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

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.

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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Beijing, Feb. 05, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that a multi-view representation learning algorithm is to deal with the data stream clustering problem. The multi-view representation learning algorithm can provide an effective solution to the data stream clustering problem. The multi-view representation learning algorithm is a method of learning and fusing data from multiple views to obtain a more comprehensive representation. In data stream clustering, multiple views can be used to represent different aspects of the data stream, such as time series view, spatial view, etc., and each view can provide different information.

Beijing, Feb. 01, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it optimized cloud task scheduling using group intelligence algorithms. A group intelligence algorithm is a computational method based on the behavior of groups in nature, which can demonstrate powerful search and optimization capabilities in solving complex problems by simulating the interactions and collaborations of individuals in a group. Using group intelligence algorithms to solve cloud task scheduling problems can improve task execution efficiency and resource utilization.

Beijing, Jan. 29, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it has been focusing on solving the bottleneck of holographic head-mounted display technology, challenging the traditional display mode and enhancing the overall user experience. WiMi's HoloAR Lens was developed by using a holographic eye-tracking focusing system, an optical system with adjustable pupil distance, and real-time FOV digital control in a product that involves optical engineering, computer vision, human-computer interaction, etc. The core innovation of the HoloAR Lens is that WiMi developed a holographic eye-tracking focusing system. It uses advanced computer vision technology that captures the user's eye movements in real-time by embedding an eye-tracking camera in the device. This data is transmitted to the built-in processing unit and analyzed with the help of algorithms to determine the user's focus position.

Beijing, Jan. 26, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed an AI-driven real-time spatial interaction perception system for interior design. The design industry has ushered in the era of personalized design, and the co-use of AI and VR technologies provides an unprecedented opportunity.

Beijing, Jan. 24, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it successfully integrated the visual-brain-computer interface (V-BCI) into the holographic eyewear interaction system, resulting in a more efficient and immersive user experience.

Beijing, Jan. 22, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced an LSTM-based data analysis system to provide clients with cutting-edge tools to trade in the complex cryptocurrency environment.

Beijing, Jan. 18, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced a Trimmed K-Means algorithm for detecting crypto-wallet fraud on the Bitcoin network. It combines symmetry and asymmetry in computer and engineering sciences to provide a novel solution for crypto-wallet fraud on the Bitcoin network. The technology not only improves detection efficiency but also identifies anomalous behavior more accurately, providing a more secure trading environment for Bitcoin investors.

Beijing, Jan. 16, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is working on a novel mixed byzantine fault tolerance (MBFT).

Beijing, Jan. 12, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is working on employing Markov Chain Monte Carlo (MCMC) to blockchain storage. A new blockchain storage strategy is being proposed that reduces the block information in different nodes according to a stochastic algorithm. This blockchain secure storage strategy utilizes MCMC to improve blockchain data storage security. The MCMC algorithm has a wide range of applications in the fields of random sampling, mathematical expectation estimation, and definite integral calculus. The security of blockchain is built on the base of randomness.

Beijing, Jan. 10, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is working on the optimization of a blockchain hash algorithm based on proactive re-configurable computing architecture (PRCA). PRCA is an operational mechanism based on multi-dimensional re-configurable functional structures, which centers on the execution structure of computation, and storage that changes dynamically with the efficiency when data is being processed, instead of improving the algorithms to improve the performance of the computation while the underlying hardware remains unchanged. PRCA has many functional equivalents, but they are accomplished by combining different hardware structures with that algorithm. The goal is to achieve high performance in computation, i.e., to deal with how the algorithm automatically senses variables to generate the optimal set of computations and re-configures the computation autonomously. In addition, PRCA can improve the processing efficiency of the blockchain by dynamically adapting the hardware configuration to different computational requirements. For example, when performing hash computing, the hardware configuration can be dynamically adjusted according to the actual computational needs, such as increasing the number of CPU cores, increasing the memory capacity, etc., in order to improve the speed of computing.

Beijing, Jan. 08, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that the probability of backtest overfitting algorithm (PBO) was used to blockchain and bitcoin trading strategies. PBO is computed based on the combinatorial symmetric cross-validation framework (CSCV) and is designed to quantify the risk of overfitting in small sample backtests. With PBO, it is possible to assess the overfitting probability of a strategy in a single backtest, thus reducing the risk of a poorly performing live model. However, relying on backtesting results alone is not enough. In order to obtain models that perform better out-of-sample, WiMi has developed a dynamic optimization algorithm based on PBO, namely the "PBO-DOA algorithm". This algorithm is important in quantitative trading because it can dynamically optimize the model's parameters to help investors build optimal portfolios.

Beijing, Jan. 05, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced an image-fused point cloud semantic segmentation method based on fused graph convolutional network, aiming to utilize the different information of image and point cloud to improve the accuracy and efficiency of semantic segmentation. Point cloud data is very effective in representing the geometry and structure of objects, while image data contains rich color and texture information. Fusing these two types of data can utilize their advantages simultaneously and provide more comprehensive information for semantic segmentation.

Beijing, Jan. 03, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed RandomPatchSpatialSpectrumClassifier (RPSSC) technology to fully utilize the complementarity between spatial and spectral information.

Beijing, Dec. 27, 2023 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that by combining cloud servers, general-purpose computers, and FPGAs, WiMi has built a blockchain heterogeneous computing framework named "HeteroBlock Framework". The framework is designed to provide users with efficient, flexible, and reliable blockchain computing services to meet the growing computing demand.

Beijing, Dec. 26, 2023 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that a decentralized file storage and sharing system is built by applying the bitcoin algorithm distributed hash table (DHT) technology.

Beijing, Dec. 22, 2023 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed an efficient hologram calculation using the wavefront recording plane method, which combines the principles of light wave interference and diffraction. The method determines the effective visible region by analyzing the diffraction characteristics of an object point on a three-dimensional object, and based on this, identifies the effective hologram size of the object point, thus realizing the rapid generation of holograms.

Beijing, Dec. 20, 2023 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it adopted a group intelligence algorithm to optimize the artificial neural network. This algorithm facilitates the process of determining the network structure and the training of the artificial neural network. The group intelligence algorithm is better at finding the optimal connection weights and biases during training compared to traditional algorithms.

Beijing, Dec. 18, 2023 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it proposed hybrid recurrent neural network architecture-based human-robot collaboration intent recognition. Hybrid recurrent neural network architecture is a model that combines recurrent neural network (RNN) and convolutional neural network (CNN). RNN is a neural network suitable for modeling and sequential data processing, which can efficiently capture temporal information and contextual relationships in the data through recurrent connections and hidden state updating, it can effectively capture temporal information and contextual relationships in sequence data. CNN can effectively extract data features. Hybrid recurrent neural network combines the advantages of RNN and CNN, which can better capture sequence information and local features, and can better handle intention recognition for human-robot collaboration.