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Ultratunable Quantum Frequency Conversion in Photonic Crystal Fiber

2022· article· en· W4308826595 on OpenAlexaff
Kent Bonsma-Fisher, Philip J. Bustard, Charlotte Parry, Thomas A. Wright, D. G. England, B. J. Sussman, Peter J. Mosley

Bibliographic record

VenuePhysical Review Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversity of OttawaNational Research Council Canada
FundersEngineering and Physical Sciences Research Council
KeywordsPhotonic-crystal fiberQuantumPhotonic crystalFiberOptoelectronicsQuantum opticsMaterials sciencePhysicsOptical fiberOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Quantum frequency conversion of single photons between wavelength bands is a key enabler to realizing widespread quantum networks. We demonstrate the quantum frequency conversion of a heralded 1551 nm photon to any wavelength within an ultrabroad (1226-1408 nm) range in a group-velocity-symmetric photonic crystal fiber, covering over 150 independent frequency bins. The target wavelength is controlled by tuning only a single pump laser wavelength. We find internal, and total, conversion efficiencies of 12(1)% and 1.4(2)%, respectively. For the case of converting 1551 to 1300 nm we measure a heralded g^{(2)}(0)=0.25(6) for converted light from an input with g^{(2)}(0)=0.034(8). We expect that this photonic crystal fiber can be used for myriad quantum networking tasks.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations27
Published2022
Admission routes1
Has abstractyes

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