In recent years, the quantum computing industry has seen significant investment and growth. However, this burgeoning industry faces a persistent labour gap: individuals with computing expertise, an understanding of quantum principles, and the ability to apply these principles to computing practices, are in increasing demand, but finding such individuals is proving challenging. We frame this problem as one of education, arguing that a computing-centred approach to learning about quantum computing is needed, and that the notion of computational thinking can help to define appropriate learning goals and outcomes in this context. We propose that metaphors can be an effective pedagogical tool in supporting the development of 'quantum computational thinking'. In this paper, we present our efforts to gather and assess a collection of metaphors that are currently used to teach quantum computing concepts. We describe a series of interviews with quantum computing experts in which we aimed to elicit such metaphors, and document our process of metaphor identification and metaphorical model synthesis. We subsequently assess these metaphors and models using both numerical rating data from experts and our own qualitative analysis. Informed by our findings, we suggest ways of developing metaphors that better support quantum computational thinking: emphasising target concepts' computational roles, acknowledging connections between concepts, and balancing procedural narratives with embodied, tangible imagery.
In recent years, the quantum computing industry has seen significant investment and growth. However, this burgeoning industry faces a persistent labour gap: individuals with computing expertise, an understanding of quantum principles, and the ability to apply these principles to computing practices, are in increasing demand, but finding such individuals is proving challenging. We frame this problem as one of education, arguing that a computing-centred approach to learning about quantum computing is needed, and that the notion of computational thinking can help to define appropriate learning goals and outcomes in this context. We propose that metaphors can be an effective pedagogical tool in supporting the development of 'quantum computational thinking'. In this paper, we present our efforts to gather and assess a collection of metaphors that are currently used to teach quantum computing concepts. We describe a series of interviews with quantum computing experts in which we aimed to elicit such metaphors, and document our process of metaphor identification and metaphorical model synthesis. We subsequently assess these metaphors and models using both numerical rating data from experts and our own qualitative analysis. Informed by our findings, we suggest ways of developing metaphors that better support quantum computational thinking: emphasising target concepts' computational roles, acknowledging connections between concepts, and balancing procedural narratives with embodied, tangible imagery.
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.
This paper introduces an adaptive learning algorithm for quantum metaphoric computing, aiming to automatically adjust computational models based on practical application contexts. The core of this approach leverages the unique properties of quantum metaphoric computation – the ability to represent and manipulate abstract concepts – to dynamically refine model parameters. The algorithm employs a novel learning framework centered around iterative refinement and contextual awareness, enabling the system to rapidly adapt to evolving demands. This research explores the potential for this approach to significantly enhance the efficiency and applicability of quantum metaphoric computing across diverse scenarios. The paper details the algorithm's architecture, the underlying mathematical principles, and preliminary results demonstrating its effectiveness in a simulated environment.
This poster explores strategies for supporting quantum computing education in academic libraries and invites collaborative discussion on building sustainable support models. As quantum computing emerges as a transformative technology with potential to solve complex research questions across disciplines, institutions of higher education must develop effective and scalable support frameworks. Successful integration of quantum computing education and support may position institutions at the forefront of breakthrough computational research and confer benefits such as strengthening the institutional ability to attract top faculty and students, boost workforce development, and deliver a competitive advantage for funding opportunities. We present our initial framework for quantum computing support developed at the University Libraries, University of Alabama. Our instructional model employs backward design principles, emphasizing hands-on engagement with open-source quantum development tools and focusing on transferable computational thinking skills that extend beyond specific tools. Our approach centers on workshops that prioritize achievable learning outcomes tailored to diverse student backgrounds, incorporate active learning strategies, and emphasize real-world applications to build quantum literacy across disciplines. Both our successes and ongoing challenges are shared to inform broader discussions. More broadly, we invite engagement with our framework and discussion across four critical dimensions of emerging technology support including pedagogical approach, assessment strategies, technology platforms and infrastructure, and collaborative support models. Pedagogically, we seek to explore which instructional methods most effectively build quantum computing literacy for students with diverse academic backgrounds as well as strategies to strike the balance between theoretical foundations and hands-on practice. With assessment, we seek meaningful assessment approaches that can address varied student backgrounds and account for steep learning curves accompanying emerging technologies. For technology, we endeavor to discover which platforms will work best within higher education and ensure access and reliability for students and educators on a broad scale. And finally, we seek to examine how academic libraries and other units collaborate to build sustainable, coordinated support infrastructure. As quantum computing and other emerging technologies reshape higher education, academic libraries may be uniquely positioned to lead support for quantum computing by democratizing access to computational resources and bridging gaps between research, instruction, and student success. To that end, we invite discussion on how academic libraries can help to build sustainable, adaptable, and collaborative models to support quantum computing. We seek experiences, challenges, and strategies to inform best practices while aligning with library and other campus computing efforts and meeting the evolving needs of students.
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.
The rapid advancement of quantum computing has, without question, outpaced the rate of the subsequent setting up of an appropriate framework to categorize its various technologies. This has resulted in essentially fragmented terminologies with research efforts being considered in isolation, limiting collaboration or adaptation by industry. This chapter intends to fill the gap by introducing a multidimensional taxonomic approach to help develop a coherent and standardized framework for terminology in quantum computing. The proposed framework separates itself from existing frameworks by unifying four significant dimensions: computational models, hardware implementations, algorithmic strategies, and application domains. Its modular nature promotes relative ease of adaptation to emerging technologies, such as diamond-based qubits and cloud-based quantum platforms. The theoretical framework upon which this model has been developed is based on systematic literature review, domain analysis, and expert consultation. Using comparative benchmarking and real-world scenarios, the framework documents its flexibility in coordinating technical capabilities with market demands. For academia, it offers a structured curriculum tool and research roadmap, whereas, for industry, it clarifies investment priorities and benchmarks technological advancements. The identified challenges such as complexity and maintenance demands, are acknowledged with solutions proposed through dynamic updating mechanisms and collaborative governance. Providing a common terminology and adaptable structure not only organizes present knowledge but also makes possible a faster transfer of theoretical breakthroughs to social impact. It allows stakeholders to conduct complex tasks on quantum computing technology and ensure equitable and sustainable utilization of its future transformative potential.
Quantum Computing is an exciting field that draws from information theory, computer science, mathematics, and quantum physics to process information in fundamentally new ways. There is an ongoing race to develop practical/reliable quantum computers and increase the quantum workforce. This needs to be accompanied by the development of quantum computing programs, courses and curricula coupled with the development and adoption of evidence-based materials and pedagogies to support the education of the next generation of quantum information scientists and workforce. At our university, we introduced an introductory course in Quantum Computing to undergraduate students and conducted a case study to investigate the strengths and difficulties of these students in quantum computing after taking the introductory course. Our goal is to contribute to the improvement of quantum computing education while understanding the topics that the students find easy to comprehend and topics that are difficult to comprehend. We conducted a series of interviews to identify these strengths and difficulties. This investigation is essential to help train the next generation of quantum scientists and researchers because it gives information and direction to quantum computing education, and it also helps us to develop research-based teaching and learning materials. We report on the results of these interviews and our initial work on the development of evidence-based materials for teaching an introductory course in Quantum Computing.