PennyLang: Pioneering LLM-Based Quantum Code Generation with a Novel PennyLane-Centric Dataset

원문 초록

Large Language Models (LLMs) offer remarkable capabilities in code generation, natural language processing, and domain-specific reasoning. However, their application in quantum software development remains underexplored, particularly for PennyLane-a leading framework for hybrid quantum-classical computing. To address this gap, we introduce a novel, high-quality dataset comprising 3,347 PennyLane-specific quantum code samples and contextual descriptions, specifically curated to support LLM training and fine-tuning for quantum code assistance. Our contributions are threefold: (1) the automatic construction and open-source release of a comprehensive PennyLane dataset derived from textbooks, official documentation, and open-source repositories; (2) a structured methodology for data curation, annotation, and formatting to enhance LLM usability and relevance; and (3) a rigorous evaluation of code generation capabilities using both baseline Retrieval-Augmented Generation (RAG) and a GraphRAG-enhanced pipeline. Using the PennyLang framework, we demonstrate that GraphRAG, when applied to a GPT-4o Mini model, substantially outperforms standard prompting and baseline RAG. Accuracy improves from 20.5% (without RAG) to 58.2% with GraphRAG, showcasing its effectiveness in reducing hallucinations and improving code correctness in quantum programming tasks. Compared to prior efforts focused largely on Qiskit, our work expands LLM-based assistance to the PennyLane ecosystem, contributing practical tools and reproducible methodologies for advancing AI-assisted quantum software development.