Quantum Circuit Learning: HCI-Driven Visualization and Agentic AI Assistance for Software Engineers

Abstract

Although most software engineering work today targets classical systems, quantum computing has become an area of growing interest for developers who want to understand alternative computational models. This thesis addresses the difficulty of learning to construct and reason about quantum circuits by focusing on small, illustrative circuit-based algorithms (Bell states, GHZ states, and a reduced Grover’s search) as structured examples around which an interactive learning platform is built. The aim is to support step-by-step understanding of these circuits while investigating how large language models (LLMs) can assist with circuit generation and explanation. The work presents a web-based platform that allows users to incrementally build circuits, visualize their layer-by-layer evolution, and inspect key properties such as gate composition and measurement probabilities. On top of this platform, two LLM-based assistance strategies are implemented: a stateless baseline that operates solely on naturallanguage prompts, and a context-aware, agentic mode that receives structured information about the current circuit and simulation results. Their behavior is compared in an offline evaluation on a fixed set of exemplar tasks, using a rubric that scores correctness of the resulting circuits, relevance of explanations to the actual circuit, and pedagogical clarity. The results indicate that the agentic, state-aware integration produces more accurate, better-grounded, and more instructionally useful outputs than the stateless baseline on the examined tasks. The thesis contributes an example-driven approach to quantum computing education for software engineers, an inspection-oriented platform design, and empirical evidence that platform-state-aware LLM agents can provide higher-quality assistance than purely prompt-based interaction in this domain.