Experimental demonstration of phase-remapping attack in a practical quantum key distribution system
Bibliographic record
Abstract
Quantum key distribution (QKD) can, in principle, provide unconditional security based on the fundamental laws of physics. Unfortunately, a practical QKD system may contain overlooked imperfections and may thus violate some of the assumptions in the security proofs of QKD. It is important to explore these assumptions. One key assumption is that the sender (Alice) can prepare the required quantum states without errors. However, such an assumption may be violated in a practical QKD system. In this paper, we perform a proof-of-principle experiment to demonstrate a technically feasible 'intercept- and-resend' attack that exploits such a security loophole in a commercial 'plug & play' QKD system. The resulting quantum bit error rate is 19.7%, which is substantially lower than the well-known 25% error rate for an intercept-and-resend attack in BB84. The attack we utilize is the phase-remapping attack (Fung et al 2007 Phys. Rev. A 75 32314) proposed by our group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".