Long-lived coherences in strongly interacting spin ensembles
Bibliographic record
Abstract
Periodic driving has emerged as a powerful tool to control, engineer, and characterize many-body quantum systems. However, the required pulse sequences are often complex, long, or require the ability to control the individual degrees of freedom. In this work, we study how a simple Carr-Purcell--Meiboom-Gill (CPMG)-like pulse sequence can be leveraged to enhance the coherence of a large ensemble of spin qubits and serve as an important characterization tool. We implement the periodic drive on an ensemble of dense nitrogen-vacancy (NV) centers in diamond and examine the effect of pulse rotation offset as a control parameter on the dynamics. We use a single diamond sample prepared with several spots of varying NV density, which, in turn, varies the NV-NV dipolar interaction strength. Counterintuitively, we find that rotation offsets deviating from the ideal $\ensuremath{\pi}$ pulse in the CPMG sequence (often classified as pulse errors) play a critical role in preserving coherence along an axis set by the $\ensuremath{\pi}$ pulses even at nominally zero rotation offset. The cause of the coherence preservation is an emergent effective field that scales linearly with the magnitude of the rotation offset for small offsets. In addition to extending coherence, we compare the rotation offset dependence of coherence to numerical simulations to measure the disorder and dipolar contributions to the Hamiltonian to quantitatively extract the densities of the constituent spin species within the diamond.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".