Hybrid Classical-Quantum Neural Network for Improving Space Weather Detection and Early Warning Alerts
Ahmad Alomari, Sathish Kumar
Abstract
Space weather events, such as solar flares and geomagnetic storms, can have significant impacts on space technologies and infrastructure. Traditional space weather detection methods are limited by their accuracy and speed, which can lead to missed or delayed warnings of these events. In this paper, we propose a Hybrid Classical-Quantum Neural Network (HCQNN) that leverages the principles of quantum computing to model and simulate space weather phenomena. The proposed HCQNN is capable of detecting space weather events with 99.9% accuracy and providing early warning alerts to mitigate potential impacts on space-based systems. Our findings indicate that the proposed approach has the potential to improve space weather detection and enhance the resiliency of critical space-based technologies. the proposed approach has the potential to reduce the economic and societal impacts of space weather events. This work contributes to the growing field of quantum computing applications in space science and technology and demonstrates the value of incorporating quantum computing principles into space weather detection and forecasting.