Quantum Key Distribution Network Architectures
Bibliographic record
Abstract
Up to now, Quantum Key Distribution (QKD) Network architectures were confined to relatively small networks. Some theoretical and standardization work was carried out at the framework level but its real-world application is still to be proven. Here, we present a novel architecture for QKD Networks, which is based on the SDN approach, and the components needed to scale to large networks. A well-defined functionality of a minimal set of universal modules allows to define the flow of information among them, and this specifies their interfaces and the architecture. We show that this scheme allows to reuse many already proposed interface definitions requiring just minor adaptation and facilitating the real-world deployment. A QKD Network testbed demonstration following these principles is mentioned. We further elaborate on interfaces needed to scale to very large networks, enabling orchestrations of different types or network monitoring tools. The latter have not been considered in depth up to now, but are needed to support automation and advanced capabilities, multidomain networks, slicing, heterogeneity, resource discovery, etc., which will be necessary to scale to large networks. The dynamicity of the quantum level in this scheme allows a significant reduction of the need of QKD devices on the metropolitan scale, an increased robustness of the service, improving its quality and overall network management, showing that the presented architecture is highly appropriate for telecom operators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".