Quantum computing's boon for complex problem solving is being tested in new ways, as Oak Ridge National Laboratory researchers explore its application in the realm of fluid dynamics, an effort that could influence everything from industrial design to energy production. In an effort facilitated by the Quantum Computing User Program, part of ORNL’s Oak Ridge Leadership Computing Facility, scientists are measuring the utility of quantum computers to tackle age-old scientific queries, specifically classical fluid dynamics problems — a study recently described on ORNL's news release.
The Hele-Shaw flow problem served as their test subject, a scenario of two flat parallel plates and the movement of fluids and gases between them, the problem serves as a microcosm of real-world challenges like oil recovery and groundwater flow and understanding it could improve processes across a range of industries, where the unsteady flow over machinery can lead to turbulence affecting performance, as detailed by ORNL. According to the study’s lead author Murali Gopalakrishnan Meena, "Scalability and accuracy are the key issues here," and while "error suppression and mitigation techniques can help," the study, which indicated quantum computing's theoretical superiority in solving the complex equations of fluid dynamics, underscores an immediate need for more research.
Traditional methods for modeling fluid dynamics combine labor-intensive physical testing with approximated equations; these methods are not without fault - they either fall short of capturing the full spectrum of physics involved or require significant computing power to digitally simulate the phenomena. Quantum computing could potentially offer a faster, more efficient solution, leveraging qubits that can encode a range of values through quantum superposition - this is an auspicious prospect for streamlining calculations in the field.
The research team deployed the Harrow-Hassidim-Lloyd algorithm on IBM quantum computers, seeking to benchmark how quantum solutions can be integrated into current models of simulating fluid flows, they uncovered challenges in managing the equations' sensitivity to numerical errors and the high error rates that can plague quantum systems. "The sensitivity of the equations grows exponentially with our problem size, so if there’s even a small numerical error then the whole solution can blow up and become unworkable, requiring more computational effort to solve the problem," Gopalakrishnan Meena told ORNL, and even as noise models and reduction algorithms were employed, simplifying and streamlining operations led to the most accurate results.
While the study points to inherent difficulties, it also suggests that with improved noise models and circuit optimizations, there is potential for quantum algorithms to revolutionize the study of fluid flows and its myriad applications. The ORNL team, comprising Murali Gopalakrishnan Meena, Kalyan Gottiparthi, Antigoni Georgiadou, Eduardo Antonio Coello Perez, and Justin Lietz from NVIDIA Corp, stresses ongoing research and enhancements to their approach, looking forward to harnessing the quantum speedup for practical fluid dynamics scenarios. The project derives support from the DOE Office of Science’s Advanced Scientific Computing Research program, with ORNL leading the charge in quantum innovation throughout the International Year of Quantum Science and Technology in 2025.
