在光子合波导阵列中单元传输矩阵的反向设计,使用神经辅助模型
Thomas W Radford1, Peter R Wiecha2, Alberto Politi1
1School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, United Kingdom.
概括
我们开发了一种使用神经网络来设计集成光学设备的新方法. 这个平台可以为量子计算和光学神经网络等应用程序实现可编程单元操作.
科学领域:
- 综合光子学 综合光子学
- 量子计算是一种量子计算.
- 光学神经网络是指光学神经网络.
背景情况:
- 低损耗可重新配置的集成光学设备对于推进光子信号处理,模拟量子计算和光学神经网络至关重要.
- 设计这些设备需要计算密集的反向设计协议来确定特定光学输出的最佳几何形状.
研究的目的:
- 引入一个新的平台来实现单元矩阵运算,使用合波导阵列的数字图案.
- 为预测设备几何形状提供一个强大而通用的反向设计协议.
主要方法:
- 使用高速神经网络的代用基梯度优化.
- 预测基于切换石化变相材料,三化 (Sb2Se3) 的折射率扰动.
- 应用数据集增强和随机噪声补充用于神经网络优化.
主要成果:
- 在3x3波导阵列中,对每个传输矩阵元素的振幅和相位都进行了证明控制.
- 在单元矩阵目标中实现了0.94的平均保真度.
- 展示了可编程单元运算符,与传统技术相比,其足迹减少了.
结论:
- 与数字扰动模式相结合的波导阵列为创建可编程单元运算符提供了新的途径.
- 这种方法适用于为量子模拟器开发紧的哈密尔顿数.
- 开发的反向设计协议增强了复杂的集成光学设备的设计过程.
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