Quantum Machine Learning and Quantum Graph Neural Networks for Enhanced Wildfire and Air Quality Management

The NASA Postdoctoral Program (NPP) offers unique research opportunities to highly-talented scientists to engage in ongoing NASA research projects at a NASA Center, NASA Headquarters, or at a NASA-affiliated research institute. These one- to three-year fellowships are competitive and are designed to advance NASA's missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology. This specific project aims to address the growing challenges of wildfire monitoring and air quality management by leveraging NASA's satellite observations and cutting-edge quantum computing techniques. By integrating Quantum Graph Neural Networks (QGNNs) and quantum-assisted neural networks with classical methods like Knowledge Graphs and Geometric Deep Learning (GDL), the study seeks to enhance the detection of wildfire smoke plumes, evaluate quantum computing's accuracy and speed advantages, and compare results with classical approaches. Utilizing advanced quantum hardware, the research explores the scalability and precision of quantum methods to process complex, high-dimensional datasets. Expected outcomes include improved detection accuracy, predictive modeling, and computational efficiency. Deliverables include open-source code, comparative analyses, and peer-reviewed publications, contributing to NASA's Earth science objectives and establishing the potential of quantum computing for actionable insights in wildfire and air quality management.