综述生成量子电路与代码的13个系统，揭示其落地部署的关键差距。
Generative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy
- 按代码类型与训练方式分类，构建生成系统评估框架
- 仅少数系统通过硬件执行验证，多数缺乏实际部署测试
- 适合关注量子算法自动化与生成模型落地的科研人员
我们通过结构化范围综述（2026年1月至2月）对Hugging Face、arXiv及溯源信息进行了检索，识别出13个用于生成量子电路与量子代码的生成系统，以及5个支持数据集。研究沿两个维度组织：生成对象类型（Qiskit代码、OpenQASM程序、电路图）与训练范式（监督微调、验证器嵌入的强化学习、扩散/图生成、代理优化）。系统应用三层评估框架：语法正确性、语义正确性、硬件可执行性。核心发现是：所有系统均处理语法问题，多数系统在语义层面有所覆盖，但无一报告在量子硬件上完成端到端评估（第3层b），表明生成电路与实际部署之间存在显著鸿沟。注：文中‘量子代码’指量子程序产物（如QASM、Qiskit），不涵盖量子纠错码（QEC）生成。
原文摘要 · Abstract (English)
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).
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