Quantum optical classifier with superexponential speedup

• URL: http://arxiv.org/abs/2404.15266v1
• Date: Tue, 23 Apr 2024 17:55:49 GMT
• Title: Quantum optical classifier with superexponential speedup
• Authors: Simone Roncallo, Angela Rosy Morgillo, Chiara Macchiavello, Lorenzo Maccone, Seth Lloyd,
• Abstract summary: We present a quantum optical pattern recognition method for binary classification tasks. It classifies an object in terms of the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer.
• Score: 3.262230127283452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a quantum optical pattern recognition method for binary classification tasks. Without direct image reconstruction, it classifies an object in terms of 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 same 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 neuron (that is at least linear in the image resolution). We provide simulations and analytical comparisons with analogous neural network architectures.

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