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Physical reservoir computing, which is a promising method for theimplementation of highly efficient artificial intelligence devices, requires aphysical system with nonlinearity, fading memory, and the ability to map inhigh dimensions. Although it is expected that spin wave interference canperform as highly efficient reservoir computing in some micromagneticsimulations, there has been no experimental verification to date. Herein, wedemonstrate reservoir computing that utilizes multidetected nonlinear spin waveinterference in an yttrium iron garnet single crystal. The subject computingsystem achieved excellent performance when used for hand-written digitrecognition, second-order nonlinear dynamical tasks, and nonlinearautoregressive moving average (NARMA). It is of particular note that normalizedmean square errors (NMSEs) for NARMA2 and second-order nonlinear dynamicaltasks were 1.81x10-2 and 8.37x10-5, respectively, which are the lowest figuresfor any experimental physical reservoir so far reported. Said high performancewas achieved with higher nonlinearity and the large memory capacity ofinterfered spin wave multi-detection.

Experimental Demonstration of High-Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multi-Detection