The Engineering of a Scalable Multi-Site Communications System Utilizing Quantum Key Distribution (QKD)
Bibliographic record
Abstract
Abstract Quantum key distribution (QKD) is a means of generating keys between a pair of computing hosts that is theoretically secure against cryptanalysis, even by a quantum computer. Although there is much active research into improving the QKD technology itself, there is still significant work to be done to apply engineering methodology and determine how it can be practically built to scale within an enterprise IT environment. Significant challenges exist in building a practical key management service (KMS) for use in a metropolitan network. QKD is generally a point-to-point technique only and is subject to steep performance constraints. The integration of QKD into enterprise-level computing has been researched, to enable quantum-safe communication. A novel method for constructing a KMS is presented that allows arbitrary computing hosts on one site to establish multiple secure communication sessions with the hosts of another site. A key exchange protocol is proposed where symmetric private keys are granted to hosts while satisfying the scalability needs of an enterprise population of users. The KMS operates within a layered architectural style that is able to interoperate with various underlying QKD implementations. Variable levels of security for the host population are enforced through a policy engine. A network layer provides key generation across a network of nodes connected by quantum links. Scheduling and routing functionality allows quantum key material to be relayed across trusted nodes. Optimizations are performed to match the real-time host demand for key material with the capacity afforded by the infrastructure. The result is a flexible and scalable architecture that is suitable for enterprise use and independent of any specific QKD technology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".