Novel implementations for reservoir computing -- from spin to charge

TL;DR

This paper explores how topological textures like skyrmions and domain walls in nanoscale magnetic and electric materials can be used for reservoir computing, offering advantages in device scaling and complexity.

Contribution

It introduces topological magnetic and electric defects as novel physical reservoirs for non-linear signal processing in reservoir computing.

Findings

Topological textures enable non-linear signal conversion.

They offer opportunities for device miniaturization and increased complexity.

Versatile input and readout options are possible with these textures.

Abstract

Topological textures in magnetic and electric materials are considered to be promising candidates for next-generation information technology and unconventional computing. Here, we discuss how the physical properties of topological nanoscale systems, such as skyrmions and domain walls, can be leveraged for reservoir computing, translating non-linear problems into linearly solvable ones. In addition to the necessary requirements of physical reservoirs, the topological textures give new opportunities for the downscaling of devices, enhanced complexity, and versatile input and readout options. Our perspective article presents topological magnetic and electric defects as an intriguing platform for non-linear signal conversion, giving a new dimension to reservoir computing and in-materio computing in general.