Core Viewpoint - WiMi Hologram Cloud Inc. is advancing its research in image classification through the development of a shallow hybrid quantum-classical convolutional neural network (SHQCNN) model, which integrates innovative quantum computing techniques to enhance performance in this field [1][4]. Group 1: SHQCNN Model Development - The SHQCNN model utilizes an enhanced variational quantum method, optimizing traditional approaches to improve efficiency in image classification tasks [2]. - The model employs a kernel encoding method in the input layer, which enhances data distinction by mapping original image data from low-dimensional to high-dimensional feature space [3]. - Variational quantum circuits are designed in the hidden layer to reduce computational complexity while effectively extracting image features [3]. Group 2: Training and Optimization Techniques - The output layer of the SHQCNN model uses a mini-batch gradient descent algorithm, which improves parameter training speed and model adaptability to changes in training data [4]. - The integration of advanced technologies such as enhanced variational quantum methods, kernel encoding, variational quantum circuits, and mini-batch gradient descent contributes to the model's stability, accuracy, and generalization capabilities [4]. Group 3: Future Potential - The continuous development of quantum computing technology and the expansion of application scenarios suggest that the SHQCNN model will have significant potential across various fields beyond image classification [4].