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Intuit: Explain Quantum Computing Concepts via AR-based Analogy

Manusha Karunathilaka, Shaolun Ruan, Linping Yuan, Jiannan Li, Zhiding Liang, Kavinda Athapaththu, Qian-Guo Guan, Yong Wang·March 2, 2025·DOI: 10.1145/3706599.3720085
Computer Science

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Abstract

Quantum computing has shown great potential to revolutionize traditional computing and can provide an exponential speedup for a wide range of possible applications, attracting various stakeholders. However, understanding fundamental quantum computing concepts remains a significant challenge for novices because of their abstract and counterintuitive nature. Thus, we propose an analogy-based characterization framework to construct the mental mapping between quantum computing concepts and daily objects, informed by in-depth expert interviews and a literature review, covering key quantum concepts and characteristics like number of qubits, output state duality, quantum concept type, and probability quantification. Then, we developed an AR-based prototype system, Intuit, using situated analytics to explain quantum concepts through daily objects and phenomena (e.g., rotating coins, paper cutters). We thoroughly evaluated our approach through in-depth user and expert interviews. The Results demonstrate the effectiveness and usability of Intuit in helping learners understand abstract concepts in an intuitive and engaging manner.

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