Quantum computing is increasingly practiced through programming, yet most educational offerings emphasize algorithmic or framework-level use rather than software engineering concerns such as testing, abstraction, tooling, and lifecycle management.
This paper reports on the design and first offering of a cross-listed undergraduate--graduate course that frames quantum computing through a software engineering lens, focusing on early-stage competence relevant to software engineering practice. The course integrates foundational quantum concepts with software engineering perspectives, emphasizing executable artifacts, empirical reasoning, and trade-offs arising from probabilistic behaviour, noise, and evolving toolchains. Evidence is drawn from instructor observations, supplemented by anonymous student feedback, a background survey, and inspection of student work.
Despite minimal prior exposure to quantum computing, students were able to engage productively with quantum software engineering topics once a foundational understanding of quantum information and quantum algorithms, expressed through executable artifacts, was established. This experience report contributes a modular course design, a scalable assessment model for mixed academic levels, and transferable lessons for software engineering educators developing quantum computing curricula.
Quantum computing is increasingly practiced through programming, yet most educational offerings emphasize algorithmic or framework-level use rather than software engineering concerns such as testing, abstraction, tooling, and lifecycle management.
This paper reports on the design and first offering of a cross-listed undergraduate--graduate course that frames quantum computing through a software engineering lens, focusing on early-stage competence relevant to software engineering practice. The course integrates foundational quantum concepts with software engineering perspectives, emphasizing executable artifacts, empirical reasoning, and trade-offs arising from probabilistic behaviour, noise, and evolving toolchains. Evidence is drawn from instructor observations, supplemented by anonymous student feedback, a background survey, and inspection of student work.
Despite minimal prior exposure to quantum computing, students were able to engage productively with quantum software engineering topics once a foundational understanding of quantum information and quantum algorithms, expressed through executable artifacts, was established. This experience report contributes a modular course design, a scalable assessment model for mixed academic levels, and transferable lessons for software engineering educators developing quantum computing curricula.
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.
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 software engineering (QSE) requires understanding application requirements, quantum programs and algorithms, quantum computation paradigms, executions, and deployment. However, relevant quantum computing (QC) concepts are often defined across physical, mathematical, and paradigm-specific levels of abstraction, making it difficult for the QSE community and hence proposed methods to refer to them consistently. To this end, we present QSoftCore, a conceptual model that reorganizes established QC concepts from a software engineering perspective and connects quantum applications, computational problems, algorithms, configurations, programs, and paradigm-specific computation specifications with state evolution, execution results, and backends. Our assessment against QSE literature across key software engineering activities indicates that QSoftCore provides shared QC referents for quantum computations, executions, deployment configurations, backends, and measurement evidence across the QSE lifecycle.
There is a growing need for scientists and engineers with Quantum Information Science and Technology (QIST) skills.To address this need for 'quantum aware' graduates we describe a two-course sequence in quantum computing suitable for undergraduate students studying electrical engineering, physics, and computer science.Our approach is unique in that it aims to take full advantage of the diverse backgrounds of second and third year undergraduate students from these three STEM disciplines.Students will work in interdisciplinary teams to (1) understand the physics and the computational theory relevant to quantum computing, (2) develop computer code that simulates a quantum computer, (3) understand the relevance and importance of existing quantum computing algorithms, and (4) appreciate the need for future research in quantum computing.Our approach is informed in part by our experience working with a pair of undergraduate students this past summer on the development of a Python-based quantum computer simulator.This experience showed that building a simulator was an effective way to teach the theory underlying quantum computing.Building a simulator also provides an excellent foundation upon which to explore student-accessible research projects in quantum computing, which is a high-impact educational practice.
Quantum computing, grounded in principles such as superposition and entanglement, is revolutionising the design, testing, and understanding of software. While the community is working on its technical challenges, the human and collaborative aspects have received little attention. This vision paper outlines a research agenda for human-centred quantum software engineering (HC-QSE). Building on current QSE foundations, the agenda spans three interrelated themes: understanding practice, designing support, and embedding responsibility. The first calls for studying how interdisciplinary teams construct and share mental models of quantum behaviour, collaborate across disciplinary boundaries, and reason under uncertainty inherent in probabilistic computation. The second focuses on creating tools, workflows, and learning environments that enhance comprehension, interpretability, and shared accountability among practitioners, educators, and learners. The third integrates ethical reflection and equity considerations into the design and governance of quantum software ecosystems. By centering human experience within QSE, this agenda encourages cooperative, reflective, and ethically grounded approaches to building quantum software.
Quantum computing promises transformative computational capabilities; however, the absence of structured and standardized software engineering significantly limits its practical scalability. Quantum Software Engineering (QSE) remains fragmented, tool-centric, and largely experimental. This paper proposes a structured Quantum Software Engineering Lifecycle Framework that systematically integrates requirements engineering, hybrid design, quantum development, verification, deployment, and governance. To validate the proposed lifecycle, a two-stage evaluation was conducted. First, a Delphi-based expert validation involving 15 domain experts assessed clarity, feasibility, scalability, and hybrid applicability. Second, a simulation-based comparative analysis using Qiskit and PennyLane evaluated the lifecycle against ad-hoc development workflows across variational and search-based quantum algorithms. Results demonstrate a 24% reduction in development time, 15–18% improvement in execution fidelity, and significant gains in modularity, reusability, and tool interoperability. These findings confirm that adopting a structured lifecycle enables more reliable, scalable, and sustainable quantum software development, positioning QSE as a mature engineering discipline rather than purely experimental practice.