Data for "Zero-field identification and control of hydrogen-related electron-nuclear spin registers in diamond"
Bibliographic record
Abstract
Spin defects in diamond serve as powerful building blocks for quantum technologies, especially for applications in quantum sensing and quantum networking. Electron-nuclear defects formed in the environment of optically active spins, such as the nitrogen-vacancy (NV) center, provide a resource for multi-qubit quantum registers. However, many of these defects have yet to be characterized, limiting their control and integration in quantum devices. Here, we apply two hybrid electron-nuclear spin control schemes to self-consistently characterize unknown spin defects at the single-spin level. We perform double electron-electron resonance at zero field (ZF-DEER) to extract hyperfine components and introduce a nuclear-electron-electron triple resonance (NEETR) protocol to control and identify the nuclear spin through the stronger electronic spin interaction. These results provide a guide to resolving the defect structures using ab initio calculations, leading to the identification of a new hydrogen-related defect structure as well as an accurate match to a previously identified nitrogen-related defect. We further apply our NEETR protocol to demonstrate initialization, unitary control, and long-lived coherence of the hydrogen nuclear spin qubit with $T_2 = 1.0(3)\,\mathrm{ms}$. Together, these characterization and control tools establish a framework to harness previously unknown electron-nuclear defects for quantum register applications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.008 |
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".