Convolutional Neural Networks for Mode On-Demand High Finesse Optical Resonator Design

TL;DR

This paper introduces a machine learning approach using convolutional neural networks to design optical resonators with specific mode shapes, enhancing photon-emitter interactions for quantum technologies.

Contribution

It presents a novel application of CNNs for inverse design of optical resonators, enabling mode on-demand creation and improved quantum emitter coupling.

Findings

Successfully designed resonator mirrors for targeted modes

Enhanced coupling strength and cooperativity achieved

Designed modes facilitate quantum information processing

Abstract

We demonstrate the use of machine learning through convolutional neural networks to solve inverse design problems of optical resonator engineering. The neural network finds a harmonic modulation of a spherical mirror to generate a resonator mode with a given target topology ("mode on-demand"). The procedure allows us to optimize the shape of mirrors to achieve a significantly enhanced coupling strength and cooperativity between a resonator photon and a quantum emitter located at the center of the resonator. In a second example, a double-peak mode is designed which would enhance the interaction between two quantum emitters, e.g., for quantum information processing.