EXAQC 用进化搜索自动设计量子电路，图像分类精度达 98.42%
Evolving Hybrid Quantum-Classical Architectures for Image Classification
这篇论文用进化算法自动搜索量子电路结构，在 CIFAR-10 上参数比 10 层 CNN 少 25 倍还保持精度，做量子机器学习的可以看看编码选择的结论。
arXiv 论文将 EXAQC 进化框架扩展到图像分类任务，自动搜索参数化量子电路（PQCs）作为中间处理模块。在 MNIST、Fashion-MNIST 和 CIFAR-10 上分别取得 98.42%、90.62% 和 85.47% 的准确率，门数量与其他量子架构搜索方法相当。与 10 层 CNN 相比，进化出的混合模型在 CIFAR-10 上以超过 25 倍更少的可训练参数达到 85.68% 的准确率。实验还发现 RX、RY、U3 等旋转编码在 CIFAR-10 上比振幅编码高出 22-25 个百分点。
Evolving Hybrid Quantum-Classical Architectures for Image Classification
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.
