Risk context
Although QLLM can detect hidden patterns in the encryption system, over-sensitization may lead to misjudgment and affect the normal operation of the system.
Quality control methods
- confidence level (math.): Adjusting model output probabilities using quantum Bayesian methods
- multilayer verification: Passing quantum detection results to classical verification modules
- duel training: Enhancing model robustness using quantum adversarial samples
- Interpretability tools: Development of quantum attention visualization and analysis tools
best practice
It is proposed to establish a three-stage defense: 1) quantum anomaly detection; 2) classical cryptography verification; and 3) expert manual review. Set up a dynamic threshold mechanism to automatically adjust the sensitivity according to the false alarm rate.
This answer comes from the articleWorld's First Quantum AI Model! SECQAI Releases QLLM for Beta Testing!The
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