Quantum Machine Learning for Advanced Climate Pattern Detection Using Satellite Data

Authors: Mrs K. Rajavadhani, Jerlin Flowrence D, Kasturi Uday Kiran, Challagundla Rakesh

Abstract: Climate pattern detection through satellite remote sensing is a critical task for understanding environmental changes and anomalies. However, current systems do not have an interactive interface for efficient visualisation and analysis of complex climate data. This study aims to develop a hybrid Quantum Machine Learning (QML) approach for sophisticated climate pattern detection through satellite data. The proposed system combines Principal Component Analysis (PCA) for dimensionality reduction with quantum-classification algorithms, such as the Variational Quantum Classifier (VQC) and the Quantum Support Vector Machine (QSVM), to detect complex climate patterns. A Streamlit web interface is designed to offer an interactive platform for data entry, visualisation, and monitoring. The system design includes a frontend interface unit, a data integration unit, and a planned AI & control unit for the backend quantum processing system. At present, the system design includes the implementation of frontend visualisation and user input modules using sample, publicly available satellite climate data. The integration of the quantum backend and direct satellite data connectivity will be done in future stages. The proposed method aims to leverage classical data preprocessing and quantum machine learning for improved anomaly detection and interpretability of satellite climate data.