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| Główni autorzy: | , , , |
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| Format: | Preprint |
| Wydane: |
2024
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| Hasła przedmiotowe: | |
| Dostęp online: | https://arxiv.org/abs/2402.06339 |
| Etykiety: |
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| _version_ | 1866910901066530816 |
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| author | Nerenberg, Sam Neill, Oliver D. Marcucci, Giulia Faccio, Daniele |
| author_facet | Nerenberg, Sam Neill, Oliver D. Marcucci, Giulia Faccio, Daniele |
| contents | Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed optical network for photonic quantum reservoir computing that is enabled by photon number-resolved detection of the output states. This significantly reduces the required complexity of the input quantum states while still accessing a high-dimensional Hilbert space. The approach is implementable with currently available technology and lowers the barrier to entry to quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06339 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Photon Number-Resolving Quantum Reservoir Computing Nerenberg, Sam Neill, Oliver D. Marcucci, Giulia Faccio, Daniele Quantum Physics Applied Physics Optics Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed optical network for photonic quantum reservoir computing that is enabled by photon number-resolved detection of the output states. This significantly reduces the required complexity of the input quantum states while still accessing a high-dimensional Hilbert space. The approach is implementable with currently available technology and lowers the barrier to entry to quantum machine learning. |
| title | Photon Number-Resolving Quantum Reservoir Computing |
| topic | Quantum Physics Applied Physics Optics |
| url | https://arxiv.org/abs/2402.06339 |