Enhanced Quantum Magnetometry with a Femtosecond Laser-Written Integrated Photonic Diamond Chip
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Ensemble negatively charged nitrogen-vacancy centers in diamond are promising quantum sensors. To optimize their sensitivity, it is crucial to increase the number of spins sampled and maximize their coupling to the detection system without degrading their spin properties. In this paper, we demonstrate enhanced quantum magnetometry via a buried laser-written waveguide in diamond with 4.5 ppm nitrogen-vacancy centers. The waveguide-coupled nitrogen-vacancy centers exhibit spin coherence properties comparable to those of nitrogen-vacancy centers in pristine diamond. Waveguide-enhanced magnetic field sensing is demonstrated in a fiber-coupled integrated photonic chip, where probing an increased volume of high-density spins results in 63 pT·Hz –1/2 of DC magnetic field sensitivity and 20 pT·Hz –1/2 of AC magnetic field sensitivity. This on-chip sensor realizes at least an order of magnitude improvement in sensitivity compared to the conventional confocal detection setup, paving the way for high-sensitivity quantum magnetometry with nitrogen-vacancy ensembles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".