Quantum sensing networks for the estimation of linear functions
dc.contributor.author | Rubio, J | |
dc.contributor.author | Knott, PA | |
dc.contributor.author | Proctor, TJ | |
dc.contributor.author | Dunningham, JA | |
dc.date.accessioned | 2020-11-30T08:58:49Z | |
dc.date.issued | 2020-08-03 | |
dc.description.abstract | The theoretical framework for networked quantum sensing has been developed to a great extent in the past few years, but there are still a number of open questions. Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal. Using this we show that if the vectors are clustered around two special subspaces, then the optimum is achieved when the correlation strength approaches its extreme values, while there is a monotonic transition between such extremes for any other geometry. Furthermore, we demonstrate that entanglement can be detrimental for estimating non-trivial global properties, and that sometimes it is in fact irrelevant. Finally, we perform a non-asymptotic analysis of these results using a Bayesian approach, finding that the amount of correlations needed to enhance the precision crucially depends on the number of measurement data. Our results will serve as a basis to investigate how to harness correlations in networks of quantum sensors operating both in and out of the asymptotic regime. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Royal Commission for the Exhibition of 1851 | en_GB |
dc.identifier.citation | Vol. 53 (34), article 344001 | en_GB |
dc.identifier.doi | 10.1088/1751-8121/ab9d46 | |
dc.identifier.grantnumber | EP/M013243/1 | en_GB |
dc.identifier.grantnumber | EP/T002875/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/123827 | |
dc.language.iso | en | en_GB |
dc.publisher | IOP Publishing | en_GB |
dc.rights | © 2020 The Author(s). Published by IOP Publishing Ltd. Open access. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | en_GB |
dc.title | Quantum sensing networks for the estimation of linear functions | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-11-30T08:58:49Z | |
dc.identifier.issn | 1751-8113 | |
dc.description | This is the final version. Available on open access from IOP Publishing via the DOI in this record | en_GB |
dc.identifier.journal | Journal of Physics A: Mathematical and Theoretical | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-06-16 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-08-03 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-11-30T08:55:43Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-11-30T08:58:53Z | |
refterms.panel | B | en_GB |
refterms.depositException | publishedGoldOA |
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Except where otherwise noted, this item's licence is described as © 2020 The Author(s). Published by IOP Publishing Ltd. Open access. Original content from this work may be used under the terms of the Creative Commons
Attribution 4.0 licence. Any further distribution of this work must maintain attribution
to the author(s) and the title of the work, journal citation and DOI.