Application of image recognition algorithm based on quantum convolutional neural network

In this paper, an innovative Quantum image recognition algorithm, Quantum Auto Gradient Descent (QAGD), is proposed. The algorithm combines the advantages of quantum convolutional neural networks (QCNN), autoencoders (AE) and low-lot gradient descent (MGD). The introduction of an automatic encoder into the quantum convolutional neural network can effectively extract image features and reduce dimensionality, reduce data dimensions while retaining key information, and improve the model's ability to capture image features. At the same time, combined with the small-batch gradient descent optimization strategy, the model parameters are iteratively updated through small-batch data, which speeds up the model convergence speed and improves the training efficiency. In the experimental part, we compare QAGD with traditional image recognition algorithms, and the results show that QAGD algorithm has significant advantages in improving image recognition performance and reducing false positive rate, with higher recognition accuracy and faster training speed. This fully verifies the feasibility and effectiveness of QAGD architecture in the application of image recognition algorithms based on quantum convolutional neural networks.