Quantum Algorithms for Computational Fluid Dynamics
This review examines quantum methods for solving complex Partial Differential Equations (PDEs) in Computational Fluid Dynamics (CFD). It explores fully quantum algorithms, hybrid approaches like Quantum Physics-Informed Neural Networks, and how Tensor Networks can bridge classical CFD data with quantum states. The conclusion is that practical advantage depends heavily on problem specifics and hardware limitations.