The emerging field of Quantum Machine Learning (QML) has shown promising advantages in accelerating processing speed and effectively handling the high dimensionality associated with complex datasets. Quantum Computing (QC) enables more efficient data manipulation through the quantum properties of superposition and entanglement. In this paper, we present a novel approach combining quantum and classical machine learning techniques to explore the impact of quantum properties for anomaly detection in Automatic Dependent Surveillance-Broadcast (ADS-B) data. We compare the performance of a Hybrid-Fully Connected Quantum Neural Network (H-FQNN) with different loss functions and use a publicly available ADS-B dataset to evaluate the performance. The results demonstrate competitive performance in detecting anomalies, with accuracies ranging from 90.17% to 94.05%, comparable to the performance of a traditional Fully Connected Neural Network (FNN) model, which achieved accuracies between 91.50% and 93.37%.

翻译：量子机器学习（QML）这一新兴领域在加速处理速度及有效处理复杂数据集的高维度方面展现出显著优势。量子计算（QC）通过量子叠加与纠缠特性实现了更高效的数据处理。本文提出一种结合量子与经典机器学习技术的新方法，以探索量子特性在广播式自动相关监视（ADS-B）数据异常检测中的影响。我们比较了混合全连接量子神经网络（H-FQNN）在不同损失函数下的性能，并使用公开的ADS-B数据集进行评估。实验结果表明，该方法在异常检测中取得了具有竞争力的性能，准确率介于90.17%至94.05%之间，与传统全连接神经网络（FNN）模型（准确率91.50%-93.37%）的表现相当。