WiMi Hologram Cloud Inc. - Class B Ordinary Shares (WIMI)

BEIJING, Jan. 02, 2026 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, released a breakthrough achievement—a hybrid quantum-classical deep learning technology based on parameter-efficient quantum modules, QB-Net (Quantum Bottleneck Network). This technology achieves a major breakthrough by embedding lightweight quantum computing modules into the classical U-Net deep learning architecture, reducing the number of parameters in the bottleneck layer by up to 30 times while maintaining performance comparable to that of the classical U-Net. This research and development outcome not only demonstrates the cutting-edge potential of hybrid quantum-classical artificial intelligence but also provides a brand-new optimization paradigm for traditional deep learning architectures.The core advantage of quantum computing lies in its ability to express high-dimensional information through the superposition states of qubits and perform linear operations in exponentially dimensional spaces, endowing it with expressive and transformative capabilities that surpass classical architectures. However, at the current stage, quantum hardware is still unable to support large-scale quantum neural networks or construct complete quantum U-Net or quantum Transformer.Therefore, WiMi has taken a completely different path: instead of building fully quantized AI models, it constructs quantum enhancement modules.This concept stems from a key observation: the bottleneck layer of deep networks is essentially a problem of high-density expression of high-dimensional features, while quantum states are naturally suited to express extremely high-dimensional vector spaces.When a classical network requires tens of thousands of parameters to accomplish a mapping task, a single quantum state can theoretically achieve the same or even higher expressive power with only a few dozen qubits. This means that as long as classical features can be mapped into quantum states and transformed through quantum circuits, it is possible to achieve equivalent capabilities with extremely low parameter counts.Based on this idea, WiMi designed a pluggable Quantum Bottleneck Module. This module takes minimal parameter count, structural stability, trainability, and the ability to be integrated into classical networks as its core objectives and has been embedded into the classical U-Net, forming QB-Net.QB-Net retains the overall structure of U-Net, including the encoder, upsampling path, and skip connections. However, at the bottleneck layer position, the traditional multiple convolutional layers are replaced with a quantum feature compression-transformation-reconstruction module. This module consists of three key steps:The first step is the encoding of classical features into quantum states. The encoding module uses techniques such as linear projection or amplitude encoding to map the classical feature tensor into a compact vector form suitable for entering quantum circuits. The design of the encoding strategy follows two major principles: minimizing the number of qubits as much as possible while preserving the key information of the features without loss.The second step is feature transformation through quantum circuits, which is the core link of the entire system and the key to parameter efficiency. A traditional convolutional bottleneck layer may contain hundreds of thousands or even millions of parameters, whereas a quantum circuit requires only tens to hundreds of adjustable rotation parameters to achieve equivalent expressive transformation.WiMi uses parameterized quantum circuits (PQC) and builds a deeply controllable quantum state transformer through layer stacking. The quantum circuit includes entanglement structures to ensure sufficient information flow between qubits, forming higher-dimensional representation capabilities than classical linear transformations.The third step is decoding the quantum state back into a classical tensor. The results obtained from quantum measurement are reconstructed through a classical integration and correction module and finally returned to the decoding path of the classical U-Net. The features compressed through the quantum bottleneck retain expressive power yet complete the filtering and abstraction of high-dimensional information with an extremely low number of parameters. The entire process can be directly embedded into existing models without modifying the U-Net architecture or changing the training paradigm, achieving true “plug-and-play quantum enhancement”.The release of WiMi's QB-Net marks a key step forward for our company on the path of quantum AI technology. It not only proves that quantum computing can deliver real value right now but also demonstrates the enormous potential of deep integration between quantum technology and deep learning. In the future, hybrid quantum-classical architectures will no longer be regarded as transitional technologies but will become one of the mainstream forms of AI for a long time to come.QB-Net represents a brand-new way of thinking: letting quantum computing become the most valuable part of artificial intelligence rather than the entirety. The hybrid deep learning framework based on parameter-efficient quantum modules will bring a new structural optimization paradigm to the global AI industry and provide a completely new performance improvement path for enterprise-level intelligent systems.

