MicroCloud Hologram Inc. Develops Quantum-Enhanced Deep Convolutional Neural Network Image 3D Reconstruction Technology

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.

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.

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.

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 quantum convolutional neural network integrates traditional convolutional layers, pooling layers, and quantum computing units, leveraging quantum superposition and quantum entanglement characteristics to efficiently extract higher-level deep features from input images, breaking through the feature extraction bottlenecks of traditional algorithms.

The quantum-enhanced parameter generation module achieves the transformation from features to models. This module precisely maps the high-dimensional feature vectors output by the quantum encoder to three-dimensional space through quantum fully connected layers or quantum optimization regression algorithms. These quantum-optimized parameters can flexibly control key attributes of the 3D model such as shape, size, and pose, achieving more refined model regulation.

The quantum-accelerated 3D reconstruction module completes the final model generation. This module inputs the quantum-enhanced parameters into the pre-built 3D model to generate high-precision 3D reconstruction results. The module incorporates quantum deconvolution layers and quantum upsampling layers, using the parallel processing capabilities of quantum computing to quickly map the feature vectors output by the encoder to three-dimensional space, significantly improving reconstruction efficiency and model precision.

The quantum-precision model evaluation and application extension module ensures technical implementation. The quantum-precision model evaluation module precisely measures the differences and errors between the generated model and the original model through quantum computing technology, optimizing algorithm parameters and improving the training dataset based on this data, continuously enhancing the precision and robustness of the 3D reconstruction model. The application interface module is responsible for the visual presentation of the 3D reconstruction model, building a convenient user interaction interface that supports users in real-time adjustment of model attributes and parameters to meet customized design and personalized needs.

Compared to traditional 3D reconstruction algorithms, the technical system proposed by HOLO, relying on the deep fusion of quantum computing and deep learning, possesses significant advantages of higher precision and stronger adaptability. Through quantum-accelerated training for deep learning on massive data, it precisely extracts image features and structural information to generate 3D models that better meet actual needs.

With the rapid development of quantum computing, deep learning, computer vision, and virtual reality technologies, the quantum-enhanced deep convolutional neural network image 3D reconstruction technology system will have broader application prospects. In the medical field, this technology can be used to achieve precise classification and diagnosis of cases; in the robotics field, it can improve the precision of robot obstacle avoidance; in manufacturing, it can achieve efficient and precise item modeling. In the future, this technology can also deeply integrate with technologies such as augmented reality and virtual reality, combined with the continuous breakthroughs in quantum computing, to expand richer application scenarios.

About MicroCloud Hologram Inc.

MicroCloud Hologram Inc. (NASDAQ: HOLO) is committed to the research and development and application of holographic technology. Its holographic technology services include holographic light detection and ranging (LiDAR) solutions based on holographic technology, holographic LiDAR point cloud algorithm architecture design, technical holographic imaging solutions, holographic LiDAR sensor chip design, and holographic vehicle intelligent vision technology, providing services to customers offering holographic advanced driving assistance systems (ADAS). MicroCloud Hologram Inc. provides holographic technology services to global customers. MicroCloud Hologram Inc. also provides holographic digital twin technology services and owns proprietary holographic digital twin technology resource libraries. Its holographic digital twin technology resource library utilizes a combination of holographic digital twin software, digital content, space data-driven data science, holographic digital cloud algorithms, and holographic 3D capture technology to capture shapes and objects in 3D holographic form. MicroCloud Hologram Inc. focuses on developments such as quantum computing and quantum holography, with cash reserves exceeding 3 billion RMB, and plans to invest more than 400 million in USD from the cash reserves to engage in blockchain development, quantum computing technology development, quantum holography technology development, and derivatives and technology development in frontier technology fields such as artificial intelligence AR. MicroCloud Hologram Inc.'s goal is to become a global leading quantum holography and quantum computing technology company.

Safe Harbor Statement

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SOURCE MicroCloud Hologram Inc.