The emerging quantum computing techniques are heavily assisted by classical computers and algorithms for their functioning. At the same time, there is a strong and growing interest in whether quantum effects can enhance the efficiency of already existing classical algorithms, such as deep learning. In this talk, I will discuss the quantum optimization technique called quantum annealing and how some of the problems within the field can be tackled using genetic algorithms and deep learning. I will also talk about applying classical-quantum hybrid neural networks to solve differential equations, with an example from the field of plasma simulations.
