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Enhancement of photon blockade via topological edge states

2024· article· en· W4393234288 on OpenAlexafffund
Jun Li, C.‐M. Hu, Yaping Yang

Bibliographic record

VenuePhysical Review Applied · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversity of Manitoba
FundersNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBlockadeEnhanced Data Rates for GSM EvolutionTopology (electrical circuits)PhotonPhysicsSet (abstract data type)MathematicsComputer scienceQuantum mechanicsCombinatoricsMedicineTelecommunicationsInternal medicine

Abstract

fetched live from OpenAlex

Quantum technologies, holding the promise of exponentially superior performance in comparison to their classical counterparts for certain tasks, have consistently encountered challenges, including instability in quantum light sources, quantum decoherence, and vulnerability to losses that topological photonics happens to adeptly address. Here, we theoretically put forth a quantum Su-Schrieffer-Heeger-type chain designed to greatly enhance single-photon blockade (single PB) effect with topological protection. By designing the deliberate coupling strengths, the quantum level lattices take the form of a one-dimensional array with a topological edge state in single-excitation space and a two-dimensional square breathing lattice with topological corner states in two-excitation space, resulting in enhanced single-photon excitation and the suppression of two-photon transitions. Therefore, the second-order correlation function is diminished by up to 2 orders of magnitude at the cavity resonance frequency, accompanied by stronger brightness. Furthermore, the PB effect is robust to local perturbations in cavity-qubit coupling and qubit frequency, benefitting from topological protection.

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 categoriesInsufficient payload (model declined to judge)
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 score0.087
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0040.000

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 head0.013
GPT teacher head0.293
Teacher spread0.280 · 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 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

Citations13
Published2024
Admission routes2
Has abstractyes

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