Scalable quantum neural networks by few quantum resources
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866917981232037888 |
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| author | Pastorello, Davide Blanzieri, Enrico |
| author_facet | Pastorello, Davide Blanzieri, Enrico |
| contents | This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement protocol. The model turns out to be equivalent to a two-layer feedforward neural network which can be realized combining small quantum modules. The advantages and the perspectives of the proposed quantum method are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_01017 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Scalable quantum neural networks by few quantum resources Pastorello, Davide Blanzieri, Enrico Quantum Physics This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement protocol. The model turns out to be equivalent to a two-layer feedforward neural network which can be realized combining small quantum modules. The advantages and the perspectives of the proposed quantum method are discussed. |
| title | Scalable quantum neural networks by few quantum resources |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2307.01017 |