Q-Bridge: Code Translation for Quantum Machine Learning via LLMs
This work provides the first reproducible framework and dataset for LLM-driven quantum code translation, addressing the lack of standardized resources for quantum machine learning development.
Q-Bridge introduces an LLM-guided framework for translating classical machine learning code into executable quantum machine learning variants, achieving faithful and interpretable quantum code generation across diverse architectures. The framework includes a self-involving pipeline that expands a verified seed codebase into a large-scale dataset, CML-2-QML.
Large language models have recently shown potential in bridging the gap between classical machine learning and quantum machine learning. However, the lack of standardized, high-quality datasets and robust translation frameworks limits progress in this domain. We introduce Q-Bridge, an LLM-guided code translation framework that systematically converts CML implementations into executable QML variants. Our approach builds on a self-involving pipeline that iteratively expands a verified seed codebase into a large-scale dataset, CML-2-QML, integrating verifiable and unverifiable code pairs. The Q-Bridge model is fine-tuned using supervised LoRA adaptation for scalable and memory-efficient training, achieving faithful and interpretable quantum code generation across diverse architectures. Empirical analysis confirms the feasibility of direct CML-to-QML translation and reveals consistent structural alignment between classical and quantum paradigms. Case studies further demonstrate that Q-Bridge can maintain deterministic correctness and also enable creative architectural exploration. This work establishes the first reproducible framework and dataset for LLM-driven quantum code translation, offering a foundation for scalable quantum AI development.