Programming Spintronic Reservoir Computing
Abstract
We present a programming framework for a spintronic reservoir computer (RC) that maps prescribed input-output relationships directly onto the readout layer, bypassing conventional data-driven black-box approaches. Our spintronic RC is based on magnetoresistive random-access memory and exploits magnetization dynamics for computation. We introduce a general metric that quantifies the system's programmability and reveals how the governing equations and system parameters constrain the class of realizable functions. We then construct externally controllable readout layers by exploiting the explicit parameter dependence of the prescribed equations. This metric and construction enable programming explicit functions on the spintronic RC, indicating a potential route to in-memory computing. Our demonstrations include neural-network emulation, bifurcation embedding, and a Newton solver for fifth-order algebraic equations. In addition, we prove the universal approximation property of the spintronic RC in the limit of infinite system size and input duration, and show its consistency with programmability.
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BibTeX
@misc{terasaki2026programming,
title = {{Programming Spintronic Reservoir Computing}},
author = {Yuichiro Terasaki and Yusuke Imai and Jason Z. Kim and Kohei Nakajima},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13169},
eprint = {2609.13169},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13169},
}
RIS
TY - UNPB
TI - Programming Spintronic Reservoir Computing
AU - Yuichiro Terasaki
AU - Yusuke Imai
AU - Jason Z. Kim
AU - Kohei Nakajima
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.13169
UR - https://arxiv.org/abs/2609.13169
AB - We present a programming framework for a spintronic reservoir computer (RC) that maps prescribed input-output relationships directly onto the readout layer, bypassing conventional data-driven black-box approaches. Our spintronic RC is based on magnetoresistive random-access memory and exploits magnetization dynamics for computation. We introduce a general metric that quantifies the system's programmability and reveals how the governing equations and system parameters constrain the class of realizable functions. We then construct externally controllable readout layers by exploiting the explicit parameter dependence of the prescribed equations. This metric and construction enable programming explicit functions on the spintronic RC, indicating a potential route to in-memory computing. Our demonstrations include neural-network emulation, bifurcation embedding, and a Newton solver for fifth-order algebraic equations. In addition, we prove the universal approximation property of the spintronic RC in the limit of infinite system size and input duration, and show its consistency with programmability.
ER -