Acoustic Modulation of Individual Nanowire Quantum Dots Integrated into a Hybrid Thin-Film Lithium Niobate Photonic Platform
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Surface acoustic waves are a powerful tool for controlling quantum systems, including quantum dots (QDs), where the oscillating strain field can modulate the emission wavelengths. We integrate InAsP/InP nanowire QDs onto a thin-film lithium niobate platform and embed them within Si 3 N 4 -loaded waveguides. We achieve a 0.70 nm peak-to-peak wavelength modulation at 13 dBm using a single focused interdigital transducer (FIDT) operating at 400 MHz, and we double this amplitude to 1.4 nm by using two FIDTs as an acoustic cavity. Additionally, we independently modulate two QDs with an initial wavelength difference of 0.5 nm, both integrated on the same chip. We show that their modulated emissions overlap, demonstrating the potential to bring them to a common emission wavelength after spectral filtering. This local strain-tuning represents a significant step toward generating indistinguishable single photons from remote emitters heterogeneously integrated on a single chip, advancing on-chip quantum information processing with multiple QDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".