Pulsed quantum frequency combs from an actively mode-locked intra-cavity generation scheme
Bibliographic record
Abstract
Summary form only given. Here, we demonstrate the first scheme to generate pulsed quantum frequency combs without the need for an external laser. We constructed a laser cavity (Fig. 1a) around a 4-port integrated micro-ring resonator, featuring narrow bandpass filters to select a single resonance, an optical gain component (erbium-doped fiber amplifier), and a mode-locking element (an intensity modulator). By setting the intensity modulation frequency to the fundamental repetition rate of the cavity (determined by the external cavity length), we achieved mode-locked pulsed operation (Fig. 1b), exciting a pulsed two-photon comb. Driving the intensity modulator at multiples of the fundamental repetition rate (upwards of 4x) also resulted in stable mode-locking (Fig. 1c), significant as this enables an increase of the photon pair generation rate without sacrificing its coincidence-to-accidental ratio (CAR). We characterized the photon statistics as a function of the average ring input power (see Fig. 1d), achieving a maximum CAR of 110 that decreases with increasing power and increasing coincidence rates. Autocorrelation measurements confirmed the purity of the generated state (pure single-frequency modes, even at multiples of the repetition rate), confirming that the resonance was excited over its entire bandwidth, and obtained a heralded anti-bunching dip of 0.245<;0.5 as expected of a source operating in the single-photon regime.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".