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Quantum Machine Learning
Making Quantum Accessible: A Seven-Category Framework for K-12 Quantum Education
arXiv
Authors: Rhea Fernandez, Sarah Hagstrom, Liesel Malanos, Lachlan McGinness, Madeline Mitchell, Saskia Schultz, Elizabeth Sexton
Year
2025
Paper ID
36430
Status
Preprint
Abstract Read
~2 min
Abstract Words
113
Citations
N/A
Abstract
We conducted a literature review and expert interviews to determine the most common methods being used to teach quantum physics and quantum computing concepts to primary and secondary students. Based on the findings of this review, we provide a framework of seven categories of teaching approaches for teaching mathematically accessible quantum concepts; they are Defamiliarization, Quantum Picturalism, Spin-First Approach, Einstein-First Approach, Many Paths Approach, Historical Development Approach and Game-based Quantum Learning. We summarise each of these teaching methods and overview their advantages and disadvantages of each method. Our framework makes it easy for physics educators to embrace the diverse methods of teaching quantum physics and quantum computing at the primary and secondary level.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- We conducted a literature review and expert interviews to determine the most common methods being used to teach quantum physics and quantum computing concepts to primary and...
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