Ultra-secure quantum protection for e-healthcare images: Hybrid chaotic one-time pad with cipher chaining encryption framework
Bibliographic record
Abstract
Quantum computing introduces major threats to conventional image encryption methods, especially in medical contexts. This paper addresses these threats by developing a quantum-resistant encryption scheme for medical images. We present a novel framework combining: (1) a novel Mixed Logistic-Ikeda-Henon (MLIH) chaotic map for pseudorandom key generation, (2) quantum image representation using the Novel Enhanced Quantum Representation (NEQR) model, and (3) a two-stage encryption process employing Controlled-Not (CNOT) gate chaining for diffusion and One-Time Pad (OTP) with MLIH-generated keys for confusion. The RGB channels are processed separately through quantum state conversion, CNOT-based diffusion, and keyed confusion before final recombination. To validate practical feasibility, the proposed encryption scheme was implemented on IBM’s 127-qubit ibm_sherbrooke quantum processor, demonstrating real-world feasibility. Experimental validation shows near-ideal entropy (7.9977), superior NPCR (99.97%) and UACI (33.89%) values, and an expansive key space (2 1952 ). The novel MLIH demonstrates a 12.7% improvement in logic gate efficiency compared to conventional chaotic and the image encryption has quantum advantage through parallel CNOT operations. The hardware execution yielded a throughput of 4,500 Circuit Layer Operations Per Second (CLOPS), indicating efficient real-time performance on NISQ devices, Moreover, the echoed cross-resonance (ECR) gate error remained within a median of 1.1 × 10⁻ 2 , supporting reliable circuit execution. The proposed scheme outperforms contemporary quantum and classical encryption approaches in terms of entropy, NPCR, UACI, and key sensitivity, all while maintaining a computational complexity of O(n), ensuring scalability. This study effectively bridges the gap between theoretical quantum security models and real-world implementation on existing NISQ devices, demonstrating resilience against classical statistical and differential attacks, as well as quantum-specific threats such as Grover’s brute-force search and quantum chosen-plaintext attacks. The successful deployment of IBM quantum hardware positions this scheme as a viable solution for secure medical image transmission in quantum-era healthcare 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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".