Multiplexed Single Photons from Deterministically Positioned Nanowire Quantum Dots
Bibliographic record
Abstract
Solid-state quantum emitters are excellent sources of on-demand indistinguishable or entangled photons and can host long-lived spin memories, crucial resources for photonic quantum-information applications. However, their scalability remains an outstanding challenge. Here, we present a scalable technique to multiplex streams of photons from multiple independent quantum dots, on chip, into a fiber network for use ``off chip.'' Multiplexing is achieved by incorporating a multicore fiber into a confocal microscope and spatially matching the multiple foci, seven in this case, to quantum dots in an array of deterministically positioned nanowires. As a proof-of-principle demonstration, we perform parallel spectroscopy on the nanowire array to identify two nearly identical quantum dots at different positions, which are subsequently tuned into resonance with an external magnetic field. Multiplexing of background-free single photons from these two quantum dots is then achieved. Our approach, applicable to all types of quantum emitters, can readily be integrated into scalable photonic based quantum technologies.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".