In the present paper, we propose and experimentally verify a concept of a magnonic reservoir computer. The system utilizes the nonlinear behavior of propagating magnetostatic surface spin waves in a yttrium-iron garnet thin film and the time delay inherent in the active ring configuration to process time-dependent data streams. Higher reservoir dimensionality is obtained through the time-multiplexing method, whereby inputs to the system are multiplied by a mask to drive complex dynamics in the ring and the output is sampled in time to create a series of “virtual” neurons for each sample. The input mask is implemented as a train of microwave pulses injected to the system. To demonstrate the efficacy of the concept, the reservoir computer is evaluated on the short-term memory and parity-check benchmark tasks, and the physical system parameters are tuned to optimize performance. By incorporating a reference line to mix the input signal directly onto the ring-resonator output, both the amplitude and phase nonlinearity of the spin waves can be exploited. The addition of a second spin-wave delay line increases the delay time of the ring and enhances the fading memory capacity of the system. Configuring the second delay line to transmit backward volume spin waves also partly compensates the dispersive pulse broadening that is present because of the large delay time.

中文翻译：

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基于时滞复用的Magnonic油藏计算机模型的实现

在本文中，我们提出并通过实验验证了磁子储层计算机的概念。该系统利用在钇铁石榴石薄膜中传播静磁表面自旋波的非线性行为和有源环配置中固有的时间延迟来处理与时间相关的数据流。更高的储层维度是通过时间复用方法获得的，系统的输入乘以掩码以驱动环中的复杂动态，输出被及时采样，为每个样本创建一系列“虚拟”神经元。输入掩码被实现为注入系统的一串微波脉冲。为了证明该概念的有效性，储层计算机在短期记忆和奇偶校验基准任务上进行了评估，并调整物理系统参数以优化性能。通过结合参考线将输入信号直接混合到环形谐振器输出上，可以利用自旋波的幅度和相位非线性。第二条自旋波延迟线的加入增加了环的延迟时间，增强了系统的衰落存储容量。配置第二条延迟线以传输反向体积自旋波也部分补偿了由于大延迟时间而存在的色散脉冲展宽。第二条自旋波延迟线的加入增加了环的延迟时间，增强了系统的衰落存储容量。配置第二条延迟线以传输反向体积自旋波也部分补偿了由于大延迟时间而存在的色散脉冲展宽。第二条自旋波延迟线的加入增加了环的延迟时间，增强了系统的衰落存储容量。配置第二条延迟线以传输反向体积自旋波也部分补偿了由于大延迟时间而存在的色散脉冲展宽。