α | D 〉 + β | H 〉 : Exploration of Quantum Deep Learning on Humanities Data from the Perspective of Digital Humanities

Abstract

The emergence and advancement of new technologies present promising opportunities for data-driven research in the Digital Humanities (DH). As an innovative intersection of quantum computing and humanities data, Quantum Humanities (QH) holds significant research potential. However, current studies related to QH remain scarce with limited applications involving quantum deep learning, and its feasibility, effectiveness, and efficiency on humanities data need in-depth exploration. To address this gap, this paper takes the role type recognition of painted faces of Beijing Opera as a case study. Several classical and competitive quantum deep learning–based and classical deep learning–based models (e.g., quantum convolutional neural networks and classical convolutional neural networks) are selected for comparison on the public image dataset Painted Faces of Beijing Opera digitized from books. Extensive empirical experimental results demonstrate that quantum deep learning–based models are of certain advantages and hold promising prospects in applied DH practices.