Reservoir computing (RC), as a brain-inspired neuromorphic computing algorithm, is
capable of fast and energy-efficient temporal data analysis and prediction. Hardware …

Magnonics addresses the physical properties of spin waves and utilizes them for data
processing. Scalability down to atomic dimensions, operation in the GHz-to-THz frequency …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

Physical neural networks are artificial neural networks that mimic synapses and neurons
using physical systems or materials. These networks harness the distinctive characteristics …

In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional
materials to perform complex computational tasks. Magnetic metamaterials are exciting …

Magnons are elementary excitations in magnetic materials and undergo nonlinear
multimode scattering processes at large input powers. In experiments and simulations, we …

In the present paper, we propose and experimentally verify a concept of a magnonic
reservoir computer. The system utilizes the nonlinear behavior of propagating magnetostatic …

Combinatorial optimization problems are known for being particularly hard to solve on
traditional von Neumann architectures. This has led to the development of Ising Machines …

Reservoir computing offers efficient processing of time-series data with exceptionally low
training cost for real-time computing in edge devices where energy and hardware resources …

Spintronic nanodevices have ultrafast nonlinear dynamic and recurrence behaviors on a
nanosecond scale that promises to enable a high-performance spintronic reservoir …