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MicroCloud Hologram Inc. Develops Quantum-Enhanced Deep Convolutional Neural Network Image 3D Reconstruction Technology
MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction technology system. This system first utilizes quantum convolutional neural network to complete the feature extraction of input images, then generates the core parameters of the 3D model through quantum fully connected layers, and finally imports these parameters into the quantum-optimized 3D model to
About this update from Microcloud Hologram Inc.
[{"type":"text","content":"SHENZHEN, China, Dec. 18, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction technology system. This system first utilizes quantum convolutional neural network to complete the feature extraction of input images, then generates the core parameters of the 3D model through quantum fully connected layers, and finally imports these parameters into the quantum-optimized 3D model to complete the reconstruction, forming a unique advantageous technical mode.","length":646,"tagName":"p"},{"type":"text","content":"This technical system encompasses six core modules: quantum-optimized dataset preparation, quantum-assisted feature extraction, quantum-enhanced parameter generation, quantum-accelerated 3D reconstruction, quantum-precision model evaluation, and interactive application interface. Each module possesses its own independent functional positioning while also collaborating and connecting with each other, jointly building a complete and efficient technical architecture.","length":468,"tagName":"p"},{"type":"text","content":"The quantum-optimized dataset preparation module is the technical foundation. The quantum-enhanced deep convolutional neural network image 3D reconstruction technology requires massive high-quality 3D model data as training samples to ensure that the deep learning algorithm can precisely learn the morphological features and structural patterns of 3D models. This module is responsible for the collection and preparation of 3D model data, while employing quantum computing technology for data preprocessing and cleaning, significantly improving the quality and usability of the dataset. High-quality datasets directly determine the precision and robustness of the algorithm. The dataset covers 3D models of various categories and morphologies, and combined with quantum data augmentation technology, further enhances the universality and generalization ability of the algorithm.","length":879,"tagName":"p"},{"type":"text","content":"The quantum-assisted feature extraction module undertakes the core processing tasks. This module uses quantum convolutional neural networks to perform feature extraction and representation on input images. The...