MétaCan
Menu
Back to cohort
Record W4307743626 · doi:10.1364/ol.471346

Biphoton engineering using modal spatial overlap on-chip

2022· article· en· W4307743626 on OpenAlexafffund
Xiangyan Ding, Jing Ma, Liying Tan, Amr S. Helmy, Dongpeng Kang

Bibliographic record

VenueOptics Letters · 2022
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesHeilongjiang Provincial Postdoctoral Science FoundationChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhotonPhotonicsOpticsSpontaneous parametric down-conversionPhysicsModalParametric statisticsQuantum opticsQuantumPolarization (electrochemistry)Quantum entanglementQuantum mechanicsMaterials scienceMathematics

Abstract

fetched live from OpenAlex

Photon pairs generated by spontaneous parametric downconversion are essential for optical quantum information processing, in which the quality of biphoton states is crucial for the performance. To engineer the biphoton wave function (BWF) on-chip, the pump envelope function and the phase matching function are commonly adjusted, while the modal field overlap has been considered as a constant in the frequency range of interest. In this work, by using modal coupling in a system of coupled waveguides, we explore the modal field overlap as a new degree of freedom for biphoton engineering. We provide design examples for on-chip generations of polarization entangled photons and heralded single photons. This strategy can be applied to waveguides of different materials and structures, offering new possibilities for photonic quantum state engineering.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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 designBench or experimental
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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueOptics LettersSame topicQuantum Information and CryptographyFrench-language works237,207