Stock Performance - The stock price of WIMI (WIMI.US) has significantly declined, closing at $1.90 on February 11, 2026, with a single-day drop of 6.86% and a cumulative decline of 32.62% over the last 20 trading days [2] Business Development - The company is constructing a full-stack AI computing innovation system, focusing on edge computing chips, quantum technology, and a holographic cloud platform, while targeting applications in vertical scenarios such as autonomous driving and industrial quality inspection [3] Future Outlook - The long-term technological layout in AI computing infrastructure and embodied intelligence has been highlighted by some market analysts, with future attention needed on commercialization progress and the realization of orders in specific segments [4]

Group 1 - Alphabet plans to issue a rare century bond, marking the first such issuance by a tech company since the late 1990s [1] - The company has raised $20 billion for its ambitious AI spending plans, exceeding the initial target of $15 billion [2] - Major tech companies, including Meta and Amazon, are expected to increase their spending to approximately $660 billion by 2026 for AI initiatives, with a significant portion being raised through the bond market [4] Group 2 - Morgan Stanley estimates that large corporations will borrow $400 billion this year, up from $165 billion in 2025, indicating a surge in capital expenditure related to AI, cloud infrastructure, and data centers, projected to reach $3 trillion by 2029 [6] - In the domestic market, Alibaba is aggressively pursuing AI infrastructure through its Qianwen app, which has seen user growth driven by a $3 billion promotional campaign [7] - ByteDance views AI as a transformative opportunity, with its Doubao product expected to reach over 100 million daily active users by the end of 2025, and plans for global expansion in AI business [9] - WIMI has established a full-stack AI ecosystem, focusing on self-developed strategies and creating a comprehensive ecosystem from foundational technology to industrial applications [11]

Core Viewpoint - The stock price of WIMI (WIMI.US) has shown a significant decline, contrasting with the market's interest in its AI computing business, closing at $1.90 on February 11, 2026, down 6.86% for the day and 32.62% over the past 20 trading days [1] Stock Performance - The stock price decline may be influenced by multiple factors, including an overall sector adjustment, with the advertising and marketing sector down 4.91% and the Nasdaq index down 0.11% during the same period [2] - Following a single-day surge of 11.54% on February 6, the stock has experienced a continuous pullback, with trading volume decreasing from 155,000 shares on February 6 to 70,000 shares on February 11, indicating a decline in short-term capital activity [2] - The company's price-to-earnings ratio (TTM) stands at 1.23, and the price-to-book ratio is 0.11, with a total market capitalization of approximately $24 million, suggesting market hesitation regarding the balance between technological implementation and short-term profitability [2] Business Development - WIMI is constructing a full-stack AI computing innovation system, focusing on edge computing chips, quantum technology, and a holographic cloud platform, targeting vertical scenarios such as autonomous driving and industrial quality inspection, aligning with the growing demand for AI computing [3] Future Development - Despite short-term pressure on the stock price, the company's long-term technological layout in AI computing infrastructure and holographic interaction is still noted by some market analysts [4] - Future attention should be directed towards the commercialization progress of its business and the realization of orders in specific segments [4]

Core Viewpoint - WiMi Hologram Cloud Inc. has announced the release of its Hybrid Quantum-Classical Neural Network (H-QNN) technology, which enhances the efficiency of MNIST binary image classification, marking a significant advancement in quantum machine learning and demonstrating the company's competitive edge in quantum intelligent algorithm research [1][10]. Group 1: Technology Overview - H-QNN technology combines quantum computing with classical deep learning, utilizing a trainable quantum feature encoding module to map raw image data into a high-dimensional quantum feature space, followed by nonlinear transformations and classification through a classical network [3][4]. - The architecture of H-QNN consists of three main components: a data preprocessing module, a quantum encoding and feature extraction module, and a classical neural classifier [4][7]. - The quantum encoding stage employs a Parameterized Quantum Circuit (PQC) to create nonlinear quantum feature space mappings, allowing for the unique representation of each sample in the quantum state space [5][6]. Group 2: Performance and Efficiency - Experimental results indicate that H-QNN achieves significantly higher classification accuracy compared to classical multi-layer perceptron (MLP) models, particularly in distinguishing between handwritten digits "0" and "1" [8]. - The computational efficiency of H-QNN is validated, with a reduction in computation time by approximately 30% compared to traditional deep networks, suggesting further improvements with the maturation of quantum hardware [8]. - The model demonstrates a nonlinear growth in feature expression capability as the number of qubits increases from 4 to 8, indicating the scalability of the quantum feature space [8]. Group 3: Future Applications and Research Directions - H-QNN is positioned as a general quantum-enhanced neural network framework, applicable to various computer vision tasks such as handwriting recognition, medical image analysis, and video frame feature extraction [9]. - WiMi plans to explore the operability and noise resistance of H-QNN on actual quantum devices and investigate integration with other quantum algorithms to develop a more comprehensive quantum intelligence framework [9]. - Future research will focus on quantum feature compression and distributed quantum learning for large-scale visual datasets, emphasizing the potential of hybrid quantum-classical neural networks in advancing artificial intelligence computing architectures [9].

