Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images

In this study, we compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the task of binary image classification. We use two distinct methodologies: using randomized NNs on dimensionality-reduced data, and applying CNNs to full image data. We evaluate these approaches on three data sets of increasing complexity: an artificial hypercube dataset, MNIST handwritten digits and real-world industrial images. We analyze correlations between classification accuracy and quantum model hyperparameters, including the number of trainable parameters, feature encoding methods, circuit layers, entangling gate type and structure, gate entangling power, and measurement operators. For random quantum NNs, we compare their performance against literature models. Classical and quantum/hybrid models achieved statistically equivalent classification accuracies across most datasets, with no approach demonstrating consistent superiority. We observe that quantum models show lower variance with respect to initial training parameters, suggesting better training stability. Among the hyperparameters analyzed, only the number of trainable parameters showed a positive correlation with the model performance. Around 94% of the best-performing quantum NNs had entangling gates, although for hybrid CNNs, models without entanglement performed equally well but took longer to converge. Cross-dataset performance analysis revealed limited transferability of quantum models between different classification tasks. Our study provides an industry perspective on quantum machine learning for practical image classification tasks, highlighting both current limitations and potential avenues for further research in quantum circuit design, entanglement utilization, and model transferability across varied applications.