qkdSim, a Simulation Toolkit for Quantum Key Distribution Including Imperfections: Performance Analysis and Demonstration of the B92 Protocol Using Heralded Photons
Bibliographic record
Abstract
Quantum key distribution (QKD) is one of the most important aspects of quantum cryptography. Using laws of quantum mechanics as the basis for security, the key-distribution process makes information theoretically secure in QKD. With the advancement and commercialization of QKD, an end-to-end QKD simulation software is required that can include experimental imperfections. Software of this kind will ensure that resources are invested only after prior performance analysis, and is faithful to experimental capacities and limitations. In this work, we introduce our QKD simulation toolkit qkdSim, which is ultimately aimed at being developed into such a software package that can precisely model and analyze any generic QKD protocol. We present the design, implementation, and testing of a prototype of qkdSim that can accurately simulate our own experimental demonstration of the B92 protocol. The simulation results match well with experiment; a representative key rate and the quantum bit error rate from experiment is $51\ifmmode\pm\else\textpm\fi{}0.5$ kbit/sec and $4.79\mathrm{%}\ifmmode\pm\else\textpm\fi{}0.01\mathrm{%}$ respectively, wherein the simulation yields $52.83\ifmmode\pm\else\textpm\fi{}0.36$ kbit/sec and $4.79\mathrm{%}\ifmmode\pm\else\textpm\fi{}0.01\mathrm{%}$, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".