无模型优化和平行架构,朝着单体混合光子电子储库计算的方向发展.
Optics express
|December 19, 2025
概括
这项研究引入了用于光子储库计算的新算法,提高了机器学习性能. 利亚普诺夫过-最小冗余最大相关性 (Lf-mRMR) 算法优化光子系统以获得卓越的结果.
科学领域:
- 神经形态计算是一种神经形态计算.
- 光子学是指光子学的使用方法.
- 机器学习 机器学习
背景情况:
- 物理储库计算 (PRC) 利用非线性物理系统进行机器学习.
- 单体混合光子电子储存计算 (MHPE RC) 结合了光子非线性与电子可调性.
研究的目的:
- 在MHPE RC.中分析光子波导网 (WGM) 的性能.
- 开发和验证一个优化算法,以提高MHPE RC性能.
主要方法:
- 平行WGM架构的数值演示.
- 开发了用于参数优化的Lyapunov过-最小冗余最大相关性 (Lf-mRMR) 算法.
- 使用芯片上的光子学进行实验验证.
主要成果:
- 平行WGM架构显示了效率和性能优越性.
- Lf-mRMR算法提高了MHPE RC性能,耐受制造错误,并降低了计算复杂性.
- 储库计算的选择性并行架构 (SPARC) 实现了与卷积神经网络相当的性能.
结论:
- Lf-mRMR算法显著提高了MHPE RC性能.
- 在芯片上的光子学成功验证了Lf-mRMR辅助RC的优势性能.
- 这种方法为先进的机器学习任务提供了一种计算效率高和强大的方法.
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