Physical Reservoir Computing with Nano Magnets

Abstract

Reservoir computing is a framework of recurrent neural networks that offers the significant advantage of not requiring learning in the intermediate reservoir layer. Leveraging this feature, the concept of “physical reservoir computing,” where computations in the reservoir layer are substituted by dynamical systems, has emerged. This novel approach has formed a broad research field that spans various domains, including not only information science but also mechanical engineering, condensed matter physics, and materials science. In this paper, I focus on physical reservoir computing using nanometer-sized magnets, describing my research achievements and discussing future prospects.