Quantum computing poses unique challenges for computer science students due to the field's reliance on mathematical formalism, abstract physical concepts, and unfamiliar notation. While computer science students are typically proficient in algorithmic thinking and systems design, they often lack the background in quantum mechanics and linear algebra necessary to navigate the field's conceptual landscape. This mismatch can result in a form of disciplinary dissonance, a ‘Quantum Babel', where understanding breaks down due to divergent cognitive and notational frameworks. This paper proposes a layered, analogy-driven pedagogical framework that re-contextualizes quantum concepts using metaphors and structures familiar to CS learners. Grounded in cognitive load theory and analogical reasoning, the approach aims to reduce extraneous cognitive load through metaphorical scaffolding. For example, the quantum Hamiltonian is recast as an operating system task scheduler, facilitating intuitive understanding of time evolution and energy landscapes in quantum systems. We present a series of conceptual mappings and illustrative examples that enable learners to build quantum intuition using analogies drawn from classical computing domains such as signal processing, debugging, and resource scheduling. Our goal is to foster quantum intuition in learners without requiring a deep background in physics, supporting the emergence of a new generation of quantum-literate computer scientists. This work contributes to the growing field of quantum software engineering education and lays the foundation for empirically validated, cross-disciplinary teaching tools.
Quantum computing poses unique challenges for computer science students due to the field's reliance on mathematical formalism, abstract physical concepts, and unfamiliar notation. While computer science students are typically proficient in algorithmic thinking and systems design, they often lack the background in quantum mechanics and linear algebra necessary to navigate the field's conceptual landscape. This mismatch can result in a form of disciplinary dissonance, a ‘Quantum Babel', where understanding breaks down due to divergent cognitive and notational frameworks. This paper proposes a layered, analogy-driven pedagogical framework that re-contextualizes quantum concepts using metaphors and structures familiar to CS learners. Grounded in cognitive load theory and analogical reasoning, the approach aims to reduce extraneous cognitive load through metaphorical scaffolding. For example, the quantum Hamiltonian is recast as an operating system task scheduler, facilitating intuitive understanding of time evolution and energy landscapes in quantum systems. We present a series of conceptual mappings and illustrative examples that enable learners to build quantum intuition using analogies drawn from classical computing domains such as signal processing, debugging, and resource scheduling. Our goal is to foster quantum intuition in learners without requiring a deep background in physics, supporting the emergence of a new generation of quantum-literate computer scientists. This work contributes to the growing field of quantum software engineering education and lays the foundation for empirically validated, cross-disciplinary teaching tools.
Quantum computing presents a transformative potential for the world of computing. However, integrating this technology into the curriculum for computer science students who lack prior exposure to quantum mechanics and advanced mathematics remains a challenging task. This paper proposes a scaffolded learning approach aimed at equipping computer science students with essential quantum principles. By introducing foundational quantum concepts through relatable analogies and a layered learning approach based on classical computation, this approach seeks to bridge the gap between classical and quantum computing. This differs from previous approaches which build quantum computing fundamentals from the prerequisite of linear algebra and mathematics. The paper offers a considered set of intuitive analogies for foundation quantum concepts including entanglement, superposition, quantum data structures and quantum algorithms. These analogies coupled with a computing-based layered learning approach, lay the groundwork for a comprehensive teaching methodology tailored for undergraduate third level computer science students.
Background and purpose of the study The rise of quantum technologies necessitates integrating foundational quantum mechanics (QM) concepts into secondary education. However, inherently abstract phenomena like quantum entanglement pose significant pedagogical challenges, as traditional formalism-based approaches are often inaccessible. This study introduces and delineates an innovative, scaffolded pedagogical model designed to foster robust conceptual understanding of entanglement in secondary STEM education, moving beyond reliance on mathematical formalism. The proposed pedagogical model The presented contribution is a detailed pedagogical sequence following a deliberate learning trajectory. It begins with a tangible analogy (magnetic interactions) as a conceptual anchor for correlation, then transitions to computational tools (Bloch sphere visualization, Qiskit simulations). These tools facilitate exploration of quantum concepts weakly addressed by the analogy (e.g., superposition) and allow more authentic engagement with quantum behavior. Underpinned by constructivism, cognitive load theory, and QM education research, the model strategically repurposes the analogy’s limitations as pedagogical opportunities to introduce and contrast key quantum features like non-locality and superposition with classical intuition. The sequence integrates exploration, guided use of representations, and critical comparative discussion. Conclusions and potential implications This paper provides a theoretically grounded pedagogical model for introducing quantum entanglement in secondary STEM education, combining tangible and computational tools in a scaffolded manner. The approach offers potential advantages over traditional methods by providing concrete starting points and explicitly using classical limitations to illuminate quantum principles. While promising, rigorous empirical validation is the essential next step. Future research should investigate the model’s effectiveness in authentic classroom settings, informing curriculum design and teacher development for incorporating QM into secondary STEM.
