Practical and Scalable Quantum Reservoir Computing
| Autor: | Zhu, Chuanzhou, Ehlers, Peter J., Nurdin, Hendra I., Soh, Daniel |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Quantum Reservoir Computing leverages quantum systems to solve complex computational tasks with unprecedented efficiency and reduced energy consumption. This paper presents a novel QRC framework utilizing a quantum optical reservoir composed of two-level atoms within a single-mode optical cavity. Employing the Jaynes-Cummings and Tavis-Cummings models, we introduce a scalable and practically measurable reservoir that outperforms traditional classical reservoir computing in both memory retention and nonlinear data processing. We evaluate the reservoir's performance through two primary tasks: the prediction of time-series data via the Mackey-Glass task and the classification of sine-square waveforms. Our results demonstrate significant enhancements in performance with increased numbers of atoms, supported by non-destructive, continuous quantum measurements and polynomial regression techniques. This study confirms the potential of QRC to offer a scalable and efficient solution for advanced computational challenges, marking a significant step forward in the integration of quantum physics with machine learning technology. Comment: 9 pages, 8 figures |
| Databáze: | arXiv |
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