基于非线性波导中的自相调制的光子科尔摩戈罗夫-阿诺德网络
Optics letters
|January 30, 2026
概括
本研究介绍了一种使用非线性波导的Kolmogorov-Arnold网络的新光子实现. 这种方法使得超快,低功耗的人工智能硬件具有竞争力的性能.
科学领域:
- 光学和光子学 在光学和光子学.
- 人工智能的人工智能
- 非线性光学是非线性光学.
背景情况:
- 光子神经网络提供低功耗,低延迟和高度并行化的硬件.
- 科尔莫戈罗夫-阿诺德网络比传统模型具有更好的可扩展性和可解释性.
研究的目的:
- 提出并实验证明Kolmogorov-Arnold网络的光子实现.
- 在非线性波导中利用自相调制来产生激活函数.
主要方法:
- 建议在非线性波导中使用自相调制.
- 实验展示了任意非线性激活函数的生成.
- 在回归和分类任务上实施和测试.
主要成果:
- 科尔莫戈罗夫-阿诺德网络的光子实现实现了具有竞争力的性能.
- 证明了超快速激活功能的产生.
- 在特定的回归和分类任务中表现优于数字基线.
结论:
- 非线性波导中的自相调制是光子神经网络的一种多功能方法.
- 这种方法克服了光子实现中的可扩展性障碍.
- 能够实现高效和高性能的光子科尔摩戈罗夫-阿诺德网络.
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