Quantum optical classifier with superexponential speedup

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 a...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Roncallo, Simone, Angela Rosy Morgillo, Macchiavello, Chiara, Maccone, Lorenzo, Lloyd, Seth
Format: Article
Language:English
Subjects:
Classification
Classifiers
Image reconstruction
Image resolution
Neural networks
Optical pattern recognition
Pattern recognition
Photons
Online Access:Get full text
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Summary: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.
ISSN:2331-8422