Experimental quantum key distribution with source flaws and tight finite-key analysis
Bibliographic record
Abstract
Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada(Dated: August 19, 2014)Decoy-state quantum key distribution (QKD) is a standard technique in current quantum cryptographic im-plementations. Unfortunately, existing experiments have two important drawbacks: the state preparation isassumed to be perfect without errors and the employed security proofs do not fully consider the finite-key ef-fects for general attacks. These two drawbacks mean that existing experiments are not guaranteed to be securein practice. Here, we perform an experiment that for the first time shows secure QKD with imperfect statepreparations at long distances and achieves rigorous finite -key security bounds for decoy-state QKD againstgeneral quantum attacks in the universally composable framework. We implement both decoy-state BB84 andthree-state protocol on top of a commercial QKD system and generate secure keys over 50 km standard telecomfiber based on a recent security analysis that is loss-tolerant to source flaws. Our work constitutes an importantstep towards secure QKD with imperfect devices.I. INTRODUCTION
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".