Security Analysis of an Untrusted Source for Quantum Key Distribution: Passive Approach
Bibliographic record
Abstract
We present a passive approach to the security analysis of quantum key distribution (QKD) with an untrusted source. A complete proof of its unconditional security is also presented. This scheme has significant advantages in real-life implementations as it does not require fast optical switching or a quantum random number generator. The essential idea is to use a beam splitter to split each input pulse. We show that we can characterize the source using a cross-estimate technique without active routing of each pulse. We have derived analytical expressions for the passive estimation scheme. Moreover, using simulations, we have considered four real-life imperfections: Additional loss introduced by the "plug & play" structure, inefficiency of the intensity monitor, noise of the intensity monitor, and statistical fluctuation introduced by finite data size. Our simulation results show that the passive estimate of an untrusted source remains useful in practice, despite these four imperfections. Also, we have performed preliminary experiments, confirming the utility of our proposal in real-life applications. Our proposal makes it possible to implement the "plug & play" QKD with the security guaranteed, while keeping the implementation practical.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".