, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WIMI" or the "Company"), a globally leading technology provider, has delved into the interdisciplinary field of quantum computing and computer vision to carry out research on quantum visual tracking algorithm technology. Deeply integrating the core characteristics of quantum computing with classic visual tracking frameworks, this algorithm reconstructs the full-process logic of algorithm training and detection, achieving technological innovations at the underlying computing power and algorithm architecture levels. Boasting both theoretical novelty and engineering implementation value, it effectively resolves the trade-off between accuracy and efficiency that plagues traditional algorithms in complex scenarios, delivering an all-new technical solution for high-performance visual tracking systems.
The quantum visual tracking algorithm developed by WIMI abandons the linear computational logic of classic visual tracking. Based on the operating mechanisms of quantum computing, it constructs a closed-loop architecture consisting of two phases: quantumized training and quantum parallel detection. Core technologies including quantum state encoding, quantum parallel computation and quantum superposition solving are embedded throughout the entire workflow, transforming the feature extraction, model training and target detection paradigms adopted by conventional algorithms. Compared with classic algorithms, this architecture fully unlocks the core strengths of quantum computing in high-dimensional spatial computation and parallel batch processing, bringing comprehensive improvements in model training efficiency, target detection accuracy and adaptability to dynamic scenarios.
The core innovation in the algorithm training phase lies in the quantum-state encoded ridge regression classifier. To address the drawbacks of classic ridge regression models — low efficiency of parameter optimization and severe multicollinearity interference when handling high-dimensional visual features and large-scale datasets — a quantumized upgrade has been implemented. Classic ridge regression introduces regularization terms to constrain model parameters and realize stable fitting of multicollinear data. Nevertheless, training with massive video feature datasets entails an extensive parameter search space, lengthy iterative optimization cycles and extremely high computing power consumption. The proposed algorithm maps and encodes the optimal fitting parameters of the ridge regression model onto the amplitude dimension of quantum states. Leveraging the natural high-dimensional spatial representation capability of quantum states, it enables high-dimensional structured storage and extraction of visual features. Meanwhile, by harnessing quantum parallel computing properties, it breaks the single linear iterative parameter optimization mode of classic algorithms, synchronously completing feature screening, parameter iteration and model fitting within the quantum high-dimensional space. This innovation drastically shortens the training cycle for large-scale visual datasets and markedly enhances the stability and precision of model parameter estimation, with more prominent computing power gains especially in training scenarios involving massive video samples and high-dimensional image features.
In the video frame target detection phase, the algorithm invokes the trained quantum-state ridge regression classifier to perform batch classification and detection on all candidate target image patches within a single video frame. Unlike the serial detection mode of classic algorithms that traverses and matches targets one by one, this quantum algorithm leverages the core property of quantum superposition to accommodate all detection tasks for candidate patches simultaneously, completing feature matching and result solving for all target samples in parallel. During detection, the classifier generates new quantum states and encodes the target response results of all candidate patches via quantum amplitudes, delivering a parallel detection effect of one computation with full-domain outputs. Additionally, the algorithm exhibits excellent numerical adaptability. When the image data matrix has a low condition number and high data complexity, it delivers logarithmic scaling performance, achieving exponential computational acceleration compared with state-of-the-art classic visual tracking algorithms. It fundamentally addresses the issues of detection latency and excessive computing load in high-complexity scenarios.
Against traditional visual tracking algorithms, the quantum visual tracking algorithm researched by WIMI achieves overwhelming technical advantages across four dimensions: computing power efficiency, tracking accuracy, scenario adaptability and resource utilization, meeting the demands of various high-end real-time visual tracking scenarios. In terms of computing power and real-time performance, the algorithm breaks through the upper limit of classic computing power by virtue of quantum superposition and parallel computation, enabling ultra-fast processing of large-scale video data and high-frequency dynamic frame sequences to satisfy the low-latency requirements of real-time tracking applications. For accuracy and robustness, the high-dimensional spatial feature representation capability of quantum systems can finely distinguish target features from complex background interferences, effectively mitigating tracking drift and target loss induced by light and shadow variations, cluttered backgrounds and target occlusion, and greatly boosting tracking stability. Furthermore, the quantum algorithm restructures computational logic to streamline redundant calculation steps. Relying on the efficient data encoding characteristics of quantum states, it significantly cuts down storage overhead and operational resource consumption for visual data. Its lightweight computational architecture is perfectly compatible with edge computing, embedded terminals and other devices with limited computing power. In terms of scenario adaptability, the algorithm can self-adapt to diverse extreme environments such as dynamic backgrounds, high-speed moving targets and complex light and shadow conditions, demonstrating strong universal compatibility. The quantum visual tracking algorithm effectively makes up for the application shortcomings of traditional visual technologies and can be deployed to empower core scenarios including high-end human-computer interaction and intelligent security monitoring, possessing extremely high practical application value.
The quantum visual tracking algorithm developed by WIMI realizes in-depth integration between quantum computing technology and the field of computer visual tracking, breaking through the technical limitations of classic visual algorithms and blazing a new trail for the iterative upgrading of computer vision technologies. With continuous iteration of quantum technologies and constant optimization of quantum algorithm systems, quantum visual technologies will further unleash their computing power advantages. The quantum visual tracking algorithm can be deployed in industrial scenarios such as intelligent robot navigation, autonomous driving visual perception and high-precision industrial visual inspection. In the future, quantum-empowered computer vision technologies will drive sustained industry development toward higher precision and higher-efficiency intelligence.
About WiMi Hologram Cloud Inc.
WiMi Hologram Cloud Inc. (NASDAQ: WIMI) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the release of its latest independently developed achievement—a quantum convolutional neural network for efficient image classification based on quantum random access memory (QRAM). This technology is oriented toward typical image classification tasks with large-scale input data and multiple output channels. It systematically addresses the key bottlenecks of existing quantum convolutional neural networks in terms of the number of qubits, circuit depth, and data loading efficiency, providing a feasible path for the practical application of quantum computing in real machine learning scenarios.
The success of traditional convolutional neural networks on classical computers relies on highly parallel matrix operations and massive storage resources. However, as input image resolution continues to increase and the number of feature channels keeps growing, the resource demands of CNNs in both training and inference stages grow exponentially. This not only increases the hardware burden but also limits the model's application space in edge computing, low-power devices, and high-real-time scenarios. The proposal of quantum convolutional neural networks is precisely an attempt to significantly compress the required computational resources while maintaining model expressiveness through quantum superposition and quantum parallelism.
However, existing QCNN models still face severe challenges at the engineering level. On one hand, the number of qubits in currently available quantum computing devices is limited and noise levels are high, making it difficult to directly handle large-scale input data; on the other hand, many quantum neural network schemes use amplitude encoding or angle encoding in the data encoding stage, requiring individual loading of each input sample—this process itself consumes a large amount of circuit depth, offsetting the potential advantages of quantum computing. How to process large-scale image data in a one-time, efficient manner under limited quantum resources has become the core problem constraining the practicalization of QCNNs.
In response to the above issues, WiMi re-examined the data access problem in quantum neural networks from the architectural level and introduced quantum random access memory (QRAM) as a key technology into the overall design of quantum convolutional neural networks. The core idea of QRAM is to use quantum superposition states to access multiple memory addresses simultaneously, enabling massive classical data to be indexed and invoked using a logarithmic number of qubits. This characteristic makes it naturally suitable for combination with the large-scale feature representation needs in deep learning.
