Quantum computing introduces abstract concepts and non-intuitive behaviors that can be challenging for students to grasp through traditional lecture-based instruction alone. This paper demonstrates how Project-Based Learning (PBL) can be leveraged to bridge that gap. This can be done by engaging students in a real-world, interdisciplinary task that combines quantum computing with their field of interest. As part of a similar assignment, we investigated the application of the Harrow-Hassidim-Lloyd (HHL) algorithm for computed tomography (CT) image reconstruction and benchmarked its performance against the classical Algebraic Reconstruction Technique (ART). Through implementing and analyzing both methods on a small-scale problem, we gained practical experience with quantum algorithms, critically evaluated their limitations, and developed technical writing and research skills. The experience demonstrated that Project-Based Learning not only enhances conceptual understanding but also encourages students to engage deeply with emerging technologies through research, implementation, and reflection. We recommend the integration of similar PBL modules in introductory quantum computing courses. The assignment also works better if students are required to write and submit a conference-style paper, supported by mentorship from faculty across the different fields. In such course interdisciplinary, real-world problems can transform abstract theory into meaningful learning experiences and better prepare students for future advancements in quantum technologies.
Quantum computing introduces abstract concepts and non-intuitive behaviors that can be challenging for students to grasp through traditional lecture-based instruction alone. This paper demonstrates how Project-Based Learning (PBL) can be leveraged to bridge that gap. This can be done by engaging students in a real-world, interdisciplinary task that combines quantum computing with their field of interest. As part of a similar assignment, we investigated the application of the Harrow-Hassidim-Lloyd (HHL) algorithm for computed tomography (CT) image reconstruction and benchmarked its performance against the classical Algebraic Reconstruction Technique (ART). Through implementing and analyzing both methods on a small-scale problem, we gained practical experience with quantum algorithms, critically evaluated their limitations, and developed technical writing and research skills. The experience demonstrated that Project-Based Learning not only enhances conceptual understanding but also encourages students to engage deeply with emerging technologies through research, implementation, and reflection. We recommend the integration of similar PBL modules in introductory quantum computing courses. The assignment also works better if students are required to write and submit a conference-style paper, supported by mentorship from faculty across the different fields. In such course interdisciplinary, real-world problems can transform abstract theory into meaningful learning experiences and better prepare students for future advancements in quantum technologies.
Quantum computing is an interdisciplinary field that spans physics, mathematics, computer engineering, and computer science. Teaching quantum computing effectively within a constrained timeframe presents a challenge, particularly for engineering students with diverse academic backgrounds. This paper presents an approach to integrating quantum computing into a broader course on Technologies of Computing Systems (TCS) through a dedicated two-week module. The course structure combines lectures, interactive problem-solving, lab exercises, and a hands-on project to ensure an engaging and practical learning experience. The curriculum covers fundamental quantum computing principles, quantum gates, circuits, algorithms, and quantum technology. Assessment includes project work, exams, and student feedback, demonstrating increased engagement and comprehension. The results indicate that this quantum computing module successfully bridges the gap between classical and quantum paradigms, fostering student interest and understanding within a short period.
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.
This thesis investigates how a hands-on introductory Quantum Computing (QC) course can be effectively designed for students of Computer Science and Media (CSM) at a University of Applied Sciences (UAS). Using a mixed-methods approach - including a literature review, focus group discussions, and a student survey - the study addresses five core research questions concerning course content, abstraction level, instructional methodology, and tool selection. The findings highlight that foundational quantum computing concepts (e.g. superposition, entanglement, interference) should be prioritized, while complex mathematical formulations may be omitted without impairing students’ practical understanding. The research supports a moderately abstract, application-driven course structure, combining theoretical foundations with hands-on programming using tools such as IBM Composer and Qiskit. Both students and lecturers emphasized the importance of accessible, cost-efficient platforms with strong visual and interactive elements. The proposed course framework promotes project-based learning, flexibility, and just-in-time theoretical instruction, accommodating the varied backgrounds of CSM students. This work contributes to the growing field of quantum education in non-physics domains and offers a blueprint for interdisciplinary, practice-oriented QC instruction. Future research is encouraged to incorporate industry perspectives, assess long-term learning outcomes, and expand course content to include topics like quantum ethics.
With the rapid evolution of quantum computing, education in quantum technology should encompass both theoretical knowledge and practical applications relevant to industry and research. We aim to create an educational model that integrates learning, application, and research in quantum computing for students of all levels. Bridging the gap between coursework and active research, a club led by students provides comprehensive learning modules, followed by mini-challenges, which aim to engage students in active research in the field. These challenges have led to the development of web and standalone applications, contributing to both community and academic knowledge. Throughout its operation, the club has introduced a series of learning materials covering fundamental to advanced quantum computing topics, combined with hands-on challenges and projects that transitioned students from theoretical understanding to practical applications. This paper discusses the structure and outcome of the club activities, highlighting specific educational strategies taken and their educational and research achievements. Results indicate the practicality of the club's structure, evidenced by the members' contributions to the field through application development and publications. By detailing the club's methodology, achievements, and challenges, this paper aims to provide insight for educators looking to cultivate similar programs that encourage active learning and research in quantum computing.
We present a practical course targeting graduate students with prior knowledge of the basics of quantum computing. The practical aims to deepen students' understanding of fundamental concepts in quantum computing by implementing quantum circuit simulators. Through hands-on experience, students learn about different methods to simulate quantum computing, including state vectors, density matrices, the stabilizer formalism, and matrix product states. By implementing the simulation methods themselves, students develop a more in-depth understanding of fundamental concepts in quantum computing, including superposition, entanglement, and the effects of noise on quantum systems. This hands-on experience prepares students to do research in the field of quantum computing and equips them with the knowledge and skills necessary to tackle complex research projects in the field. In this work, we describe our teaching approach and the structure of our practical, and we discuss evaluations and lessons learned.
Abstract The revolutionary new field of quantum computing (QC) continues to gain attention in industry, academia, and government in both research and education. At educational institutions, there is a proliferation of introductory courses at various academic levels signaling a growing interest and recognition of the significance of this field. A crucial and often overlooked aspect is the development of research-based materials and pedagogical approaches to effectively teach the complexities of QC to diverse cohorts of learners across multiple disciplines. There is a great need for empirical investigations of the effectiveness of learning materials and pedagogical approaches in this new interdisciplinary field. We present an empirical investigation done at an R1 institution using the multiple case study method. We compare a case study on students in an introductory QC course without research-based mini-tutorials to a study of students taking the QC course with research-based mini-tutorials. We compare the strengths and difficulties of students in the two courses, discuss the general strengths and difficulties of students across both courses, postulate the effectiveness of the mini-tutorials and discuss how they can be revised. Strengths across both classes include the ability to apply single-qubit and two-qubit gates, favoring application of Dirac notation, and a reasonable understanding of normalization, probability and teleportation described qualitatively. Difficulties across both classes included use of matrix representation, use of rotation gates, and an ability to recall and analyze quantitatively a circuit representing teleportation. We postulate that the mini-tutorials on gates, normalization, probability and a qualitative description of teleportation may have been effective in increasing students’ understanding of the concepts. We also identify mini-tutorials that may need revision.