标题： 数字-模拟量子卷积神经网络用于图像分类 显示英文标题

标题： Digital-analog quantum convolutional neural networks for image classification

摘要： 我们提出数字-模拟量子核，以增强图像分类中复杂特征的检测。 我们考虑源自中性原子量子处理器中原始伊辛相互作用的多体纠缠模拟块，并将单独操作作为数字步骤来实现该协议。 为了进一步提高对复杂特征的检测，我们根据硬件约束改变量子比特连接性，应用多个量子核。 一种结合不可训练量子核和标准卷积神经网络的架构被用于分类现实中的医学图像，包括乳腺癌和肺炎疾病，且参数数量显著减少。 尽管如此，该模型的表现优于其经典对应模型，并根据公共基准实现了可比较的指标。 这些发现证明了数字-模拟编码的相关性，为在图像识别中超越经典模型并接近量子优势领域铺平了道路。 显示英文摘要

摘要： We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improving the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines non-trainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings demonstrate the relevance of digital-analog encoding, paving the way for surpassing classical models in image recognition approaching us to quantum-advantage regimes.