Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection

• URL: http://arxiv.org/abs/2412.19843v2
• Date: Tue, 29 Apr 2025 07:45:17 GMT
• Title: Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection
• Authors: Owais Ishtiaq Siddiqui, Nouhaila Innan, Alberto Marchisio, Mohamed Bennai, Muhammad Shafique,
• Abstract summary: Quantum Machine Learning has shown promise in diverse applications such as environmental monitoring, healthcare diagnostics, and financial modeling.<n>One critical challenge is handling imbalanced datasets, where rare events are often misclassified due to skewed data distributions.<n>This paper introduces a Bayesian approach utilizing QBNs to classify satellite-derived imbalanced datasets, distinguishing oil-spill'' from non-spill'' regions.
• Score: 3.9554540293311864
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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