标题： 量子和混合卷积神经网络中不同编码、尝试态和测量方法的影响的实用见解 显示英文标题

标题： Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks

摘要： 本研究调查了参数化量子电路（PQCs）在量子和混合卷积神经网络（HQNN 和 QCNN）架构中的设计选择，应用于使用 EuroSAT 数据集的卫星图像分类任务。 我们系统地评估了数据编码技术、变分回路和测量在约 500 种不同模型配置中的性能影响。 我们的分析揭示了对模型性能的影响层次。 对于与它们直接的经典对应物（例如，移除了 PQCs 的相同架构）进行基准测试的混合架构，数据编码策略是主要因素，不同嵌入的验证准确率变化超过 30%。 相比之下，变分回路和测量基的选择影响较小，验证准确率的变化保持在 5% 以下。 对于仅限于幅度编码的纯量子模型，性能最依赖于测量协议和数据到幅度的映射。 测量策略使验证准确率变化高达 30%，而编码映射则变化约 8 个百分点。 显示英文摘要

摘要： 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 ans\"atze, 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 ans\"atze 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.