Quantum Image Encryption Using 4D Hamiltonian System and Bit-Plane Encoding
Bibliographic record
Abstract
This paper presents a new quantum image encryption (QIE) algorithm that integrates a newly developed 4D Quantum Logistic-Jerk Hyperchaotic System (4D-QLJHS) with a quantum image representation (QIR) model, enabling secure and efficient image transmission.By reducing qubit usage and circuit depth, the proposed QIE framework significantly improves quantum resource efficiency.Additionally, incorporating the enhanced 4D-LJHS map strengthened security and resistance against both quantum and classical attacks, surpassing the robustness and scalability of current quantum image encryption techniques.The new 4D-QLJHS is created by combining a logistic map and a jerk system, and then converted to a quantum hyperchaotic system using a Hamiltonianbased method.These systems help create a Quantum Pseudo-Random Number Generator (QPRNG) that generates random bit sequences used to alter and rearrange data at the quantum bit-plane level during the encryption process.The quantum encryption method uses the Quantum Image Representation based on Bit Planes (QIRBP) model, which enables modifying individual pixels and color channels via CNOT and SWAP gates.We evaluate various statistical tests to confirm the security and efficiency of our system.The experiments demonstrate that the system is highly secure, featuring adequate randomness, robust protection against specific attacks, and uniform distribution in the encrypted image data.Experimental simulations of some images indicate that the system provides a satisfactory level of security for image encryption, given the computational costs.This makes it suitable for real-time image communication where security is a priority.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".