Reservoir Neuromorphic Computing with Photonic Superconducting Circuits

Abstract

The focus of this thesis is to understand the applicability of Artificial Intelligence mainly, Neural Network models in the prediction of the optical responses of superconducting metamaterials which is inherently a quantum phenomenon. It discusses the viability of several Neural Network architectures in solving this problem and lays out quantitative results comparing the different architectures for the reader to choose based on their approach and parameters such as nature of the task and resources. Superconducting metamaterials like Niobium show the unique phenomenon of plasmon polariton formation, whether they are surface plasmon polaritons or bulk plasmon polaritons, when exposed to radiation in a specific wavelength range. This thesis also focuses on the determination and replication of the mechanisms of plasmon polariton formation in the Niobium metamaterial via simulation environments and paves the way for future Machine Learning work in the ever-changing world of plasmonics.