Teaching Quantum Computing through Lab-Integrated Learning: Bridging Conceptual and Computational Understanding

TL;DR

This paper describes a lab-integrated course at LSU that combines intuitive conceptual teaching with practical programming to improve understanding of quantum computing, highlighting benefits and ongoing challenges.

Contribution

It introduces a novel lab-integrated teaching approach for quantum computing that combines conceptual and computational learning for undergraduates and graduates.

Findings

Hands-on labs increased student confidence and conceptual clarity.

Students faced persistent challenges in debugging and understanding probabilistic outcomes.

The course effectively integrated intuitive concepts with professional quantum programming tools.

Abstract

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.