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Record W1728138922 · doi:10.1103/physreva.93.013801

Shaping the joint spectrum of down-converted photons through optimized custom poling

2016· article· en· W1728138922 on OpenAlexafffund
Annamaria Dosseva, Łukasz Cincio, Agata M. Brańczyk

Bibliographic record

VenuePhysical review. A/Physical review, A · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotorefractive and Nonlinear Optics
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersIndustry CanadaOntario Ministry of Research, Innovation and ScienceGovernment of CanadaJohn Templeton Foundation
KeywordsPolingPhotonSimulated annealingParametric statisticsOpticsSpontaneous parametric down-conversionQuantum opticsPhysicsQuasi-phase-matchingPhase matchingOptoelectronicsAnnealing (glass)Computer scienceQuantumMaterials scienceNonlinear opticsAlgorithmQuantum mechanicsMathematicsQuantum entanglementLaser

Abstract

fetched live from OpenAlex

We present a scheme for engineering the joint spectrum of photon pairs created via spontaneous parametric down-conversion. Our method relies on customizing the poling configuration of a quasi-phase-matched crystal. We use simulated annealing to find an optimized poling configuration which allows almost arbitrary shaping of the crystal's phase-matching function. This has direct application in the creation of pure single photons---currently one of the most important goals of single-photon quantum optics. We describe the general algorithm and provide code, written in C++, that outputs an optimized poling configuration given specific experimental parameters.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.366
Teacher spread0.329 · 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

Citations91
Published2016
Admission routes2
Has abstractyes

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