Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum...

Area of Science:

• Quantum Information Science
• Artificial Intelligence
• Network Security

Background:

• Traditional Quantum Key Distribution (QKD) faces limitations in dynamic environments due to static protocols.
• Network fluctuations and potential attacks compromise the security and efficiency of existing QKD systems.
• High key generation rates and robust security are crucial for advanced communication networks.

Purpose of the Study:

• To enhance the security and efficiency of quantum communication protocols using AI and machine learning.
• To address the limitations of conventional QKD systems in dynamic and adversarial environments.
• To propose adaptive and resilient quantum security solutions.

Main Methods:

• Deep Reinforcement Learning (DRL) for adaptive QKD protocol optimization.
• Variational Autoencoder (VAE) for anomaly detection and eavesdropping identification in quantum networks.
• Multi-Agent Deep Q-Networks (MADQN) for optimizing cryptographic protocols in distributed quantum networks.

Main Results:

• DRL approach increased secure key generation rate by 15-20% and suppressed Quantum Bit Error Rate (QBER) by 30-40% under noisy conditions.
• VAE model achieved 85-90% attack detection accuracy with a 25% reduction in false positives.
• MADQN system reduced attack vulnerabilities by 15-18% and computational complexity by 20-25%.

Conclusions:

• Integrating AI and machine learning significantly enhances quantum communication system security and efficiency.
• The proposed models overcome critical limitations of conventional QKD systems.
• This research paves the way for more resilient and adaptive quantum security solutions.