标题： 用于石油泄漏检测的量子贝叶斯网络 显示英文标题

标题： Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection

摘要： 量子机器学习（QML）在环境监测、医疗诊断和金融建模等多样化应用中显示出前景。 然而，其实际实施面临挑战，包括有限的量子硬件和将量子算法与经典系统集成的复杂性。 一个关键挑战是处理不平衡数据集，其中罕见事件由于数据分布偏斜而经常被错误分类。 量子贝叶斯网络（QBNs）通过增强特征提取和提高对罕见事件（如石油泄漏）的分类能力来解决这个问题。 本文介绍了一种利用QBNs的贝叶斯方法，用于对卫星衍生的不平衡数据集进行分类，区分“石油泄漏”和“非泄漏”区域。 QBNs利用概率推理和量子态制备，将量子增强整合到经典机器学习架构中。 我们的方法实现了0.99的AUC分数，证明了其在异常检测中的有效性，并推动了精确的环境监测和管理。 虽然集成提高了分类性能，但数据集特定的挑战仍需要进一步优化。 显示英文摘要

摘要： 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.