Quantum key distribution with entangled photon sources
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Bibliographic record
Abstract
A parametric down-conversion (PDC) source can be used as either a triggered single-photon source or an entangled-photon source in quantum key distribution (QKD). The triggering PDC QKD has already been studied in the literature. On the other hand, a model and a post-processing protocol for the entanglement PDC QKD are still missing. We fill in this important gap by proposing such a model and a post-processing protocol for the entanglement PDC QKD. Although the PDC model is proposed to study the entanglement-based QKD, we emphasize that our generic model may also be useful for other non-QKD experiments involving a PDC source. Since an entangled PDC source is a basis-independent source, we apply Koashi and Preskill's security analysis to the entanglement PDC QKD. We also investigate the entanglement PDC QKD with two-way classical communications. We find that the recurrence scheme increases the key rate and the Gottesman-Lo protocol helps tolerate higher channel losses. By simulating a recent $144\text{\ensuremath{-}}\mathrm{km}$ open-air PDC experiment, we compare three implementations: entanglement PDC QKD, triggering PDC QKD, and coherent-state QKD. The simulation result suggests that the entanglement PDC QKD can tolerate higher channel losses than the coherent-state QKD. The coherent-state QKD with decoy states is able to achieve highest key rate in the low- and medium-loss regions. By applying the Gottesman-Lo two-way post-processing protocol, the entanglement PDC QKD can tolerate up to $70\phantom{\rule{0.3em}{0ex}}\mathrm{dB}$ combined channel losses ($35\phantom{\rule{0.3em}{0ex}}\mathrm{dB}$ for each channel) provided that the PDC source is placed in between Alice and Bob. After considering statistical fluctuations, the PDC setup can tolerate up to $53\phantom{\rule{0.3em}{0ex}}\mathrm{dB}$ channel losses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it