Insider Brief
- Scientists at PNNL have developed a quantum computing approach to simulate turbulence, a problem too complex for even the most powerful supercomputers.
- Their method reformulates the Boltzmann equation to potentially provide a quantum speed advantage in modeling fluid dynamics across multiple scales.
- The research has implications for climate science, biophysics, and chemical systems, though current quantum computers are not yet capable of running these simulations at scale.
- Image: PNNL
Scientists at Pacific Northwest National Laboratory (PNNL) have developed a new approach using quantum computing to simulate turbulence, a problem that even the world’s most powerful supercomputers struggle to solve, according to an article on the PNNL website. Their findings, published in Physical Review Research, could lead to more precise climate models and advancements in fields ranging from chemistry to biophysics.
Turbulence — the chaotic movement of air, water, or other fluids — remains one of the most difficult problems in science. It affects everything from weather patterns to industrial processes. Scientists typically have to choose between highly detailed but computationally expensive simulations or approximate models that introduce uncertainties. Even the largest supercomputers can’t efficiently model turbulence across all scales, from microscopic interactions to massive cloud formations.
The PNNL team, which includes physicists, quantum computing experts, biologists, chemists, and Earth scientists, devised a quantum algorithm that reformulates the Boltzmann equation, which is a fundamental equation in fluid dynamics. Their approach suggests that quantum computing could offer a speed advantage over classical methods, potentially making complex turbulence simulations feasible. However, today’s quantum computers are not yet advanced enough to run these simulations at scale.
“This formulation will allow us to cross so many scales where complex transport phenomena occur. This includes the native size of turbulence all the way up to the size of clouds,” said Gregory Schenter, a PNNL scientist involved in the project.
Climate Science Implications
The research has direct implications for climate science, the team reports. Clouds, which influence weather and long-term climate trends, remain one of the largest sources of uncertainty in climate models. Better simulations of how turbulence affects cloud formation and precipitation could refine predictions about global warming and extreme weather events.
“In climate projections, cloud physics — including turbulence — is the largest source of uncertainty,” said Johannes Mülmenstädt, the project’s principal investigator. “If we want to solve climate problems, we need to resolve cloud processes.”
Beyond climate modeling, turbulence plays a role in biological and chemical systems. Margaret Cheung, a biophysicist on the team, noted that turbulence affects molecular interactions in cells. Understanding these interactions could improve biomedical research and industrial processes.
“One aspect of microbial research focuses on the coupled changes in the chemical reactions through the metabolic networks in time and space,” said Cheung. “With our formulation, quantum computing may provide speedup in advancing the simulations of this complex system. This provides a basis and motivation for creating quantum algorithms of complex systems to increase society’s predictive power relevant to health and environmental queries, ultimately leading to a better bioeconomy for addressing energy and environmental problems.”
The research effort began with a chance collaboration. Xiangyu Li, an Earth scientist at PNNL, gave a talk on cloud turbulence, which caught the interest of Schenter and chemical engineer Jaehun Chun. Chun had previously published influential research on turbulence in aerosols while at Cornell University. The researchers formed a study group to explore the problem further, and their discussions led them to quantum computing.
“I’d been dreaming about working on quantum computing for turbulence or even quantum turbulence since 2016,” said Li.
The team members came from varied background, however none of the team were quantum computing experts. They enrolled in PNNL’s annual Quantum Bootcamp, where they met Nathan Wiebe, a quantum computing specialist and professor at the University of Toronto. Wiebe joined the project and helped the team apply quantum principles to their turbulence models.
The project was supported by the Department of Energy and PNNL’s internal research funding. The team acknowledged that their work is high risk, as quantum computing remains in its early stages. But they believe the potential rewards justify the effort.
“In many places, it would be extremely difficult to get support for such high-risk work,” Li said. “I’d been wanting to pursue this project for years; it wasn’t until I came to PNNL that I actually could.”
Mülmenstädt echoed that sentiment.
“When I first came to PNNL, I decided I would use my honeymoon period with the lab management to see how willing they were to entertain some crazy ideas,” said Mülmenstädt. “And it turns out they were very interested because I think they recognize that we need some blue sky–type projects.”
The team now hopes this work opens up the path to study other complex systems, including biophysics and plasma physics.
