From theory to autonomy: a topic modelling study of quantum finance through the lens of Datatopia and TOE

Quantum finance is an emerging frontier that combines quantum theory with computational finance to deal with complex financial market dynamics. Despite of its rapid expansion, the field literature still remains fragmented, making it difficult to trace its intellectual development. So, Structural Topic Modeling, an AI-based unsupervised machine learning technique widely used in natural language processing, together with the Mann-Kendall trend test, to identify the latent thematic structure of the corpus and examine their temporal trends. Through STM, six topics are identified. The MK trend test is used for analyzing the temporal trends of articles collected based on PRISMA guidelines from both the databases, Scopus and Web of Science. The six identified topics are placed into the Datatopia quadrants, highlighting how the field spans from conceptually oriented "Aspiring Creativity" to the technologically autonomous, "Sorcerer's Apprentice," reflecting its expanding conceptual and technical foundations and the field's growing alignment with AI-driven financial innovation. The TOE (Technology-Organization-Environment) framework further elucidates how technological advances, organisational adaptation and environmental factors shaped this transition toward AI-enabled financial innovation. The Mann-Kendall test showed positive thematic trends across AI-related topics, particularly "Quantum ML and Prediction," while the final results also highlighted "algorithmic governance" as a new form of sustainability within AI-driven financial systems. Overall, this study provides a comprehensive view of the field's development and demonstrates how combining topic modelling with conceptual frameworks can offer a systematic and scalable approach for analysing emerging interdisciplinary fields such as quantum finance.