Quantum Reservoir Computing Using Bose-Einstein Condensate with Damping
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910573100269568 |
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| author | Kurokawa, Yuki Takahashi, Junichi Yamanaka, Yoshiya |
| author_facet | Kurokawa, Yuki Takahashi, Junichi Yamanaka, Yoshiya |
| contents | Quantum reservoir computing is a type of machine learning in which the high-dimensional Hilbert space of quantum systems contributes to performance. In this study, we employ the Bose-Einstein condensate of dilute atomic gas as a reservoir to examine the effect of reduction in the number of condensed particles, damping, and the nonlinearity of the dynamics. It is observed that for the condensate to function as a reservoir, the physical system requires damping. The nonlinearity of the dynamics improves the performance of the reservoir, while the reduction in the number of condensed particles degrades the performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_09577 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Reservoir Computing Using Bose-Einstein Condensate with Damping Kurokawa, Yuki Takahashi, Junichi Yamanaka, Yoshiya Quantum Gases Quantum reservoir computing is a type of machine learning in which the high-dimensional Hilbert space of quantum systems contributes to performance. In this study, we employ the Bose-Einstein condensate of dilute atomic gas as a reservoir to examine the effect of reduction in the number of condensed particles, damping, and the nonlinearity of the dynamics. It is observed that for the condensate to function as a reservoir, the physical system requires damping. The nonlinearity of the dynamics improves the performance of the reservoir, while the reduction in the number of condensed particles degrades the performance. |
| title | Quantum Reservoir Computing Using Bose-Einstein Condensate with Damping |
| topic | Quantum Gases |
| url | https://arxiv.org/abs/2408.09577 |