Quantum computing has emerged as one of the most influential technological developments of the twenty-first century. While considerable attention has been devoted to hardware development, algorithm design, and industrial applications, comparatively less emphasis has been placed on the educational challenges associated with introducing quantum concepts to engineering students. Most available learning resources are designed primarily for physics students and often require prior knowledge of advanced quantum mechanics. Consequently, engineering students frequently encounter significant conceptual barriers when attempting to understand quantum computation. This chapter presents a mathematics-first educational framework aimed at simplifying the learning process for engineering students. Instead of introducing quantum theory through wave mechanics and advanced physical interpretations, the framework begins with familiar mathematical concepts such as vectors, matrices, probability, and linear transformations. The chapter demonstrates how core quantum concepts can be taught using accessible mathematical tools already encountered in undergraduate engineering curricula. A structured educational model is proposed and illustrated through practical examples, matrix operations, simple quantum gates, and engineering-oriented learning activities. The framework is intended to reduce cognitive barriers, improve conceptual understanding, and encourage interdisciplinary learning among future engineers. The chapter further discusses potential applications of quantum mathematics in thermal engineering, computational fluid dynamics, optimization, artificial intelligence, and advanced manufacturing.
Quantum computing is an emerging technology with a burgeoning industry and growing workforce needs. The demand for computer scientists with quantum computing knowledge is increasing, as their skills are needed to make quantum computing a scalable and useful resource. However, few computer scientists are formally educated in quantum computing, with most education occurring in physics curricula. Although some open source learning resources and software tools for quantum computing are available, they employ a quantum mechanical, hardware-driven framing that poses accessibility challenges for computing-focused learners. Educational games have seen some success in quantum computing outreach initiatives, particularly in building intuition for quantum mechanical concepts, but rarely relate to computing practices. This submission outlines a doctoral research programme that seeks to develop computing-centred interactions with quantum computing systems, and to explore how play can be leveraged in these interactions as a means of building quantum intuitions in a computing context.
Quantum computing education requires students to move beyond classical programming intuitions related to state, determinism, and debugging, and to develop reasoning skills grounded in probability, measurement, and interference. This paper reports on the design and delivery of a combined undergraduate and graduate course at Louisiana State University that employed a lab-integrated learning model to support conceptual change and progressive understanding. The course paired lectures with weekly programming labs that served as environments for experimentation and reflection. These labs enabled students to confront misconceptions and refine their mental models through direct observation and evidence-based reasoning. Instruction began with Quantum Without Linear Algebra (QWLA), which introduced core concepts such as superposition and entanglement through intuitive, dictionary representations. The course then transitioned to IBM Qiskit, which provided a professional framework for circuit design, noise simulation, and algorithm implementation. Analysis of student work and feedback indicated that hands-on experimentation improved confidence, conceptual clarity, and fluency across representations. At the same time, it revealed persistent challenges in debugging, reasoning about measurement, and understanding probabilistic outcomes. This paper presents the course structure, instructional strategies, and lessons learned, and argues that lab-integrated learning offers an effective and accessible approach to teaching quantum computing in computer science education.
Quantum computing is increasingly discussed outside physics departments, yet its foundational ideas, superposition, entanglement, interference and decoherence, have no equivalent in everyday experience, which makes them resistant to plain-language explanation. This makes analogies one of the few practical tools available for teaching these ideas to non-expert audiences such as students, policymakers and the general public. It is not well understood, however, which analogies actually transfer the correct structure of the physics and which ones quietly introduce wrong intuitions. This paper designs and evaluates three analogies, a chess-engine analogy for general quantum computation, a soap-bubble analogy for decoherence, and a noisy-crowd analogy for amplitude amplification in Grover's algorithm, using Gentner's structure-mapping theory as the analytical framework. Each analogy is tested against the underlying physics on four criteria: how many core quantum concepts it maps, where the mapping breaks down, whether it could mislead a learner, and how it performed in front of a live, non-expert audience. It is found that analogies which map multiple quantum concepts simultaneously, rather than just one, draw more engaged questions from audiences, but that this same richness increases the risk of overextension if the limits of the analogy are not stated explicitly alongside it. All three analogies were delivered live to a mixed audience of physics evaluators and students at a state-government-organised science competition, where each achieved its intended communicative purpose; this single presentation is reported plainly as the basis for that finding, alongside next steps that would extend it further. The paper concludes with a practical checklist for constructing and presenting quantum analogies responsibly.