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

Umar Farooq, Krishna Upadhyay

TL;DR

The paper tackles the challenge of teaching quantum computing to computer science students by blending conceptual learning with hands-on programming in a lab-integrated course at LSU. It introduces a two-stage progression from Quantum Without Linear Algebra (QWLA) to IBM Qiskit, aligning weekly labs with foundational lectures to bridge intuition and formal implementation. Findings indicate increased student confidence and fluency across representations, though persistent difficulties remain in debugging probabilistic systems and interpreting measurements. The study offers a replicable instructional framework and actionable insights for balancing workload, scaffolding concepts, and incorporating feedback and visualization tools to enhance QC education in CS programs.

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.

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

TL;DR

The paper tackles the challenge of teaching quantum computing to computer science students by blending conceptual learning with hands-on programming in a lab-integrated course at LSU. It introduces a two-stage progression from Quantum Without Linear Algebra (QWLA) to IBM Qiskit, aligning weekly labs with foundational lectures to bridge intuition and formal implementation. Findings indicate increased student confidence and fluency across representations, though persistent difficulties remain in debugging probabilistic systems and interpreting measurements. The study offers a replicable instructional framework and actionable insights for balancing workload, scaffolding concepts, and incorporating feedback and visualization tools to enhance QC education in CS programs.

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.

Paper Structure

This paper contains 17 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Theoretical Foundation
  4. Core Ideas in QC Education
  5. Conceptual Change and Evolution
  6. Progressive Shift to Quantum Programming
  7. Instructional Design
  8. Learning Approach
  9. Course Structure
  10. Assessment and Learning Outcomes
  11. Implementation and Delivery
  12. Lecture Format
  13. Laboratory Sessions
  14. Projects
  15. Student Interaction and Support
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Comparison of classical and quantum reasoning. In the classical view, a bit is either 0 or 1, while in the quantum view, a qubit can exist in a superposition of both states until measurement. The figure illustrates the conceptual shift students experience when learning to reason about state, probability, and measurement in quantum programs.