General-purpose methodologies for technological forecasting tend to rely on expert opinions on the state of a technological field. However, such analyses are generally considered incomplete in multidisciplinary fields where a single expert is typically incapable of having a complete overview of the area. To address this, such forecasts are usually augmented with unbiased and quantitative data mining approaches. This chapter aims to provide such an overview, focusing on the convergence potential in the quantum computing field. Here, recent advances in generative large language models (LLMs) development are leveraged to perform a bibliometric analysis of scientific literature on quantum computing. The analysis focuses on arXiv preprints, which are considered to be representative of the state-of-the-art in quantum computing research. Semantic triples are extracted from these preprints to represent the factual claims made in the papers. These triples are then analyzed to uncover emerging technological convergences. The results indicate an increasing convergence of core quantum computing technologies and computational complexity analysis through quantum circuits, likely indicating a rapid development of architectures dedicated to executing algorithms with a better quantum complexity. Additionally, a general indication of increased research maturity is observed, focusing on the utilization of existing technologies over the last two years.