TRAILBLAZER: Quantum Computing and Machine Learning for Fluid Dynamics Research

Computational fluid dynamics is an area of engineering that predicts complicated flows, such as air flow around supersonic aircraft, hurricanes, and fuel combustion processes inside an automobile engine. Accurate and rapid prediction of these complicated flows, particularly their chaotic or turbulent patterns which are collectively called nonlinear processes, is an ongoing challenge for engineering. Quantum computers, which rely on the principles of quantum mechanics, can perform calculations at much greater speed than traditional supercomputers. Currently however, quantum computers cannot be used to predict complicated flows. The central problem is to make quantum data processing, which is based on linear processes, work with nonlinear processes associated with complex flows. To address this problem, this project will combine quantum computing with classical supercomputing, using artificial intelligence to accelerate connection between these two computing methods. If successful, this project will enable rapid prediction of complicated flows associated with natural systems such as wind gusts and engineered systems such as supersonic transport. To help disseminate these new methods, the project will host workshops at national scientific meetings, and work with private companies, both large and small, to test out the computer code. Additionally, students will be trained in a collaborative environment that includes engineers, computer scientists, and physicists to build a quantum science and engineering workforce here in the U.S. The lure of quantum computing for solving complex fluid flows is the promise of exponential advantage in memory and speed compared to classical computing. At present, quantum computing is limited to very simple fluid flow models, and the potential for solving complex problems has remained unrealized. Applications of quantum computing for prediction of complex flow phenomena face the inherently nonlinear and dissipative nature of chaotic processes, including turbulence. Novel quantum protocols with end-to-end utility are needed, including data loading, computation, and data readout that accommodate these nonlinear processes. This project will develop new tools for this purpose, including iterative matrix multiplication and inversion, detection of extrema, and approximation of non-unitary and non-Hermitian quantum systems by combinations of unitary quantum bits. The quantum computing component will then be integrated with classical computing and tuned by machine learning to enable prediction of complex fluid flow phenomena. Engineering applications include prediction of supersonic, combustion-driven, and atmospheric flows relevant to national energy and security goals. The proposed research also addresses national needs in quantum computing articulated in the U.S. National Quantum Initiative Act, the National Quantum Initiative Reauthorization Act, and the U.S. CHIPS and Science Act. Anticipated Transformative Impact: Prediction of complex flows e.g. supersonic transport and hurricane formation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.