论文标题
量子卷积神经网络（QCNN）的教程
A Tutorial on Quantum Convolutional Neural Networks (QCNN)
论文作者
论文摘要
卷积神经网络（CNN）是计算机视觉中的流行模型，具有充分利用数据相关信息的优势。但是，如果数据或模型的给定维度太大，CNN有效地学习有效学习。量子卷积神经网络（QCNN）为使用量子计算环境或改善现有学习模型的性能的方向提供了解决问题的新解决方案。提出的第一项研究提出了一个模型，以通过将CNN的结构应用于量子计算环境，从而有效地解决量子物理和化学中的分类问题。该研究还提出了可以使用多尺度纠缠重归于ANSATZ（MERA）的O（log（log（n））深度计算的模型。第二项研究介绍了一种通过在现有计算机视觉中使用的CNN学习模型中添加量子计算来提高模型性能的方法。该模型也可以在小量子计算机中使用，并且可以通过在CNN模型中添加量子卷积层或用卷积层替换混合学习模型。本文还验证了通过使用Tensorflow量子平台使用MNIST数据集进行训练与CNN相比，QCNN模型是否能够有效学习。
Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn efficiently if the given dimension of data or model becomes too large. Quantum Convolutional Neural Network (QCNN) provides a new solution to a problem to solve with CNN using a quantum computing environment, or a direction to improve the performance of an existing learning model. The first study to be introduced proposes a model to effectively solve the classification problem in quantum physics and chemistry by applying the structure of CNN to the quantum computing environment. The research also proposes the model that can be calculated with O(log(n)) depth using Multi-scale Entanglement Renormalization Ansatz (MERA). The second study introduces a method to improve the model's performance by adding a layer using quantum computing to the CNN learning model used in the existing computer vision. This model can also be used in small quantum computers, and a hybrid learning model can be designed by adding a quantum convolution layer to the CNN model or replacing it with a convolution layer. This paper also verifies whether the QCNN model is capable of efficient learning compared to CNN through training using the MNIST dataset through the TensorFlow Quantum platform.
