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Record W4415046912 · doi:10.1103/nxhw-7nf6

Phase-dependent quantum optical coherence tomography

2025· article· en· W4415046912 on OpenAlexafffund
Mayte Y. Li-Gomez, Taras Hrushevskyi, Kayla McArthur, Pablo Yepiz-Graciano, Alfred B. U’Ren, Shabir Barzanjeh

Bibliographic record

VenuePhysical Review Research · 2025
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterferometryQuantum imagingCoherence (philosophical gambling strategy)Interference (communication)Optical coherence tomographyQuantumPhotonRealization (probability)Quantum sensorQuantum key distribution

Abstract

fetched live from OpenAlex

Two-photon interference is a cornerstone of quantum optics, enabling imaging, sensing, and precision measurements that surpass classical limits. By harnessing the quantum interference of photon pairs, as demonstrated by the Hong-Ou-Mandel effect, this approach offers superior axial resolution and intrinsic dispersion cancellation, along with strong noise suppression arising from the precise temporal correlations between photons. Building on these principles, we present the theoretical framework and experimental realization of phase-dependent quantum optical coherence tomography (QOCT), a technique that employs phase-modulated two-photon interference for noninvasive morphological analysis of multilayered samples. We demonstrate that introducing controlled phase shifts to photon pairs in a Hong-Ou-Mandel interferometer effectively eliminates artifacts caused by reflections at different sample layers, thereby greatly improving the accuracy and reliability of QOCT measurements. This work advances the fundamental understanding and practical deployment of two-photon interference, overcoming a key limitation in applying QOCT to real-world applications such as biomedical imaging and materials characterization.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.433
Teacher spread0.370 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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