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arXiv 2202.03359physics.optics
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卷积神经网络在按需模式高精细度光学谐振腔设计中的应用

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

Denis V. Karpov, Sergei Kurdiumov, Peter Horak

摘要:

我们展示了通过卷积神经网络使用机器学习来解决光学谐振腔工程的逆向设计问题。神经网络找到球面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”)。该过程允许我们优化镜的形状,以实现谐振腔光子和位于谐振腔中心的量子发射器之间的耦合强度和协同性的显着增强。在第二示例中,设计了双峰模式,其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。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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