In this released technical scheme, QRAM is not merely used as a simple data loading tool but is deeply embedded into the feature extraction and channel mapping process of QCNN, forming an entirely new model structure. After the input image is mapped into a data structure suitable for quantum storage during the classical preprocessing stage, the corresponding quantum state representation is constructed through QRAM. Unlike traditional pixel-by-pixel or block-by-block loading methods, this scheme allows multiple spatial locations and feature dimensions to exist simultaneously in the quantum state, thereby enabling parallel access to large-scale input data in a single quantum operation.
At the convolution computation level, the model proposed by WiMi redefines the implementation of quantum convolution kernels. In traditional QCNN, convolution operations are often realized through local quantum gate combinations, which significantly increase circuit depth as input scale grows. This technology, leveraging the parallel addressing capability provided by QRAM, matches convolution kernel parameters and input features in the form of quantum states, transforming the convolution process into a series of controlled quantum operations. This design significantly weakens the coupling relationship between the depth growth of the convolution layer and input size, allowing the model to maintain a relatively shallow quantum circuit structure even when processing high-resolution images or multi-channel features.
In terms of output channel expansion, this technology also embodies the design philosophy of resource efficiency. In classical CNNs, increasing the number of output channels often leads to linear or even super-linear growth in computation and storage demands, while in quantum architectures, blindly expanding channels would quickly exhaust available qubit resources. The channel mapping mechanism based on QRAM proposed by WiMi introduces auxiliary index registers in the quantum state, allowing multiple output channels to exist in superposition within the same quantum circuit, with selective readout during measurement to complete classification decisions. This mechanism significantly reduces the direct consumption of quantum resources caused by channel count growth, enabling the model to adapt to more complex classification tasks.
In the overall training and inference process, this QCNN model adopts a typical hybrid quantum-classical architecture. Parameter updates and loss function evaluation are completed on the classical computing side, while core feature extraction and mapping processes are handled by quantum circuits. This design fully considers the current development stage of quantum hardware, avoiding dependence on large-scale fault-tolerant quantum computers while maximizing the advantages of quantum computing in high-dimensional feature space processing. Through repeated iterative optimization, the model achieves a good balance among qubit count, circuit depth, and runtime while ensuring classification accuracy.
During the experimental validation phase, WiMi conducted systematic evaluations of this model on multiple sets of image classification tasks with different scales. The results show that, under conditions of significantly increased input data scale and continuously expanded output channel count, the quantum convolutional neural network based on QRAM outperforms existing similar QCNN schemes in both resource consumption and circuit depth while maintaining competitive classification performance. This result validates the feasibility of this technology in processing large-scale machine learning tasks in resource-constrained quantum environments.
WiMi's quantum convolutional neural network for efficient image classification based on quantum random access memory (QRAM) not only expands the design space of quantum machine learning models in theory but also provides a practically feasible solution path for large-scale data processing at the engineering level. With the continuous improvement of quantum hardware capabilities, the QRAM-based QCNN architecture is expected to further amplify its parallel computing advantages, offering new solutions for complex visual tasks and laying a solid foundation for the actual deployment of future quantum intelligent systems.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced that they are researching the use of neural networks for machine learning to optimize parameters in the dual-field quantum key distribution (TF-QKD) system. This innovative approach aims to leverage the powerful fitting ability and generalization performance of neural networks to directly predict the optimal parameter configuration for the TF-QKD system, significantly reducing computation time and resource consumption.
In the study, WiMi trained and evaluated three different types of neural network models:
Backpropagation Neural Network (BPNN): Based on the error backpropagation algorithm, BPNN minimizes prediction errors by continuously adjusting the network weights and biases. Due to its flexibility and wide applicability, BPNN has become the preferred model in many fields.
Radial Basis Function Neural Network (RBFNN): Using radial basis functions as activation functions for the hidden layer neurons, RBFNN efficiently handles nonlinear problems and is particularly suitable for high-dimensional data and scenarios requiring high precision.
Generalized Regression Neural Network (GRNN): Based on probability density estimation, GRNN uses kernel function methods to achieve nonlinear regression, excelling in handling small sample data and uncertainty issues.
Through training and testing these three neural network models, WiMi found that all models could accurately predict the optimal parameters of the TF-QKD system to some extent. Among them, RBFNN and GRNN performed especially well in high-dimensional parameter spaces, showing higher prediction accuracy. Compared to LSA, the neural network-based prediction method achieved a significant reduction in computation time, cutting it by multiple orders of magnitude. BPNN, due to its relatively simple structure, had the fastest computation speed; whereas RBFNN and GRNN, though slightly more complex in terms of computational cost, still remained within acceptable limits, and their enhanced prediction accuracy often brought more practical application value.
Considering the varying optimization needs of different TF-QKD systems (such as real-time requirements and precision demands), WiMi also conducted a comprehensive comparison of prediction accuracy and time consumption. The results indicate that for scenarios requiring rapid response with lower precision demands, BPNN is the ideal choice. On the other hand, for applications that prioritize high accuracy and can tolerate certain computation time, RBFNN or GRNN is more suitable.
The main technical advantage of using neural networks for TF-QKD system parameter optimization lies in significantly reducing the computational complexity of parameter optimization, accelerating the key generation rate, and enhancing the system's real-time responsiveness. Neural networks can automatically learn and adapt to changes in the quantum communication environment, providing the possibility for dynamic adjustment of system parameters. As quantum communication technology develops, neural network models can be further upgraded and optimized to accommodate more complex quantum key distribution protocols and higher security requirements.
In the future, WiMi will continue to deepen its research into neural networks for TF-QKD parameter optimization, exploring more advanced neural network architectures and training strategies, such as deep learning, reinforcement learning, etc., with the aim of achieving more efficient and intelligent quantum key distribution systems. At the same time, it will strengthen integration with quantum communication hardware platforms to promote the practical application and commercialization of quantum communication technologies, contributing to the development of secure and efficient quantum communication networks.
About WiMi Hologram Cloud
WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
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, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WIMI" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, has completed systematic benchmark testing on fully parameterized quantum convolutional neural networks. Its research team has proposed a quantum neural network model inspired by classical convolutional neural networks. Throughout the entire computational process, this model only adopts two-qubit interactions. It not only retains the simplicity of the network architecture but also greatly lowers the implementation difficulty of quantum circuits, laying a crucial foundation for its practical deployment on noisy intermediate-scale quantum computers in the future.
Unlike traditional deep learning networks, classical convolutional neural networks generally rely on massive parameters and sophisticated hierarchical structures to perform feature extraction. For instance, in image classification tasks, convolutional layers continuously scan input images to extract local features, after which pooling layers compress feature dimensions, and fully connected layers ultimately generate classification decisions. Although this architecture has achieved remarkable success, the training and inference costs surge exponentially as model scales keep expanding.
The research team at WIMI holds that quantum computing is inherently capable of processing high-dimensional feature spaces. A quantum system consisting of n qubits can represent a 2ⁿ-dimensional state space simultaneously, which enables it to tackle complex pattern recognition tasks with far fewer parameters than classical algorithms. This potential advantage has driven the research team to rethink the implementation of convolutional neural networks and attempt to build convolutional quantum neural network architectures via quantum gate operations.
