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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.16938 |
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| _version_ | 1866913704940929024 |
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| author | Wang, Longhan Sun, Peijie Kong, Ling-Jun Sun, Yifan Zhang, Xiangdong |
| author_facet | Wang, Longhan Sun, Peijie Kong, Ling-Jun Sun, Yifan Zhang, Xiangdong |
| contents | Quantum reservoir computing (QRC) exploits the information-processing capabilities of quantum systems to tackle time-series forecasting tasks, which is expected to be superior to their classical counterparts. By far, many QRC schemes have been theoretically proposed. However, most of these schemes involves long-time evolution of quantum systems or networks with quantum gates. This poses a challenge for practical implementation of these schemes, as precise manipulation of quantum systems is crucial, and this level of control is currently hard to achieve with the existing state of quantum technology. Here, we propose a different way of QRC scheme, which is friendly to experimental realization. It implements the quantum version of nonlinear vector autoregression, extracting linear and nonlinear features of quantum data by measurements. Thus, the evolution of complex networks of quantum gates can be avoided. Compared to other QRC schemes, our proposal also achieves an advance by effectively reducing the necessary training data for reliable predictions in time-series forecasting tasks. Furthermore, we experimentally verify our proposal by performing the forecasting tasks, and the observation matches well with the theorectial ones. Our work opens up a new way toward complex tasks to be solved by using the QRC, which can herald the next generation of the QRC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation Wang, Longhan Sun, Peijie Kong, Ling-Jun Sun, Yifan Zhang, Xiangdong Quantum Physics Quantum reservoir computing (QRC) exploits the information-processing capabilities of quantum systems to tackle time-series forecasting tasks, which is expected to be superior to their classical counterparts. By far, many QRC schemes have been theoretically proposed. However, most of these schemes involves long-time evolution of quantum systems or networks with quantum gates. This poses a challenge for practical implementation of these schemes, as precise manipulation of quantum systems is crucial, and this level of control is currently hard to achieve with the existing state of quantum technology. Here, we propose a different way of QRC scheme, which is friendly to experimental realization. It implements the quantum version of nonlinear vector autoregression, extracting linear and nonlinear features of quantum data by measurements. Thus, the evolution of complex networks of quantum gates can be avoided. Compared to other QRC schemes, our proposal also achieves an advance by effectively reducing the necessary training data for reliable predictions in time-series forecasting tasks. Furthermore, we experimentally verify our proposal by performing the forecasting tasks, and the observation matches well with the theorectial ones. Our work opens up a new way toward complex tasks to be solved by using the QRC, which can herald the next generation of the QRC. |
| title | Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2502.16938 |