Harney, Cillian, Banchi, Leonardo and Pirandola, Stefano orcid.org/0000-0001-6165-5615 (2021) Ultimate Limits of Thermal Pattern Recognition. Physical Review A. 052406. ISSN 1094-1622
Abstract
Quantum Channel Discrimination (QCD) presents a fundamental task in quantum information theory, with critical applications in quantum reading, illumination, data-readout and more. The extension to multiple quantum channel discrimination has seen a recent focus to characterise potential quantum advantage associated with quantum enhanced discriminatory protocols. In this paper, we study thermal imaging as an environment localisation task, in which thermal images are modelled as ensembles of Gaussian phase insensitive channels with identical transmissivity, and pixels possess properties according to background (cold) or target (warm) thermal channels. Via the teleportation stretching of adaptive quantum protocols, we derive ultimate limits on the precision of pattern classification of abstract, binary thermal image spaces, and show that quantum enhanced strategies may be used to provide significant quantum advantage over known optimal classical strategies. The environmental conditions and necessary resources for which advantage may be obtained are studied and discussed. We then numerically investigate the use of quantum enhanced statistical classifiers, in which quantum sensors are used in conjunction with machine learning image classification methods. Proving definitive advantage in the low loss regime, this work motivates the use of quantum enhanced sources for short-range thermal imaging and detection techniques for future quantum technologies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 15 pages, 7 figures. Close to published version. © 2021 American Physical Society. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Keywords: | quant-ph,cs.LG,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: | 23 Feb 2022 12:30 |
Last Modified: | 16 Oct 2024 18:15 |
Published Version: | https://doi.org/10.1103/PhysRevA.103.052406 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1103/PhysRevA.103.052406 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184032 |