对比了量子卷积网络中编码、电路结构和测量方式的影响，发现编码策略最关键。
Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
- 系统测试500种配置，发现数据编码对混合模型影响最大
- 不同编码使准确率波动超30%，而电路设计影响不足5%
- 适合关注量子机器学习设计的工程师和研究者
本研究针对量子与混合卷积神经网络（HQNN和QCNN）中的参数化量子电路（PQC）设计选择展开分析，应用于使用EuroSAT数据集的卫星图像分类任务。通过约500种不同模型配置的系统评估，揭示了各项设计因素对性能的影响层级。在混合架构中，数据编码策略是决定性因素，不同嵌入方式导致验证准确率波动超过30%；而变分电路结构和测量基的选择影响较小，准确率变化低于5%。对于纯量子模型（仅限振幅编码），性能主要依赖测量协议和数据到振幅的映射：测量策略可使准确率变化达30%，编码映射则带来约8个百分点的差异。
原文摘要 · Abstract (English)
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to the task of satellite image classification using the EuroSAT dataset. We systematically evaluate the performance implications of data encoding techniques, variational ansätze, and measurement in approx. 500 distinct model configurations. Our analysis reveals a clear hierarchy of influence on model performance. For hybrid architectures, which were benchmarked against their direct classical equivalents (e.g. the same architecture with the PQCs removed), the data encoding strategy is the dominant factor, with validation accuracy varying over 30% for distinct embeddings. In contrast, the selection of variational ansätze and measurement basis had a comparatively marginal effect, with validation accuracy variations remaining below 5%. For purely quantum models, restricted to amplitude encoding, performance was most dependent on the measurement protocol and the data-to-amplitude mapping. The measurement strategy varied the validation accuracy by up to 30% and the encoding mapping by around 8 percentage points.
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