This Innovative Practice Work in Progress Paper describes the development and assessment of a web-based simulation lab exercise introducing basic quantum computing concepts in a sophomore signals and systems course. Specifically, students make the connection between quantum computing and signals and systems theory through a comparative study of using Quantum Fourier Transforms and Fast Fourier Transforms for a speech analysis-synthesis application. In addition, quantum noise models are introduced and simulated to show their effect on computation performance. Statistics from pre/post quizzes show that there is significant knowledge improvement by completing the lab exercise.
This Innovative Practice Work in Progress Paper describes the development and assessment of a web-based simulation lab exercise introducing basic quantum computing concepts in a sophomore signals and systems course. Specifically, students make the connection between quantum computing and signals and systems theory through a comparative study of using Quantum Fourier Transforms and Fast Fourier Transforms for a speech analysis-synthesis application. In addition, quantum noise models are introduced and simulated to show their effect on computation performance. Statistics from pre/post quizzes show that there is significant knowledge improvement by completing the lab exercise.
This paper presents the development of Quantum Fourier transform (QFT) education tools in the object-oriented Java-DSP (J-DSP) simulation environment. More specifically, QFT and Inverse QFT (IQFT) user-friendly J-DSP functions are developed to expose undergraduate students to quantum computing. These functions provide opportunities to examine QFT resolution, precision (qubits), and the effects of quantum measurement noise. In our study, we also describe a laboratory exercise on QFT-based speech analysis-synthesis which has been deployed in our senior-level DSP class and in our NSF workforce development programs. The software and the laboratory exercise are evaluated using formative and summative assessments.
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 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.
As quantum computers are becoming available for experiments, their enormous potential is being recognized among college communities. The goal of introducing quantum computing in college level computer science programs is becoming attractive. However, this goal comes with opportunities and challenges. of the challenges is that quantum computing appears to require substantial background in physics as well as in computer science. However, it is hard to convince students to major in these two fields. Computer Science students with a minor in physics can also be considered. Several alternative curriculum structures are possible. A non-consecutive two-course framework is proposed here. A course on Introduction to Quantum Computing may be offered to sophomore level students. This introductory course may teach fundamental ideas about quantum computing with basic concepts behind BQP (Bounded Error Quantum Polynomial) and related topics. This introductory course may be followed up by some physics courses (on related topics such as quantum mechanics) and some computer science courses. Then, a senior level course on Advanced Quantum Computing may be offered that may include some well-known algorithms (such as Shor's Algorithm for Quantum Factorization and Grover's Algorithm for Quantum Search) and their implementation and practices on some simulators (such as QuIDE from http://www.quide.eu/) or some prototype quantum processors (such as IBM's prototype quantum processors). For advanced level courses, some course materials are available from various sources (such as MIT OpenCourseWare). The greater challenge would be in teaching the introductory course mentioned earlier that motivates students towards investing time and efforts in learning quantum computing and related physics, math and computational topics. The challenges of teaching the introductory course should not be underestimated. Some flexible strategies can be suggested for this course so that the biggest challenges can be overcome. Some initial assessments of the students may help adjusting the introduction strategies with student's preparation. What is recommended is that the instructor should be able to adjust the coverage of topics, rigor and depth of treatment. That is, the introductory course should not be rigidly defined. It should be recognized that the main emphasis of the introductory course is actually instilling motivations and curiosity in student's mind about potentials of quantum computing through adaptive teaching of some selected topics where traditional digital computers do not provide tractable solutions. Ideally a balance could be achieved among aspects of depth of treatment, rigor, inspiration, motivation and coverage of topics. Students should be able to realize that the emerging quantum computing technology would be able to provide elegant solutions to some of the classical intractable problems combining ideas from physics, math and computer science in an innovative way. One size fits all is not a good approach and discussion of issues may help us face the challenges better by pragmatically considering a range of possible alternatives. Resources that are currently available may also need to be reviewed for the purpose of introducing quantum computing adequately.
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.
Contribution:QC has potential for significant speedup in data and signal analysis in industrial, academic, and government applications. This article introduces new software tools integrated in the online Java-digital signal processing (J-DSP) simulation platform for undergraduate (UG) exposition to quantum computing (QC).Background:In response to national calls for early UG exposition to QC, we develop new J-DSP functionality and quantum Fourier Transform (QFT) tools. In addition, we describe the deployment and evaluation of a hands-on online exercise to introduce UG students to QFT-based signal analysis-synthesis.Intended Outcomes:The primary goal is to expose students to QC by leveraging the new interactive QFT tools in J-DSP. These tools enable rapid exploration of key concepts, such as QFT resolution, qubit precision, and noise effects in signal analysis. Students use the QFT functions in an online QC exercise that we designed.Application Design:The J-DSP interactive online education tools are designed for quick UG exposition to QC. A QFT exercise on speech analysis-synthesis is designed and evaluated. UG Students explore in single hands-on exposition session simple compression steps, including QFT peak picking used in audio coding applications. The QFT functions are designed for quick deployment and UG exposition outside the classroom.Findings:Evaluations of the QFT tools are conducted with pre and post-assessments and interviews examining satisfaction with the system and impact on knowledge acquisition. To analyze the impact of this supplementary QC exposition tool, students were recruited from two senior UG signal processing classes and from a summer research experience for UG (REU) cohort. A paired samples t-test was used to examine changes from pre to post-assessments. The students indicated increased level of understanding at post-assessments. However, a few students reported having at times difficulty with the system. However, the majority of the participants indicated the program was suitable for introductory/exposition sessions.Key Insight:Generally, students found the J-DSP quantum tools effective and easy to use and suggested that adding more qualitative video materials and applying QC to other digital signal processing (DSP) functions would further enhance the learning experience.