To this end, WIMI has designed a fully parameterized Quantum Convolutional Neural Network (QCNN) architecture. The network mainly comprises a quantum data encoding layer, a quantum convolutional layer, a quantum pooling layer, a feature compression layer and a quantum classification layer. Distinct from many complex quantum networks built on multi-qubit gate operations, this model takes two-qubit interactions as its basic computational unit, which effectively controls circuit depth and mitigates noise accumulation.
In the data input phase, raw classical images first go through preprocessing modules for dimensionality reduction and normalization. Given the limited number of qubits supported by current quantum hardware, researchers need to map high-dimensional image data onto a finite qubit space. To address this challenge, WIMI has investigated multiple classical data preprocessing strategies including principal component analysis, feature selection and image compression techniques, ensuring the input data adapts to quantum computing resource constraints while preserving core information.
Once preprocessing finishes, data proceeds to the quantum encoding stage. As a cornerstone of quantum machine learning, quantum encoding converts classical data into quantum state representations. WIMI has systematically compared a range of quantum encoding schemes such as angle encoding, amplitude encoding and hybrid encoding methods. Experimental results reveal that different encoding strategies directly determine the network's representation capacity and training efficiency. Angle encoding leverages rotation gates to map data into the parameter space of quantum states, featuring straightforward implementation and strong noise robustness. By contrast, amplitude encoding can express higher-dimensional data with fewer qubits yet comes with relatively higher implementation complexity.
After data is encoded into quantum states, the network executes quantum convolution operations. Classical convolutional neural networks extract features by sliding convolutional kernels over local regions, while QCNN implements convolution via parameterized two-qubit gates. WIMI has constructed a series of trainable quantum gate arrays that establish correlations between different features through quantum entanglement. Since quantum states can exist as superpositions of multiple states concurrently, the network can process numerous potential feature combinations in parallel within a single computation, delivering more efficient feature extraction than classical convolution methods.
Notably, the model follows a fully parameterized design principle. While certain quantum gate parameters are often fixed in conventional quantum neural networks, the architecture proposed by WIMI allows all key quantum gate parameters to be updated during training. This design substantially boosts the model's expressive power, enabling it to learn intricate data distribution patterns.
Following quantum convolution, the network enters the quantum pooling stage. Classical pooling layers reduce feature dimensions through max pooling or average pooling, whereas quantum pooling screens valid information via measurement, entanglement reconstruction and quantum state compression. By gradually cutting down the number of qubits involved in computation, the network reduces subsequent computational complexity while retaining critical feature information.
From an information processing perspective, this mechanism mirrors the feature abstraction process in classical deep learning. As network layers deepen, low-level features including edges, textures and shapes in input images are progressively converted into high-level semantic abstract representations, supporting the final classification task.
WIMI has tested multiple QCNN configurations covering diverse parameterized quantum circuit architectures, data encoding schemes, loss functions and optimization algorithm combinations. Experimental results demonstrate that QCNN achieves outstanding classification performance across most test scenarios. Most importantly, even with far fewer parameters than classical convolutional neural networks, QCNN matches or even exceeds the classification accuracy of traditional CNNs. This outcome proves that quantum neural networks deliver higher parameter utilization efficiency; in other words, quantum models can learn richer data features with fewer trainable parameters and thus achieve superior overall performance.
Further analysis by WIMI attributes this competitive edge to the high-dimensional feature representation capability enabled by quantum entanglement. In classical neural networks, information exchange between neurons is constrained by connection topology, yet quantum entanglement builds non-classical correlations among multiple qubits, empowering the network with stronger representation capacity under limited computing resources. To enhance training stability, the research team has thoroughly explored optimization workflows. Training quantum neural networks frequently suffers from vanishing gradients and flat parameter regions, an issue known as the barren plateau phenomenon. To resolve this problem, the team evaluated various optimizers including stochastic gradient descent, Adam optimizer and quantum-specific optimization algorithms, and analyzed how different cost functions affect model training.
Experimental results verify that well-designed parameter initialization strategies and optimization pipelines can effectively ease training difficulties, allowing the network to converge stably within fewer training epochs. This research delivers valuable practical experience for training large-scale quantum neural networks in the future.
It is foreseeable that with the continuous advancement of quantum computing technologies, quantum convolutional neural networks will evolve into a core component of next-generation intelligent computing. From current classical data classification experiments to large-scale artificial intelligence applications in complex future scenarios, QCNN exhibits enormous potential to reshape the evolution of machine learning. WIMI's research not only advances the theoretical development of quantum machine learning but also pioneers innovative technical routes for developing efficient, low-parameter and high-performance new artificial intelligence systems, laying a solid foundation for the advent of the quantum intelligence era.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, has announced its research into the Synergic Quantum Generative Network (SQGEN).
Addressing the issues of unstable training, high quantum resource consumption, and low training efficiency in traditional Quantum Generative Adversarial Networks (QGAN), WiMi has designed a new parallel quantum learning framework and algorithm optimizations aimed at achieving a dual breakthrough in the performance and practicality of quantum generative machine learning.
The SQGEN architecture completes technological innovation across four aspects: operating framework, algorithm optimization, function design, and communication mechanism. Different from the traditional QGAN serial operation mode, SQGEN establishes a brand-new parallel quantum learning framework that enables the generator and discriminator to run synchronously and interact in real time within a quantum computing environment. Leveraging the superposition and entanglement characteristics of qubits, the model can process multiple sets of data samples in parallel and simultaneously complete the data generation and authenticity discrimination processes. This fundamentally accelerates the model training process from the underlying architecture and significantly improves the overall algorithm operation efficiency.
At the quantum circuit optimization level, SQGEN innovatively introduces the Nelder-Mead optimization algorithm, overturning the traditional gradient-based optimization mode. This algorithm does not rely on gradient information calculations, perfectly adapting to the technical issue of difficult precise gradient computation in quantum computing scenarios, thereby greatly improving the adaptability and operational stability of the quantum circuits. At the same time, the team at WiMi has carried out special optimization on the model cost function by relaxing the reversibility constraints and raising the lower bound of cost function computation, effectively reducing the number of function evaluations within a single training cycle. This optimization not only significantly reduces ineffective consumption of quantum hardware resources but also avoids training oscillation problems at the algorithm level, enhancing model training stability. SQGEN takes the game balance between the generator and the discriminator as the core measurement standard of the cost function. When the two core components both reach their optimal operating states, the cost function achieves its maximum value, ensuring that the model continues to iterate and converge to the optimal solution.
Meanwhile, the technology leverages quantum entanglement characteristics to build a dedicated quantum communication channel, achieving high-speed and synchronous information transmission between modules, and is equipped with an efficient synchronous update mechanism. This solves the core problems of asynchronous module updates and poor model stability in traditional QGAN training processes, significantly improving the model's robustness and generalization capability.
Compared with traditional QGAN models, the SQGEN architecture researched by WiMi has significant technical advantages. The parallelization framework and optimized quantum circuits effectively shorten the model convergence cycle and achieve a significant improvement in training speed; the cost function optimization and lightweight evaluation mechanism greatly reduce quantum resource consumption. At the same time, the dynamic game optimization and synchronous operation mechanism effectively enhance the authenticity, diversity, and precision of the generated data, solving the problems of unstable training and poor generation quality in traditional models.
