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Record W2952631779 · doi:10.1103/physreva.97.043847

Demonstration of analyzers for multimode photonic time-bin qubits

2018· article· en· W2952631779 on OpenAlexafffund
Jeongwan Jin, Sascha Agne, Jean‐Philippe Bourgoin, Yanbao Zhang, Norbert Lütkenhaus, Thomas Jennewein

Bibliographic record

VenuePhysical review. A/Physical review, A · 2018
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of Waterloo
FundersOffice of Naval ResearchIndustry CanadaCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsMulti-mode optical fiberQubitPhysicsAstronomical interferometerQuantum entanglementPhotonicsInterferometryQuantum information scienceBeam splitterOpticsQuantumOptical fiberQuantum mechanicsLaser

Abstract

fetched live from OpenAlex

We demonstrate two approaches for unbalanced interferometers as time-bin qubit analyzers for quantum communication, robust against mode distortions and polarization effects as expected from free-space quantum communication systems including wavefront deformations, path fluctuations, pointing errors, and optical elements. Despite strong spatial and temporal distortions of the optical mode of a time-bin qubit, entangled with a separate polarization qubit, we verify entanglement using the Negative Partial Transpose, with the measured visibility of up to $0.85\ifmmode\pm\else\textpm\fi{}0.01$. The robustness of the analyzers is further demonstrated for various angles of incidence up to $0.{2}^{\ensuremath{\circ}}$. The output of the interferometers is coupled into multimode fiber yielding a high system throughput of 0.74. Therefore, these analyzers are suitable and efficient for quantum communication over multimode optical channels.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.357
Teacher spread0.341 · 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.

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

Citations48
Published2018
Admission routes2
Has abstractyes

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