Visualization enhances Problem Solving in multi-Qubit Systems

TL;DR

This study shows that visualizing multi-qubit systems with Dimensional Circle Notation improves problem-solving performance and reduces cognitive load for learners with limited quantum physics experience, especially when they transition less between representations.

Contribution

It investigates the conditions under which visualization benefits quantum problem solving, highlighting the role of representational competence and transition frequency between notations.

Findings

DCN increases performance for inexperienced learners.

DCN reduces extraneous cognitive load for certain participants.

Transitions between notations influence visualization benefits.

Abstract

Quantum Information Science (QIS) is a vast, diverse, and abstract field. In consequence, learners face many challenges. Science, Technology, Engineering, and Mathematics (STEM) education research has found that visualizations are valuable to aid learners in complex matters. The conditions under which visualizations pose benefits are largely unexplored in QIS education. In this eye-tracking study, we examine the conditions under which the visualization of multi-qubit systems with the Dimensional Circle Notation (DCN) in addition to the mathematical symbolic Dirac Notation (DN) is associated with a benefit for solving problems on the ubiquitously used Hadamard gate operation in terms of performance, Extraneous Cognitive Load (ECL) and Intrinsic Cognitive Load (ICL). We find that DCN increases performance and reduces cognitive load for participants with little experience in quantum physics. In addition, representational competence is able to predict reductions in ECL with DCN, but not performance or ICL. Analysis of the eye-tracking results indicates that task solvers with more transitions between DN and DCN benefit less from the visualization. We discuss the generalizability of the results and practical implications.