In the last year, quantum computing has rapidly transitioned from a purely theoretical field into applied engineering, where multiple algorithms can approach combinatorial optimization problems, machine learning, and cryptography in novel ways, often achieving speedup compared to classical solutions. In this context, there is an increasing need for educational tools that connect mathematical principles from quantum information theory with practical experimentation. Yet, most existing educational resources remain either highly theoretical or provide fragmented code examples, with limited analysis and applications to real-world use cases. As a result, students’ experiences appear restricted, with non-interactive tools for learning. This work presents a comprehensive framework for teaching quantum computing through interactive resources, developed via MATLAB Live Scripts. By combining theory, executable code, and visualization tools within a single environment, this framework enhances learners’ ability to see and reason about quantum algorithms step by step. Following the principles of Project-Based Learning in STEM education, we present a diverse collection of quantum resources, including algorithms for optimization, communication, and error correction. Each project operates as a self-contained learning unit, offering modularity, white-box implementations, and tools for visualization and analysis. Beyond its educational impact, this framework also employs and extends the MATLAB Support Package for Quantum Computing, offering an open-source, reproducible environment suitable for courses and independent study.
In the last year, quantum computing has rapidly transitioned from a purely theoretical field into applied engineering, where multiple algorithms can approach combinatorial optimization problems, machine learning, and cryptography in novel ways, often achieving speedup compared to classical solutions. In this context, there is an increasing need for educational tools that connect mathematical principles from quantum information theory with practical experimentation. Yet, most existing educational resources remain either highly theoretical or provide fragmented code examples, with limited analysis and applications to real-world use cases. As a result, students’ experiences appear restricted, with non-interactive tools for learning. This work presents a comprehensive framework for teaching quantum computing through interactive resources, developed via MATLAB Live Scripts. By combining theory, executable code, and visualization tools within a single environment, this framework enhances learners’ ability to see and reason about quantum algorithms step by step. Following the principles of Project-Based Learning in STEM education, we present a diverse collection of quantum resources, including algorithms for optimization, communication, and error correction. Each project operates as a self-contained learning unit, offering modularity, white-box implementations, and tools for visualization and analysis. Beyond its educational impact, this framework also employs and extends the MATLAB Support Package for Quantum Computing, offering an open-source, reproducible environment suitable for courses and independent study.
Quantum education has been increasingly emphasized at both the post-secondary and secondary education stages, in line with the advent of the quantum computing paradigm. Traditional quantum computing education, however, necessitates an extensive background in physics, mathematics, and information science, which can be challenging for beginners. This study presents the experience of implementing a college curriculum at National Chi Nan University in Taiwan, designed to enable learners at all levels to grasp the concept of a quantum algorithm using accessible math yet rigorous enough for verification. The study succinctly conveys the concept of the quantum Grover algorithm, provides necessary background knowledge, and elucidates the ideas using three distinct visualization methods. Furthermore, this study offers a novel presentation for aligning the results of geometrical representation with the corresponding quantum circuit layout. The hands-on project demonstration motivates learners to explore solutions using their visualization tool, enhancing their engagement and sense of achievement. Developing these tools deepens learners' understanding and promotes active peer education, thereby improving their participation and retention of quantum knowledge. The visualization tools developed through this process serve as a valuable contribution to quantum education, offering an effective quantum tool and a user-friendly interface for learning, teaching, researching, and providing fresh insights and perspectives. By making quantum computing concepts more understandable through accessible math and visualization, we aim to lower the entry-level barrier to learning quantum computing, encouraging more talents to engage in quantum computing for their research studies.
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.
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.
The last few years have seen many new ways to get hands-on with quantum computing, including publicly accessible prototype hardware and software frameworks. Such tools provide new education opportunities, not just for quantum computing specifically, but also more broadly for quantum information science and even quantum physics as a whole. In this paper we present a case study of one education resource which aims to take advantage of the opportunities: the open-source online textbook ‘Learn Quantum Computation using Qiskit’. This blends traditional text with executable code and interactive elements. We give an overview of the form taken by the textbook in 2020, summary of the topics covered and an explanation of the approach taken.
Unlock the future of quantum technology with this comprehensive guide that bridges the gap between complex quantum theory and real-world applications, giving you the practical edge needed to solve the next generation’s toughest scientific and industrial problems. At its core, quantum computing leverages the principles of quantum mechanics to process information in fundamentally different ways from classical computing. As physical limits constrain the continued scaling of classical technologies, quantum computing offers a fundamentally new pathway toward solving complex problems in science, engineering, and industry. This book is a comprehensive guide designed to bridge the gap between theoretical principles and real-world quantum computing applications. It explores quantum information theory, a cornerstone of quantum computing, to explain how information is encoded, manipulated, and measured in a quantum system. Topics such as quantum gates, circuits, and the Bloch sphere representation are covered in detail, offering readers a solid grounding in the mechanics of quantum operations. With a focus on practical implementation, this book is tailored for students, researchers, and professionals seeking to delve into the transformative world of quantum computing, where the boundaries of classical computation are redefined. Readers will find the book: Uses step-by-step guides and hands-on tutorials for building quantum circuits and running algorithms; Explores core algorithms through detailed walkthroughs of quantum algorithms, including Shor’s, Grover’s, and Quantum Fourier Transform; Provides hands-on learning through exercises, coding challenges, and projects to reinforce practical skills. Audience Academics, researchers, quantum software developers and hardware engineers, data scientists, AI specialists, and security experts focused on the study and implementation of quantum systems and processors.
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.