Exploring Quantum-Inspired Fluid Dynamics Simulations for Real-World Applications

The direct numerical simulation of complex fluid flows is computationally expensive and quickly becomes infeasible with increasing turbulence, requiring alternate approaches. This is particularly relevant for the automotive industry since the accurate simulation of fluid flow around a car is crucial for efficient vehicle design. Recently, tensor network algorithms, such as Matrix Product State (MPS) algorithms, typically used to study complex quantum many-body systems, have shown promise in fluid dynamics. By treating the interscale correlations in the velocity field like local correlations between quantum states, these quantum-inspired methods offer a way to efficiently capture the underlying structure of turbulence by reducing the bond dimension in MPS using the Schmidt decomposition. We investigate applying these novel methods to real-world scenarios by extending the existing techniques to flows at higher Reynolds no. with more realistic boundaries and analyzing the computational complexity. We benchmark this algorithm using a 2D turbulent jet and the flow around a 2D cylinder. Our findings provide valuable insights into the scalability and suitability of these quantum-inspired approaches for solving fluid dynamics problems, and identify scenarios that can potentially benefit from computation on quantum computers. This work uses cuTensorNet, a high-performance library for tensor network computations in NVIDIA's cuQuantum SDK.