Quantum reservoir networks based on decoherence-free subspaces
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914605536641024 |
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| author | Akshay, V. V. Altaisky, M. V. Kaputkina, N. E. |
| author_facet | Akshay, V. V. Altaisky, M. V. Kaputkina, N. E. |
| contents | We present numerical simulation of a six-qubit quantum reservoir network with an output implemented on a 5-dimensional decoherence-free subspace (DFS), working as a classifier between entangled and product states of the input quantum system, fed to the reservoir during a finite learning time. Since the dynamics of DFS is not affected by external fluctuations, no cooling is required, and the proposed model seems a promising candidate for future quantum artificial intelligence systems working at room temperatures and free of huge energy consumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27427 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum reservoir networks based on decoherence-free subspaces Akshay, V. V. Altaisky, M. V. Kaputkina, N. E. Quantum Physics We present numerical simulation of a six-qubit quantum reservoir network with an output implemented on a 5-dimensional decoherence-free subspace (DFS), working as a classifier between entangled and product states of the input quantum system, fed to the reservoir during a finite learning time. Since the dynamics of DFS is not affected by external fluctuations, no cooling is required, and the proposed model seems a promising candidate for future quantum artificial intelligence systems working at room temperatures and free of huge energy consumption. |
| title | Quantum reservoir networks based on decoherence-free subspaces |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2605.27427 |