Core Viewpoint - WiMi Hologram Cloud Inc. is actively exploring Scalable Quantum Convolutional Neural Networks (SQCNN) technology, which shows superior performance in classification accuracy compared to existing quantum neural network models [1][2]. Group 1: Technology Advancements - The scalable quantum convolutional neural network model improves classification accuracy by optimizing qubit utilization and employing a unique network architecture design, allowing for better feature extraction from images [2]. - This model enhances generalization capabilities, enabling accurate classification even with new data, thus providing stability and reliability in practical applications [2]. - Training efficiency is significantly improved as the model reduces the time required for training through optimized quantum algorithms, enhancing overall application efficiency [2]. Group 2: Unique Features of SQCNN - The quantum circuit of the scalable quantum convolutional neural network utilizes superposition and entanglement properties of quantum gates, allowing for simultaneous processing of multiple features, which greatly improves processing efficiency [3]. - The design allows multiple independent quantum devices to extract features in parallel, significantly accelerating feature extraction speed compared to traditional sequential methods [4]. - The system can dynamically adapt to the scale of quantum devices, balancing computational resources with task complexity, which is beneficial for high-real-time and high-complexity scenarios such as autonomous driving and medical image analysis [5]. Group 3: Company Overview - WiMi Hologram Cloud, Inc. is a comprehensive technical solution provider focusing on holographic AR technologies, including automotive HUD software, 3D holographic pulse LiDAR, and holographic cloud software [6][7].