用量子模型分析行人过街时的应激反应，探索智能交通新方法。
Quantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling
- 采用8比特量子支持向量机与量子神经网络建模
- 量子神经网络测试准确率达55%，优于经典模型
- 为交通心理状态监测提供量子计算新思路
量子计算为解决复杂机器学习任务提供了新机遇，例如智能交通系统中常见的高维数据表示。本文通过虚拟现实过街实验，利用量子机器学习建模复杂的皮肤电反应（SCR）事件以反映行人应激状态。基于Pennylane构建了八比特ZZ特征映射的量子支持向量机（QSVM）和采用树张量网络参数化的量子神经网络（QNN）。数据集包含皮肤电导测量值及响应幅值、时间等特征，并按幅值分类。QSVM训练准确率高但存在过拟合，测试准确率仅为45%，影响分类可靠性；而QNN测试准确率达到55%，优于QSVM及经典模型，表现更优。
原文摘要 · Abstract (English)
Quantum computing has opened new opportunities to tackle complex machine learning tasks, for instance, high-dimensional data representations commonly required in intelligent transportation systems. We explore quantum machine learning to model complex skin conductance response (SCR) events that reflect pedestrian stress in a virtual reality road crossing experiment. For this purpose, Quantum Support Vector Machine (QSVM) with an eight-qubit ZZ feature map and a Quantum Neural Network (QNN) using a Tree Tensor Network ansatz and an eight-qubit ZZ feature map, were developed on Pennylane. The dataset consists of SCR measurements along with features such as the response amplitude and elapsed time, which have been categorized into amplitude-based classes. The QSVM achieved good training accuracy, but had an overfitting problem, showing a low test accuracy of 45% and therefore impacting the reliability of the classification model. The QNN model reached a higher test accuracy of 55%, making it a better classification model than the QSVM and the classic versions.
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