Physical reservoir computing is a promising approach to realize high-performance artificial intelligence systems utilizing physical devices. Recently, it has been experimentally found that nonlinear interfered spin wave multi-detection shows excellent performance for processing nonlinear time-series data due to its outstanding features: nonlinearity, short-term memory, and the ability to map in high dimensional space. However, said performance is considerably inferior to reservoir computing utilizing an optical circuit with a large volume. Herein, we develop reservoir computing with nonlinear interfered spin wave coupled with light switching, namely opto-magnonic reservoir computing. The spin wave was modulated through a crystal field transition that occurred in two different Fe sites of YFeO by visible light switching, and it was found that the spin wave modulated by visible light switching dramatically reduced normalized mean square errors to 4.96 × 10, 0.163, and 3.66 × 10 for NARMA2, NARMA10, and second-order nonlinear dynamical equation tasks. Said excellent performance results from the strong nonlinearity caused by chaos and large memory capacity induced by reservoir states diversified by visible light switching.

中文翻译：

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非线性干涉自旋波与可见光开关耦合的光磁储层计算


物理储层计算是利用物理设备实现高性能人工智能系统的一种有前途的方法。近年来，实验发现非线性干涉自旋波多重检测因其非线性、短期记忆、高维空间映射能力等突出特点，在处理非线性时序数据方面表现出优异的性能。然而，所述性能远不如利用大体积光路的储层计算。在此，我们开发了非线性干涉自旋波与光开关耦合的储层计算，即光磁储层计算。通过可见光切换，通过在 YFeO 的两个不同 Fe 位点发生的晶体场跃迁来调制自旋波，发现可见光切换调制的自旋波将归一化均方误差显着降低至 4.96 × 10, 0.163，对于 NARMA2、NARMA10 和二阶非线性动力学方程任务，为 3.66 × 10。这种优异的性能源于混沌引起的强非线性和可见光切换多样化的储存状态引起的大存储容量。