论文标题
有效的基于CNN学习的有效量子提取
Efficient Quantum Feature Extraction for CNN-based Learning
论文作者
论文摘要
最近的工作已开始探索作为一般函数近似值的参数化量子电路（PQC）的潜力。在这项工作中，我们提出了一个量子古典的深网结构，以增强经典的CNN模型可区分性。卷积层使用线性过滤器来扫描输入数据。此外，我们构建了PQC，它是一个更有效的函数近似器，具有更复杂的结构，可捕获接受场内的特征。通过以与CNN相似的方式将PQC滑到输入上，从而获得了特征图。我们还为提出的模型提供了培训算法。我们设计中使用的混合模型通过数值模拟验证。我们演示了MNIST上合理的分类性能，并将表演与不同设置中的模型进行比较。结果表明，具有高表达性的ANSATZ模型可实现较低的成本和更高的准确性。
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.