As a brand-new quantum machine learning generative framework, SQGEN provides an innovative technical solution for the field of quantum generative network technology through multiple innovations in architecture, algorithms, communication, and optimization. In the future, with the continuous upgrading of quantum computing hardware technology and the continuous iteration of quantum resources, SQGEN technology is expected to be widely applied in multiple industry fields, driving technological innovation and industrial upgrading in the intersection of quantum computing and artificial intelligence.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announces the release of a core technology for hybrid Quantum Convolutional Neural Network (QCNN), proposing and implementing a Quantum Kernel Convolution (QKC) scheme capable of running on current noisy intermediate-scale quantum (NISQ) devices, thereby providing a practically feasible engineering path for quantum-enhanced image classification models.
The core objective of this technology is not simply to embed quantum circuits into classical neural networks, but rather, starting from the computationally intensive core operation of convolution, to rethink the computational approach to feature extraction and dimensionality reduction. WiMi points out that classical convolutional layers essentially rely on sliding windows and linear weighted summation to accomplish local feature extraction, whereas quantum computing inherently possesses the capability of high-dimensional Hilbert space representation and quantum parallelism. If local image patches can be mapped into quantum states and feature mixing can be achieved through controlled entanglement evolution, it becomes possible to realize an equivalent or even more expressive feature extraction mechanism under a lower parameter scale.
WiMi points out that this pooling approach is essentially an information reallocation and selection mechanism, which can achieve dimensionality compression without explicitly discarding information, thereby significantly reducing the computational burden on subsequent quantum circuits and classical networks.
At the overall system architecture level, this hybrid QCNN adopts a layered design of classical-quantum synergy. The classical neural network is responsible for completing preliminary normalization of input data, dimensionality adjustment, and final classification decisions, while the quantum convolutional layer is embedded at the critical position of feature extraction, functioning as a quantum acceleration module. This design enables the model to fully leverage mature classical deep learning toolchains while introducing quantum advantages at key computational nodes, thereby avoiding, from an engineering perspective, the scalability issues that fully quantum models face under current hardware conditions.
In terms of technical implementation, WiMi, based on the Qiskit quantum computing development framework, completed a complete engineering realization from quantum circuit construction, parameterized training, to integration with classical deep learning frameworks. The quantum convolutional layer is encapsulated as a reusable module interface that can be directly embedded into existing deep learning training workflows. During the training process, the model adopts a hybrid optimization strategy: classical backpropagation algorithms are used to update the parameters of the classical network, while the parameter-shift rule is utilized to estimate gradients for the quantum circuit parameters, achieving end-to-end joint training. This implementation path effectively addresses the challenge of gradient propagation between quantum and classical components, providing engineering assurance for the trainability of hybrid models.
In the experimental validation phase, WiMi selected the MNIST handwritten digit dataset as the benchmark task and conducted a systematic evaluation of the proposed hybrid QCNN model. The experimental results show that, with a significantly lower number of parameters compared to traditional CNN models, this hybrid model is still able to achieve classification accuracy comparable to that of classical models. Particularly noteworthy is that after replacing some classical convolutional layers with quantum convolutional layers, the overall parameter scale and computational complexity of the model are effectively controlled while maintaining stable convergence performance. These results demonstrate that quantum kernel convolution possesses practical feasibility in real tasks, rather than remaining merely at the theoretical level.
Furthermore, through analysis of intermediate quantum states and measurement outcomes, WiMi verified the effectiveness of the entanglement-based quantum pooling mechanism in the dimensionality reduction process. Experiments show that quantum pooling not only compresses feature dimensions but also preserves discriminative information critical to the classification task. This finding provides a new entry point for interpretability research in quantum neural networks and lays the foundation for subsequent extensions to more complex datasets and tasks.
This hybrid quantum convolutional neural network technology is not an isolated algorithmic innovation, but rather an important step taken around WiMi's long-term strategic goal of deployable quantum-enhanced artificial intelligence. By emphasizing low depth, modularity, and high compatibility with existing AI ecosystems, this technology provides a realistic path for quantum computing to move from the laboratory to practical applications. In the future, further exploration will be conducted on the application potential of this architecture in higher-resolution images, multi-channel data, and other perception tasks, while continuously optimizing circuit designs in conjunction with the development of quantum hardware.
The release of WiMi's hybrid neural network quantum kernel convolution technology marks an important step forward for quantum machine learning, moving from proof-of-concept toward engineering implementation. It not only demonstrates the practical value of quantum computing in real-world image recognition tasks but also provides clear design ideas for the future construction of quantum-classical collaborative computing systems. With the continuous improvement of quantum hardware performance and the ongoing maturation of development toolchains, the hybrid QCNN framework built by WiMi is expected to play a role in a broader range of artificial intelligence applications, becoming an important component of next-generation intelligent computing technology.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
WiMi Hologram Cloud Inc. (NASDAQ: WIMI - Get Free Report) was the target of a significant drop in short interest in March. As of March 13th, there was short interest totaling 143,634 shares, a drop of 67.9% from the February 26th total of 446,801 shares. Currently, 2.3% of the shares of the stock are sold short.
WiMi Hologram Cloud Inc. (NASDAQ:WIMI – Get Free Report) was the recipient of a large increase in short interest during the month of March. As of March 31st, there was short interest totaling 347,820 shares, an increase of 142.2% from the March 15th total of 143,634 shares. Based on an average trading volume of 61,224 shares, the days-to-cover ratio is currently 5.7 days. Approximately 5.5% of the company’s stock are sold short.
WiMi Hologram Cloud Price Performance NASDAQ WIMI opened at $1.85 on Monday. WiMi Hologram Cloud has a 12 month low of $1.56 and a 12 month high of $7.26. The stock has a 50 day moving average of $1.86 and a 200-day moving average of $2.77.
Institutional Investors Weigh In On WiMi Hologram Cloud A hedge fund recently bought a new stake in WiMi Hologram Cloud stock. XTX Topco Ltd acquired a new position in shares of WiMi Hologram Cloud Inc. (NASDAQ:WIMI – Free Report) in the 4th quarter, according to its most recent 13F filing with the Securities and Exchange Commission (SEC). The firm acquired 30,569 shares of the company’s stock, valued at approximately $72,000. XTX Topco Ltd owned about 0.31% of WiMi Hologram Cloud as of its most recent SEC filing. Institutional investors own 0.96% of the company’s stock.
Analyst Upgrades and Downgrades Separately, Weiss Ratings reaffirmed a “sell (d)” rating on shares of WiMi Hologram Cloud in a research report on Wednesday, January 21st. One research analyst has rated the stock with a Sell rating, According to data from MarketBeat, WiMi Hologram Cloud has an average rating of “Sell”.
View Our Latest Analysis on WIMI
About WiMi Hologram Cloud (Get Free Report)
WiMi Hologram Cloud Inc is a technology company specializing in the development and delivery of cloud-based holographic augmented reality (AR) solutions. Its core offering is a proprietary holographic cloud platform that integrates advanced computer vision, artificial intelligence and 3D rendering algorithms to power immersive AR experiences. The company’s services span content creation, distribution and deployment, enabling enterprises and developers to integrate holographic effects into applications ranging from live streaming and virtual events to interactive advertising campaigns.
Through its platform, WiMi Hologram Cloud distributes a digital library of holographic assets—such as 3D models, animations and special effects—that can be accessed on demand.
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, /PRNewswire/ -- WiMi Hologram Cloud Inc. (Nasdaq: WIMI) ("WiMi" or the "Company"), a leading hologram augmented reality ("AR") technology provider, today announced that it has filed its annual report on Form 20-F for the fiscal year ended December 31, 2025, with the Securities and Exchange Commission (the "SEC").
