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Fiber-integrated microwave-to-optical quantum transducer

2023· article· en· W4387541174 on OpenAlexafffund
Wenfang Li, Jinjin Du, C. M. Wilson, Michal Bajcsy

Bibliographic record

VenuePhysical Review Applied · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Waterloo
FundersCanada First Research Excellence FundCMC Microsystems
KeywordsMicrowaveOptical fiberTransducerPhotonOptoelectronicsResonatorMaterials scienceOpticsCladding (metalworking)PhysicsAcoustics

Abstract

fetched live from OpenAlex

We propose an on-chip fiber-based quantum transducer to convert microwave photons into optical-telecommunication-band photons. The device consists of a coplanar-waveguide microwave resonator and a pair of coupled optical cavities integrated into a rare-earth-doped fiber. The microwave resonator is formed by two aluminum strips deposited onto the cladding of the fiber, whose diameter has been etched down to approximately $8\phantom{\rule{0.2em}{0ex}}\text{\ensuremath{\mu}}\mathrm{m}$. We study the conversion process using the cavity electro-optic formalism for a system operating at liquid-helium temperature (around 4 K), as well as using a three-wave mixing approach for operation at millikelvin temperatures achievable in a dilution refrigerator. Both methods predict that unity conversion efficiency could be achievable with realistically achievable experimental parameters. The proposed device acts as a quantum transducer for coherent microwave-to-optical conversion, which has great promise for application in all-fiber integrated quantum devices for quantum information 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.021

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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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