Reversible Tuning of Nanowire Quantum Dot to Atomic Transitions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Quantum dots embedded in semiconductor photonic nanowires (NW-QDs) can deterministically produce single-photons and entangled photon pairs at high repetition rates. These photons can be efficiently coupled from the photonic nanowire into free space or optical fibers thanks to the sharp tip of the nanowire, which provides impedance matching. However, precise control of the NW-QD emission frequency in a way that is reversible, does not degrade the properties of the emitted photons, and can be used independently for individual NW-QDs on the same chip has so far remained a challenge. Resolving this issue is crucial for applications when interfacing the photons with quantum systems that require MHz to sub-GHz precision, such as atomic ensembles acting as memories in a quantum network. Here, we demonstrate a reversible tuning method that can tune the emission frequency of a NW-QD by more than 300 GHz with sub-GHz precision. We achieve this through gas condensation that is then partially reversed with localized laser ablation. This process finely adjusts stress applied to the quantum dots, thereby tuning their emission frequency. We validate the precision and stability of this method by tuning the frequency of the emitted single-photons across an atomic resonance to probe its absorption and dispersion. We observed up to 80% absorption of the single-photons from NW-QD in hot cesium vapor at the D 1 -line resonances and a 75-fold decrease in group velocity associated with the hyperfine transitions of the D 1 -line ground states. We observed no discernible effects in the second-order autocorrelation function, lifetime, or linewidth of the NW-QD emission for up to 300 GHz of tuning and we saw minimal effects on the fine structure splitting of the NW-QD when tuning up to 100 GHz.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".