发表机构
Rochester Institute of Technology（罗切斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于演化框架EXAQC自动发现量子电路，生成紧凑混合量子-经典模块用于图像分类，在多个数据集上以更少参数达到与经典网络相当的准确率。
AI 中文摘要
混合量子-经典神经网络将参数化量子电路（PQCs）与成熟的深度学习架构相结合，但其性能在很大程度上取决于量子电路架构的选择，而这一选择目前仍主要依赖人工。现有方法大多依赖手工设计或固定的电路拟设，需要预先指定电路结构、门组成和量子比特连接方式，且无法保证其适合目标任务。这一局限性在图像分类中尤为突出，因为量子电路必须转换经典网络提取的特征，同时保持足够紧凑以支持实际训练，而通用的、与任务无关的拟设难以同时满足这些要求。我们将EXAQC（一个用于自动化量子电路发现的演化框架）扩展到图像分类。EXAQC将参数化量子电路演化为中间处理模块，同时保留经典特征提取和预测层。在MNIST、Fashion-MNIST和CIFAR-10数据集上，EXAQC分别达到98.42%、90.62%和85.47%的准确率，同时使用的门数量与其他量子架构搜索方法相当。与经典网络相比，演化出的混合模型在保持相当准确率的同时，可训练参数大幅减少，在CIFAR-10上达到85.68%的准确率，其参数比10层CNN少25倍以上。编码方式的选择也很重要：基于旋转的编码（RX、RY、U3）在CIFAR-10上比振幅编码高出22-25个百分点。这些结果表明，自动化电路发现能够产生紧凑的量子模块，可在视觉架构中替代较大的经典组件，同时保持有竞争力的准确率。
英文摘要
Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depends strongly on the choice of quantum circuit architecture, a choice that remains largely manual. Most existing approaches rely on hand-designed or fixed circuit ansätze, requiring circuit structure, gate composition, and qubit connectivity to be specified in advance with no guarantee that they suit the task. This limitation is especially acute in image classification, where quantum circuits must transform features extracted by classical networks while remaining compact enough for practical training, requirements that generic, task-agnostic ansätze are unlikely to satisfy simultaneously. We extend EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification. EXAQC evolves PQCs as intermediate processing modules while retaining classical feature-extraction and prediction layers. On MNIST, Fashion-MNIST, and CIFAR-10, EXAQC achieves 98.42%, 90.62%, and 85.47% accuracy, respectively, while using comparable gate counts to other quantum architecture-search methods. Against classical networks, evolved hybrid models maintain comparable accuracy with substantially fewer trainable parameters, reaching 85.68% on CIFAR-10 with over 25$\times$ fewer parameters than a 10-layer CNN. Encoding choice also matters: rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10. These results demonstrate that automated circuit discovery yields compact quantum modules that can replace larger classical components in vision architectures while retaining competitive accuracy.
CommentsUnder Review at The Fifteenth International Conference on Learning Representations 2027
