Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification BEIJING, Feb.06, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the release of a Hybrid Quantum-Classical Neural Network (Hybrid Quantum-Classical Neural Network, H-QNN) technology for efficient MNIST binary image classification. This ...

Core Viewpoint - WiMi Hologram Cloud Inc. has launched a new Quantum Convolutional Neural Network for Multi-Channel Supervised Learning (MC-QCNN), which allows for efficient processing of multi-channel data and offers significant advantages in various industries such as image classification, medical imaging, and video analysis [1][5]. Group 1: Technological Breakthrough - The MC-QCNN technology features a fully hardware-adaptable quantum convolution kernel design, which enables the processing of multi-channel data efficiently [1][2]. - The architecture includes a systematic design scheme that incorporates convolution kernel structure, qubit layout, and channel interaction encoding, allowing for robust performance against quantum decoherence [2][3]. - WiMi's quantum convolution kernels utilize single-bit rotation gates and controlled parameterized gates, enabling the model to learn complex multi-channel correlations through quantum superposition and entanglement [2][3]. Group 2: Training and Performance - The training framework combines classical and quantum computing, where the classical module handles loss function calculations and the quantum module manages forward propagation and state evolution [3][5]. - The model captures nonlinear correlations between multiple channels, enhancing its ability to recognize joint features in data, such as color distribution patterns in RGB images [3][4]. - Experimental results indicate that the new pooling structure is more stable than traditional methods, maintaining a higher feature retention rate [3]. Group 3: Future Developments - WiMi plans to refine its technology by developing more efficient quantum convolution kernel structures and exploring integration with models like Transformer for processing multimodal data [6]. - The company envisions that quantum deep learning will evolve beyond small-scale tasks to become a significant component in next-generation general AI models [6]. - The combination of quantum computing and artificial intelligence is expected to be a core trend in technological development over the next decade [6]. Group 4: Company Overview - WiMi Hologram Cloud Inc. specializes in holographic cloud services, focusing on areas such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, and metaverse holographic AR/VR devices [7]. - The company provides a comprehensive range of holographic AR technologies, including software development and interactive virtual communication solutions [7].

Core Viewpoint - WiMi Hologram Cloud Inc. is exploring a blockchain privacy protection system utilizing post-quantum threshold algorithms to create a secure and efficient blockchain ecosystem [1][11]. Blockchain Technology Overview - The fundamental structure of blockchain consists of transaction data, timestamps, and hashes of previous blocks, ensuring data integrity and immutability [2]. - Asymmetric encryption algorithms like RSA and ECC are commonly used for data encryption and digital signatures, but their security is threatened by advancements in quantum computing [2]. Post-Quantum Cryptography - Post-quantum cryptography aims to develop algorithms resilient to quantum attacks, utilizing new mathematical structures such as lattice-based systems and hash functions [3]. - These algorithms must meet blockchain requirements for efficiency, scalability, and decentralization while maintaining security [3]. Threshold Key Sharing Technology - This technology divides a private key into multiple parts distributed among participants, requiring cooperation to reconstruct the key, enhancing security and fault tolerance [4]. - Challenges include ensuring trust among nodes and maintaining system scalability [4]. WiMi's Blockchain Privacy Protection System - The system integrates post-quantum cryptography and threshold key sharing technology, providing a robust security solution for blockchain [5][11]. - It employs post-quantum encryption algorithms to protect data against quantum computing threats [5]. Distributed Key Management and Fault Tolerance - The system distributes private keys across multiple consensus nodes, ensuring that the compromise of a single node does not jeopardize overall security [6]. - This mechanism enhances fault tolerance, allowing the system to function normally even if some nodes are attacked or fail [6]. Flexible Data Authorization and Access Control - A threshold-based data authorization mechanism controls access to data, requiring a specific number of nodes to agree before granting permissions [7]. - The system allows for fine-grained access control policies tailored to user needs [7]. Efficiency and Scalability - The design optimizes consensus algorithms and data processing workflows to achieve efficient data processing and transaction confirmation [8]. - It supports dynamic scalability, enabling the addition of new consensus and data storage nodes as required [9]. Smart Contracts and Decentralized Applications - The system facilitates the deployment of smart contracts, allowing users to create custom logic and rules [10]. - This capability enhances the functionality of the system and promotes the development of decentralized applications [10]. Future Implications - WiMi's blockchain privacy protection system addresses security risks in the quantum era and enhances blockchain flexibility through various advanced features [11]. - As quantum computing and blockchain technology evolve, this system is expected to play a crucial role in user privacy protection and secure data sharing [11].

Core Insights - WiMi Hologram Cloud Inc. has announced the development of a Scalable Quantum Neural Network (SQNN) technology [1] Company Overview - WiMi is recognized as a leading global provider of Hologram Augmented Reality (AR) technology [1]

Core Viewpoint - WiMi Hologram Cloud Inc. is exploring an innovative solution called Synergic Quantum Generative Network (SQGEN), which utilizes a parallel quantum learning framework to enhance the efficiency of generative learning in quantum computing [1][4]. Group 1: SQGEN Overview - SQGEN features a parallel quantum learning framework where the generator and discriminator operate simultaneously, improving training speed and efficiency [1][3]. - The architecture employs the Nelder-Mead optimization algorithm, which is effective in quantum computing environments where gradient calculations are challenging [2]. - SQGEN introduces a cost function that relaxes the reversibility condition, enhancing stability and reducing quantum resource consumption during training [2][3]. Group 2: Technological Advantages - The collaborative quantum generative network architecture significantly improves training efficiency and reduces the time to reach a converged state compared to traditional methods [3][4]. - By optimizing quantum communication mechanisms and reducing cost function evaluations, SQGEN makes quantum generative learning more economically feasible [3][4]. - The synchronization mechanism within SQGEN ensures that the generator and discriminator remain in sync, addressing training instability and enhancing model robustness [2][3]. Group 3: Future Implications - With ongoing advancements in quantum computing technology, SQGEN has the potential for broader applications in machine learning and artificial intelligence [4]. - The improvements in training speed and data quality achieved by SQGEN validate its effectiveness and suggest new methodologies for quantum generative learning [3][4]. Group 4: Company Background - WiMi Hologram Cloud, Inc. specializes in holographic AR technologies, including automotive applications, 3D holographic pulse LiDAR, and holographic software development [5]. - The company is positioned as a leading provider in the global Hologram Augmented Reality technology sector, focusing on innovative solutions and applications [5].

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].