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

Darko Zibar; Francesco Da Ros

doi:10.1117/12.3017112

18 June 2024 Machine learning techniques for inverse system design and control of photonic systems

Darko Zibar, Francesco Da Ros

Proceedings Volume PC13017, Machine Learning in Photonics; PC1301701 (2024) https://doi.org/10.1117/12.3017112
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

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.

Conference Presentation

Citation Download Citation

Darko Zibar and Francesco Da Ros "Machine learning techniques for inverse system design and control of photonic systems", Proc. SPIE PC13017, Machine Learning in Photonics, PC1301701 (18 June 2024); https://doi.org/10.1117/12.3017112

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