The company reported that its net income increased by RMB 243.8 million, or 235.9%, from RMB 103.3 million for the year ended December 31, 2024, to RMB 347.1 million (USD 49.4 million) for the year ended December 31, 2025, representing two consecutive years of profitability with substantial profit growth and significantly improved performance.
For the year ended December 31, 2025, the company incurred approximately RMB 147.6 million (USD 21.0 million) in operating expenses, representing a decrease of approximately RMB 35.4 million, or 19.4%, from approximately RMB 183.1 million for the year ended December 31, 2024, reflecting outstanding cost-reduction effects, remarkable expense optimization, and enhanced cost control capabilities.
Moreover, the company's working capital was approximately RMB 2,611.6 million (USD 371.6 million) as of December 31, 2025, representing a 105.8% increase from RMB 1,269.2 million (USD 176.6 million) as of December 31, 2024. The company maintained sufficient cash flow and capital reserves, demonstrating extremely strong risk resistance capacity.
The information disclosed in this press release does not purport to be complete and is qualified in its entirety by reference to the Company's annual report on Form 20-F. The annual report, which contains the Company's audited consolidate statements, can be accessed on the SEC's website at http://www.sec.gov and on the Company's investor relations website at http://ir.wimiar.com/.
The Company will provide a copy of its annual report containing the audited consolidated financial statements, free of charge, to its shareholders upon request. Requests should be directed to Investor Relations Department, Room#1508, 4th Building, Zhubang 2000 Business Center, No. 97, Balizhuang Xili, Chaoyang District, Beijing,The People's Republic of China.
About WIMI Hologram Cloud Inc.
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), whose commercial operations began in 2015, is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies. For more information, please visit http://ir.wimiar.com.
Safe Harbor / Forward-Looking Statement
This press release contains "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates" and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release, as well as the Company's strategic and operational plans, contain forward-looking statements. The Company may also make written or oral forward-looking statements in its periodic reports to the U.S. Securities and Exchange Commission ("SEC") on Forms 20-F and 6-K, in its annual report to shareholders, in press releases and other written materials and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. A number of factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services. Further information regarding these and other risks is included in the Company's annual report on Form 20-F and current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release, and the Company does not undertake any obligation to update any forward-looking statement, except as required under applicable laws.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, launched a breakthrough technological achievement—a Multi-Scale Fusion Quantum Deep Convolutional Neural Network for Text Classification. This technology is based on an advanced quantum convolutional architecture and an innovative multi-scale feature fusion mechanism, aimed at solving bottlenecks in the field of natural language processing (NLP) such as high model complexity, limited embedding representations, and difficulty in scaling quantum networks.
This brand-new Quantum Deep Convolutional Neural Network (QDCNN) not only achieves breakthrough improvements in key dimensions such as parameter scale, computational complexity, and training efficiency but also realizes unified modeling of word-level and sentence-level information, obtaining performance superior to existing state-of-the-art quantum models on multiple standard datasets. The release of this technology is regarded as an important milestone in promoting quantum natural language processing from theory to practical application.
In recent years, with the widespread success of large neural network architectures such as Transformer in the NLP field, the industry's demand for fast, scalable, and low-energy-consumption natural language processing models has been growing. Quantum machine learning is considered a potential key technological direction to break through this dilemma. After in-depth analysis of these bottlenecks, the WiMi R&D team realized that a more structured, scalable network framework capable of fully leveraging the advantages of quantum computing is essential. Therefore, they focused their attention on convolutional structures—a neural network architecture that has been the most time-tested and scalable in human modeling of visual and textual data.
The first key innovation proposed by WiMi is Quantum Depthwise Separable Convolution.
Traditional depthwise separable convolution consists of two steps:
Depthwise Convolution performed independently on different channels; Pointwise Convolution that linearly combines multiple channels. This structure significantly reduces the number of parameters in classical neural networks, making it the core idea of lightweight CNN design. The WiMi research team cleverly mapped this concept to the quantum circuit architecture, achieving multiple breakthroughs:
First, by encoding input features into quantum states and applying quantum convolution operations in a per-channel manner, the model avoids the exponential increase in parameter consumption that occurs when traditional quantum networks expand in width or depth. Quantum depthwise convolution allows each qubit or qubit cluster to independently process word-level local semantics, thereby preserving the locality advantage of convolution operations.
Second, in the pointwise quantum convolution module, trainable quantum gate combinations are used to achieve interaction and channel fusion between quantum states, successfully compressing multi-dimensional representations into a more expressive semantic space. Quantum gates can achieve higher expressive power than classical linear layers, particularly when operating in high-dimensional Hilbert space, where their modeling capability has a natural advantage.
Ultimately, this quantum depthwise separable convolution significantly reduces the number of controlled rotation gates required by traditional quantum convolution structures, making the model's execution efficiency on simulators and real quantum hardware several times higher than existing quantum convolution models. Quantum depthwise separable convolution not only brings a lightweight structure to quantum NLP models but also solves the scalability problem of quantum networks in text processing, making quantum convolution a core building block for NLP QNNs.
In addition, text classification is a task that relies on both local information and overall semantics. For example, sentiment analysis needs to focus on the polarity of individual words as well as understand the contextual semantics of the entire sentence. To this end, traditional NLP architectures often adopt multi-layer convolution, bidirectional LSTM, or Transformer self-attention structures to simultaneously capture features at different semantic scales.
In quantum NLP research, how to enable quantum models to simultaneously understand local word meanings and global sentence meanings has been a problem that has not yet been fully solved. The multi-scale feature fusion mechanism (Multi-Scale Fusion Mechanism) proposed by WiMi this time effectively fills this gap.
This mechanism consists of two key parts.
The first part is word-level feature extraction, utilizing quantum convolution to extract local n-gram representations, such as sentiment polarity words, adjective structures, negation word combinations, etc. Quantum states can simultaneously encode multiple word combination patterns in a superposition manner, thus having a natural advantage in n-gram modeling.
The second part is sentence-level feature extraction, extracting semantic structures across sentence levels through multi-layer quantum convolution and quantum pooling operations. Quantum pooling achieves dimension compression through measurement and incomplete observation mechanisms while preserving key information in the quantum state, enabling the model to effectively capture the overall theme and paragraph structure of sentences.
Most critically, the proposed feature fusion module can merge word-level and sentence-level features into the same semantic space in a trainable quantum gate manner. The fused quantum state possesses both local sensitivity and the ability to reflect overall semantics, exhibiting richer feature capabilities than traditional QNNs. Through ablation experiments, WiMi found that this multi-scale feature fusion mechanism contributes significantly to the model's final performance improvement, bringing more than 6% accuracy gains on multiple datasets.
On two public text classification benchmarks, the research team conducted complete experimental validation. The results show that this quantum deep convolutional model achieves leading performance on multiple metrics: it improves accuracy while reducing model parameters by more than 30% compared to classical CNNs; it outperforms various existing quantum models, including QRNN, QSAM, and QTF (Quantum Transformer), by 4% to 10% in accuracy; it maintains stable performance even in noisy quantum hardware simulation environments, demonstrating strong noise resistance.
Ablation experiments further validate the practical value of multi-scale feature fusion and quantum depthwise separable convolution, proving that each design element of this architecture makes a key contribution to overall performance.
