Quantum Cryptography Methods for Securing Communication Networks

Abstract

The rising demand for secure communication networks requires the implementation of new techniques like quantum cryptography to mitigate flaws in traditional encryption methods. This research investigates a thorough framework for safeguarding communication networks through Quantum Noise Filtering, Hybrid Classical-Quantum Techniques, and Quantum Machine Learning Models. Quantum noise filtering reduces errors and improves data integrity in quantum key distribution, facilitating dependable communication in noisy settings. Hybrid classical-quantum methodologies connect classical preprocessing with quantum computing, refining feature selection and improving encryption techniques. Quantum machine learning models, including Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs), are utilised for the classification of encrypted communication states and the identification of potential threats. The suggested method is assessed in several network settings, showing substantial enhancements in accuracy, scalability, and resistance to quantum noise. This framework utilises the advantages of quantum and classical paradigms to offer a scalable solution for safeguarding communication networks, facilitating the development of next-generation cryptographic algorithms for practical applications.