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Record W2793050864 · doi:10.1364/oe.26.007528

Resolution limits of quantum ghost imaging

2018· article· en· W2793050864 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Express · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Ottawa
FundersH2020 Marie Skłodowska-Curie ActionsH2020 European Research CouncilEngineering and Physical Sciences Research CouncilCanada Excellence Research Chairs, Government of CanadaEuropean CommissionQuantIC
KeywordsGhost imagingPoint spread functionOpticsQuantum imagingPhysicsImage resolutionResolution (logic)PhotonDetectorOptical transfer functionImage qualityPhoton countingQuantumComputer scienceImage (mathematics)Quantum informationComputer visionArtificial intelligenceQuantum network

Abstract

fetched live from OpenAlex

Quantum ghost imaging uses photon pairs produced from parametric downconversion to enable an alternative method of image acquisition. Information from either one of the photons does not yield an image, but an image can be obtained by harnessing the correlations between them. Here we present an examination of the resolution limits of such ghost imaging systems. In both conventional imaging and quantum ghost imaging the resolution of the image is limited by the point-spread function of the optics associated with the spatially resolving detector. However, whereas in conventional imaging systems the resolution is limited only by this point spread function, in ghost imaging we show that the resolution can be further degraded by reducing the strength of the spatial correlations inherent in the downconversion process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.249
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it