WiMi Hologram Cloud Inc (WIMI)

WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key DistributionJune 29, 2026 10:50 AM
PR Newswire (US) BEIJING, June 29, 2026 /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 CloudWiMi 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.Safe Harbor StatementsThis press release contains "forward-looking statements" within 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 and 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 US 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. Several 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 the 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. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.  View original content:https://www.prnewswire.com/news-releases/wimi-explores-the-application-of-neural-networks-in-parameter-optimization-for-dual-field-quantum-key-distribution-302813268.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key Distribution

WiMi Develops Quantum Convolutional Neural Network Model for Classical Data ClassificationJune 24, 2026 8:30 AM
PR Newswire (US) BEIJING, June 24, 2026 /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 CloudWiMi 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 DisclaimerThe 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. View original content:https://www.prnewswire.com/news-releases/wimi-develops-quantum-convolutional-neural-network-model-for-classical-data-classification-302809230.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Develops Quantum Convolutional Neural Network Model for Classical Data Classification

WiMi Implements a Quantum Kernel Convolution (QKC) Scheme Capable of Running on Current Noisy Intermediate-Scale Quantum (NISQ) DevicesJune 15, 2026 10:00 AM
PR Newswire (US) BEIJING, June 15, 2026 /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 CloudWiMi 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 DisclaimerThe 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.  View original content:https://www.prnewswire.com/news-releases/wimi-implements-a-quantum-kernel-convolution-qkc-scheme-capable-of-running-on-current-noisy-intermediate-scale-quantum-nisq-devices-302800358.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Implements a Quantum Kernel Convolution (QKC) Scheme Capable of Running on Current Noisy Intermediate-Scale Quantum (NISQ) Devices

WiMi Explores Multi-Dimensional Pooling Optimization Technology under the Variational Quantum Algorithm FrameworkJune 8, 2026 8:40 AM
PR Newswire (US) BEIJING, June 8, 2026 /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 CloudWiMi 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 DisclaimerThe 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. View original content:https://www.prnewswire.com/news-releases/wimi-explores-multi-dimensional-pooling-optimization-technology-under-the-variational-quantum-algorithm-framework-302793940.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Explores Multi-Dimensional Pooling Optimization Technology under the Variational Quantum Algorithm Framework

WiMi Proposes a New High-Performance Fault-Tolerant Quantum Computing Technology Based on Multi-Hypercube CodesJune 2, 2026 10:40 AM
PR Newswire (US) BEIJING, June 2, 2026 /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 CloudWiMi 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 DisclaimerThe 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.  View original content:https://www.prnewswire.com/news-releases/wimi-proposes-a-new-high-performance-fault-tolerant-quantum-computing-technology-based-on-multi-hypercube-codes-302788701.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Proposes a New High-Performance Fault-Tolerant Quantum Computing Technology Based on Multi-Hypercube Codes

WiMi Achieves Breakthrough in Deep Convolutional Neural Network Technology Based on Quantum Parameterized CircuitsMay 28, 2026 12:30 PM
PR Newswire (US) BEIJING, May 28, 2026 /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 CloudWiMi 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 DisclaimerThe 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. View original content:https://www.prnewswire.com/news-releases/wimi-achieves-breakthrough-in-deep-convolutional-neural-network-technology-based-on-quantum-parameterized-circuits-302784741.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Achieves Breakthrough in Deep Convolutional Neural Network Technology Based on Quantum Parameterized Circuits

WiMi Deploys Quantum Computing Optimization Based on Multi-Objective Deep Reinforcement LearningMay 21, 2026 8:20 AM
PR Newswire (US) BEIJING, May 21, 2026 /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 CloudWiMi 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 DisclaimerThe 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. View original content:https://www.prnewswire.com/news-releases/wimi-deploys-quantum-computing-optimization-based-on-multi-objective-deep-reinforcement-learning-302778891.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Deploys Quantum Computing Optimization Based on Multi-Objective Deep Reinforcement Learning

WiMi Releases Next-Generation Quantum Neural Network Feature Mapping Technology: Repeated Amplitude Encoding Significantly Enhances Expressive Power of Quantum ModelsMay 11, 2026 11:00 AM
PR Newswire (US) BEIJING, May 11, 2026 /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 CloudWiMi 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 DisclaimerThe 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. View original content:https://www.prnewswire.com/news-releases/wimi-releases-next-generation-quantum-neural-network-feature-mapping-technology-repeated-amplitude-encoding-significantly-enhances-expressive-power-of-quantum-models-302768316.htmlSOURCE WiMi Hologram Cloud Inc. Original: WiMi Releases Next-Generation Quantum Neural Network Feature Mapping Technology: Repeated Amplitude Encoding Significantly Enhances Expressive Power of Quantum Models

WiMi Studies Multi-Scale Feature Fusion Quantum Deep Convolutional Neural Network for Text Classification
BEIJING, May 6, 2026 /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 origina

Yes, very sad. PM-trend not looking good. Hope there are chances for a rebound.

