A systematic analysis on the relationship between reservoir-computing capacities and physical properties of spin waves
Yuta Miyasaka, Akira Hirose, Ryosho Nakane · APL Machine Learning · 2026
We experimentally demonstrate spin-wave-based reservoir computing (RC) using a device with one-input and nine-output antennas on a yttrium iron garnet film, and we study the relationship between nonlinear phenomena and computational capacities to clarify the mechanism of computing. First, a single series of random binary data is converted into 72 different input voltage signals by varying four encoding parameters: amplitude, time-step length, frequency, and duty ratio. These waveforms are then fed into the reservoir. We propose a “nonlinear transformation index (NT)” to quantitatively evaluate the strength of nonlinear phenomena in reservoir output signals. The values of NT are directly estimated from discrete Fourier transformation spectra of input and output signals to reveal that NT changes with the input parameters. Then, RC is performed for short-term memory tasks and temporal exclusive-OR tasks using a series of random binary data. It is found that there is a strong relationship between NT and the computational capacities and that a trade-off between capacities for linear and nonlinear tasks originates from the strength of linear and nonlinear phenomena. As a result, our NT approach can quantitatively identify the relationship between nonlinear phenomena and computational capacities, unlike the qualitative suggestions in previous studies. Furthermore, with the help of our characterization, enhancement of computional performance can be achieved when some input conditions that have moderate NT values (0.4–0.6) are selectively collected. Our systematic analysis can provide further understanding of physical RC as well as a guideline toward the establishment of a practical computing scheme.