We review thirteen generative systems and five supporting datasets for quantum circuit and quantum code generation, identified through a structured scoping review of Hugging Face, arXiv, and provenance tracing (January-February 2026). We organize the field along two axes: artifact type (Qiskit code, OpenQASM programs, circuit graphs); crossed with training regime (supervised fine-tuning, verifier-in-the-loop RL, diffusion/graph generation, agentic optimization); and systematically apply a three-layer evaluation framework covering syntactic validity, semantic correctness, and hardware executability. The central finding is that while all reviewed systems address syntax and most address semantics to some degree, none reports end-to-end evaluation on quantum hardware (Layer 3b), leaving a significant gap between generated circuits and practical deployment. Scope note: quantum code refers throughout to quantum program artifacts (QASM, Qiskit); we do not cover generation of quantum error-correcting codes (QEC).

翻译：本文通过结构化范围综述方法（涵盖Hugging Face平台、arXiv预印本库及溯源追踪，时间跨度为2026年1月至2月），系统梳理了十三种生成式系统及五个支撑数据集在量子电路与量子代码生成领域的研究进展。我们通过双重维度构建领域分类体系：其一是生成产物类型（Qiskit代码、OpenQASM程序、电路图）；其二是训练范式（监督微调、验证器在环强化学习、扩散/图生成、智能体优化）。在此基础上，我们系统化应用涵盖语法有效性、语义正确性及硬件可执行性的三层评估框架。核心发现表明：尽管所有被综述系统均能处理语法层面问题，且多数能在一定程度上处理语义问题，但尚无系统报告在量子硬件上的端到端评估结果（第三层评估的硬件执行阶段），这导致生成电路与实际部署之间存在显著断层。范围说明：本文所涉"量子代码"均指量子程序产物（QASM、Qiskit），不包含量子纠错码的生成研究。