Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection

TL;DR

This paper introduces Quantum Bayesian Networks to improve classification of imbalanced satellite data for oil-spill detection, achieving high accuracy and demonstrating potential for environmental monitoring.

Contribution

It presents a novel quantum-enhanced Bayesian approach for classifying imbalanced datasets in environmental monitoring applications.

Findings

Achieved 0.99 AUC score in oil-spill detection

Enhanced feature extraction with quantum Bayesian Networks

Improved classification of rare environmental events

Abstract

Quantum Machine Learning (QML) has shown promise in diverse applications such as environmental monitoring, healthcare diagnostics, and financial modeling. However, its practical implementation faces challenges, including limited quantum hardware and the complexity of integrating quantum algorithms with classical systems. One critical challenge is handling imbalanced datasets, where rare events are often misclassified due to skewed data distributions. Quantum Bayesian Networks (QBNs) address this issue by enhancing feature extraction and improving the classification of rare events such as oil spills. This paper introduces a Bayesian approach utilizing QBNs to classify satellite-derived imbalanced datasets, distinguishing ``oil-spill'' from ``non-spill'' regions. QBNs leverage probabilistic reasoning and quantum state preparation to integrate quantum enhancements into classical machine learning architectures. Our approach achieves a 0.99 AUC score, demonstrating its efficacy in anomaly detection and advancing precise environmental monitoring and management. While integration enhances classification performance, dataset-specific challenges require further optimization.