Core Idea - WiMi Hologram Cloud Inc. has introduced a new Lean Classical-Quantum Hybrid Neural Network (LCQHNN) framework designed to enhance learning efficiency while minimizing quantum circuit complexity, marking a significant advancement in the practical application of quantum neural networks [1][11]. Technical Overview - The LCQHNN framework focuses on quantum feature amplification combined with classical stability optimization, creating an efficient interaction mechanism between classical and quantum computing [2]. - The architecture consists of a Classical Front-End for feature extraction and a Quantum Back-End utilizing variational quantum circuits for classification [2]. - The classical component employs lightweight convolutional layers for data preprocessing, embedding results into quantum state space for feature transformation [3]. - The quantum section features a four-layer variational quantum circuit (4-layer VQC) that optimizes circuit parameters to enhance classification performance while reducing resource consumption [4]. Workflow Stages - The workflow includes several key stages: 1. Data Preprocessing and Classical Encoding, where images are processed into medium-dimensional vectors for quantum encoding [5]. 2. Quantum State Preparation and Entanglement Structure Construction, enhancing correlations between qubits to improve model performance [6]. 3. Parameterized Quantum Evolution and Measurable Readout, utilizing adjustable parameters for efficient training and measurement [7]. 4. Classical Feedback and Hybrid Optimization, coordinating classical and quantum parameter updates to minimize classification errors [8]. 5. Classification Decision and Feature Visualization, where final results are decoded back to the classical domain, demonstrating strong inter-class separability [9]. Future Directions - WiMi plans to expand the LCQHNN model into multimodal learning, integrate with quantum support vector machines and quantum convolutional networks, and promote prototype deployment on quantum hardware to validate performance in real-world scenarios [10]. - The company aims to combine quantum parallel optimization with federated learning frameworks to develop secure and efficient quantum intelligent systems [10]. Company Background - WiMi Hologram Cloud Inc. specializes in holographic cloud services, focusing on various professional applications including AR technologies, holographic devices, and metaverse solutions [12].

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 Insights - WiMi Hologram Cloud Inc. has developed a hybrid quantum-classical deep learning technology called QB-Net, which integrates lightweight quantum computing modules into the classical U-Net architecture, achieving a reduction in parameters by up to 30 times while maintaining comparable performance [1][9][10] Technology Overview - The core advantage of quantum computing is its ability to express high-dimensional information and perform operations in exponentially dimensional spaces, surpassing classical architectures [2] - WiMi's approach focuses on constructing quantum enhancement modules rather than fully quantized AI models, addressing the bottleneck layer of deep networks with quantum states that can express high-dimensional features efficiently [3][4] - QB-Net retains the U-Net structure but replaces traditional convolutional layers at the bottleneck with a quantum feature compression-transformation-reconstruction module, which consists of three key steps: encoding classical features into quantum states, transforming features through quantum circuits, and decoding quantum states back into classical tensors [5][8] Quantum Feature Transformation - The encoding step minimizes qubit usage while preserving key information, using techniques like linear projection or amplitude encoding [5] - The transformation step utilizes parameterized quantum circuits (PQC) to achieve expressive transformations with significantly fewer parameters compared to traditional convolutional layers [6][7] - The decoding step reconstructs the quantum measurement results into classical tensors, allowing for efficient filtering and abstraction of high-dimensional information [8] Strategic Implications - The introduction of QB-Net signifies a significant advancement in quantum AI technology, demonstrating the real value of quantum computing and its potential integration with deep learning [9] - The hybrid quantum-classical architecture is expected to become a mainstream form of AI, providing a new optimization paradigm for the global AI industry and enhancing enterprise-level intelligent systems [10]

