Ortolano, Giuseppe, Napoli, Carmine, Harney, Cillian et al. (6 more authors) (2023) Quantum-enhanced pattern recognition. Physical Review Applied. 024072. ISSN 2331-7019
Abstract
The challenge of pattern recognition is to invoke a strategy that can accurately extract features of a dataset and classify its samples. In realistic scenarios this dataset may be a physical system from which we want to retrieve information, such as in the readout of optical classical memories. The theoretical and experimental development of quantum reading has demonstrated that the readout of optical memories can be dramatically enhanced through the use of quantum resources (namely entangled input-states) over that of the best classical strategies. However, the practicality of this quantum advantage hinges upon the scalability of quantum reading, and up to now its experimental demonstration has been limited to individual cells. In this work, we demonstrate for the first time quantum advantage in the multi-cell problem of pattern recognition. Through experimental realizations of digits from the MNIST handwritten digit dataset, and the application of advanced classical post-processing, we report the use of entangled probe states and photon-counting to achieve quantum advantage in classification error over that achieved with classical resources, confirming that the advantage gained through quantum sensors can be sustained throughout pattern recognition and complex post-processing. This motivates future developments of quantum-enhanced pattern recognition of bosonic-loss within complex domains.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 American Physical Society. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | quant-ph,physics.optics |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 01 Sep 2023 09:40 |
Last Modified: | 21 Dec 2024 00:24 |
Published Version: | https://doi.org/10.1103/PhysRevApplied.20.024072 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1103/PhysRevApplied.20.024072 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202931 |