Waveguide integrated superconducting nanowire single-photon detectors for integrated photonics
Bibliographic record
Abstract
Abstract Integrated photonics is expected to play a key role in the scalability of quantum systems for applications such as quantum computing, quantum communications, quantum internet, and quantum metrology. One of the primary components of quantum integrated photonics is a single photon detector, which reads out the quantum information encoded in photons. Amongst available single-photon detection schemes, superconducting nanowire single photon detectors (SNSPDs) remain the most promising technology for effective on-chip coupling, because they can be seamlessly integrated with a wide range of waveguide materials and substrates and have shown unparalleled performance from visible to the mid-infrared regime. Here, we review different aspects of SNSPDs and schemes for their on-chip integration for different integrated photonics applications. Although mostly concentrated on quantum applications, we also cover some of the important wider photonics applications including imaging, AI and machine learning, and single-photon spectroscopy, and conclude the review with a future outlook discussing emerging research areas enabled by photonic integrated circuits based on SNSPDs.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".