The launch of this technology by WiMi has profound industrial significance. As quantum computing enters the early practicalization stage, the scale, depth, and adaptability of quantum models will become key competitive points. It is not only a technological innovation but also a profound breakthrough in the field of quantum natural language processing. It validates the feasibility, effectiveness, and scalability of quantum convolutional structures in text processing, bringing a new paradigm to quantum NLP architectures.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, released a key foundational technology oriented toward quantum neural networks—the Repeated Amplitude Encoding method (Repeated Amplitude Encoding, RAE). This technology effectively enhances the mapping capability of quantum neural networks to complex feature spaces by performing repeated amplitude encoding of the same set of classical data across multiple qubit blocks, thereby providing an entirely new engineered path for constructing quantum neural network models that possess high expressive power while maintaining controllable resource usage.
From a technical background perspective, existing mainstream quantum neural networks generally rely on parameterized quantum gates to encode input data during the feature mapping stage. These quantum gates are mathematically linear or unitary transformations in essence, and the feature mappings formed by their combinations are often limited by circuit depth, the number of qubits, and the scale of trainable parameters. Although the quantum state itself resides in an exponentially high-dimensional space, in practical models, the limited encoding methods make it difficult to fully unleash this high-dimensional advantage, resulting in issues such as insufficient mapping capability and weak category scalability in complex classification tasks.
To address the above bottlenecks, WiMi, starting from the fundamental mechanism of quantum state representation, re-examined the way classical data enters the quantum system. The traditional amplitude encoding method typically maps a set of normalized classical feature vectors into the probability amplitudes of a single quantum state. Its advantage lies in high qubit usage efficiency, but the disadvantage is that the feature distribution after a single encoding is easily diluted by linear operations during the evolution of the quantum circuit, thereby limiting the ability of subsequent quantum neural networks to model complex nonlinear structures.
To verify the effectiveness of this technology in real tasks, WiMi used the classic image classification benchmark dataset MNIST as the experimental platform and conducted a systematic evaluation of the repeated amplitude encoding method. In the experiments, researchers embedded this method into various typical quantum neural network architectures and compared it with mainstream data loading methods such as traditional amplitude encoding and angle encoding.
The experimental results show that, under the condition of a fixed number of classes, quantum neural networks adopting repeated amplitude encoding outperform the control methods in classification accuracy, convergence stability, and robustness to parameter initialization. This indicates that, even under the same task complexity, repeated amplitude encoding can provide the model with more discriminative feature representations.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, is researching quantum computing optimization based on multi-objective deep reinforcement learning. The core of this innovative solution lies in breaking the limitations of traditional single-objective optimization and constructing a global optimization framework that takes into account multi-dimensional constraints.
By using the single-process quantum control optimization results as the truncation threshold and reward function migration strategy for multi-objective optimization, effective reuse of optimization knowledge is achieved. This not only avoids redundant computation during the multi-objective optimization process but also improves the model's convergence speed. At the same time, by designing a multi-objective reward function that comprehensively considers various key indicators in the quantum control process, synergistic optimization of multiple factors such as quantum gate fidelity, operational efficiency, noise suppression, and energy consumption control is realized, ultimately obtaining a globally optimal control solution rather than a locally optimal solution targeting only a single error metric, effectively improving the control precision and robustness of the quantum system. WiMi's multi-objective deep reinforcement learning method, through deep learning and modeling of the dynamic characteristics of qubits, can adapt in real time to the dynamic changes of quantum systems, automatically adjust control strategies, and effectively suppress the impact of environmental noise and crosstalk effects.
The control of quantum systems essentially involves precisely regulating external physical fields to enable qubits to complete a series of processes such as state preparation, quantum gate operations, and state readout according to preset logic. Its core challenge lies in the openness and complexity of quantum systems—qubits are susceptible to environmental noise, crosstalk effects, decoherence, and other factors. Moreover, in multi-process quantum control, there exist multiple mutually constraining optimization objectives. Traditional control methods struggle to achieve global optimality. Traditional quantum control strategies are mostly based on model-driven optimization algorithms that rely on precise mathematical modeling of quantum systems. However, the dynamic characteristics of actual quantum systems are complex and easily affected by external interference, leading to deviations between the model and the actual system, which in turn affects control precision. At the same time, traditional methods mostly optimize for a single control objective, making them prone to falling into local optimal solutions. They cannot balance multi-dimensional requirements such as quantum gate fidelity, operation speed, and energy consumption control, making it difficult to adapt to the control scenarios of large-scale quantum systems.
The rapid iteration of machine learning technology has provided a completely new approach to solving the challenges of quantum control. Its powerful data-driven learning capability and adaptive optimization characteristics can effectively adapt to the complexity and uncertainty of quantum systems. Among them, reinforcement learning, as an important branch of machine learning, breaks through the dependence of traditional optimization algorithms on complete parameter sets. Through real-time interaction between the agent and the environment, it dynamically adjusts control strategies during the trial-and-error process to achieve gradual convergence of optimization objectives. This closed-loop mechanism of interaction-feedback-iteration highly aligns with the real-time control requirements of quantum systems, providing core technical support for the optimization of control strategies in quantum computing.
Quantum computing, as the core development direction of next-generation information technology, cannot achieve its practical application process without continuous breakthroughs in core technologies. In the future, WiMi will continue to focus on the forefront of quantum technology, taking technological innovation as the core driving force, deeply cultivating the interdisciplinary fields of quantum control, quantum algorithms, and artificial intelligence, continuously breaking through technical bottlenecks, promoting the development of quantum computing technology, and assisting various industries in achieving transformation and upgrading with the help of quantum computing.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company") is a leading global Hologram Augmented Reality ("AR") Technology provider. A quantum deep convolutional neural network technology oriented toward image recognition tasks has achieved phased progress. This technology provides a new technical path to address the challenges faced by traditional deep learning in terms of computational complexity, memory consumption, and training efficiency by constructing a quantum deep convolutional neural network model and combining it with a quantum-classical hybrid training mechanism. The proposal of this technology marks a further deepening of the application of quantum machine learning in typical artificial intelligence tasks such as image recognition, and also provides a new research direction for the realization of future large-scale quantum intelligent computing systems.
WiMi has proposed a quantum deep convolutional neural network model for image recognition tasks. The model takes quantum parameterized circuits as its core computing structure, performs feature extraction on image data through quantum convolutional layers, and utilizes a quantum classification layer to complete the final recognition task. The overall architecture draws on the hierarchical structure of classical deep convolutional neural networks in terms of design philosophy, while fully leveraging the parallel computing capability of quantum circuits, enabling the model to achieve higher computational efficiency when processing high-dimensional data.
At the technical architecture level, the quantum deep convolutional neural network consists of a data encoding module, a quantum convolutional layer module, a quantum feature fusion module, and a quantum classification module. The system first maps classical image data to the quantum state space through the data encoding module. Since quantum computers process quantum state information, it is necessary to convert pixel information into probability amplitudes of qubits through specific encoding strategies. This process is usually achieved through amplitude encoding, angle encoding, or hybrid encoding methods, enabling image data to be effectively processed by quantum circuits.
After completing data encoding, the quantum convolutional layer begins to perform feature extraction on the quantum states. Similar to the convolution kernels in classical convolutional neural networks, the quantum convolutional layer operates on local qubits through a set of parameterized quantum gates. These quantum gates can form functions similar to convolution filters, performing feature mapping on the input quantum states. Since quantum gate operations can act on multiple superposition states simultaneously, they enable highly parallel feature extraction processes when handling complex image structures.
