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arXiv 2405.00548quant-ph
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用于图像分类的数字-模拟量子卷积神经网络

Digital-analog quantum convolutional neural networks for image classification

Anton Simen, Carlos Flores-Garrigos, Narendra N. Hegade, Iraitz Montalban, Yolanda Vives-Gilabert, Eric Michon, Qi Zhang, Enrique Solano, José D. Martín-Guerrero

备注：
7 pages, 3 figures
发表信息：
Phys. Rev. Research 6, L042060 (2024)
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AI总结：

本文提出数字-模拟量子核与卷积神经网络结合的架构，利用中性原子量子处理器的伊辛相互作用和可变量子比特连接性，在显著减少参数量的情况下对乳腺癌和肺炎医学图像实现优于经典模型的分类性能。

摘要：

我们提出数字-模拟量子核，以增强图像分类中对复杂特征的检测。我们考虑源自中性原子量子处理器中固有伊辛相互作用的多方纠缠模拟块，并将单个操作作为数字步骤来实现该协议。为进一步改善对复杂特征的检测，我们根据硬件约束改变量子比特连接性，从而应用多个量子核。一种结合不可训练量子核与标准卷积神经网络的架构被用于分类来自乳腺癌和肺炎疾病的真实医学图像，且参数量显著减少。尽管如此，该模型表现出优于其经典对应模型的性能，并根据公开基准达到了可比的指标。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铺平了道路，使我们接近量子优势区域。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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