  Physical reservoir computing, which is a promising method for the
implementation of highly efficient artificial intelligence devices, requires a
physical system with nonlinearity, fading memory, and the ability to map in
high dimensions. Although it is expected that spin wave interference can
perform as highly efficient reservoir computing in some micromagnetic
simulations, there has been no experimental verification to date. Herein, we
demonstrate reservoir computing that utilizes multidetected nonlinear spin wave
interference in an yttrium iron garnet single crystal. The subject computing
system achieved excellent performance when used for hand-written digit
recognition, second-order nonlinear dynamical tasks, and nonlinear
autoregressive moving average (NARMA). It is of particular note that normalized
mean square errors (NMSEs) for NARMA2 and second-order nonlinear dynamical
tasks were 1.81x10-2 and 8.37x10-5, respectively, which are the lowest figures
for any experimental physical reservoir so far reported. Said high performance
was achieved with higher nonlinearity and the large memory capacity of
interfered spin wave multi-detection.
