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.

翻译：本研究探讨了应用于EuroSAT数据集卫星图像分类任务的量子与混合卷积神经网络（HQNN与QCNN）架构中参数化量子电路的设计选择。我们系统评估了约500种不同模型配置中数据编码技术、变分拟设及测量策略对性能的影响。分析结果显示各因素对模型性能的影响存在明确层级关系。在与其直接经典等效架构（即移除参数化量子电路的相同架构）进行基准对比的混合架构中，数据编码策略是主导因素，不同嵌入方式可使验证准确率差异超过30%。相比之下，变分拟设与测量基的选择影响相对有限，验证准确率差异保持在5%以下。对于仅限于振幅编码的纯量子模型，性能主要取决于测量协议与数据-振幅映射方式：测量策略可使验证准确率差异高达30%，而编码映射方式的影响约为8个百分点。