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Record W4387159310 · doi:10.1002/qute.202300195

High‐Resolution Single Photon Level Storage of Telecom Light Based on Thin Film Lithium Niobate Photonics

2023· article· en· W4387159310 on OpenAlex

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.

Bibliographic record

VenueAdvanced Quantum Technologies · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersDanmarks GrundforskningsfondVillum Fonden
KeywordsLithium niobatePhotonicsMaterials scienceOptoelectronicsPhotonTelecommunicationsThin filmLithium (medication)3D optical data storageOpticsPhysicsNanotechnologyComputer science

Abstract

fetched live from OpenAlex

Abstract This study presents an experimental analysis of high‐resolution single photon buffers based on low‐loss thin film lithium niobate (TFLN) photonic devices operating at room temperature. While dynamically controlling writing and reading operations within picosecond timescales poses a challenge, the devices are capable of resolving 102.8 4.6 ps time step with ‐0.89 dB loss per round‐trip and 197.7 6.6 ps time steps with ‐1.29 dB loss per round‐trip, respectively. These results imply that the devices are at the cutting edge of on‐chip technology, performing in the current state of the art at the single photon level. Both of the single photon buffers do not introduce any detrimental effects and provide a high signal‐to‐noise ratio (SNR). The room‐temperature, low‐loss, and voltage‐controlled TFLN buffers combine scalable architecture with relatively high buffering capacity in the sub‐nanosecond regime and are expected to unlock many novel photonics applications such as temporally multiplexed single photon sources.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.229
Teacher spread0.210 · 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