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Multimode nonlinear integrated optics for quantum and machine learning-assisted signal processing

2023· article· en· W4386087719 on OpenAlexaff
Luigi Di Lauro, Imtiaz Alamgir, Stefania Sciara, Pavel Dmitriev, Celine Mazoukh, Hao Yu, Seyedeh Nazanin Kamali, Riza Fazili, Aadhi A. Rahim, Bennet Fischer, Brent E. Little, David Moss, Zhiming Wang, Roberto Morandotti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMulti-mode optical fiberPhotonicsComputer scienceSignal processingLeverage (statistics)Nonlinear opticsFour-wave mixingQuantum opticsIntegrated opticsElectronic engineeringTelecommunicationsOpticsOptical fiberPhysicsEngineeringLaserArtificial intelligence

Abstract

fetched live from OpenAlex

Integrated optics offer advanced platforms for the development of new classical and quantum telecom-based applications. In this work, we leverage on-chip photonic devices that support multimode interaction via four-wave mixing, to develop novel, highly efficient, and secure smart technologies for telecom signal processing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

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

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 head0.043
GPT teacher head0.279
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2023
Admission routes1
Has abstractyes

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