用大模型设计更高效量子电路，提升金融数据生成效果
LLM-Guided Ansätze Design for Quantum Circuit Born Machines in Financial Generative Modeling
- 用大模型根据硬件条件生成适配的量子电路架构
- 在12量子比特真实设备上，电路深度更浅且生成效果更好
- 适合关注量子计算落地的金融与算法研究者
使用量子电路玻恩机（QCBM）进行量子生成建模展现出实现实际量子优势的潜力。然而，在噪声中等规模量子（NISQ）设备上，如何发现兼具表达力与硬件效率的量子线路结构仍是关键挑战。本文提出一种基于提示词的框架，利用大语言模型（LLMs）生成考虑硬件特性的QCBM架构。提示词基于量子比特连通性、门错误率和硬件拓扑，通过包含KL散度、电路深度和有效性在内的迭代反馈进行优化。我们在日本政府债券（JGB）利率日变动的金融建模任务上评估该方法。结果表明，在真实IBM量子硬件上使用12个量子比特执行时，由大模型生成的线路显著更浅，并在生成性能上优于标准基线。这些发现验证了大模型驱动量子架构搜索的实际价值，为近期量子设备上的鲁棒可部署生成模型指明了可行路径。
原文摘要 · Abstract (English)
Quantum generative modeling using quantum circuit Born machines (QCBMs) shows promising potential for practical quantum advantage. However, discovering ansätze that are both expressive and hardware-efficient remains a key challenge, particularly on noisy intermediate-scale quantum (NISQ) devices. In this work, we introduce a prompt-based framework that leverages large language models (LLMs) to generate hardware-aware QCBM architectures. Prompts are conditioned on qubit connectivity, gate error rates, and hardware topology, while iterative feedback, including Kullback-Leibler (KL) divergence, circuit depth, and validity, is used to refine the circuits. We evaluate our method on a financial modeling task involving daily changes in Japanese government bond (JGB) interest rates. Our results show that the LLM-generated ansätze are significantly shallower and achieve superior generative performance compared to the standard baseline when executed on real IBM quantum hardware using 12 qubits. These findings demonstrate the practical utility of LLM-driven quantum architecture search and highlight a promising path toward robust, deployable generative models for near-term quantum devices.
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