R/S last year = now super small ss

$WIMI great news  last friday 🔥🔥🔥🔥🚀🚀🚀🚀👌

https://investorshub.advfn.com/boards/read_msg.aspx?message_id=177534715

profit growth
https://investorshub.advfn.com/boards/read_msg.aspx?message_id=177534715

and interesting business, in a great future-oriented sector,
https://investorshub.advfn.com/boards/read_msg.aspx?message_id=177534715

operating in a very exciting 🔥🔥🔥🚀🚀🚀🚀👌

https://investorshub.advfn.com/boards/read_msg.aspx?message_id=177534715

huge cash reserves,
https://investorshub.advfn.com/boards/read_msg.aspx?message_id=177534715

super revenue growth 🔥🔥🔥🔥🚀🚀🚀🚀👌

https://investorshub.advfn.com/boards/read_msg.aspx?message_id=177534715

fantastic figures,


🚀🚀🚀🚀👌

https://investorshub.advfn.com/boards/read_msg.aspx?message_id=177534715

Just a few years ago, stocks like $WIMI, with fantastic figures, superb revenue growth, huge cash reserves, operating in a very exciting and interesting business, in a great future-oriented sector, with profit growth – exactly those kinds of stocks would have exploded and then experienced a long, significant rise over weeks, months, maybe even years, because that was the basic idea back then. Now, in 2026, such stocks with such fantastic figures are being pushed into the red, and despite good news, they're being played into the red (like $WIMI on Friday, lol, just crazy, and normal retailers don't dare to buy anymore, or if they are invested, they're afraid to hold for weeks = that's bad in my opinion). Does anyone find that beautiful? Maybe that's exactly what's ruining the stock market?
imho

WiMi Hologram Cloud Posts Strong Profit Growth and Improved Balance Sheet in 2025 FilingApril 24, 2026 10:42 AM
IH Market News
WiMi Hologram Cloud Inc. (NASDAQ:WIMI), a Beijing-based provider of holographic augmented reality technologies, reported a sharp increase in profitability and stronger financial positioning in its latest annual filing.



2025 Results Highlight Profit Surge



On April 24, 2026, the company announced the filing of its Form 20-F for the year ended December 31, 2025, revealing net income of RMB 347.1 million—an increase of 235.9% compared with 2024 and marking its second consecutive year of profitability.Operating expenses declined 19.4% to RMB 147.6 million, reflecting tighter cost management, while working capital more than doubled to RMB 2,611.6 million, pointing to significantly improved liquidity.



Strengthening Financial Position



The results underline enhanced operational efficiency and financial resilience, with improved margins and a stronger balance sheet potentially reinforcing WiMi’s competitive standing in the holographic AR sector.



Broad AR Technology Portfolio



WiMi focuses on a wide range of holographic AR applications, including automotive head-up display (HUD) software, 3D holographic LiDAR, head-mounted holographic devices, semiconductor technologies and cloud-based platforms.Its solutions are used across industries such as automotive, advertising, entertainment, navigation, payments and interactive communications.



More about WiMi Hologram Cloud



WiMi Hologram Cloud Inc., listed on Nasdaq under WIMI, delivers integrated holographic cloud and AR technologies, targeting professional and industrial users.Since launching commercial operations in 2015, the company has developed both hardware and software solutions, positioning itself as a vertically integrated provider in the growing augmented reality and holographic imaging market.WiMi Hologram Cloud stock price

Original: WiMi Hologram Cloud Posts Strong Profit Growth and Improved Balance Sheet in 2025 Filing

WiMi announced a net income of RMB 347 million for 2025, a year-on-year increase of 235%April 24, 2026 7:10 AM
PR Newswire (US)

BEIJING, April 24, 2026 /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 StatementThis 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.



