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Record W4406262170 · doi:10.1109/qce60285.2024.10424

Radio-Frequency Excitation for Quantum Sensing Based on Diamond NV Center Using Coplanar Waveguide Transmission Lines

2024· article· en· W4406262170 on OpenAlexaff
Aashutosh Kumar, Maxime Colson, Richard Al Hadi, Bora Ung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsCoplanar waveguideCenter frequencyDiamondRadio frequencyElectric power transmissionExcitationOptoelectronicsTransmission (telecommunications)Center (category theory)Materials sciencePhysicsOpticsTelecommunicationsElectrical engineeringComputer scienceEngineeringBand-pass filter

Abstract

fetched live from OpenAlex

The Nitrogen-Vacancy (NV) center in diamond is a prominent quantum system leveraged for advanced quantum metrology, particularly in high-precision magnetometry and high-resolution quantum imaging, exploiting the Optically Detected Magnetic Resonance (ODMR) spectroscopy technique. Proper radio-frequency (RF) excitation of NV centers is critical in order to manipulate these quantum states effectively. Our research focuses on optimizing the continuous-wave RF excitation of ensembles of NV centers on coplanar waveguide transmission lines (CPW-TL), measuring the centers' photoluminescence (PL) signal as a function of RF power. This setup enhances the sensitivity and resolution of the NV center PL signal. By combining CPW-TL lines with nanodiamonds, our approach aims to develop a scalable and tunable platform that can be deployed in a variety of quantum sensing scenarios, including in biomedical applications, navigation systems, environmental monitorlng, and beyond.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.022
GPT teacher head0.260
Teacher spread0.239 · 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
Published2024
Admission routes1
Has abstractyes

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