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A reconfigurable entanglement distribution network suitable for connecting multiple ground nodes with a satellite

2025· preprint· en· W4399911340 on OpenAlexafffund
Stéphane Vinet, Ramy Tannous, Thomas Jennewein

Bibliographic record

VenueEPJ Quantum Technology · 2025
Typepreprint
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsSimon Fraser UniversityNational Research Council CanadaUniversity of Waterloo
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of Canada
KeywordsQuantum entanglementSatelliteComputer scienceDistribution (mathematics)Computer networkTelecommunicationsPhysicsAerospace engineeringEngineeringMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Satellite-based quantum communication channels are important for ultra-long distances. Given the short duration of a satellite pass, it can be challenging to efficiently connect multiple users of a city-wide network while the satellite is passing over that area. We propose a network with dual-functionality: during a brief satellite pass, the ground network is configured as a multipoint-to-point topology where all ground nodes establish entanglement with a satellite receiver. During times when this satellite is not available, the satellite up-link is rerouted via a single optical switch to the ground nodes, and the network is configured as a pair-wise ground network. We numerically simulate a pulsed hyper-entangled photon source and study the performance of the proposed network configurations for quantum key distribution. We find favourable scaling in the case that the satellite receiver exploits time-multiplexing whereas the ground nodes utilize frequency-multiplexing. The scalability, simple reconfigurability, and easy integration with fibre networks make this architecture a promising candidate for quantum communication of many ground nodes and a satellite, an important step towards interconnection of ground nodes at a global scale.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.257
Teacher spread0.233 · 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 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

Citations8
Published2025
Admission routes2
Has abstractyes

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