Hybrid Quantum-Classical Neural Networks for Secure Data Transmission Cryptographic Protocol Optimization
Keywords:
Hybrid quantum-classical neural networks, cryptographic protocols, secure data transmissionAbstract
Many industries need safe data transit, hence cryptographic protocol innovation is crucial. Despite their success, classical computers' processing power is challenging traditional encryption methods. Combining quantum computing and classical machine learning, hybrid quantum-classical neural networks (HQ-CNNs) may tackle these issues. The study analyzes how HQ-CNNs may optimize cryptography approaches for secure data transit. Quantum computing with traditional neural networks may enable faster, more secure encryption in next-generation cryptography systems. This paper covers HQ-CNN basics, cryptography advantages, and implementation challenges. Case studies and future proposals for hybrid model integration into cryptographic systems are also discussed. HQ-CNNs may improve cryptography efficiency, scalability, and security despite their early study.
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