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Imperfect phase randomization and generalized decoy-state quantum key distribution

2023· article· en· W4389918573 on OpenAlexafffund
Shlok Nahar, Twesh Upadhyaya, Norbert Lütkenhaus

Bibliographic record

VenuePhysical Review Applied · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum key distributionComputer scienceLaserKey (lock)Independent and identically distributed random variablesImperfectPhase (matter)Pulse (music)State (computer science)Key generationProtocol (science)PhysicsPhotonAlgorithmQuantum mechanicsMathematicsTelecommunicationsComputer securityStatisticsCryptography

Abstract

fetched live from OpenAlex

Decoy-state methods are essential to perform quantum key distribution (QKD) at large distances in the absence of single-photon sources. However, the standard techniques apply only if laser pulses are used that are independent and identically distributed. Moreover, they require that the laser pulses are fully phase randomized. However, realistic high-speed QKD setups do not meet these stringent requirements. In this work, we generalize decoy-state analysis to accommodate laser sources that emit imperfectly phase-randomized states. We also develop theoretical tools to prove the security of protocols with lasers that emit pulses that are independent, but not identically distributed. These tools can be used with recent work [G. Curr\'as-Lorenzo, S. Nahar, N. L\"utkenhaus, K. Tamaki, and M. Curty, Quantum Sci. Technol. (2023)] to prove the security of laser sources with correlated phase distributions as well. We quantitatively demonstrate the effect of imperfect phase randomization on key rates by computing the key rates for a simple implementation of the three-state protocol.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.300
Teacher spread0.287 · 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 designTheoretical or conceptual
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

Citations16
Published2023
Admission routes2
Has abstractyes

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