ABSTRACT This paper presents a method for teaching quantum computing for engineering students of the Master's degree in Quantum Computing at the International University of La Rioja. The approach is designed so that students can acquire the basic concepts of quantum computing in a context in which they have no knowledge of quantum mechanics. The focus is therefore on the mathematical, physical and, above all, computational knowledge acquired by the student in engineering. Theoretical and practical concepts of quantum computation are introduced, as well as the most important quantum algorithms. The student is also taught to work with a real quantum device. The method was successfully tested using the IBM Quantum platform, which consists of real quantum devices and simulators where students were able to test the acquired knowledge and implement the learned algorithms. In a context where quantum computing‐related degrees and subjects are starting to emerge, the aim of this work is to help not only future generations of engineers to study quantum computing, but also those responsible people for implementing such studies.
ABSTRACT This paper presents a method for teaching quantum computing for engineering students of the Master's degree in Quantum Computing at the International University of La Rioja. The approach is designed so that students can acquire the basic concepts of quantum computing in a context in which they have no knowledge of quantum mechanics. The focus is therefore on the mathematical, physical and, above all, computational knowledge acquired by the student in engineering. Theoretical and practical concepts of quantum computation are introduced, as well as the most important quantum algorithms. The student is also taught to work with a real quantum device. The method was successfully tested using the IBM Quantum platform, which consists of real quantum devices and simulators where students were able to test the acquired knowledge and implement the learned algorithms. In a context where quantum computing‐related degrees and subjects are starting to emerge, the aim of this work is to help not only future generations of engineers to study quantum computing, but also those responsible people for implementing such studies.
Quantum computing has emerged as one of the most influential technological developments of the twenty-first century. While considerable attention has been devoted to hardware development, algorithm design, and industrial applications, comparatively less emphasis has been placed on the educational challenges associated with introducing quantum concepts to engineering students. Most available learning resources are designed primarily for physics students and often require prior knowledge of advanced quantum mechanics. Consequently, engineering students frequently encounter significant conceptual barriers when attempting to understand quantum computation. This chapter presents a mathematics-first educational framework aimed at simplifying the learning process for engineering students. Instead of introducing quantum theory through wave mechanics and advanced physical interpretations, the framework begins with familiar mathematical concepts such as vectors, matrices, probability, and linear transformations. The chapter demonstrates how core quantum concepts can be taught using accessible mathematical tools already encountered in undergraduate engineering curricula. A structured educational model is proposed and illustrated through practical examples, matrix operations, simple quantum gates, and engineering-oriented learning activities. The framework is intended to reduce cognitive barriers, improve conceptual understanding, and encourage interdisciplinary learning among future engineers. The chapter further discusses potential applications of quantum mathematics in thermal engineering, computational fluid dynamics, optimization, artificial intelligence, and advanced manufacturing.
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.
Investments in quantum technologies, which are based on the effects of quantum mechanics, have escalated in recent years, with big players such as IBM, Google, and Microsoft engaging in the race for quantum supremacy. Some potential benefits of this cutting-edge technology include developing new drugs and materials through accurate molecule simulation, establishing more secure and reliable communication channels, and finding faster solutions for complex optimization problems. As quantum technologies evolve, however, the demand for new software, interfaces, and end-to-end systems to properly program and explore the advantages of quantum mechanics becomes paramount. This requires engineers, especially software engineers, and computer scientists, to have multidisciplinary knowledge and skills. Unfortunately, introductory quantum computing and technologies courses are mostly available in physics, and most engineering and computer science courses do not have modules on quantum physics, leading to a talent gap in the current job market. Therefore, this paper proposes a short teaching plan for introducing quantum computing to students with a more technology-based background, such as computer science and engineering students. This plan uses active learning methodologies, such as Challege-Based Teaching, to better engage these students and contextualize the many applications of this new technology. The initial teaching plan consists of a 3-hour seminar introducing the main quantum mechanics concepts and the technology applications, a 3-hour introduction to the math fundamentals and quantum logic gates, and a 3-hour handson workshop using the IBM Qiskit platform. Two versions of this plan were executed in two different moments: (1) a one-day event open to undergraduate students from different institutions and backgrounds, and (2) a 3-day internal training course for computer science and information technology management undergraduate students from our higher education institution. Data was collected during both events through online forms and interviews to measure the learning outcomes of the produced material and better understand the public interest in the field. The results identified key elements for better teaching quantum computing for computer scientists and engineers, providing guidelines for developing a more extensive and term-long teaching plan for undergraduate technology courses.
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 aninterdisciplinary field that lies at the intersection of mathematics, quantum physics, and computer science, and finds applications in areas including optimization, machine learning, and simulation of chemical, physical, and biological systems. It has the potential to help solve problems that so far have no satisfying method solving them, and to provide significant speedup to solutions when compared with their best classical approaches. In turn, quantum computing may allow us to solve problems for inputs that so far are deemed practically intractable. With the computational power of quantum computers and the proliferation of quantum development kits, quantum computing is anticipated to become mainstream, and the demand for a skilled workforce in quantum computing is expected to increase significantly. Therefore, quantum computing education is ramping up. This article describes our experiences in designing and delivering quantum computing workshops for youth (Grades 9–12). We introduce students to the world of quantum computing in innovative ways, such as newly designed unplugged activities for teaching basic quantum computing concepts. We also take a programmatic approach and introduce students to the IBM Quantum Experience using Qiskit and Jupyter notebooks. Our contributions are as follows. First, we present creative ways to teach quantum computing to youth with little or no experience in science, technology, engineering, and mathematics areas; second, we discuss diversity and highlight various pathways into quantum computing from quantum software to quantum hardware; and third, we discuss the design and delivery of online and in-person motivational, introductory, and advanced workshops for youth.
Quantum computing is an emerging field that promises to revolutionize various domains, such as simulation optimization, data processing, and more, by leveraging the principles of quantum mechanics. This paper outlines innovative pedagogical strategies developed by university lecturers in Fin-land and Spain for teaching quantum computer programming and quantum software engineering. Our curriculum integrates essential tools and methodologies such as containerization with Docker, Qiskit, PennyLane, and Ocean SDK to provide a comprehensive learning experience. The approach consists of several steps, from introducing the fundamentals of quantum mechanics to hands-on labs focusing on practical use cases. We believe quantum computer programming is an important topic and one that is hard to teach, so having a teaching agenda and guidelines for teaching can be of great help.