Cavity electro-optics in thin-film lithium niobate for efficient microwave-to-optical transduction
Bibliographic record
Abstract
Linking superconducting quantum devices to optical fibers via microwave-optical quantum transducers may enable large-scale quantum networks. For this application, transducers based on the Pockels electro-optic (EO) effect are promising for their direct conversion mechanism, high bandwidth, and potential for low-noise operation. However, previously demonstrated EO transducers require large optical pump power to overcome weak EO coupling and reach high efficiency. Here, we create an EO transducer in thin-film lithium niobate, a platform that provides low optical loss and strong EO coupling. We demonstrate on-chip transduction efficiencies of up to and of optical pump power. The transduction efficiency can be improved by further reducing the microwave resonator’s piezoelectric coupling to acoustic modes, increasing the optical resonator quality factor to previously demonstrated levels, and changing the electrode geometry for enhanced EO coupling. We expect that with further development, EO transducers in thin-film lithium niobate can achieve near-unity efficiency with low optical pump power.
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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".