Nanowire-based sources of non-classical light
Bibliographic record
Abstract
Sources of quantum light that utilize photonic nanowire designs have emerged as potential candidates for high efficiency non-classical light generation in quantum information processing. In this review we cover the different platforms used to produce nanowire-based sources, highlighting the importance of waveguide design and material properties in achieving optimal performance. The limitations of the sources are identified and routes to optimization are proposed. State-of-the-art nanowire sources are compared to other solid-state quantum emitter platforms with regard to the key metrics of single photon purity, indistinguishability and entangled-pair fidelity to maximally entangled Bell states. We also discuss the unique ability of the nanowire platform to incorporate multiple emitters in the same optical mode and consider potential applications. Finally, routes to on-chip integration are discussed and the challenges facing the development of a nanowire-based scalable architecture are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".