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.
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This paper is submitted and accepted in the Fourth Annual Quantum Science and Engineering Education Conference (QSEEC25), which is collocated with the IEEE International Conference on Quantum Computing & Engineering (QCE25), part of IEEE Quantum Week 2025
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.
Quantum computing harnesses quantum laws of nature to enable new types of algorithms, not efficiently possible on traditional computers, that may lead to breakthroughs in crucial areas like materials science and chemistry. There is rapidly growing demand for a quantum workforce educated in the basics of quantum computing, in particular in quantum programming. However, there are few offerings for non-specialists and little information on best practices for training computer science and engineering students. In this report we describe our experience teaching an undergraduate course on quantum computing using a practical, software-driven approach. We centered our course around teaching quantum algorithms through hands-on programming, reducing the significance of traditional written assignments and relying instead on self-paced programming exercises ("Quantum Katas''), a variety of programming assignments, and a final project. We observed that the programming sections of the course helped students internalize theoretical material presented during the lectures. In the survey results, students indicated that the programming exercises and the final project contributed the most to their learning process. We describe the motivation for centering the course around quantum programming, discuss major artifacts used in this course, and present our lessons learned and best practices for a future improved course offering. We hope that our experience will help guide instructors who want to adopt a practical approach to teaching quantum computing and will enable more undergraduate programs to offer quantum programming as an elective.
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.
As the field of quantum computing rapidly advances, there is a growing demand for skilled professionals adept in quantum computing and programming. Recognizing this need, in this paper, we share our experiences teaching an introductory-level quantum computing course to students at Cleveland State University (CSU). The course integrates dedicated hands-on programming labs, allowing students to verify their experimental results with corresponding examples from the textbook. These labs cover a diverse range of topics, including fundamental elements such as quantum gates and circuits, quantum key distribution protocols, and quantum algorithms. As educators, our goal is to share teaching insights and resources with fellow instructors in the field. This article elucidates the rationale behind the design of each experiment, providing a deeper understanding of quantum computing.
Quantum computing is a highly abstract scientific discipline, which, however, is expected to have great practical relevance in future information technology. This forces educators to seek new methods to teach quantum computing for students with diverse backgrounds and with no prior knowledge of quantum physics. We have developed an online course built around an interactive quantum circuit simulator designed to enable easy creation and maintenance of course material with ranging difficulty. The immediate feedback and automatically evaluated tasks lower the entry barrier to quantum computing for all students, regardless of their background.
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 is redefining computational boundaries, offering solutions to complex problems that classical computing struggles to address. A deep understanding of quantum principles, algorithms, and practical tools is essential for professionals and researchers navigating this transformative technology. Quantum computing is no longer a distant dream—it is rapidly becoming a cornerstone of modern innovation. This book provides a structured introduction to quantum computing, covering foundational principles such as superposition and entanglement, quantum gates, and circuits. Readers will gain hands-on experience with Qiskit and IBM Q, explore quantum error correction, and understand the real-world applications of quantum computing across industries. Alongside theoretical concepts, readers are introduced to hands-on quantum programming using Qiskit and IBM Q. The book also addresses practical challenges like quantum error correction and showcases real-world applications across industries, including cybersecurity, artificial intelligence, and material science. By the end of this book, readers will have a solid grasp of quantum computing fundamentals and hands-on experience with practical tools. Equipped with both conceptual understanding and hands-on experience, readers will be prepared to contribute meaningfully to research, solve industry-relevant problems, and engage confidently with the rapidly evolving field of quantum computing. --