The core of the quantum convolutional layer lies in the design of parameterized quantum circuits. WiMi's circuit consists of basic quantum logic gates such as rotation gates, control gates, and entanglement gates, controlling the evolution process of quantum states through trainable parameters. Rotation gates are used to adjust the state angles of qubits, control gates are used to construct correlation relationships between qubits, and entanglement gates enable the establishment of complex quantum entanglement structures among multiple qubits. Through these operations, the quantum circuit can form feature extraction capabilities similar to those of classical convolutional layers while possessing higher expressive power.
As the quantum convolutional layers are stacked layer by layer, the network is able to gradually extract higher-level image features. Shallow quantum convolutional layers are primarily responsible for capturing low-level features such as edges and textures in the image, while deeper quantum convolutional layers can identify more complex shapes and structural information. Since quantum states remain in superposition during the computation process, the entire feature extraction process can be performed in parallel within an exponentially large state space, thereby significantly improving computational efficiency.
Following the quantum convolutional layers, the system introduces a quantum feature fusion module. This module integrates feature information from different qubits through additional quantum gate operations. Through quantum entanglement mechanisms, image features from different regions can be effectively fused, thereby forming higher-dimensional feature representations with greater discriminative power. Compared to the feature fusion methods in traditional neural networks that rely on matrix multiplication, quantum feature fusion completes information integration through the quantum state evolution process, which can reduce some computational overhead.
After completing feature extraction and fusion, the network enters the quantum classification stage. The quantum classification layer outputs classification results by measuring the probability distribution of the quantum states. Specifically, the quantum circuit measures several key qubits in the final stage and determines the category of the input image through statistical analysis of the measurement probabilities. This process is similar to the fully connected classification layer in classical neural networks, but its computation occurs in the quantum state space, thus enabling the use of quantum parallelism to improve computational efficiency.
To train this quantum deep convolutional neural network, WiMi proposed a quantum-classical hybrid training scheme. Since current quantum hardware is still in the development stage, relying entirely on quantum devices for large-scale training presents certain difficulties. Therefore, the hybrid training strategy has become an effective solution. In this scheme, the quantum circuit is responsible for executing the forward computation process, while parameter updates are completed by classical computers.
During the training process, the system first encodes the image data into quantum states and completes feature extraction and classification calculations through the quantum circuit. Subsequently, by statistically analyzing the quantum measurement results, the error between the network output and the true label is obtained. Classical optimization algorithms calculate gradient information based on this error and update the trainable parameters in the quantum circuit. These parameters are then reloaded into the quantum circuit for the next round of computation, thereby forming a training process in which quantum and classical computing work collaboratively.
WiMi's hybrid training mechanism draws on the design philosophy of variational quantum algorithms. Variational quantum algorithms combine parameterized quantum circuits with classical optimizers, enabling quantum computing to solve complex problems under limited quantum resources. In this quantum deep convolutional neural network, the idea of variational quantum algorithms is applied to network parameter updates, thereby realizing the feasibility of model training.
From the perspective of computational complexity, the model is theoretically capable of providing significant computational advantages. Traditional deep convolutional neural networks typically exhibit polynomial growth in computational complexity with increasing network scale when processing high-dimensional image data. In contrast, the quantum deep convolutional neural network leverages quantum superposition and parallel computing capabilities to simultaneously process a large number of data states in an exponentially large state space, thereby achieving exponential computational acceleration in certain tasks.
In terms of experimental validation, WiMi conducted quantitative experimental testing of the model on a quantum simulation platform. The experimental results show that the quantum deep convolutional neural network can effectively learn image features in image classification tasks and achieve stable recognition performance. Although the current experimental scale is still limited by the number of qubits, the results have already demonstrated the feasibility of the model in image recognition tasks.
In terms of system implementation, the R&D team has built a complete software and algorithm framework. This framework supports functions such as quantum circuit construction, data encoding, training optimization, and model evaluation, enabling the quantum deep convolutional neural network to run on existing quantum simulation environments and early quantum hardware.
From the perspective of technological development trends, with the continuous advancement of quantum hardware, quantum machine learning models are expected to gradually move toward practical applications. As one of the important directions in quantum machine learning, quantum deep convolutional neural networks have broad application prospects in fields such as image recognition and video analysis. By leveraging the parallel capabilities of quantum computing, such models may demonstrate significant advantages when processing ultra-large-scale data.
WiMi stated that it will continue to optimize the structural design of this quantum deep convolutional neural network in the future, including improving the quantum convolutional layer structure, optimizing quantum data encoding methods, and enhancing the training efficiency of quantum circuits. At the same time, it also plans to explore more complex quantum neural network architectures, such as quantum residual networks, quantum attention mechanisms, and quantum generative models, to further improve the performance of quantum machine learning systems.
In addition, with the integration of quantum computing and high-performance computing platforms, hybrid quantum-classical computing architectures are expected to become an important component of next-generation intelligent computing systems. The research on quantum deep convolutional neural network technology not only provides a new technical route for image recognition but also lays an important foundation for the future development of quantum artificial intelligence.
WiMi's technology demonstrates the potential value of quantum computing in the field of artificial intelligence. By combining the physical properties of quantum computing with the model structures of deep learning, new intelligent systems with higher expressive power and stronger computational efficiency can be constructed. As quantum hardware gradually matures, such technologies are expected to achieve larger-scale applications in the future. Overall, this quantum deep convolutional neural network technology for image recognition provides new ideas for the development of quantum machine learning. In the future, as quantum computing technology continues to advance, such innovative technologies that integrate quantum computing and artificial intelligence are expected to play an even more important role in the field of intelligent computing.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, proposes a new high-performance fault-tolerant quantum computing technology based on multi-hypercube codes. This technology constructs a cascaded high-rate small-size quantum error-detection code system, which, while ensuring high fault-tolerant capability, significantly improves the quantum encoding rate and achieves high parallelism in logical gate operations. Compared with traditional quantum error correction frameworks, this new architecture can not only reduce physical resource consumption but also enhance logical computation throughput, providing a new technical path for building truly scalable large-scale quantum computers in the future.
The entire multi-hypercube code system is not simply a stacking of multiple quantum codes, but establishes logical associations through a special geometric mapping mechanism. WiMi utilizes the topological connection relationships between hypercube dimensions, enabling efficient information interaction between different logical quantum regions while maintaining low coupling complexity. The greatest advantage of this structure is that its logical gate operations can be executed in parallel simultaneously across multiple hypercube modules, without generating severe error correction conflicts as in traditional schemes.
From a structural perspective, the multi-hypercube code forms an organization similar to a quantum computing array. Each hypercube module can independently complete local error detection and can also participate in higher-level logical operations. Through this hierarchical structure, the system can decompose complex fault-tolerant tasks into a large number of localized small-scale tasks, thereby significantly reducing the overall error correction complexity.
WiMi stated that although traditional high-rate quantum codes can theoretically improve encoding efficiency, they often face the problem that logical gate operations are difficult to parallelize. Because in many high-density quantum codes, a single logical gate operation may affect a large number of qubit regions, resulting in strong coupling and conflicts between operations. The multi-hypercube code, however, restricts logical operations to specific hypercube regions through a geometric partitioning mechanism, allowing multiple logical gates to be executed simultaneously.
