The Lorenz 63 model by quantum machine learning

Complex fluid flows often require extensive analysis which can be avoided with easier and faster machine learning models for various tasks. These models tend to utilize high dimensional linear algebra that can be realized effectively on quantum computers. In this talk, we present a quantum reservoir computing model for the Lorenz 63 model which describes two-dimensional thermal convection between two free-slip walls right above the onset of thermal convection. It is taken to show the usability of the approach for chaotic fluid flow problems where known data can be used to predict or reproduce single or multiple states of a nonlinear model or system. We summarize some critical steps of the implementation on real quantum computers.