On-chip single photon filtering and multiplexing in hybrid quantum photonic circuits
Bibliographic record
Abstract
Quantum light plays a pivotal role in modern science and future photonic applications. Since the advent of integrated quantum nanophotonics different material platforms based on III-V nanostructures-, colour centers-, and nonlinear waveguides as on-chip light sources have been investigated. Each platform has unique advantages and limitations; however, all implementations face major challenges with filtering of individual quantum states, scalable integration, deterministic multiplexing of selected quantum emitters, and on-chip excitation suppression. Here we overcome all of these challenges with a hybrid and scalable approach, where single III-V quantum emitters are positioned and deterministically integrated in a complementary metal-oxide-semiconductor-compatible photonic circuit. We demonstrate reconfigurable on-chip single-photon filtering and wavelength division multiplexing with a foot print one million times smaller than similar table-top approaches, while offering excitation suppression of more than 95 dB and efficient routing of single photons over a bandwidth of 40 nm. Our work marks an important step to harvest quantum optical technologies' full potential.Combining different integration platforms on the same chip is currently one of the main challenges for quantum technologies. Here, Elshaari et al. show III-V Quantum Dots embedded in nanowires operating in a CMOS compatible circuit, with controlled on-chip filtering and tunable routing.
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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 | 0.001 | 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".