This parallelization capability is crucial for future quantum computing. As the scale of quantum algorithms continues to expand, quantum computers must execute massive numbers of logical gate operations simultaneously. If logical gates cannot be parallelized, the overall computing speed will be severely limited. Especially in scenarios such as quantum machine learning, quantum chemistry simulation, and quantum optimization, large-scale parallel quantum operations are an important foundation for achieving practicality.
To further improve system performance, WiMi has also developed dedicated quantum decoders and quantum encoders. Traditional quantum decoding usually needs to handle extremely complex error correlation relationships, but because the multi-hypercube code has a clear geometric structure, it can utilize topological path analysis methods to quickly locate error regions. The system can complete error inference and recovery operations in an extremely short time by analyzing error propagation patterns between hypercubes.
When designing the decoder, WiMi introduced a hierarchical local decoding mechanism. The system first performs error detection and preliminary repair within local hypercubes, and then handles cross-module error propagation through higher-level structures. This method avoids the exponential complexity growth problem brought by traditional global decoding.
In terms of encoder design, WiMi focused on optimizing the loading efficiency of logical quantum states. Since the multi-hypercube code has a natural modular structure, logical quantum states can be written into the system layer by layer in a pipelined manner, without the need to complete complex global initialization at once. This not only reduces the depth of the initialization circuit but also decreases the risk of error propagation during the initialization phase.
This technology can still achieve a relatively high error threshold under circuit-level noise models. The so-called error threshold refers to the ability of a quantum system to maintain stable computation through error correction under a certain physical error rate. The higher the error threshold, the stronger the system's tolerance to hardware noise, and the lower the difficulty of actual hardware implementation.
Through simulations, WiMi found that under circuit-level random noise environments, the multi-hypercube code can maintain a stable trend of decreasing logical error rates. This means that as the encoding levels increase, the system can continuously improve logical reliability without performance collapse due to increased complexity. Moreover, due to its adoption of a local modular structure, the multi-hypercube code offers higher flexibility in physical implementation. This structure can adapt to two-dimensional, three-dimensional, or even higher-dimensional quantum chip layouts, and can dynamically adjust the hypercube mapping method according to hardware connectivity constraints. This means that in the future, this technology is expected to become a universal fault-tolerant quantum computing architecture.
In superconducting quantum chips, the multi-hypercube code can utilize nearest-neighbor coupling to achieve local stabilizer measurements, reducing the demand for long-distance quantum communication. In ion-trap platforms, the ion chain reconfiguration capability can be used to dynamically establish hypercube connection structures. In photonic quantum platforms, the hypercube structure can also be combined with photonic cluster state computing modes to achieve high-speed parallel logical operations. In addition to hardware adaptation advantages, the multi-hypercube code may also have a significant impact on future quantum operating systems. Traditional quantum computing architectures often treat quantum error correction as an underlying function, whereas the multi-hypercube code, due to its natural hierarchical structure, is more suitable for deep integration with quantum task scheduling systems.
WiMi proposed that future quantum operating systems can dynamically allocate hypercube resource regions according to algorithm load conditions, mapping different logical tasks to different modules for execution. This can not only improve quantum resource utilization but also reduce interference between logical tasks. In large-scale quantum cloud computing scenarios, the multi-hypercube code may even form a quantum virtualization mechanism. Different user tasks can run in different hypercube logical regions, while the system ensures overall stability through dynamic error correction and resource isolation mechanisms. This means that future quantum computing centers may operate like today's data centers, enabling large-scale multi-task concurrent execution.
Currently, the technology has completed theoretical modeling, structural verification, and noise simulation analysis. In the next stage, WiMi plans to further optimize the hypercube cascading structure and conduct experimental verification in real quantum hardware environments. At the same time, it will also study the fusion mechanisms between the multi-hypercube code and quantum low-density parity-check codes, surface codes, and topological quantum codes.
As quantum computing gradually moves toward the era of practical application, fault-tolerant capability will become a core indicator for measuring the competitiveness of quantum computing platforms. The new high-rate fault-tolerant system represented by the multi-hypercube code is opening up new development space for future high-performance quantum computers. If this technology can maintain its theoretical performance in real hardware environments in the future, it is expected to become an important component of next-generation quantum computing infrastructure and drive quantum computing from the laboratory research stage to truly enter the industrial application stage.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, is exploring multi-dimensional pooling optimization technology under the variational quantum algorithm framework, proposing an innovative solution that integrates the Quantum Haar Transform (QHT) with quantum partial measurement, and constructing a quantum pooling mechanism that possesses both local feature preservation capability and dimension compression efficiency.
From a technical principle perspective, the Haar transform, as a core technology in the field of classical signal processing, is widely used in data compression and feature extraction. As its quantized extension, QHT maps high-dimensional classical data to the quantum state space through parameterized quantum gate groups, achieving a breakthrough improvement in computational efficiency over the classical Haar transform. In this mapping process, each qubit corresponds to one feature dimension of the data, and the superposition coefficients of the quantum state encode the feature intensity information. At the same time, correlations between feature dimensions are constructed through quantum entanglement, which not only fully preserves the global structural information of the data but also reinforces the correlations of local features through the local action domain constraints of quantum gates, effectively solving the problem of exponentially increasing computational complexity that the classical Haar transform faces in high-dimensional data processing. After QHT completes the data mapping, quantum partial measurement technology undertakes the core function of multi-dimensional data pooling. Its core logic differs from the crude dimensionality reduction mode of traditional pooling that directly discards redundant data, instead utilizing the probabilistic characteristics of quantum states combined with preset pooling strategies to selectively extract key feature information from quantum states in probabilistic form.
As the core driver of the entire optimization scheme, VQA constructs a hybrid optimization framework by integrating quantum computing and classical optimization technologies. Its core architecture consists of a parameterized quantum circuit (PQC) and a classical optimizer. By iteratively adjusting the parameters of the quantum circuit to minimize a preset loss function, it ensures that the pooling operation can accurately capture the key features of high-dimensional data while balancing computational efficiency and precision. In multi-dimensional pooling optimization scenarios, the core value of VQA is reflected in three aspects: first, realizing direct pooling of multi-dimensional data without the need to reduce high-dimensional data to one-dimensional space, fundamentally solving the problem of local feature loss caused by traditional pooling and fully preserving the spatial structure and local correlations of the data; second, leveraging the characteristics of quantum superposition and entanglement to obtain richer feature representations of multi-dimensional data in the quantum state space, enabling the extraction of fine and complex features that classical pooling methods cannot capture; third, relying on quantum parallelism to significantly reduce the computational complexity of high-dimensional data pooling, achieving polynomial-level computational acceleration and substantially improving model training and inference efficiency. In addition, the VQA framework possesses good scalability. By adjusting the parameters and gate structures of the quantum circuit, it can flexibly adapt to the processing needs of unstructured data of different dimensions and types, such as one-dimensional audio, two-dimensional images, three-dimensional point clouds, and hyperspectral data, demonstrating broad application prospects.
The VQA-driven multi-dimensional pooling optimization technology researched by WiMi will break through the locality preservation limitations of traditional pooling methods in high-dimensional data processing, fully unleash the inherent advantages of quantum computing in feature representation and computational efficiency, and provide key technical support for the practical application of QML in complex multi-dimensional data tasks.
In the future, with the iterative upgrading of quantum hardware and the continuous optimization of algorithms, the multi-dimensional pooling optimization technology under the VQA framework is expected to achieve practical application in more fields.
About WiMi Hologram Cloud
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
