Quantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling

TL;DR

This paper investigates the application of quantum machine learning techniques, specifically QSVM and QNN, to model pedestrian stress responses in a virtual reality setting, highlighting potential advantages and current limitations.

Contribution

It introduces quantum machine learning models for pedestrian stress classification and compares their performance to classical methods in a transportation context.

Findings

QNN achieved 55% test accuracy, outperforming QSVM.

QSVM had overfitting issues with 45% test accuracy.

Quantum models show promise but need further refinement.

Abstract

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.