Bibliographic record
Abstract
Quantum key distribution is information-theoretically secure and a promising candidate for security against quantum computers. However, experimental implementations might deviate from assumptions made in the security proofs and leave the system vulnerable. Measurement device-independent QKD (MDI-QKD) scheme closes all the loopholes on the detection side, which is the most susceptible part of QKD. Therefore, the state preparation part of QKD systems becomes the only source of imperfections that needs to be considered in the security proofs of MDI-QKD. We examine our polarization encoding module and how imperfection in its components can affect state preparation. Our experiment demonstrates the feasibility of generating a secure key with imperfect states prepared by off-the-shelf devices. Another practical challenge that we address in implementing fibre-based MDI-QKD is polarization compensation. Polarization variations due to temperature fluctuations and fibre birefringence are inevitable and require polarization stabilization. Before this work, polarization compensation schemes for QKD either reduced the key sharing cycle or required extra equipment, making them less scalable or commercially viable and preventing the wide adaptation of polarization encoding. We propose and implement a novel polarization compensation scheme in the MDI-QKD systems that avoids the abovementioned drawbacks by using part of discarded detections. Our scheme evaluates the polarization drift in real-time based on single measurements corresponding to decoy intensities. We successfully implemented this active polarization compensation for four hours over 40 km of buffered fibre spool without thermal insulation or vibrational isolation. This thesis is a decisive step toward making polarization encoding MDI-QKD practical and secure, paving the way to realizing a polarization encoding MDI-QKD network. Future networks could be hybrid (ground and satellite links), and polarization encoding is a suitable candidate if an efficient stabilization scheme for fibre links is present. To this aim, our novel compensation scheme solely relies on recycling detections and retains a simple structure for a user node so that expanding the network could be feasible in terms of cost and practicality. Also, it does not reduce the key-sharing cycle, which is essential in networks where the detection system is shared, and each user has limited access time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".