Researchers across several international institutions have announced a significant stride in deciphering the patterns and inner workings of turbulence—a phenomenon found in countless fluid systems, from rushing rivers and ocean currents to swirling smoke, storm clouds, chemical reactions, blood flow, and even the plasma surrounding stars. This development represents what some experts are calling a “rare advance” in tackling a puzzle that has eluded complete explanation for approximately two centuries.
Turbulent flow manifests in a chaotic, unpredictable manner, marked by vortices or eddies spinning off into ever-smaller ones. Although it has been studied extensively, the complexity of turbulence has thwarted scientists’ efforts to model it comprehensively. Despite the use of state-of-the-art supercomputers, only the simplest forms of turbulent systems have been simulated with complete accuracy. A full-scale understanding of turbulence remains tantalizingly out of reach.
Now, in a paper published on January 29 in the journal Science Advances, an international team of physicists presents a novel approach to simulating turbulence, drawing inspiration from quantum computing methods. By utilizing techniques adapted from quantum information science, the researchers believe they can model turbulent flows more efficiently and effectively than ever before, potentially shaving off computational time that previously extended into days, if not weeks, on even the most powerful conventional supercomputers.
According to lead author Nik Gourianov, a researcher in the Department of Physics at the University of Oxford, this innovative method could have many practical implications. Accurate modeling of turbulence underlies improvements in multiple engineering and scientific arenas, including the design of aircraft wings, propellers, automobile parts, artificial organs for medical use, and more precise systems for predicting weather.
“Turbulence was and still is an unsolved problem in the sense that we cannot exactly simulate realistic flows on computers,” Gourianov explained. “Even now, we rely on wind tunnels when designing aircraft wings. But developments like ours gradually reduce the gap between theoretical understanding and practical applications, pushing the frontier of what we can achieve.”
A Probabilistic Approach
Historically, most simulations of turbulence have relied on deterministic models: starting with a precise set of initial conditions, such models will always deliver the same outcome. In the newly published work, however, the research team embraced a probabilistic framework, which better captures the random fluctuations that emerge within turbulent flows. This key shift in perspective provides a new lens through which turbulence can be understood and, crucially, calculated.
Harnessing a quantum computing-inspired algorithm, the team found they could carry out intricate calculations in just a few hours, whereas a traditional algorithm might consume multiple days on an entire supercomputer. Gourianov and his colleagues attribute this remarkable efficiency to insights drawn from the way quantum computers handle information. Rather than relying solely on bits—binary units that can represent zero or one—quantum computers use quantum bits (often called “qubits”), which can assume zero, one, or both states simultaneously. This capacity enables significantly more complex computations to run in far less time.
In their experiments, the scientists did not use an actual quantum computer; instead, they drew on a mathematical framework known as tensor networks, which can approximate the behavior of quantum systems. By representing the data in a structure that simplifies multiple variables at once, the researchers saw enormous savings in both the memory and processing power required to model fluid turbulence.
Wider Implications and Expert Insights
James Beattie, a postdoctoral research associate at Princeton University’s Department of Astrophysical Sciences, lauded this new work as an exciting advance in the ongoing quest to understand turbulence. Although he was not involved in the study, he pointed to the team’s ability to compress a previously massive computational task into a format suitable for a single laptop computer.
“They’re simulating fluid flows of two chemicals mixing and reacting,” Beattie said. “By employing their representation, they can drastically reduce memory usage—enough that even a relatively ordinary computer can handle the load. Observing such a leap in efficiency, from needing an entire supercomputer to running it on a laptop, is a rarity.”
Beattie also highlighted the researchers’ claims of realizing a million-fold increase in memory efficiency and a thousand-fold boost in computational speed, calling these achievements uncommon and signaling an “exciting step forward” in turbulent-flow modeling.
However, he was quick to clarify that the study did not fully solve the multi-scale nature of turbulence, which extends over vastly divergent size ranges—from mere fractions of an inch to light-years across. Turbulence researchers often seek to understand how these scales interact with and influence each other, yet capturing every level of detail in a single computer model remains a formidable challenge. The resource demands for such an endeavor typically stretch even the most extensive supercomputing facilities to their limits.
Persistent Mysteries
Other experts, such as Yongxiang Huang—an associate professor at the State Key Laboratory of Marine Environmental Science & College of Ocean and Earth Sciences at Xiamen University in China—also described the achievement as “highly impressive.” He emphasized the novelty of reducing both memory requirements and computational overhead so dramatically, while underscoring that this result is but one part of a very large puzzle.
Indeed, many specialists refer to turbulence as one of the oldest unsolved problems in physics. Werner Heisenberg, the famed German theoretical physicist, is rumored to have remarked on his deathbed that he had two questions for God: “Why relativity? And why turbulence?”—believing that the Almighty might have a direct response to the first, but perhaps not the second.
Gourianov likewise made clear that as groundbreaking as these findings are, the core enigma of turbulence remains intact. He views the advantage of the new technique mainly as an avenue to further research on topics previously inaccessible due to computational constraints. Ultimately, he believes that unlocking the grand secrets of turbulence may demand fresh algorithms or hardware that surpasses current technological capacities in fundamental ways.
“This problem has been examined by some of the most brilliant scientific minds, and yet we have scarcely inched toward a definitive solution,” he said. “Our approach is a meaningful contribution, but there is still a long journey ahead before turbulence is fully understood.”
Thanks to this quantum-inspired modeling strategy, researchers can test theories and ideas about turbulence more readily. Although a full resolution may yet be far off, each advancement—like this one—brings the physics community closer to tackling the elusive chaos that lies at the heart of so many fluid systems, both on Earth and throughout the universe.