QUANTUM LEARNING: INSIGHTS FROM CASE STUDY IN AI ENGINEERING EDUCATION

This communication examines the integration of quantum computing into AI engineering education, with a specific focus on aligning the course design with CDIO Standards. Authored by the module's instructor and three participating students, it narrates the experience of integrating Quantum Computing into the curriculum and the interdisciplinary knowledge it encompasses. The course curriculum drew from essential scientific domains such as linear algebra, differential and integral calculus, statistics, group theory, quantum mechanics, electromagnetism, information theory, algorithmic thinking, computational complexity, and numerical simulation. These domains are traditionally part of the foundational studies in an undergraduate engineering program and were effectively utilized as building blocks for understanding Quantum Computing. The paper explores the idea of introducing this novel field early in the undergraduate engineering cycle, using it as a cornerstone that ties together various fundamental and specialized fields of knowledge. A survey conducted among the students indicated a strong agreement that the Quantum Computing course fits naturally within the earlier stages of their engineering education, allowing for a practical application of many concepts learned during this period. The paper demonstrates the feasibility and benefits of employing Quantum Computing as a comprehensive educational tool. It argues that such integration aligns with and enhances the core objectives of undergraduate engineering programs, providing a cohesive learning journey from fundamental principles to advanced applications. The student survey results reinforce this proposition, suggesting that such a course could serve as an effective pedagogical strategy to consolidate and apply the broad spectrum of topics covered in the engineering curriculum.