A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG

arXiv:2503.02497v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Their application to quantum software development remains limited, in part because of the lack of high-quality datasets both for LLM training and as dependable knowledge sources. To bridge this gap, we introduce \textit{PennyLang}, an off-the-shelf, high-quality dataset of 3,347 PennyLane-specific qua