Quantum optical classifier with superexponential speedup
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917983037685760 |
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| author | Roncallo, Simone Morgillo, Angela Rosy Macchiavello, Chiara Maccone, Lorenzo Lloyd, Seth |
| author_facet | Roncallo, Simone Morgillo, Angela Rosy Macchiavello, Chiara Maccone, Lorenzo Lloyd, Seth |
| contents | Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $\mathcal{O}(1)$ complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_15266 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum optical classifier with superexponential speedup Roncallo, Simone Morgillo, Angela Rosy Macchiavello, Chiara Maccone, Lorenzo Lloyd, Seth Quantum Physics Computational Physics Optics Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $\mathcal{O}(1)$ complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron. |
| title | Quantum optical classifier with superexponential speedup |
| topic | Quantum Physics Computational Physics Optics |
| url | https://arxiv.org/abs/2404.15266 |