Space Mission Quantum GenAI and XAI System for Predictive Anomaly Detection and Mission Optimization

Humanity’s exploration of space has reached unprecedented levels of complexity. Launching satellites, deploying rovers, and conducting manned missions require extreme precision, accurate anomaly prediction, and resource optimization. This research presents a hybrid Quantum Neural Network (QNN) and Classical Explainable AI (XAI) system, integrating Generative AI (GenAI) capabilities, designed to predict anomalies, human errors, and mechanical failures in space missions. The system leverages PyTorch-based classical models, QNN layers for high-dimensional embedding, and vector database storage for mission-critical data. By simulating 100 missions, we demonstrate a theoretical predictive accuracy of 88.5%, while providing interpretable recommendations for mission control. Anomaly classification is prioritized into Minor, Medium, and Severe, allowing preemptive mitigation strategies. The system further optimizes launch site selection using DGGS and UTM geospatial algorithms, predicts material and time requirements, and adapts over successive missions. This work establishes a foundation for fully autonomous, quantum-enhanced space mission planning, capable of both prediction and explanation in highly uncertain environments.