Reservoir computing by thin film embedded with magnetic impurities
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915974173687808 |
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| author | Kamakura, Shuto Ohtsuki, Tomi Ohe, Jun-ichiro |
| author_facet | Kamakura, Shuto Ohtsuki, Tomi Ohe, Jun-ichiro |
| contents | The reservoir computing based on the thin film embedded with magnetic impurities in the presence of the long-range (the dipole-dipole) interaction is numerically investigated. We simulated the magnetization dynamics by taking into account the dipole-dipole interaction and performed the handwritten-digit recognition task. Although the training data is prepared by taking spatial average in the sample, the high classification accuracy is achieved. Our result demonstrates that the long range interaction effectively encodes the complex spatial input pattern into the time domain, even when only a spatially averaged output is accessible. The proposed system paves the way for easily realizable magnetic reservoir computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01360 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reservoir computing by thin film embedded with magnetic impurities Kamakura, Shuto Ohtsuki, Tomi Ohe, Jun-ichiro Disordered Systems and Neural Networks The reservoir computing based on the thin film embedded with magnetic impurities in the presence of the long-range (the dipole-dipole) interaction is numerically investigated. We simulated the magnetization dynamics by taking into account the dipole-dipole interaction and performed the handwritten-digit recognition task. Although the training data is prepared by taking spatial average in the sample, the high classification accuracy is achieved. Our result demonstrates that the long range interaction effectively encodes the complex spatial input pattern into the time domain, even when only a spatially averaged output is accessible. The proposed system paves the way for easily realizable magnetic reservoir computing. |
| title | Reservoir computing by thin film embedded with magnetic impurities |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2605.01360 |