Thin-film Al <sub>0.30</sub> Ga <sub>0.70</sub> As (111) as a ‘flat’ source of high-purity orthogonally polarized entangled photons
Bibliographic record
Abstract
Flat-optics platforms offer new opportunities for the generation of entangled photons by relaxing traditional phase-matching constraints, enabling the use of a broader range of nonlinear materials. Among these, gallium arsenide and aluminum gallium arsenide stand out for their exceptionally high second-order nonlinearities, but their conventional orientation (001) has limited their applicability for photon-pair generation. By transitioning to crystals with (111) surface orientation, we overcome these limitations. We demonstrate a flat-optics-based telecom-range SPDC source using Al 0.30 Ga 0.70 As that achieves a high photon-pair generation rate per pump power and bandwidth of up to 0.24 Hz/mW/nm. The choice of 30% aluminum concentration allowed us to reduce pump absorption and photoluminescence background for photon pairs generation at telecom wavelengths by at least an order of magnitude compared to that of GaAs. The specific layer orientation facilitates the generation of orthogonally polarized entangled photons, a prerequisite for polarization-entangled states. Rather than directly probing entanglement, we observe the effect of hidden polarization. Our results highlight AlGaAs (111) as a promising platform for scalable quantum photonic sources and shed light on nonclassical polarization effects accessible through flat-optics engineering.
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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".