Topological and conventional nanophotonic waveguides for directional integrated quantum optics
Bibliographic record
Abstract
Directionality in integrated quantum photonics has emerged as a promising route towards achieving scalable quantum technologies with nonlinearities at the single-photon level. Topological photonic waveguides have been proposed as a novel approach to harnessing such directional light-matter interactions on-chip. However, uncertainties remain regarding the strength of the directional coupling of embedded quantum emitters to topological waveguides in comparison to conventional line defect waveguides. In this work we present an investigation of directional coupling in a range of waveguides using a combination of experimental, theoretical, and numerical analyses. We quantitatively characterize the position dependence of the light-matter coupling on several topological photonic waveguides and benchmark their directional coupling performance against conventional line defect waveguides. We conclude that topological waveguides underperform in comparison to conventional line defect waveguides, casting their directional optics credentials into doubt. To demonstrate this is not a question of the maturity of the field; we show that state-of-the-art inverse design methods, while capable of improving the directional emission of these topological waveguides, still place them significantly behind the operation of a conventional (glide-plane) photonic crystal waveguide. Our results and conclusions pave the way towards improving the implementation of quantitatively predicted quantum nonlinear effects on-chip. Published by the American Physical Society 2024
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".