# Hybrid Magnonic Reservoir Computing

arXiv: https://arxiv.org/abs/2405.09542 · HTML: https://mlsanity.com/paper/2405.09542/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)

## Built on by (most cited first, 1 of 1)

- [Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing](https://mlsanity.com/paper/2506.04523/) (2025)

## Abstract (arXiv)

Magnonic systems have been a major area of research interest due to their potential benefits in speed and lower power consumption compared to traditional computing. One particular area that they may be of advantage is as Physical Reservoir Computers in machine learning models. In this work, we build on an established design for using an Auto-Oscillation Ring as a reservoir computer by introducing a simple neural network midstream and introduce an additional design using a spin wave guide with a scattering regime for processing data with different types of inputs. We simulate these designs on the new micro magnetic simulation software, Magnum.np, and show that the designs are capable of performing on various real world data sets comparably or better than traditional dense neural networks.