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 supervised binary image classification. We keep the employed quantum circuits compatible with near-term quantum devices and use two distinct methodologies: applying randomized NNs on dimensionality-reduced data and applying CNNs to full image data. We evaluate these approaches on three fully-classical data sets of increasing complexity: an artificial hypercube data set, MNIST handwritten digits and industrial images. Our central goal is to shed more light on how quantum and classical models perform for various binary classification tasks and on what defines a good quantum model. Our study involves a correlation analysis between classification accuracy and quantum model hyperparameters, and an analysis on the role of entanglement in quantum models, as well as on the impact of initial training parameters. We find classical and quantum-classical hybrid models achieve statistically-equivalent classification accuracies across most data sets with no approach consistently outperforming the other. Interestingly, we observe that quantum NNs show lower variance with respect to initial training parameters and that the role of entanglement is nuanced. While incorporating entangling gates seems advantageous, we also observe the (optimizable) entangling power not to be correlated with model performance. We also observe an inverse proportionality between the number of entangling gates and the average gate entangling power. Our study provides an industry perspective on quantum machine learning for binary image classification tasks, highlighting both limitations and potential avenues for further research in quantum circuit design, entanglement utilization, and model transferability across varied applications.

翻译：本研究比较了随机化经典与量子神经网络（NNs），以及经典与量子-经典混合卷积神经网络（CNNs）在监督式二值图像分类任务中的性能。我们确保所采用的量子电路与近期量子设备兼容，并运用两种不同方法：对降维数据应用随机化神经网络，以及对完整图像数据应用卷积神经网络。我们在三个复杂度递增的完全经典数据集上评估这些方法：人工超立方数据集、MNIST手写数字数据集和工业图像数据集。我们的核心目标是进一步揭示量子与经典模型在各种二值分类任务中的表现差异，以及如何定义优秀的量子模型。研究内容包括分类准确率与量子模型超参数之间的相关性分析、量子模型中纠缠作用的分析，以及初始训练参数的影响分析。我们发现，在大多数数据集上，经典模型与量子-经典混合模型达到统计上等效的分类准确率，没有一种方法持续优于另一种。有趣的是，我们观察到量子神经网络在初始训练参数方面表现出更低的方差，且纠缠的作用具有微妙性。虽然引入纠缠门似乎有益，但我们同时发现（可优化的）纠缠能力与模型性能无相关性。我们还观察到纠缠门数量与平均门纠缠能力之间存在反比关系。本研究从工业应用视角探讨了量子机器学习在二值图像分类任务中的应用，强调了量子电路设计、纠缠利用以及模型在不同应用间可迁移性方面的局限性和潜在研究方向。