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Exploiting Nonlocal Correlations for Dispersion-Resilient Quantum Communications

2025· article· en· W4411055957 on OpenAlexafffund
Hao Yu, Benjamin Crockett, Nicola Montaut, Stefania Sciara, Mario Chemnitz, Sai T. Chu, Brent E. Little, David Moss, Zhiming Wang, José Azaña, Roberto Morandotti

Bibliographic record

VenuePhysical Review Letters · 2025
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsHydro-QuébecInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesChina Scholarship CouncilCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaMitacsCanada Research ChairsNatural Sciences and Engineering Research Council of CanadaEuropean CommissionBanting Research Foundation
KeywordsStatistical physicsQuantumDispersion (optics)PhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Encoding quantum information via time-bin entangled states has had a profound impact on the development of quantum communications. However, dispersive propagation limits their achievable transmission distances. Here we describe a regime for nonlocal dispersion cancellation where the sum of arrival times of photons undergoing identical dispersion remains highly correlated. We exploit this effect to mitigate dispersive effects in a quantum key distribution fiber link, allowing an increase in the secret key rate by over a factor of 5 after 80 km of optical fiber dispersion.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.323
Teacher spread0.297 · 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 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

Citations8
Published2025
Admission routes2
Has abstractyes

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