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Photon transport in bimodal atom-cavity systems with waveguide coupling: Application to deterministic photon subtraction

2025· article· en· W4406099961 on OpenAlexafffund
Abdolreza Pasharavesh, Michal Bajcsy

Bibliographic record

VenuePhysical Review Research · 2025
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
FundersCanada First Research Excellence Fund
KeywordsPhotonPhysicsSubtractorPhotonicsSubtractionQuantumComputational physicsOpticsQuantum mechanicsOptoelectronicsMathematicsAdder

Abstract

fetched live from OpenAlex

The single-photon Raman interaction has emerged as a promising tool for deterministic photon subtraction, acting as a photon-activated switch in which the first arriving photon renders the system transparent to subsequent photons. Here, we employ the input-output formalism to perform a single-photon transport analysis within a photon subtractor consisting of a three-level quantum emitter coupled to a bimodal cavity-waveguide system. The analysis provides the frequency response of the system and reveals how it is affected by the key physical parameters of the cavity modes, such as coupling strengths and decay rates. We utilize these results to calculate the probability of successful photon subtraction from pulses of different temporal shapes, durations, and carrier frequencies. Subsequent analysis combines the input-output formalism with the quantum trajectory method to numerically verify the analytical findings as well as to assess the impact of photon number of the input pulse on the subtractor's performance. Our findings highlight the single-photon model's capability in predicting the system's response to multiphoton inputs in the pulsed regime as well as its potential utility in the design of efficient single-photon subtractors.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.393
Teacher spread0.353 · 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 designSimulation or modeling
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

Citations4
Published2025
Admission routes2
Has abstractyes

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