MicroCloud Hologram Inc. (NASDAQ:Â HOLO), (“HOLO” or the “Company”), a technology service provider, developed a quantum-driven 3D intelligent model, which is a 3D modeling and image processing system that deeply integrates quantum computing and artificial intelligence technologies. This model utilizes quantum deep learning technology to precisely analyze massive data, efficiently extracts core features, and automatically generates high-precision 3D models and images that meet user needs without excessive manual intervention.
The model adopts a quantum-optimized distributed architecture, assembled collaboratively from multiple functional subsystems. Each subsystem has clear functional positioning and responsibility division, providing users with high-quality 3D model and image generation services through collaborative operation. The core advantage of this architecture design lies in its ability to flexibly achieve expansion and upgrading of subsystems, leveraging the parallel processing capabilities of quantum computing to enhance the overall system’s stability and scalability, thereby meeting the diverse needs of enterprises of different scales and personalized users.
The core subsystems of the model include six major modules, all of which incorporate quantum technology to achieve performance upgrades: The quantum-enhanced data acquisition subsystem is responsible for collecting, organizing, and storing raw data from various data sources, while completing data cleaning through quantum data preprocessing technology. This subsystem supports multiple types and formats such as 3D modeling data and image data, using quantum computing to convert data into a unified format for storage and management, significantly improving the accuracy and stability of subsequent model training and image generation, while also taking into account data security and privacy protection under quantum encryption.
The quantum-accelerated model training subsystem, as the core of the model, employs quantum deep learning algorithms to conduct in-depth analysis of the collected data, precisely extracting data features and adaptively optimizing model parameters to achieve high-precision recognition and prediction of sample data, while combining the advantages of quantum computing to balance the relationships between data volume, model structure, and training duration, completing model verification and performance evaluation. The quantum intelligent autonomous generation subsystem integrates complex quantum computer vision algorithms, 3D modeling technology, and quantum data stream processing technology, utilizing the trained mature model to quickly generate standard-compliant 3D models and images based on user needs and input parameters, while balancing efficient processing capabilities with a high-quality user interaction experience.
In addition, the quantum secure data management subsystem is responsible for coordinating the management of collected data, model parameters, generation results, and other information, covering functions such as quantum encrypted storage, backup, recovery, version control, and access control, possessing high availability, high scalability, and quantum-level security protection capabilities. The quantum-empowered data visualization subsystem presents the generated 3D models and images in an intuitive form through a graphical interface, leveraging quantum computing to enhance visualization rendering efficiency, helping users more conveniently understand and operate the model. The quantum-fortified system security subsystem adopts technical means such as quantum encrypted communication, quantum access control, and secure log quantum desensitization recording, providing all-round assurance for data security, privacy protection, and stable system operation, building a solid security defense line for the model.
Communication between subsystems is achieved through quantum encrypted interfaces, efficiently sharing data and resources. Each subsystem is deployed in an independent container, enabling independent deployment and upgrading, relying on a quantum distributed architecture to enhance the system’s concurrent processing capabilities and overall performance, while reducing the impact range of a single subsystem failure on the overall model.
Compared to traditional 3D autonomous generation systems, the model proposed by HOLO this time relies on quantum technology and has three core advantages: leveraging the fusion of quantum intelligent algorithms and distributed computing technology to efficiently process massive data; completing data training and analysis through quantum deep learning algorithms, which can precisely extract features to generate high-quality 3D models and images, meeting users’ diversified needs, achieving parameterized autonomous generation, reducing manual intervention and time costs, and improving work efficiency; relying on a quantum-optimized distributed architecture to achieve rapid, flexible, and stable system expansion, paired with multiple layers of quantum security technology, which can ensure the security and privacy protection of user data.