标题： 通过大型语言模型自动设计量子特征映射 显示英文标题

标题： Automating quantum feature map design via large language models

摘要： 量子特征映射是量子机器学习的关键组成部分，它们将经典数据编码为量子态，以利用高维希尔伯特空间的表达能力。 尽管它们在理论上具有前景，但设计出在实际应用中优于经典方法的量子特征映射仍然是一个开放性挑战。 在本工作中，我们提出了一种代理系统，该系统使用大型语言模型自主生成、评估和优化量子特征映射。 该系统由五个组件组成：生成、存储、验证、评估和评审。 通过这些组件，它迭代地改进量子特征映射。 在MNIST数据集上的实验表明，它可以成功发现并优化特征映射，而无需人工干预。 生成的最佳特征映射优于现有的量子基线，并在MNIST、Fashion-MNIST和CIFAR-10上与经典核方法相比达到了具有竞争力的准确率。 我们的方法提供了一个探索数据集自适应量子特征的框架，并突显了LLM驱动自动化在量子算法设计中的潜力。 显示英文摘要

摘要： Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces. Despite their theoretical promise, designing quantum feature maps that offer practical advantages over classical methods remains an open challenge. In this work, we propose an agentic system that autonomously generates, evaluates, and refines quantum feature maps using large language models. The system consists of five component: Generation, Storage, Validation, Evaluation, and Review. Using these components, it iteratively improves quantum feature maps. Experiments on the MNIST dataset show that it can successfully discover and refine feature maps without human intervention. The best feature map generated outperforms existing quantum baselines and achieves competitive accuracy compared to classical kernels across MNIST, Fashion-MNIST, and CIFAR-10. Our approach provides a framework for exploring dataset-adaptive quantum features and highlights the potential of LLM-driven automation in quantum algorithm design.