Quantum Safe Lightweight Cryptography with Quantum Permutation Pad
Bibliographic record
Abstract
Quantum permutation pad or QPP was first proposed by Kuang and Bettenburg in 2020 [15]. QPP is a generic quantum algorithm consisting of multiple n-qubits quantum permutation gates. As a quantum algorithm, QPP can be implemented both in a quantum computing system as a quantum circuit operating on n-qubits' state for transformation and in a classical computing system represented by a pad of n-bit permutation matrices. QPP has two unique characteristics: huge Shannon information entropy and non-commutativity between permutation matrices or the generalized uncertainty principal. Permutation transformation is bijective mapping between input information space and output ciphertext space. That means, QPP has the property of Shannon perfect secrecy with reusability due to the uncertainty relationship. QPP is the generalization of One-Time-Pad or OTP over Hilbert space and OTP is the simplification of QPP over a Galois field. Based on those, this paper explores a variant of AES for a quantum safe lightweight cryptography by incorporating AES ShiftRows and MixColumns with QPP or called AES-QPP. AES-QPP unifies the SubBytes and AddRoundKey with the same QPP of 16 8-bit permutation matrices, essentially SubBytes to be a special 8-bit permutation matrix and AddRoundKey to be 16 8-bit permutation matrices selected from XOR operations. By randomly selecting 16 permutation matrices with a secret key material, AES-QPP could hold a total equivalent 26,944 bits of Shannon entropy. It not only improves the security against differential and linear attacks but also largely reduces the number of rounds to 5 rounds. AES-QPP could be a good candidate for quantum safe lightweight cryptography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".