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Achieving Ultimate Noise Tolerance in Quantum Communication

2021· article· en· W3127890241 on OpenAlexaff
Frédéric Bouchard, Duncan England, Philip J. Bustard, Kate L. Fenwick, Ebrahim Karimi, Khabat Heshami, Benjamin Sussman

Bibliographic record

VenuePhysical Review Applied · 2021
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of OttawaNational Research Council Canada
Fundersnot available
KeywordsComputer scienceNoise (video)Quantum information scienceQuantumPhotonQuantum cryptographyMode (computer interface)Quantum networkTelecommunicationsPhysicsQuantum informationOpticsQuantum mechanicsQuantum entanglementArtificial intelligenceHuman–computer interaction

Abstract

fetched live from OpenAlex

Quantum communication is the most advanced cryptographic method to distribute secret keys securely through public channels, but actual performance is still limited, due to noise. This study explores the ultimate noise tolerance of quantum communication by considering a method based on spectral, temporal, and spatial mode filtering of single photons with high efficiency. The authors also show that by actively filtering photons to a nearly single mode, quantum communication can occur in environments that are noisier by three orders of magnitude than what traditional methods tolerate. This approach could bring quantum communication under realistic conditions a step closer to reality.

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)0.003
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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

Citations30
Published2021
Admission routes1
Has abstractyes

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