Recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as general function approximators. In this work, we propose a quantum-classical deep network structure to enhance classical CNN model discriminability. The convolutional layer uses linear filters to scan the input data. Moreover, we build PQC, which is a more potent function approximator, with more complex structures to capture the features within the receptive field. The feature maps are obtained by sliding the PQCs over the input in a similar way as CNN. We also give a training algorithm for the proposed model. The hybrid models used in our design are validated by numerical simulation. We demonstrate the reasonable classification performances on MNIST and we compare the performances with models in different settings. The results disclose that the model with ansatz in high expressibility achieves lower cost and higher accuracy.
翻译:最近的工作已经开始探索半调量子电路(PQCs)作为一般功能近效器的潜力。 在这项工作中,我们提出一个量子古典深网络结构,以加强古典CNN模型的分布性。 卷发层使用线性过滤器扫描输入数据。 此外,我们建造了PQC,这是一个更强大的功能近效器,其结构更复杂,以捕捉可接收域内的特征。 地貌图是通过以与CNN类似的方式将PQs对输入进行滑动而获得的。 我们还为拟议的模型提供了一种培训算法。 我们设计中使用的混合模型经过数字模拟验证。 我们展示了MMSIS的合理分类性能,我们将不同环境中的性能与模型进行比较。结果显示,高清晰度的atz模型成本较低,准确度更高。