Quantum Computational Modeling for Affective Assessment in Virtual Reality Systems - Bayro, Allison; Jeong, Heejin (2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2024)
This research explores the use of quantum machine learning-more specifically, the Quantum Support Vector Machine (QSVM)-to predict emotional reactions triggered in virtual reality (VR) settings. The study's experimental setup involved provoking different emotional states via VR gameplay elements and capturing the physiological signals of participants. These signals were paired with the subjects' self-reported emotions, measured through the Self-Assessment Manikin. The study then employed both conventional Support Vector Machines (SVM) and QSVM to classify the data. Notably, QSVM surpassed the traditional SVM in accurately predicting the levels of arousal and valence, achieving higher precision with a reduced set of features. This enhanced efficiency is likely due to QSVM's superior handling of the intricate patterns in emotional data and the quantum models' more effective computational resource usage. These results hold considerable potential for the field of affective computing within VR environments, indicating the advantageous prospects of quantum machine learning for the domain.