Investigating How Quantum Cryptographic Techniques Can Enhance the Security of Blockchain-Based Artificial Intelligence (AI) Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integration of blockchain and artificial intelligence (AI) has revolutionized secure, transparent, and decentralized applications. However, the security of blockchain-based AI models remains reliant on classical cryptographic techniques, such as RSA and ECC, which are increasingly vulnerable to emerging quantum computing threats. This study investigates how quantum cryptographic techniques can enhance the security and resilience of blockchain-based AI applications. Specifically, it explores the role of Quantum Key Distribution (QKD) in securing key exchanges, post-quantum cryptographic (PQC) algorithms in fortifying data encryption, and quantum hashing techniques in protecting blockchain consensus mechanisms. The research evaluates the feasibility, implementation challenges, and performance implications of quantum-enhanced security frameworks for AI-driven blockchain networks. By addressing these concerns, this study establishes a foundation for developing quantum-secured blockchain infrastructures that safeguard AI transactions against future quantum threats while ensuring trust, transparency, and scalability in decentralized applications.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it