Entropy Transformation and Expansion with Quantum Permutation Pad for 5G Secure Networks
Bibliographic record
Abstract
This paper proposes a quantum safe solution for 5G networks using a quantum permutation pad (or QPP) algorithm, originated from quantum computing logic gates or quantum permutation gates. All permutation gates form a unique permutation space, just like a classical key space. An n-bit permutation space consists of the entire 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ! permutation gates, or permutation matrices over its computational basis. The huge equivalent Shannon entropy of this permutation space would be a nice entropy source for information security. Kuang and Bettenburg in 2020 first proposed QPP and proved it to be the Shannon type of perfect secrecy. Here, we extend its capability of entropy transformations for distribution over the Internet to entropy expansions for 5G networks. We analyze the randomnesses following transformations and expansions with QPP, using industry randomness testing suites. Testing results confirm that QPP can maintain the original randomness of QRNG random numbers for transformations and expansions. Leveraging its strong diffusion capability, QPP may also improve the byte-level randomness of input random numbers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".