Q-Bridge: Code Translation for Quantum Machine Learning via LLMs

TL;DR

Q-Bridge is a novel LLM-guided framework that translates classical machine learning code into quantum machine learning code, supported by a large dataset and efficient training methods.

Contribution

It introduces the first reproducible framework and dataset for translating classical ML code to quantum ML using LLMs, enabling scalable quantum AI development.

Findings

Successful direct translation from CML to QML demonstrated.

Structural alignment observed between classical and quantum code.

Q-Bridge maintains correctness and supports architectural exploration.

Abstract

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.