量子学习金字塔（QLP）：一种用于量子计算教育的新颖、全面、适用于行业的课程和教学方法
The Quantum Learning Pyramid (QLP): A Novel, Holistic, Industry-Ready Curriculum and Pedagogical Methodology for Quantum Computing Education
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中文总结 AI 辅助
针对量子计算教育需求，提出量子学习金字塔（QLP）框架，采用四层结构，基于螺旋式推进等设计课程，涵盖多方面主题，借助多种平台支持实践学习，为培养相关人才提供可扩展路线图。
中文摘要 AI 辅助
随着行业需求和国家举措迅速增长，量子计算教育变得迫在眉睫。本文介绍了量子学习金字塔（QLP），这是一个用于量子信息与计算本科及研究生教育的统一教学框架。QLP采用四层结构，整合了现象学理解、计算思维、硬件感知开发和社会背景。课程设计采用螺旋式推进、基于能力的路径和真实评估。教学以主动式和项目式学习为基础，符合ACM/IEEE课程指南。核心主题包括量子力学基础、量子比特操作和关键算法，高级模块涉及纠错、密码学和量子硬件。通过模拟平台、云访问量子处理器和混合实验室环境支持实践学习。贯穿了跨学科案例研究和实际系统实验。该框架架起了理论与实践的桥梁，为培养具备量子能力的劳动力和有科学素养的公民提供了可扩展的路线图。
英文摘要
Quantum computing education is becoming urgent as industry demand and national initiatives grow rapidly. This paper introduces the Quantum Learning Pyramid (QLP), a unified pedagogical framework for undergraduate and graduate education in quantum information and computing. The QLP follows a four-tier structure that integrates phenomenological understanding, computational thinking, hardware-aware development, and societal context. The curriculum is designed using spiral progression, competency-based pathways, and authentic assessment. Instruction is grounded in active and project-based learning, aligned with ACM/IEEE curriculum guidelines. Core topics include quantum mechanics fundamentals, qubit operations, and key algorithms, while advanced modules address error correction, cryptography, and quantum hardware. Hands-on learning is supported through simulation platforms, cloud-accessible quantum processors, and hybrid laboratory environments. Interdisciplinary case studies and real-system experimentation are embedded throughout. The proposed framework bridges theory and practice and provides a scalable roadmap for developing a quantum-ready workforce and scientifically informed citizens.
