Quantum Transport Reservoir Computing
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917057687191552 |
|---|---|
| author | Jing, Yecheng Wang, Pengfei Zhang, Shuai Zeng, Zhoujie Liang, Shi-Jun Chen, Wei |
| author_facet | Jing, Yecheng Wang, Pengfei Zhang, Shuai Zeng, Zhoujie Liang, Shi-Jun Chen, Wei |
| contents | Reservoir computing (RC), a neural network designed for temporal data, enables efficient computation with low-cost training and direct physical implementation. Recently, quantum RC has opened new possibilities for conventional RC and introduced novel ideas to tackle open problems in quantum physics and advance quantum technologies. Despite its promise, it faces challenges, including physical realization, output readout, and measurement-induced back-action. Here, we propose to implement quantum RC through quantum transport in mesoscopic electronic systems. Our approach possesses several advantages: compatibility with existing device fabrication techniques, ease of output measurement, and robustness against measurement back-action. Leveraging universal conductance fluctuations, we numerically demonstrate two benchmark tasks, spoken-digit recognition and time-series forecasting, to validate our proposal. This work establishes a novel pathway for implementing on-chip quantum RC via quantum transport and expands the mesoscopic physics applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Transport Reservoir Computing Jing, Yecheng Wang, Pengfei Zhang, Shuai Zeng, Zhoujie Liang, Shi-Jun Chen, Wei Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Reservoir computing (RC), a neural network designed for temporal data, enables efficient computation with low-cost training and direct physical implementation. Recently, quantum RC has opened new possibilities for conventional RC and introduced novel ideas to tackle open problems in quantum physics and advance quantum technologies. Despite its promise, it faces challenges, including physical realization, output readout, and measurement-induced back-action. Here, we propose to implement quantum RC through quantum transport in mesoscopic electronic systems. Our approach possesses several advantages: compatibility with existing device fabrication techniques, ease of output measurement, and robustness against measurement back-action. Leveraging universal conductance fluctuations, we numerically demonstrate two benchmark tasks, spoken-digit recognition and time-series forecasting, to validate our proposal. This work establishes a novel pathway for implementing on-chip quantum RC via quantum transport and expands the mesoscopic physics applications. |
| title | Quantum Transport Reservoir Computing |
| topic | Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2509.07778 |