Quantum cryptographic dynamics: modeling cryptosystems via entropy operators
Bibliographic record
Abstract
This paper introduces Quantum Cryptographic Dynamics (QCD), a novel theoretical framework that models cryptographic processes through the lens of entropy injection and ejection. Drawing inspiration from classical mechanics, QCD establishes three fundamental laws: the Entropy Inertial Law (conservation of entropy in isolated systems), the Entropy Evolution Law (transformation of entropy via injection operators), and the Entropy Redistribution Law (reversibility through ejection operators). Applying these principles, we provide a unified, entropy-centric interpretation of classical and quantum cryptographic schemes, including symmetric-key systems, public-key protocols, and post-quantum cryptography (PQC) algorithms such as Learning With Errors (LWE), Kyber, and Homomorphic Polynomial Public Key (HPPK). By shifting the focus from computational hardness assumptions to the fundamental dynamics of entropy manipulation, QCD offers new insights into the security foundations of these cryptographic primitives. Furthermore, we reinterpret the Quantum Permutation Pad Random Number Generator (QPP-RNG) within the QCD framework. QPP-RNG is modeled as an entropy-driven process that harnesses system jitter to generate unpredictable random numbers, which in turn fuel PQC schemes like Kyber and HPPK for quantum-secure key establishment, forming a self-sustained quantum-secure eco-cryptosystem. This framework provides a rigorous approach to security analysis, unifying cryptographic security models across classical, quantum, and post-quantum domains. QCD establishes a robust foundation for designing quantum-resistant cryptographic primitives and assessing the fundamental security properties of cryptographic 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 | 0.001 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".