Reservoir computing by thin film embedded with magnetic impurities

TL;DR

This study demonstrates that magnetic thin films with impurities can perform reservoir computing, effectively encoding complex spatial patterns into temporal signals for tasks like digit recognition.

Contribution

It introduces a magnetic reservoir computing system utilizing long-range dipole interactions, showing high accuracy with spatially averaged outputs.

Findings

High classification accuracy achieved in digit recognition task.

Long-range interactions encode complex spatial patterns into time domain.

System enables easy realization of magnetic reservoir computing.

Abstract

The reservoir computing based on the thin film embedded with magnetic impurities in the presence of the long-range (the dipole-dipole) interaction is numerically investigated. We simulated the magnetization dynamics by taking into account the dipole-dipole interaction and performed the handwritten-digit recognition task. Although the training data is prepared by taking spatial average in the sample, the high classification accuracy is achieved. Our result demonstrates that the long range interaction effectively encodes the complex spatial input pattern into the time domain, even when only a spatially averaged output is accessible. The proposed system paves the way for easily realizable magnetic reservoir computing.