Refreshments at 14:30

In this talk, we explore the use of quantum computing and machine learning to improve performance of computational fluid dynamics calculations.  

Regarding quantum computing, a novel quantum high-order finite-difference method will be presented based on the use of a variational quantum algorithm for solving linear systems of algebraic equations and tested for simple 1D/2D model cases.  

Regarding machine learning, the physics-informed neural networks (PINNs) approach to solving partial differential equations (PDEs) will be presented and extension to quantum PINNs will be proposed.