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卷积神经网络用于按需模式的高精度光学共振器设计
Denis V Karpov1, Sergei Kurdiumov2, Peter Horak1
1Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK.
Scientific reports
|September 20, 2023
概括
使用卷积神经网络的机器学习使"按需模式"的光学共振器工程成为可能. 这优化了镜子形状,用于增强量子发射器相互作用和量子信息处理应用.
科学领域:
- 光学和光子学 在光学和光子学.
- 量子技术 量子技术是一种量子技术.
- 机器学习应用 机器学习应用
背景情况:
- 光学共振器对于控制光物质相互作用至关重要.
- 为特定的量子现象设计共振器是具有挑战性的.
- 反向设计方法为定制光学系统提供了一条途径.
研究的目的:
- 在光学共振器工程中应用机器学习用于反向设计.
- 为了实现对共振器模式的"按需模式"生成.
- 为了增强量子应用的光物质相互作用.
主要方法:
- 使用卷积神经网络 (CNN) 进行反向设计.
- 训练神经网络找到球形镜子的调制.
- 为所需的共振器模式拓优化镜像形状.
主要成果:
- 证明成功生成目标共振器模式.
- 实现了显著增强的合强度和合作性.
- 设计了一种双峰模式,以促进两个量子发射器之间的相互作用.
结论:
- 机器学习为光学共振器反向设计提供了一个有效的工具.
- "按需模式"的方法使定制的共振器特性成为可能.
- 这种方法对量子信息处理和增强的光物质相互作用有重大影响.
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