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

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.