Security Proofs of Quantum Key Distribution with Imperfect Source Devices: From Practical Devices to Passive System
Bibliographic record
Abstract
Quantum key distribution (QKD) is a method that enables two parties separated by distance to share a secret data string. QKD is based on the principles of quantum mechanics and offers information-theoretical security, making it a strong contender for the next generation of cryptography. However, the use of imperfect devices can lead to vulnerabilities and potential quantum hacking schemes. It is crucial to address these imperfections and restore the security of QKD, especially for individuals who use realistic devices for secure communication. QKD can be categorized into two types based on key encoding methods: discrete-variable (DV) QKD and continuous-variable (CV) QKD. It can also be classified based on the transmission medium: fiber-based QKD, free-space-based QKD, and chip-based QKD. This thesis focuses on chip-based DVQKD and fiber-based CVQKD. In the first part, we study the chip-based DVQKD system in the presence of phase-and polarization-dependent loss. We develop innovative methods to restore the security and improve the key rate. The solution is practical, requiring no hardware modifications, and has been adopted by other groups. The second part focuses on fiber-based CVQKD systems that use intensity fluctuating sources. We establish the security by incorporating the concept from DVQKD. Our proposed techniques are practical and do not require any hardware adjustments for existing CVQKD systems. Lastly, we present a general framework for passive CVQKD. Our proposed passive protocol offers a one-time solution to eliminate all potential side channels or imperfections in the source. Interestingly, we discover that the passive source is an excellent choice for the discrete-modulated CVQKD protocol, since the passive protocol offers the same key rate as its active counterpart, while removing all modulator side channels that have plagued the active ones. To sum up, this thesis introduces novel techniques and practical solutions to address imperfections in source devices in different QKD systems. We also propose a passive protocol that can handle all loopholes in source devices in a one-time process. We anticipate that this thesis will facilitate significant advancements in chip-based and fiber-based QKD implementations in the future.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".