BEIJING, Oct. 23, 2025 (GLOBE NEWSWIRE) -- BEIJING, Oct. 23, 2025––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that they are actively exploring a shallow hybrid quantum-classical convolutional neural network (SHQCNN) model, bringing innovative breakthroughs to the field of image classification.Variational quantum methods, as an important technical means in the field of quantum computing, provide effective pathways for the design and implementation of quantum algorithms by transforming the optimization problems of quantum states into classical optimization problems. WiMi has adopted an enhanced variational quantum method in the SHQCNN model, laying a solid foundation for the model's efficient operation in image classification tasks. The enhanced variational quantum method has undergone multi-faceted optimizations based on traditional methods. First, in terms of quantum state representation, by introducing more complex combinations of quantum gates and parameterized forms, it can more precisely describe the quantum features of image data. Second, in the optimization algorithm, advanced adaptive optimization strategies are employed, which can dynamically adjust optimization parameters based on real-time feedback during the training process, accelerating convergence speed and improving the model's training efficiency. This enhanced variational quantum method enables the SHQCNN model to fully leverage the advantages of quantum computing when handling image classification tasks, while avoiding the complexity issues brought by increasing layers in traditional QNNs.In image classification tasks, the quality and distinguishability of input data directly affect the model's performance. The SHQCNN model adopts the kernel encoding method in the input layer; this method is like a precise key, enhancing the efficiency of data distinction and processing. The core idea of the kernel encoding method is to map the original image data from low-dimensional space to high-dimensional feature space through nonlinear mapping, making image data that is difficult to distinguish in the low-dimensional space easier to separate in the high-dimensional space. Through the kernel encoding method, the SHQCNN model optimizes the data processing at the input stage, providing high-quality input for the computations of subsequent hidden and output layers, thereby improving the classification accuracy of the entire model.And the hidden layer, as the core part of the neural network, undertakes the important task of feature extraction and transformation of input data. In traditional QNNs, as the number of layers increases, the computational complexity of the hidden layer rises sharply, leading to the training process becoming extremely difficult. The SHQCNN model designs variational quantum circuits in the hidden layer, cleverly solving this problem. Variational quantum circuits are composed of a series of quantum gates, which can perform specific transformations on the input quantum states. Compared to the hidden layers of traditional deep neural networks, variational quantum circuits have a more concise structure and lower computational complexity. Through rationally designing the types and arrangement order of quantum gates, variational quantum circuits can achieve effective extraction of image features within fewer layers.At the same time, the parameters of the variational quantum circuit can be trained through classical optimization algorithms, enabling the model to perform adaptive optimization based on different image classification tasks, further improving the model's generalization ability.The output layer, as the final module of the neural network, is responsible for classification decisions on the features extracted by the hidden layer. The SHQCNN model adopts the mini-batch gradient descent algorithm in the output layer; this innovative application of the algorithm brings significant improvements to the model's parameter training and learning speed. The mini-batch gradient descent algorithm is a variant of the gradient descent algorithm. In each iteration, instead of using all the training data, it randomly selects a small batch of data from the training set for computation. Compared to the traditional batch gradient descent algorithm, the mini-batch gradient descent algorithm has faster computation speed and better convergence. In the SHQCNN model, by performing weight updates more frequently, the mini-batch gradient descent algorithm can timely adjust the model's parameters, enabling the model to adapt more quickly to changes in the training data.The shallow hybrid quantum-classical convolutional neural network model (SHQCNN) researched by WiMi, through the integrated application of a series of advanced technologies such as enhanced variational quantum methods, kernel encoding methods, variational quantum circuits, and mini-batch gradient descent algorithms, has significant advantages in terms of stability, accuracy, and generalization, and will bring new solutions to the field of image classification. With the continuous development of quantum computing technology and the continuous expansion of application scenarios, the SHQCNN model will demonstrate its enormous potential in more fields.

