arXiv:2508.14084 · IEEE International Conference on Quantum Computing and Engineering (QCE)
Q-BEAST: A Practical Course on Experimental Evaluation and Characterization of Quantum Computing Systems
Minh Thanh Chung, Yaknan John Gambo, Burak Mete, Xiao-Ting Michelle To, Florian Krötz, Korbinian Staudacher, Martín Letras, Xiaolong Deng, Mounika Vavilala, Amir Raoofy, Jorge Echavarria, Luigi Iapichino, Laura Brandon Schulz, Josef Weidendorfer, Martin Schulz
Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and handson experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.
Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and handson experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.
As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum computing lectures are strongly physics-oriented and have little intersection with the remaining curriculum of computer science. This fact makes designing an appealing course very difficult, in particular for non-physicists. Furthermore, in the academic community, there is consensus that quantum computers are going to be used only for specific computational tasks (e.g., in computational science), where hybrid systems - combined classical and quantum computers - facilitate the execution of an application on both quantum and classical computing resources. A hybrid system thus executes only certain suitable parts of an application on the quantum machine, while other parts are executed on the classical components of the system. To fully exploit the capabilities of hybrid systems and to meet future requirements in this emerging field, we need to prepare a new generation of computer scientists with skills in both distributed computing and quantum computing. To bridge this existing gap in standard computer science curricula, we designed a new lecture and exercise series on Hybrid Quantum-Classical Systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum. While learning the inherent concepts underlying quantum systems, students are obligated to apply techniques and methods they are already familiar with, making the entrance to the field of quantum computing comprehensive yet appealing and accessible to students of computer science.
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.
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.
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.
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.
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.