Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 158 tok/s
Gemini 2.5 Pro 50 tok/s Pro
GPT-5 Medium 36 tok/s Pro
GPT-5 High 35 tok/s Pro
GPT-4o 112 tok/s Pro
Kimi K2 177 tok/s Pro
GPT OSS 120B 452 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Benchmarking a magnon-scattering reservoir with modal and temporal multiplexing (2502.02271v2)

Published 4 Feb 2025 in cond-mat.mes-hall

Abstract: Physical reservoir computing has emerged as a powerful framework for exploiting the inherent nonlinear dynamics of physical systems to perform computational tasks. Recently, we presented the magnon-scattering reservoir, whose internal nodes are given by the fundamental wave-like excitations of ferromagnets called magnons. These excitations can be geometrically-quantized and, in response to an external stimulus, show transient nonlinear scattering dynamics that can be harnessed to perform memory and nonlinear transformation tasks. Here, we test a magnon-scattering reservoir in a single magnetic disk in the vortex state towards two key performance indicators for physical reservoir computing, the short-term memory and parity-check tasks. Using time-resolved Brillouin light scattering microscopy, we measure the evolution of the reservoir's spectral response to an input sequence consisting of random binary inputs encoded in microwave pulses with two distinct frequencies. Two different output spaces of the reservoir are defined, one based on the time-averaged frequency spectra and another based on temporal multiplexing. Our results demonstrate that the memory and nonlinear transformation capability do not depend on the chosen read-out scheme as long as the dimension of the output space is large enough to capture all nonlinear features provided by the magnon-magnon interactions. This further shows that solely the nonlinear magnons in the physical system, not the read-out, determine the reservoir's capacity.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 tweet and received 1 like.

Upgrade to Pro to view all of the tweets about this paper:

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube