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Record W4414230080 · doi:10.1117/12.3063677

Dynamical resonance florescence (RF) in cavity-QED and waveguide-QED

2025· article· en· W4414230080 on OpenAlexaff
Stephen J. Hughes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsQueen's University
Fundersnot available
KeywordsExcited stateResonance (particle physics)ExcitationPhotonQuantumPopulationQuantum dotFock spaceReflection (computer programming)Tensor (intrinsic definition)

Abstract

fetched live from OpenAlex

We discuss the long sought after regime of “dynamical resonance florescence,” which adds significant modification and control to the usual CW resonance florescence schemes such as the Mollow triplet, when using excitation pulses whose time duration is shorter than the inverse decay time of the quantum emitter. We present several examples, including (i) semiconductor quantum dot cavity systems, where we will show recent experiments and simulations side by side, and (ii) waveguide QED systems excited with few photon Fock state pulses. Using state of the art quantum simulation algorithms, including tensor networks, we describe how the usual emission spectrum and intensity outputs are dynamically modified with short pulse excitation, and demonstrate how even single photon population effects are uniquely accessed in this regime. These short-pulsed emission regimes allow for the generation of a variety of exotic quantum states of light.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.005
GPT teacher head0.218
Teacher spread0.214 · 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 designTheoretical or conceptual
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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