Strain-Tunable Quantum Integrated Photonics
Bibliographic record
Abstract
Semiconductor quantum dots are crucial parts of the photonic quantum technology toolbox because they show excellent single-photon emission properties in addition to their potential as solid-state qubits. Recently, there has been an increasing effort to deterministically integrate single semiconductor quantum dots into complex photonic circuits. Despite rapid progress in the field, it remains challenging to manipulate the optical properties of waveguide-integrated quantum emitters in a deterministic, reversible, and nonintrusive manner. Here we demonstrate a new class of hybrid quantum photonic circuits combining III-V semiconductors, silicon nitride, and piezoelectric crystals. Using a combination of bottom-up, top-down, and nanomanipulation techniques, we realize strain tuning of a selected, waveguide-integrated, quantum emitter and a planar integrated optical resonator. Our findings are an important step toward realizing reconfigurable quantum-integrated photonics, with full control over the quantum sources and the photonic circuit.
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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".