论文标题
卷积神经网络，用于按需高技巧的光学谐振器设计
Convolutional Neural Networks for Mode On-Demand High Finesse Optical Resonator Design
论文作者
论文摘要
我们证明了通过卷积神经网络使用机器学习来解决光学谐振器工程的反设计问题。神经网络找到了球形镜的谐波调制，以生成具有给定目标拓扑的谐振器模式（“按需模式”）。该过程使我们能够优化镜子的形状，以达到谐振器光子和位于谐振器中心的量子发射器之间的显着增强的耦合强度和协同性。在第二个示例中，设计了双峰模式，该模式将增强两个量子发射器之间的相互作用，例如用于量子信息处理。
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.
