IID-Based QPP-RNG: A Random Number Generator Utilizing Qpp Generated from System Jitter
Bibliographic record
Abstract
We propose a groundbreaking random number generator that achieves truly uniform, independent, and identically distributed (IID) randomness by integrating Quantum Permutation Pads (QPP) with system jitter-derived entropy, herein called IID-based QPP-RNG. Unlike conventional RNGs that use raw timing variations, our design uses system jitter solely to generate ephemeral QPP pads and derives 8-bit outputs directly from permutation counts, eliminating the need for post-processing. This approach leverages the factorial complexity of permutation sorting to systematically accumulate entropy from dynamic hardware interactions, ensuring non-deterministic outputs even from fixed seeds. Notably, IID-based QPP-RNG achieves a min-entropy of 7.85-7.95 bits per byte from IID minentropy estimate, surpassing ID Quantique's QRNG (7.157042 bits per byte), which marks a breakthrough in randomness quality. Our implementation employs a dynamic seed evolution protocol that continuously refreshes the internal state with unpredictable system jitter, effectively decoupling the QPP sequence from the initial seed. Cross-platform validation on macOS (x86 and ARM) and Windows (x86) confirms uniformly distributed outputs, while evaluations compliant with NIST SP 800-90B show a Shannon entropy of 7.9999 bits per byte. Overall, IID-based QPP-RNG represents a significant advancement in random number generation, offering a scalable, system-based, software-only, postquantum secure solution for a wide range of cryptographic applications.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".