Gaussian-modulated coherent state quantum key distribution experiment over 20km telecommunication fiber
Bibliographic record
Abstract
In most implementations of quantum-key-distribution (QKD) protocols the secure keys originate from single-photon signals. However, due to the unavoidable channel losses and the low efficiencies of single photon detectors, the key generation rate of a single-photon QKD system is low. Recently, there has been a growing interest in the Gaussian-modulated coherent state (GMCS) QKD protocol because it can be implemented with conventional laser sources and high efficiency homodyne-detectors. Here, we present our experimental results with a fully fiber-based one-way GMCS QKD system. Our system employed a double Mach-Zehnder interferometer (MZI) configuration in which the weak quantum signal and the strong local-oscillator (LO) go through the same fiber between Alice and Bob. We employed two novel techniques to suppress system excess noise. First, to suppress the LO's leakage, an important contribution to the excess noise, we implemented a scheme combining polarization and frequency multiplexing, achieving an extinction ratio of 70dB. Second, to further minimize the system excess noise due to phase drift of the double MZI, the sender simply remaps her data by performing a rotation operation. Under a realistic model, the secure key rates determined with a 5km and a 20km fiber link are 0.3bit/pulse and 0.05bit/pulse, respectively. These key rates are significantly higher than that of a practical BB84 QKD system.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".