Quantum optical shallow networks
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908505066176512 |
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| author | Roncallo, Simone Morgillo, Angela Rosy Lloyd, Seth Macchiavello, Chiara Maccone, Lorenzo |
| author_facet | Roncallo, Simone Morgillo, Angela Rosy Lloyd, Seth Macchiavello, Chiara Maccone, Lorenzo |
| contents | Classical shallow networks are universal approximators. Given a sufficient number of neurons, they can reproduce any continuous function to arbitrary precision, with a resource cost that scales linearly in both the input size and the number of trainable parameters. In this work, we present a quantum optical protocol that implements a shallow network with an arbitrary number of neurons. Both the input data and the parameters are encoded into single-photon states. Leveraging the Hong-Ou-Mandel effect, the network output is determined by the coincidence rates measured when the photons interfere at a beam splitter, with multiple neurons prepared as a mixture of single-photon states. Remarkably, once trained, our model requires constant optical resources regardless of the number of input features and neurons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum optical shallow networks Roncallo, Simone Morgillo, Angela Rosy Lloyd, Seth Macchiavello, Chiara Maccone, Lorenzo Quantum Physics Classical shallow networks are universal approximators. Given a sufficient number of neurons, they can reproduce any continuous function to arbitrary precision, with a resource cost that scales linearly in both the input size and the number of trainable parameters. In this work, we present a quantum optical protocol that implements a shallow network with an arbitrary number of neurons. Both the input data and the parameters are encoded into single-photon states. Leveraging the Hong-Ou-Mandel effect, the network output is determined by the coincidence rates measured when the photons interfere at a beam splitter, with multiple neurons prepared as a mixture of single-photon states. Remarkably, once trained, our model requires constant optical resources regardless of the number of input features and neurons. |
| title | Quantum optical shallow networks |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2507.21036 |