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Record W4388300548 · doi:10.1002/qute.202300227

Plug‐and‐Play Fiber‐Coupled Quantum Dot Single‐Photon Source via Photonic Wire Bonding

2023· article· en· W4388300548 on OpenAlexafffund
Marco De Gregorio, Shangxuan Yu, Donald Witt, Becky Lin, Matthew Mitchell, Łukasz Dusanowski, Christian Schneider, Lukas Chrostowski, Tobias Huber, Sven Höfling, Jeff F. Young, Andreas Pfenning

Bibliographic record

VenueAdvanced Quantum Technologies · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersStewart Blusson Quantum Matter Institute, University of British ColumbiaNatural Sciences and Engineering Research Council of CanadaJulius-Maximilians-Universität WürzburgBayerisches Staatsministerium für Wissenschaft, Forschung und KunstBundesministerium für Forschung und TechnologieMitacsBundesministerium für Bildung und Forschung
KeywordsSingle-photon sourcePhotonicsQuantum dotPhotonOptoelectronicsWaveguideOptical fiberPhysicsOpticsMaterials science

Abstract

fetched live from OpenAlex

Abstract The collection of single‐photon emission from a quantum dot (QD) in a Bragg waveguide through a photonic wire bond (PWB) via free‐space resonant frequency pumping at 1.6 K is demonstrated. The in‐fiber single photons show a small multiphoton contribution, quantified by a low second order photon autocorrelation value of (background‐corrected) or (raw data). The decay time of the QD is measured to be ps. The PWB obviates the need for in‐cryostat alignment of the single‐photon source with an optical fiber and thus offers a route to scalable integration of quantum photonic devices in a cryogenic environment. Uniquely, the approach combines the QD‐waveguide technique, enabling resonant driving of individual QDs without the need for cross‐polarization filtering, and the PWB for deterministic, alignment‐free coupling of single‐photon sources to optical fibers.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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