DGL22: A practical Quantum key distribution
Bibliographic record
Abstract
Abstract Quantum key distribution (QKD) protocols are unconditionally secure, providing that quantum devices are truthful. Device-independent quantum key distribution (DI-QKD) protocols offer the prospect of distributing secret keys with minimal assumptions. The DI-QKD protocols are hard to implement using the current technologies as they require all the quantum devices to be uncharacterized. Measurement device independent quantum key distribution (MDI-QKD) protocols assume that the measurement devices are not truthful; the adversary might make them. MDI-QKD is secure but more challenging to implement. Receiver-device-independent quantum key distribution (RDI-QKD) protocols assume that the receiver's measurement devices can be viewed as a black box while the sender's device can be trusted. RDI-QKD relies on reasonable assumptions that make it easy to implement using the current state-of-the-art technology. We propose an RDI-QKD protocol, called DGL22. DGL22 is secure against all known attacks that are allowed by quantum mechanics. DGL22 provides several benefits over previous protocols, including the capability to attain high communication and sifting efficiency.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".