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Record W4308022378 · doi:10.1103/physreva.106.053702

Subradiant states for two imperfect quantum emitters coupled by a nanophotonic waveguide

2022· article· en· W4308022378 on OpenAlexfundno aff
Xiao‐Liu Chu, Vasiliki Angelopoulou, Peter Lodahl, Nir Rotenberg

Bibliographic record

VenuePhysical review. A/Physical review, A · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersHorizon 2020Natural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeCanada Foundation for InnovationDanmarks Grundforskningsfond
KeywordsDephasingQuantum decoherencePhysicsNanophotonicsPhotonQuantumWaveguidePhotonicsRealization (probability)OptoelectronicsPopulationQuantum opticsQuantum computerQuantum technologyQuantum mechanicsOpen quantum system

Abstract

fetched live from OpenAlex

Coherent interactions between quantum emitters in tailored photonic structures is a fundamental building block for future quantum technologies, but remains challenging to observe in complex solid-state environments, where the role of decoherence must be considered. Here, we investigate the optical interaction between two quantum emitters mediated by one-dimensional waveguides in a realistic solid-state environment, focusing on the creation, population, and detection of a subradiant state, in the presence of dephasing. We show that as dephasing increases, the signatures of subradiance quickly vanish in intensity measurements, yet remain pronounced in photon correlation measurements, particularly when the two emitters are pumped separately so as to populate the subradiant state efficiently. The applied Green's tensor approach is used to model a photonic crystal waveguide, including the dependence on the spatial position of the integrated emitter. The work lays out a route to the experimental realization of subradiant states in nanophotonic waveguides containing solid-state emitters.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.308
Teacher spread0.299 · 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.

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

Citations15
Published2022
Admission routes1
Has abstractyes

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