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Record W4412969450 · doi:10.1117/12.3066076

Waveguide-QED in a nanowire quantum dot system

2025· article· en· W4412969450 on OpenAlexaff
Tarun Patel, Matteo Pennacchietti, Sayan Gangopadhyay, Philip J. Poole, Dan Dalacu, Robin L. Williams, Michael E. Reimer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaUniversity of Waterloo
Fundersnot available
KeywordsQuantum dotNanowireWaveguideOptoelectronicsPhysicsQuantum dot laserGallium arsenideMaterials scienceSemiconductor

Abstract

fetched live from OpenAlex

A single two-level system coupled strongly to a one-dimensional optical waveguide mode has been a challenge in realizing a true waveguide-QED system. Such a system can be realized with a single quantum dot (QD) grown on the axis of a nanowire (NW) with a single optical waveguide mode. In this study, we investigate light injection into a tapered InP NW with an InAsP QD using k-space extinction microscopy. Numerical simulations estimate that > 80% extinction of reflected laser light under mode matching conditions corresponds to ≈ 90% in-coupling of the input light into the fundamental waveguide mode of the NW. We experimentally verify this claim of near-ideal mode matching and reflect light from the quantum dot under the Heitler regime. We observe that the reflected light has the coherence of the laser with strong antibunching (g2(0) < 0.1). Mode-matching the input laser and the reflected signal removes the requirement for any additional filtering techniques that were necessary in previous works to supress the laser light that does not interact with the QD. Our work demonstrates a true realization of a single two-level system coupled to a one-dimensional electromagnetic mode.

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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