Pereira, Jason L., Zhuang, Quntao and Pirandola, Stefano orcid.org/0000-0001-6165-5615 (2020) Optimal environment localization. Physical Review Research. 043189. ISSN 2643-1564
Abstract
Quantum channels model many physical processes. For this reason, hypothesis testing between quantum channels is a fundamental task in quantum information theory. Here we consider the paradigmatic case of channel position finding, where the aim is to determine the position of a target quantum channel within a sequence of background channels. We explore this model in the setting of bosonic systems, considering Gaussian channels with the same transmissivity (or gain) but different levels of environmental noise. Thus the goal of the problem becomes detecting the position of a target environment among a number of identical background environments, all acting on an input multi-mode system. We derive bounds for the ultimate error probability affecting this multi-ary discrimination problem and find an analytic condition for quantum advantage over protocols involving classical input states. We also design an explicit protocol that gives numerical bounds on the ultimate error probability and often achieves quantum advantage. Finally, we consider direct applications of the model for tasks of thermal imaging (finding a warmer pixel in a colder background) and quantum communication (for localizing a different level of noise in a sequence of lines or a frequency spectrum).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 12 pages, 7 figures © 2020, The Author(s). |
Keywords: | quant-ph |
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: | 06 Nov 2020 08:50 |
Last Modified: | 16 Oct 2024 17:00 |
Published Version: | https://doi.org/10.1103/PhysRevResearch.2.043189 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1103/PhysRevResearch.2.043189 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167722 |