This study empirically investigates the career intentions and employability perceptions of graduates specializing in the emerging field of quantum computing. Against the backdrop of a rapidly expanding quantum technology sector, which faces a critical shortage of qualified professionals, this research addresses a significant gap in understanding the human capital pipeline. A structured survey was administered to graduates from quantum computing-related programs, with the collected data subjected to both descriptive statistical analysis and advanced machine learning techniques, specifically K-means clustering, to identify latent patterns and profiles. The analysis reveals a complex landscape of graduate preparedness and aspiration. While a majority express a strong intention to pursue careers directly related to their quantum specialization, a significant proportion experience considerable anxiety and uncertainty regarding their job prospects. The clustering analysis delineates three distinct graduate profiles: the confident specialists, who are well-aware and focused; the anxious explorers, characterized by high pressure and uncertainty; and the pragmatic learners, who adopt a flexible, skill-oriented approach. Key findings indicate that perceived gaps in specialized skills and practical experience are primary factors hindering employment confidence. The study concludes that bridging the gap between academic training and industry requirements is paramount. It proposes a multi-faceted set of recommendations targeted at both higher education institutions and graduates themselves. These include enhancing curricula with practical, industry-relevant modules, strengthening university career support systems, and encouraging proactive career planning. This research contributes to the broader discourse on workforce development in high-technology fields by providing a nuanced, data-driven perspective on the factors shaping the career trajectories of quantum computing graduates.

Zhougeng Lin. Employability and Career Intentions of Quantum Computing Graduates Investigated by a Clustering Algorithm. International Journal of Frontiers in Engineering Technology (2026), Vol. 8, Issue 2: 63-71. https://doi.org/10.25236/IJFET.2026.080209.

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