Core Insights - WiMi Hologram Cloud (WIMI) has shown a stock price increase of 2.88% on December 16, reaching $2.86 per share, with a total market capitalization of $36.78 million [1] - For the fiscal year ending December 31, 2024, WIMI reported total revenue of 542 million RMB, a year-over-year decrease of 7.42%, while net profit attributable to shareholders was 71.64 million RMB, reflecting a significant year-over-year increase of 117.01% [1] - The company is registered in the Cayman Islands and operates primarily through its domestic subsidiary, Beijing WiMi Hologram Cloud Software Co., Ltd. [1] Company Overview - WiMi focuses on computer vision holographic cloud services and aims to become a leading and internationally influential holographic cloud platform in China [1] - The company provides a comprehensive range of services in holographic AR technology, including holographic computer vision AI synthesis, visual presentation, interactive software development, online and offline advertising, SDK payment solutions, 5G holographic communication software, facial recognition, and AI face-swapping [1] - WiMi has established itself as one of the largest comprehensive technology solution providers in the holographic cloud sector in China [1] Industry Developments - WIMI has made significant breakthroughs in various holographic application fields such as advertising, entertainment, education, and 5G communications [2] - The company is committed to deep research and market application across all stages of holographic 3D computer vision, including collection, AI synthesis, transmission, and presentation [2] - WIMI aims to build a strong, open service platform that bridges the application of holographic technology and computer vision presentation, promoting cross-industry development [2]

Core Viewpoint - WiMi Hologram Cloud Inc. is advancing its research in image classification through the development of a shallow hybrid quantum-classical convolutional neural network (SHQCNN) model, which integrates innovative quantum computing techniques to enhance performance in this field [1][4]. Group 1: SHQCNN Model Development - The SHQCNN model utilizes an enhanced variational quantum method, optimizing traditional approaches to improve efficiency in image classification tasks [2]. - The model employs a kernel encoding method in the input layer, which enhances data distinction by mapping original image data from low-dimensional to high-dimensional feature space [3]. - Variational quantum circuits are designed in the hidden layer to reduce computational complexity while effectively extracting image features [3]. Group 2: Training and Optimization Techniques - The output layer of the SHQCNN model uses a mini-batch gradient descent algorithm, which improves parameter training speed and model adaptability to changes in training data [4]. - The integration of advanced technologies such as enhanced variational quantum methods, kernel encoding, variational quantum circuits, and mini-batch gradient descent contributes to the model's stability, accuracy, and generalization capabilities [4]. Group 3: Future Potential - The continuous development of quantum computing technology and the expansion of application scenarios suggest that the SHQCNN model will have significant potential across various fields beyond image classification [4].

Core Viewpoint - WiMi Hologram Cloud Inc. has developed a disruptive single-qubit quantum neural network technology that integrates quantum computing with artificial intelligence, addressing the limitations of traditional neural networks in multi-class classification tasks [1][4]. Group 1: Technology Development - The single-qubit quantum neural network technology (SQ-QNN) demonstrates the feasibility of high-dimensional quantum systems for efficient learning, providing a pathway for deep integration of quantum computing and AI [1][3]. - This technology addresses the challenges of training large neural networks, which often require billions of parameters and significant data center resources, leading to high power consumption and costs [2][3]. Group 2: Quantum Neural Network Advantages - Quantum Neural Networks (QNN) utilize qubits and quantum multi-level systems to represent high-dimensional data spaces, overcoming the resource limitations of classical computing [3]. - Compared to traditional deep learning, QNN can achieve complex mappings with shallow quantum circuits, enhancing model compactness and computational efficiency [3][4]. Group 3: Implementation Details - The SQ-QNN technology uses a single high-dimensional qudit to handle multi-class classification tasks, simplifying the model structure compared to classical neural networks [5][6]. - Each category corresponds to a dimension of the quantum system, and the classification process is executed through a high-dimensional unitary operator, reducing circuit depth and training overhead [6][8]. Group 4: Training Methodology - WiMi's technology incorporates a hybrid training method that combines extended activation functions with Support Vector Machine (SVM) optimization, enhancing the network's nonlinear representational capabilities [7][10]. - The training framework allows for efficient parameter optimization and quick convergence to global optimal solutions, effectively sharing the burden on quantum hardware [10][11]. Group 5: Company Overview - WiMi Hologram Cloud Inc. specializes in holographic cloud services, focusing on various applications such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, and metaverse holographic technologies [12].