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Record W3110332284 · doi:10.1364/fio.2020.jm6b.27

Modeling INQNET’s time-bin qubit teleportation experiments using phase space methods

2020· article· en· W3110332284 on OpenAlexaff
Nikolai Lauk, Raju Valivarthi, Samantha I. Davis, Lautaro Narváez, Yewon Gim, Meraj Hussein, George Iskander, Boris Korzh, Andrew Mueller, Daniel Oblak, Cristián Peña, M. Rominsky, Matthew D. Shaw, Christoph Simon, Neil Sinclair, Panagiotis Spentzouris, M. Spiropulu, Dawn Tang, S. Xie

Bibliographic record

VenueFrontiers in Optics / Laser Science · 2020
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsQuantum teleportationSuperdense codingTeleportationQubitQuantum channelPhysicsBinQuantum networkComputer sciencePhotonQuantumQuantum computerQuantum mechanicsTopology (electrical circuits)Quantum entanglementAlgorithmMathematics

Abstract

fetched live from OpenAlex

We apply phase space methods to simulate time-bin qubit teleportation experiments at the Caltech Quantum Network (CQNET) and Fermilab Quantum Network (FQNET) [1]. Our model includes all experimentally relevant parameters and contributions from the multi-photon events.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.046
GPT teacher head0.349
Teacher spread0.304 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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