Secure Anomaly Detection in Advanced Reactor through Quantum Federated Learning with Advanced Cryptography

This paper explores the transformative potential of Small Modular Reactors (SMRs) as a sustainable solution in the global energy landscape. SMRs offer distinct advantages over traditional nuclear power plants, including modularity, scalability, and enhanced safety, making them suit-able for diverse applications. The integration of advanced sensor technologies ensures operational efficiency and safety through real-time monitoring and anomaly detection. Machine learning algo-rithms analyze reactor sensor data to facilitate predictive maintenance and optimize performance, while federated learning allows SMRs to collaboratively improve models while preserving data privacy. Quantum cryptography secures data transmission, enhancing security and the integrity of sensitive operational information. The paper highlights how these technologies can enhance SMR safety, efficiency, and reliability. SMRs, supported by cutting-edge technologies, can play a key role in the transition to a low-carbon energy future, addressing energy security and environmental sustainability. Future research should focus on optimizing these technologies and exploring hy-brid systems combining SMRs with renewable energy.