Dynamic Strain Modulation of a Nanowire Quantum Dot Compatible with a Thin-Film Lithium Niobate Photonic Platform
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The integration of indistinguishable single photon sources in photonic circuits is a major prerequisite for on-chip quantum applications. Among the various high-quality sources, nanowire quantum dots can be efficiently coupled to optical waveguides because of their preferred emission direction along their growth direction. However, local tuning of the emission properties remains challenging. In this work, we transfer a nanowire quantum dot onto a bulk lithium niobate substrate and show that its emission can be dynamically tuned by acousto-optical coupling with surface acoustic waves. The purity of the single photon source is preserved during the strain modulation. We further demonstrate that the transduction is maintained even with a SiO 2 encapsulation layer deposited on top of the nanowire acting as the cladding of a photonic circuit. Based on these experimental findings and numerical simulations, we introduce a device architecture consisting of a nanowire quantum dot efficiently coupled to a thin-film lithium niobate rib waveguide and strain-tunable by surface acoustic waves.
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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 | 0.000 | 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".