Nanowires as a single photon source for quantum photonic integrated circuits
Bibliographic record
Abstract
Single photons and quantum interference between indistinguishable pairs of photons are promising resources in the ongoing development of quantum information technologies. On-demand generation of such photons on a photonic integrated circuit (PIC) is desirable as it can allow for stable operation and device scalability alongside other requisite components. Solid-state two-level emitters—in particular, epitaxial semiconductor quantum dots—have demonstrated to be a good source of single photons, though efficient integration onto PICs remains a challenge. Hybrid integration of such dots into on-chip photonic circuitry can provide a basis for testing practical implementations of quantum communication devices. In this talk, I will discuss NRC's InP-based nanowire quantum dots and our work integrating these onto silicon nitride integrated photonics. The cryogenic environment poses challenges in the operation of key components such as optical phase shifters, tunable filters, and on-chip detectors. With this in mind, I will review our progress and near-term plans for realizing on-chip quantum information processing. Also examined is our recent work developing nanowire sources that emit in telecom O or C bands—a key requirement for practical long distance quantum communications—and coherent control schemes for optical pumping.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".