Parallel Fast Random Bit Generation Based on Spectrotemporally Uncorrelated Random Laser Comb
Bibliographic record
Abstract
Abstract Correlations existing among spectral components in multi‐wavelength lasers have remained a fundamental constraint impeding their development as chaotic comb entropy sources for parallel random bit generation. Herein, spectrotemporally uncorrelated multi‐order Stokes/anti‐Stokes emissions are achieved by exploiting cascaded stimulated Brillouin scattering and quasi‐phase‐matched four‐wave mixing in a random fiber laser. The proposed configuration introduces random instabilities arising from random mode resonance while enabling disordered energy redistribution across different lasing lines, which thereby effectively eliminates the inherent correlation between multiple Stokes/anti‐Stokes emission lines, realizing a spectrotemporally uncorrelated chaotic frequency comb. Parallel fast random bit generation is fulfilled using 31 channels, with a single‐channel bit rate of 35‐Gbps and a total bit rate of 1.085‐Tbps. This work, in a simple and efficient way, breaks the spectrotemporally correlation barrier for utilizing a multi‐wavelength laser to achieve a high‐quality chaotic laser source, opening new avenues for achieving greatly accelerated random bit generation through parallelization and offering potential benefits for future developments in secure communication and high‐performance computing systems.
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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 | 0.001 |
| Open science | 0.000 | 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".