This paper analyzes extant studies on the teaching of quantum computing within the K-12 education levels.This analysis was necessitated by the limited information on the incorporation of quantum computing in the K-12 curriculum, including the pedagogical approaches, resources utilized, and how quantum computing can be promoted in schools.A systematic literature review approach was adopted to provide answers to the research questions using four reputable academic databases.The databases include ACM, Scopus, IEEE, and Web of Science where articles published between 2012 and March 2023 were retrieved.Fifty articles were found eligible for review from the numerous papers the search yielded.The analysis of the papers indicates that the main teaching methods adopted by researchers were direct instruction, hands-on activity, collaborative learning, inquiry-based learning, participatory learning, game-based learning, project-based learning, and design-oriented learning.The result further showed that the key assessment methods adopted mostly to evaluate the teaching of quantum computing include self-report evaluation, process observation, and knowledge and acquisition questions.The findings were discussed in relation to their implication for promoting quantum computing education in schools.This study offers insight for educators, computing and educational researchers, and education policymakers.
This paper analyzes extant studies on the teaching of quantum computing within the K-12 education levels.This analysis was necessitated by the limited information on the incorporation of quantum computing in the K-12 curriculum, including the pedagogical approaches, resources utilized, and how quantum computing can be promoted in schools.A systematic literature review approach was adopted to provide answers to the research questions using four reputable academic databases.The databases include ACM, Scopus, IEEE, and Web of Science where articles published between 2012 and March 2023 were retrieved.Fifty articles were found eligible for review from the numerous papers the search yielded.The analysis of the papers indicates that the main teaching methods adopted by researchers were direct instruction, hands-on activity, collaborative learning, inquiry-based learning, participatory learning, game-based learning, project-based learning, and design-oriented learning.The result further showed that the key assessment methods adopted mostly to evaluate the teaching of quantum computing include self-report evaluation, process observation, and knowledge and acquisition questions.The findings were discussed in relation to their implication for promoting quantum computing education in schools.This study offers insight for educators, computing and educational researchers, and education policymakers.
A revolutionary technology impacting the field of science, engineering, and education is Quantum computing. Quantum Computing in education is a discipline regarding its application to teaching and learning. The focus of the paper is a bibliometric analysis of academic literature on quantum computing in education to identify the intellectual structure thereof, patterns of growth, and future research perspectives. It is based on the Scopus database as a primary source of data. It covers 104 relevant publications published during 2011-2025. They were sorted and analyzed with the help of Bibliometrix package and its Biblioshiny interface in R to show the annual trends of publications. The findings indicate that quantum computing education is becoming of academic interest, and the number of research materials is growing rapidly since 2020. The highly interdisciplinary nature, with engineering, computer science, physics, and social sciences, respectively, playing the dominant role. There is a transition towards more basic quantum concepts into applied educational concepts, such as curriculum development, engineering education, teaching practices, and student learning. The paper has an important implication for researchers, teachers, and policy formulators. It also outlines the direction of future research that can be used to advance the sustainable development of quantum computing education.
Quantum computing is emerging as a key enabler of digital transformation in data science and STEM education. This study investigates how quantum computing can be meaningfully integrated into higher education by combining a dual approach: a structured assessment of the specialized literature and a practical evaluation of educational tools. First, a science mapping study based on 281 peer-reviewed publications indexed in Scopus (2015–2024) identifies growth trends, thematic clusters, and international collaboration networks at the intersection of quantum computing, data science, and education. Second, a comparative analysis of widely used educational platforms—such as Qiskit, Quantum Inspire, QuTiP, and Amazon Braket—is conducted using pedagogical criteria including accessibility, usability, and curriculum integration. The results highlight a growing convergence between quantum technologies, artificial intelligence, and data-driven learning. A strategic framework and roadmap are proposed to support the gradual and scalable adoption of quantum literacy in university-level STEM programs.
