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Record W2106886959 · doi:10.1109/isit.2006.261920

Simulation and Implementation of Decoy State Quantum Key Distribution over 60km Telecom Fiber

2006· preprint· en· W2106886959 on OpenAlexaff
Yi Zhao, Bing Qi, Xiongfeng Ma, Hoi‐Kwong Lo, Li Qian

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuantum key distributionTelecommunicationsKey (lock)Computer scienceComputer networkQuantumPhysicsComputer securityQuantum mechanics

Abstract

fetched live from OpenAlex

Decoy state quantum key distribution (QKD) has been proposed as a novel approach to improve dramatically both the security and the performance of practical QKD set-ups. Recently, many theoretical efforts have been made on this topic and have theoretically predicted the high performance of decoy method. However, the gap between theory and experiment remains open. In this paper, we report the first experiments on decoy state QKD, thus bridging the gap. Two protocols of decoy state QKD are implemented: one-decoy protocol over 15 km of standard telecom fiber, and weak+vacuum protocol over 60 km of standard telecom fiber. We implemented the decoy state method on a modified commercial QKD system. The modification we made is simply adding commercial acousto-optic modulator (AOM) on the QKD system. The AOM is used to modulate the intensity of each signal individually, thus implementing the decoy state method. As an important part of implementation, numerical simulation of our set-up is also performed. The simulation shows that standard security proofs give a zero key generation rate at the distance we perform decoy state QKD (both 15 km and 60 km). Therefore decoy state QKD is necessary for long distance secure communication. Our implementation shows explicitly the power and feasibility of decoy method, and brings it to our real-life

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.294
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations61
Published2006
Admission routes1
Has abstractyes

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