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Record W4327744364 · doi:10.1103/physreva.107.032611

High-contrast interaction-free quantum imaging method

2023· article· en· W4327744364 on OpenAlexafffund
Sepideh Ahmadi, Erhan Sağlamyürek, Shabir Barzanjeh, Vahid Salari

Bibliographic record

VenuePhysical review. A/Physical review, A · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGhost imagingQuantum imagingPhotonPhysicsContrast (vision)VisibilityOpticsComputer scienceQuantumQuantum sensorNoise (video)Object (grammar)Computer visionArtificial intelligenceQuantum informationQuantum networkQuantum mechanicsImage (mathematics)

Abstract

fetched live from OpenAlex

Quantum imaging techniques offer enhanced resolution, contrast, and precision at ultralow illumination levels compared to traditional imaging approaches. Relying on the unique properties of entangled photon pairs, two of these techniques stand out: the correlation-based quantum imaging technique provides visibility enhancement in imaging of a low-reflectivity object which is subject to excessive noise and losses, while the interaction-free ghost imaging allows for probing the presence of an object with an ultimately low number of photons. Here we propose a quantum imaging scheme that combines the unique advantages of these two approaches. We show that this scheme offers high-contrast imaging of objects with a minimal number of photons that can minimize thermal noise efficiently and create background-free images. We anticipate that this approach can find application in the imaging of photosensitive biological tissues in a noninvasive and harm-free fashion.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.376
Teacher spread0.361 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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