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Record W4416874253 · doi:10.1109/qce65121.2025.00111

Temperature Dependence of a Warm Ensemble Based Memory on Quantum Communication Rates

2025· article· W4416874253 on OpenAlexaff
Kenny Gregory, Khaled Mnaymneh, Connor Kupchak, Andrew I. MacRae

Bibliographic record

Venuenot available
Typearticle
Language
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversity of VictoriaNational Research Council CanadaCarleton University
Fundersnot available
KeywordsQuantum key distributionQuantum sensorQuantumQuantum networkQuantum information scienceQuantum entanglementMeasure (data warehouse)Quantum memoryQuantum channel

Abstract

fetched live from OpenAlex

The success of long-distance quantum communication will hinge on quantum repeater nodes equipped with robust and scalable quantum memories. Such memories must possess specific operating characteristics to make these nodes practical. An attractive option for these memory devices is an atomic warm vapor system operating under the conditions of electromagnetically induced transparency (EIT). These systems are easy to implement and require relatively low equipment overhead. A key metric in atomic-based quantum memory performance is optical depth, as it is closely tied to the strength of light-matter coupling. Here, we simulate and measure the optical depthbased performance of an EIT quantum memory using a warm atomic vapor cell. We then translate the simulated and measured optical depth and efficiency metrics into expected entanglement distribution rates. Our analysis evaluates the feasibility of these memories in optical quantum repeater nodes, which are essential for robust long-distance quantum communication.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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