Machine learning techniques for inverse system design and control of photonic systems

Machine learning techniques are proving to be very useful for design of optical amplifiers, noise characterization of frequency combs, optimization of fiber-optic communications systems, inverse design of photonics components and quantum-noise limited signal detection. In this talk, we will review some of the successful applications of machine learning in photonics, and look into what is next in this emerging field. More specifically, we will look into how reinforcement learning can be used for the generation of programmable pulse shapes, which has a broad range of applications in classical and quantum engineering.