Quantum Readout of Imperfect Classical Data.

quantum channel discrimination quantum enhanced measurement quantum hypothesis testing

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
15 Mar 2022
Historique:
received: 01 02 2022
revised: 08 03 2022
accepted: 11 03 2022
entrez: 26 3 2022
pubmed: 27 3 2022
medline: 27 3 2022
Statut: epublish

Résumé

The encoding of classical data in a physical support can be done up to some level of accuracy due to errors and the imperfection of the writing process. Moreover, some degradation of the stored data can happen over time because of physical or chemical instability of the system. Any readout strategy should take into account this natural degree of uncertainty and minimize its effect. An example are optical digital memories, where the information is encoded in two values of reflectance of a collection of cells. Quantum reading using entanglement, has been shown to enhances the readout of an ideal optical memory, where the two level are perfectly characterized. In this work, we analyse the case of imperfect construction of the memory and propose an optimized quantum sensing protocol to maximize the readout accuracy in presence of imprecise writing. The proposed strategy is feasible with current technology and is relatively robust to detection and optical losses. Beside optical memories, this work have implications for identification of pattern in biological system, in spectrophotometry, and whenever the information can be extracted from a transmission/reflection optical measurement.

Identifiants

pubmed: 35336438
pii: s22062266
doi: 10.3390/s22062266
pmc: PMC8949242
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : European Union's Horizon 2020 Research and Innovation Action (Quantum readout techniques and technologies, QUARTET)
ID : 862644

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Auteurs

Giuseppe Ortolano (G)

Istituto Nazionale di Ricerca Metrologica, Strada Delle Cacce, 10135 Torino, Italy.
Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.

Ivano Ruo-Berchera (I)

Istituto Nazionale di Ricerca Metrologica, Strada Delle Cacce, 10135 Torino, Italy.

Classifications MeSH