Oak Ridge tests quantum computing for fluid dynamics problems

Researchers at Oak Ridge National Laboratory (ORNL) have used quantum computing to tackle classic fluid dynamics challenges. They focused on modeling fluid flow between two parallel plates, a situation important for areas like oil recovery and bioengineering. The study was published in the journal Physics of Fluids. It aimed to see if a quantum algorithm could solve fluid flow equations faster than traditional computers. Lead author Murali Gopalakrishnan Meena noted that while techniques to reduce errors showed promise, more research is needed. Modeling fluid flow is crucial across many industries. Current methods involve physical testing and complex equations, which can be time-consuming and costly. Traditional simulations often miss essential physics, while accurate digital simulations demand significant computing power. Quantum computers use qubits that can represent multiple states at once. This could potentially enhance the ability to solve complex problems. However, Meena explained that setting up the problem correctly is a significant challenge. The team received time on IBM quantum computers and applied the Harrow-Hassidim-Lloyd (HHL) algorithm, designed to solve linear equations. But they encountered issues with numerical errors, which can escalate quickly in fluid dynamics problems. High error rates in quantum systems were a hurdle, but the researchers found that simplifying quantum circuits helped improve accuracy. They noted the need for better noise models and more efficient algorithms for future studies. Meena emphasized the importance of refining these quantum methods. They hope to apply their findings to various fluid dynamics problems, including combustion and fusion, to leverage the advantages of quantum computing.