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Record W2611446273 · doi:10.1103/physreva.95.053816

Quantum frequency conversion with ultra-broadband tuning in a Raman memory

2017· article· en· W2611446273 on OpenAlex
Philip J. Bustard, Duncan England, Khabat Heshami, Connor Kupchak, Benjamin Sussman

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

VenuePhysical review. A/Physical review, A · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversity of OttawaNational Research Council Canada
FundersNational Research Council Canada
KeywordsPhotonPhotonicsRaman spectroscopyOptoelectronicsPhysicsMaterials scienceQuantumQuantum efficiencySpontaneous parametric down-conversionQuantum networkFrequency combOpticsQuantum informationLaserQuantum mechanicsQuantum entanglement

Abstract

fetched live from OpenAlex

Quantum frequency conversion is a powerful tool for the construction of hybrid quantum photonic technologies. Raman quantum memories are a promising method of conversion due to their broad bandwidths. Here we demonstrate frequency conversion of THz-bandwidth, fs-duration photons at the single-photon level using a Raman quantum memory based on the rotational levels of hydrogen molecules. We shift photons from 765 nm to wavelengths spanning from 673 to 590 nm---an absolute shift of up to 116 THz. We measure total conversion efficiencies of up to 10% and a maximum signal-to-noise ratio of 4.0(1):1, giving an expected conditional fidelity of 0.75, which exceeds the classical threshold of 2/3. Thermal noise could be eliminated by cooling with liquid nitrogen, giving noiseless conversion with wide tunability in the visible and infrared.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.347
Teacher spread0.331 · 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