This study aims to design and implement a quantum computing education program suitable for high school students, given the growing significance of quantum computing in diverse scientific and industrial fields. While quantum computing is rapidly gaining attention for its potential to revolutionize computational speed and address complex problems, the gap in early education for pre-university students remains notable, especially in South Korea. This research examined international and domestic trends in quantum computing education, developed an activity-based curriculum integrating simulation tools and collaborative problem solving, and applied it to a group of high school students. Through pre- and post-program surveys, activity sheets, and qualitative feedback, the results show a significant increase in students’ understanding and interest in quantum computing concepts such as superposition, entanglement, and quantum gates. The study highlights that an experiential, STEAM-integrated approach using visual and hands-on resources effectively lowers conceptual barriers and enhances engagement. However, challenges such as a lack of prior knowledge and limited educational materials persist. The research suggests policy and curriculum improvements as well as extended teacher training are required to bridge the global educational gap in quantum computing, and indicates that systematic integration of quantum concepts at the high school level is both feasible and impactful.
Background and Context Quantum computing (QC) is an emerging discipline with the potential to transform multiple fields. However, comprehensive syntheses of the existing educational research on QC in K-12 settings remain limited.Objective This early systematic review examined how QC is being studied in K-12 settings by education researchers.Method The study adopted the PRISMA protocol, leading to the inclusion of 24 articles following stringent screening.Findings Among other findings, the study identified six key reasons that have motivated education researchers to study QC in K-12 contexts, including its strategic national importance, interdisciplinary relevance, and the need for early exposure, among others. Researchers most frequently examined foundational quantum concepts such as superposition, entanglement, and measurement.Implications The study highlights the urgent need for more rigorous studies within the education discipline and the development of age-appropriate and user-friendly methodologies to enhance QC learning in K-12 education.
This paper presents a comprehensive systematic and bibliometric review of the rapidly expanding literature on quantum computing. The study addresses six research questions concerning prominent themes, temporal evolution, types of key findings, frequently reported research gaps, higher-order gap typologies, and future research directions. Utilizing a researcher-curated database, the final corpus comprises 153 studies compiled from non-Scopus, Scopus and Web of Science. The results demonstrate that quantum hardware and architecture, algorithms, and theoretical foundations dominate the current literature. Furthermore, publication output has risen significantly between 2018 and 2025. Among the reported findings, algorithmic innovations, performance claims, and hardware advancements are the most common. Despite this progress, the field faces substantial barriers; technical, scalability, fault-tolerance, and methodological-standardization gaps are the most frequently reported. Based on these identified gaps, eight actionable future research directions emerge to guide the scientific community. Ultimately, this review concludes that quantum computing is actively transitioning from a purely physics-driven domain into a multidisciplinary, engineering- and application-oriented science. However, its continued advancement is presently constrained by a hardware-methodology-application triad, wherein reporting practices often outpace empirical verification and standardized benchmarking.
Abstract Quantum computing has emerged as a promising field with the potential to revolutionize information processing and storage. Understanding its current state and research trajectory is crucial. This study aims to map the current literature on quantum computing, to identify how quantum computing is being used in solving quantum software problems and improving quantum algorithms. Moreover, it examines evaluation parameters and the challenges faced in this field. A systematic mapping study was conducted using the SCOPUS database, including all papers up to December 2024. Relevant articles were selected based on inclusion criteria, and a content analysis was performed to categorize and synthesize the findings. Information was gathered from 33 primary studies. Findings show that quantum computing is mainly applied in machine learning and optimization, though classical methods still outperform it. IBM Quantum is the most used platform. Research focuses on algorithm efficiency, verification over validation, and resource optimization, but lacks standardized evaluation methods. Key limitations include hardware constraints, noise, and scalability, while challenges involve integration, error correction, and problem formulation. Quantum computing continues to face significant technological challenges, but current research lays the groundwork for future advances. More practical studies on real hardware and strategies to improve the implementation and performance of quantum algorithms are needed. Effective evaluation, verification, and validation of quantum solutions must be further investigated.