2025/10/17 by Umar Farooq, Farooq, Umar, Krishna Upadhyay +1 · 1 voice
Computer Science · Engineering · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Nanotechnology research and applications #Quantum Computing Algorithms and Architecture #Space Technology and Applications #cs.CY
paper · pdf · doi:10.48550/arxiv.2511.02844
openalex publication_date 2025/10/17 · arxiv published 2025/10/17 · openalex created_date 2025/11/07 · arxiv updated 2026/01/25 · openalex updated_date 2026/07/28
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.