Key predictors of student proficiency in quantum computing

Quantum computing is an emerging field that presents unique challenges for student learning, particularly in higher education. Understanding the factors that influence student proficiency is critical for designing effective and inclusive quantum computing curricula. This study investigates key predictors of student proficiency in quantum computing by analyzing data of undergraduate and graduate students in computing-related programs at a public university in the United States. Using Exploratory Factor Analysis (EFA) (n = 63), we identified three latent constructs. Principal Component Analysis (PCA) explained 94.18 % of the total variance, capturing dimensions related to broad understanding, practical application, and conceptual depth. Multiple regression analysis revealed that general understanding, exposure to topics, and qubit knowledge significantly contribute to proficiency, while topics such as quantum algorithms and teleportation were negatively associated, suggesting areas of greater instructional difficulty. These findings can guide actionable insights for curriculum development. Specifically, tailoring content to academic level, reinforcing foundational knowledge, and providing targeted support for challenging topics can enhance student proficiency in quantum computing. By empirically identifying the key educational factors linked to student success, this study contributes a data-driven perspective to the growing field of quantum education and supports instructional design in STEM curricula.