynamics problem,” Bryngelson said. “GTRI and Georgia Tech have already made progress in this area, and also work well together, so it has been a good experience.” 

The project has been supported by GTRI’s Independent Research and Development (IRAD) Program, winning an IRAD of the Year award in fiscal year 2023, and the Defense Advanced Research Projects Agency (DARPA). 


Writer: Anna Akins 
Photos: Christopher Moore 
Art Credit:, Adobe 
GTRI Communications
Georgia Tech Research Institute
Atlanta, Georgia

The Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech). Founded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,900 employees, supporting eight laboratories in over 20 locations around the country and performing more than $940 million of problem-solving research annually for government and industry. GTRI's renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, state, and industry.

GT's Quantum Computing Research Team

The team leading this project includes, from left to right: Bryan Gard, a GTRI senior research scientist; Spencer Bryngelson, an assistant professor in Georgia Tech's School of Computational Science and Engineering; and Zhixin "Jack" Song, a Georgia Tech graduate student who is researching quantum algorithms for CFD (Photo Credit: Christopher Moore, GTRI).

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