BEIJING, Oct. 20, 2025 (GLOBE NEWSWIRE) -- BEIJING, Oct. 20, 2025––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of single-qubit quantum neural network technology for multi-task design. This technology has extremely disruptive significance; this technology, by demonstrating the feasibility of high-dimensional quantum systems in efficient learning, provides a realistic path for the deep integration of future quantum computing and artificial intelligence.Nowadays, training large neural networks often requires billions of parameters and massive data center resources, and the sharp rise in power consumption and hardware costs has become a real bottleneck in the development of artificial intelligence. At the same time, although traditional neural networks have achieved high accuracy in multi-class classification problems, as the number of categories increases, the model structure also expands accordingly, leading to a decline in inference latency and computational efficiency.The rise of quantum computing provides new possibilities for this dilemma. Quantum bits (qubits) and quantum multi-level systems (qudits) can utilize superposition and entanglement to achieve natural representation of high-dimensional data spaces, thereby breaking the resource limitations of classical computing. In this field, Quantum Neural Networks (QNN) have become a frontier direction of research. Compared to traditional deep learning, QNN can achieve complex mappings through shallow quantum circuits, greatly improving model compactness and computational efficiency.In the wave of quantum machine learning, the single-qudit quantum neural network technology proposed by WiMi not only meets the actual needs of high-dimensional data classification but also breaks through the implementation bottlenecks under the constraints of quantum hardware, becoming an important step in promoting industrial progress.The core idea of the single-qudit quantum neural network technology proposed by WiMi is to use the state space of a single high-dimensional qudit to directly handle multi-class classification tasks. Unlike classical neural networks that rely on thousands of neurons and complex hierarchical structures, SQ-QNN leverages the high-dimensional characteristics of quantum systems to efficiently encode and distinguish category information within a compact circuit scale.In this design, each category corresponds to one dimension of the quantum system, and the overall classification process is completed through the action of a high-dimensional unitary operator. WiMi uses the Cayley transform of skew-symmetric matrices to construct the unitary operator; this method not only possesses good mathematical stability but also ensures efficiency in quantum circuit implementation. In this way, the evolution of the quantum state directly establishes a mapping relationship with the category labels, greatly reducing the circuit depth and training overhead.Additionally, this technology introduces a hybrid training method when optimizing network parameters. It combines extended activation functions with the optimization framework of Support Vector Machines (SVM). The extended activation function originates from the truncated multivariate Taylor series expansion and can effectively introduce nonlinear representational capabilities in the quantum state space, while SVM optimization further ensures the stability of parameter optimization and the acquisition of global optimal solutions.The entire technical logic of WiMi's SQ-QNN can be divided into three levels: quantum state encoding, unitary evolution design, and hybrid training optimization.First is the quantum state encoding. In multi-class classification problems, assuming the number of categories is $d$, a $d$-dimensional qudit system is constructed to carry the data. After appropriate data preprocessing, the input samples are mapped to the amplitude or phase information of the quantum state. In this process, traditional feature extraction steps are greatly simplified, allowing data to directly enter the neural network in quantum form.Second is the unitary evolution design. WiMi proposes using the Cayley transform of skew-symmetric matrices to generate $d$-dimensional unitary operators. The properties of skew-symmetric matrices make their Cayley transform results naturally satisfy unitarity, thereby ensuring the physical rationality and implementability of quantum state evolution. Through this unitary operator, the input quantum state completes the mapping and differentiation of category information in the high-dimensional Hilbert space. Unlike the multi-layer propagation in classical neural networks, this scheme can achieve complex decision boundaries through a single-step evolution, significantly reducing the circuit depth.Finally, it is the hybrid training optimization. In the parameter training phase, this scheme does not solely rely on quantum computing but adopts a hybrid quantum-classical training method. The introduction of extended activation functions enables the quantum neural network to possess nonlinear classification capabilities while maintaining a shallow structure. At the same time, the support vector machine optimization mechanism provides an efficient path for parameter search, allowing the network to quickly converge to the global optimal solution. Under this training framework, the burden on quantum hardware is effectively shared, and training efficiency is significantly improved.

The financial markets are buzzing today, October 1, 2025, as WiMi Hologram Cloud Inc. (NASDAQ: WIMI) announced a groundbreaking advancement in artificial intelligence: a quantum-assisted unsupervised data clustering technology based on neural networks. This pivotal development is set to redefine how large-scale and high-dimensional datasets are processed and analyzed, promising

October 1, 2025 – The long-anticipated future of holographic technology is rapidly materializing, moving beyond science fiction to become a tangible force poised to revolutionize numerous sectors. As of October 2025, groundbreaking advancements in interactive 3D holograms, compact display solutions, and AI integration are heralding a new era of immersive experiences.

October 1, 2025 – The burgeoning field of quantum computing has taken significant strides forward with recent breakthroughs from MicroCloud Hologram Inc., a subsidiary of WiMi Hologram Cloud (NASDAQ: WIMI), in quantum link efficiency optimization, and its parent company, WiMi Hologram Cloud (NASDAQ: WIMI), in quantum-assisted data clustering. These innovations, announced

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Gainers comScore (NASDAQ: SCOR) shares increased by 32.2% to $8.09 during Monday's pre-market session. The market value of their outstanding shares is at $30.6 million.

BEIJING, Aug. 08, 2025 (GLOBE NEWSWIRE) -- Beijing, August 8, 2025 – WiMi Hologram Cloud Inc. (Nasdaq: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced a significant improvement in its operating performance, with a notable increase in cash reserves.

Beijing, April 23, 2025 (GLOBE NEWSWIRE) -- WiMi Developed a Quantum Computing-Based Feedforward Neural Network (QFNN) Algorithm

Beijing, April 22, 2025 (GLOBE NEWSWIRE) -- WiMi Turned an Annual Loss into a Significant Profit, with Cash Reserves Reaching a Record High

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