MétaCan
Menu
Back to cohort
Record W4416595784 · doi:10.1021/acs.nanolett.5c04530

Mode-Matched Resonant Excitation of a Nanowire Quantum Dot in a Nanophotonic Waveguide

2025· article· en· W4416595784 on OpenAlexafffund
Sayan Gangopadhyay, Lingxi Yu, Tarun Patel, Matteo Pennacchietti, David B. Northeast, Robin L. Williams, Philip J. Poole, Michael E. Reimer, Dan Dalacu

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsNational Research Council CanadaUniversity of OttawaUniversity of Waterloo
FundersNational Research Council CanadaCanadian Space AgencyNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsQuantum dotNanowireExcitationNanophotonicsWaveguidePhotonicsCoherent controlPhotonQuantum dot laserLaser

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Nanowire-based quantum dots as sources of single photons are promising candidates for the implementation of quantum photonic technologies. Achieving coherent control of these sources is essential for generating indistinguishable single photons─a key requirement for quantum interference. However, coherent excitation of nanowire quantum dots via resonant pumping has remained a long-standing challenge due to high laser suppression requirements. Here we establish a reliable technique to implement resonant excitation of a quantum dot in a tapered single-mode nanowire waveguide by complementing polarization–rejection with mode-matching to minimize the amount of backscattered laser. We demonstrate low multiphoton emission [ g X (2) (0) = 0.019] and multiple Rabi oscillations under pulsed resonant excitation. We also report on two-photon indistinguishability under resonant excitation, achieving an interference visibility of 41%. This is a significant improvement over incoherent excitation and represents an important step in the development of a scalable approach for producing coherent 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.

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 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 score0.125
Threshold uncertainty score0.513

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 integrity0.0000.000
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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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.

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

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueNano LettersSame topicPhotonic Crystals and ApplicationsFrench-language works237,207