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Record W2193109712 · doi:10.1038/srep17449

W-state Analyzer and Multi-party Measurement-device-independent Quantum Key Distribution

2015· article· en· W2193109712 on OpenAlexafffund
Changhua Zhu, Feihu Xu, Changxing Pei

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaOffice of Naval ResearchUniversity of TorontoHigher Education Discipline Innovation ProjectAir Force Office of Scientific ResearchChina Scholarship Council
KeywordsQuantum key distributionComputer scienceSpectrum analyzerQubitState (computer science)Protocol (science)Key (lock)QuantumDetectorPhotonComputer networkPhysicsOpticsTelecommunicationsComputer securityQuantum mechanicsAlgorithm

Abstract

fetched live from OpenAlex

W-state is an important resource for many quantum information processing tasks. In this paper, we for the first time propose a multi-party measurement-device-independent quantum key distribution (MDI-QKD) protocol based on W-state. With linear optics, we design a W-state analyzer in order to distinguish the four-qubit W-state. This analyzer constructs the measurement device for four-party MDI-QKD. Moreover, we derived a complete security proof of the four-party MDI-QKD, and performed a numerical simulation to study its performance. The results show that four-party MDI-QKD is feasible over 150 km standard telecom fiber with off-the-shelf single photon detectors. This work takes an important step towards multi-party quantum communication and a quantum network.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.051
GPT teacher head0.264
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2015
Admission routes2
Has abstractyes

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