Randomized tensor network reservoir computing: validity and learnability phase transitions
Reservoir computing (RC) systems, traditionally based on echo state networks (ESN) or liquid state machines, have shown significant potential in dynamic temporal data modeling, such as weather forecasting and astronomical predictions. However, these frameworks are known not being applicable to quant...
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| Format: | Article |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/aded56 |
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| author | Shinji Sato Daiki Sasaki Chih-Chieh Chen Kodai Shiba Tomah Sogabe |
| author_facet | Shinji Sato Daiki Sasaki Chih-Chieh Chen Kodai Shiba Tomah Sogabe |
| author_sort | Shinji Sato |
| collection | DOAJ |
| description | Reservoir computing (RC) systems, traditionally based on echo state networks (ESN) or liquid state machines, have shown significant potential in dynamic temporal data modeling, such as weather forecasting and astronomical predictions. However, these frameworks are known not being applicable to quantum dynamics-based RC. Tensor networks (TNs), with their efficient representation of high-dimensional quantum information and entanglement, are powerful tools for modeling correlated quantum dynamics. Introducing randomized effects into TNs, akin to randomization of recurrent connections in traditional RC, is expected to generate diverse quantum correlation patterns and provide robust, generalizable quantum-inspired dynamic models through randomization. In this work, we propose a novel randomized TN-based RC scheme, experimentally demonstrating its validity. A theoretical model selection criterion is constructed to find the optimal TNRC hyperparameters. Critical phenomena along with the phase transitions of learnability near the edge of chaos in TN RC are clearly identified. The distribution-independent universality in phase transitions observed in TN RC is captured by the newly developed learning theory and a self-consistent mean-field theory of the spin-glass type. The performance advantage of TNRC over ESN is demonstrated in several forecasting experiments. Our findings lay the groundwork for future explorations into randomized TN quantum machine learning, phase transitions in quantum RC, and the manipulation of critical phenomena in complex systems. |
| format | Article |
| id | doaj-art-5f68a90fd4264fabb10d4eb024265171 |
| institution | DOAJ |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-5f68a90fd4264fabb10d4eb0242651712025-08-20T03:08:18ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303501110.1088/2632-2153/aded56Randomized tensor network reservoir computing: validity and learnability phase transitionsShinji Sato0Daiki Sasaki1https://orcid.org/0009-0006-9601-0773Chih-Chieh Chen2https://orcid.org/0000-0003-3092-4346Kodai Shiba3Tomah Sogabe4https://orcid.org/0000-0001-9258-6130Engineering Department, The University of Electro-Communications , Chofu 182-8585, Tokyo, JapanEngineering Department, The University of Electro-Communications , Chofu 182-8585, Tokyo, JapanGrid Inc. , Minato-ku 171-0061, Tokyo, JapanGrid Inc. , Minato-ku 171-0061, Tokyo, JapanEngineering Department, The University of Electro-Communications , Chofu 182-8585, Tokyo, Japan; Grid Inc. , Minato-ku 171-0061, Tokyo, Japan; i-PERC, The University of Electro-Communications , Chofu 182-8585, Tokyo, JapanReservoir computing (RC) systems, traditionally based on echo state networks (ESN) or liquid state machines, have shown significant potential in dynamic temporal data modeling, such as weather forecasting and astronomical predictions. However, these frameworks are known not being applicable to quantum dynamics-based RC. Tensor networks (TNs), with their efficient representation of high-dimensional quantum information and entanglement, are powerful tools for modeling correlated quantum dynamics. Introducing randomized effects into TNs, akin to randomization of recurrent connections in traditional RC, is expected to generate diverse quantum correlation patterns and provide robust, generalizable quantum-inspired dynamic models through randomization. In this work, we propose a novel randomized TN-based RC scheme, experimentally demonstrating its validity. A theoretical model selection criterion is constructed to find the optimal TNRC hyperparameters. Critical phenomena along with the phase transitions of learnability near the edge of chaos in TN RC are clearly identified. The distribution-independent universality in phase transitions observed in TN RC is captured by the newly developed learning theory and a self-consistent mean-field theory of the spin-glass type. The performance advantage of TNRC over ESN is demonstrated in several forecasting experiments. Our findings lay the groundwork for future explorations into randomized TN quantum machine learning, phase transitions in quantum RC, and the manipulation of critical phenomena in complex systems.https://doi.org/10.1088/2632-2153/aded56tensor networksreservoir computingphase transitions |
| spellingShingle | Shinji Sato Daiki Sasaki Chih-Chieh Chen Kodai Shiba Tomah Sogabe Randomized tensor network reservoir computing: validity and learnability phase transitions Machine Learning: Science and Technology tensor networks reservoir computing phase transitions |
| title | Randomized tensor network reservoir computing: validity and learnability phase transitions |
| title_full | Randomized tensor network reservoir computing: validity and learnability phase transitions |
| title_fullStr | Randomized tensor network reservoir computing: validity and learnability phase transitions |
| title_full_unstemmed | Randomized tensor network reservoir computing: validity and learnability phase transitions |
| title_short | Randomized tensor network reservoir computing: validity and learnability phase transitions |
| title_sort | randomized tensor network reservoir computing validity and learnability phase transitions |
| topic | tensor networks reservoir computing phase transitions |
| url | https://doi.org/10.1088/2632-2153/aded56 |
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