View original content:https://www.prnewswire.com/news-releases/wimi-announced-a-net-income-of-rmb-347-million-for-2025-a-year-on-year-increase-of-235-302752819.htmlSOURCE WiMi Hologram Cloud Inc.

Original: WiMi announced a net income of RMB 347 million for 2025, a year-on-year increase of 235%

holy shi_ , i',m out now 🤮🤮🤮🤮🤮🤦🤦🤦🤦🤦

Yes, I was thinking the very sme thing! Best day it's had all year! No news, so either shorts started covering, or some group bought 1 mil shares. I'm not counting my chickens. (YET)

Why is $WIMI starting to rise today? Is this the start of an upward trend?

WiMi Studies Hybrid Quantum-Classical Inception Neural Network Model for Image ClassificationFebruary 18, 2026 8:00 AM
PR Newswire (US)

BEIJING, Feb. 18, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, proposed a brand-new technological innovation—a hybrid quantum-classical Inception neural network model for image classification. This is a brand-new hybrid architecture that naturally integrates quantum computing with classical deep learning through Inception-style parallel feature channels, achieving triple improvements in performance, efficiency, and robustness. The core goal of this technology is to utilize the high-dimensional feature expression capability of quantum computing to solve the expressiveness bottleneck of image classification models, while enhancing engineering implementability through classical network structures, and building a research path regarding the relationship between quantum expressiveness, quantum entanglement degree, and model performance, laying the foundation for future hybrid quantum AI research.Past quantum neural network research has mostly focused on constructing some kind of variational quantum circuit and attempting to embed it into traditional neural network structures. Although this method can achieve improvements in small-scale tasks, overall performance growth is slow and has not fully tapped the potential of quantum computing. For this reason, the WiMi research team realized that to allow quantum computing to play a true role in image classification, its parallel structure must be redesigned, especially needing to break through the structural limitations of single-path quantum networks.The core idea of the Inception structure is to allow multiple sub-networks with different receptive fields and expression methods to extract features in parallel and then complete multi-scale fusion through concatenation. By re-examining the quantum-classical hybrid network through this idea, WiMi proposed three parallel feature paths:Quantum feature extraction path: utilizing the multi-dimensional Hilbert space of quantum circuits to perform quantum encoding on local regions of images, and then extracting complex features through parameterized quantum gates and entanglement structures.Classical feature extraction path: using efficient convolution and lightweight feature extraction units to enhance model stability and macroscopic structure recognition capabilities.Hybrid quantum-classical path: taking the output of classical convolutional layers as input to quantum circuits, allowing classical features to be mapped into quantum space to obtain higher-order nonlinear expressive capabilities.The three paths together constitute the parallel Inception module, and then the outputs are concatenated into the final feature tensor to enter the subsequent classifier.Such a design not only enables the model to simultaneously possess three major advantages—quantum high-dimensional expression, classical strong stability, and cross-domain feature fusion—but also thoroughly solves the industry pain point of training difficulties caused by excessively deep circuits in pure quantum networks. The quantum part does not need to construct extremely deep circuits but instead achieves more expressive space in shallow layers through parallel structures, fundamentally improving model trainability and scalability.The key to building a hybrid quantum-classical Inception network lies in how to effectively map image data to quantum circuits. WiMi adopted an encoding strategy based on parameterized rotation gates, mapping image blocks to multi-qubit rotation angles so that they can represent complete local features in the quantum state space. Subsequently, the team designed controlled rotation gates, entanglement structures, and depth-adapted quantum circuits to enable quantum states to achieve the highest possible expressiveness in limited depth.The design of the quantum path follows the principles of shallow circuits, high entanglement, and strong expression. By introducing multiple sets of entanglement constructions, quantum states can rapidly diffuse between different layers and generate higher-order feature combinations. The structural selection of quantum circuits is no longer based on manual guessing but on systematic research into the relationships between expressiveness, entanglement degree, and training stability, thereby constructing the circuit topology most suitable for image classification.The classical path adopts lightweight convolutional networks to maintain good generalization ability and training efficiency. In the hybrid path, WiMi embeds features extracted by classical convolutions into new quantum circuits for secondary enhancement, enabling the model to possess the capability of first classical understanding followed by quantum enhancement.The entire Inception module provides the classifier with a richer, more three-dimensional, multi-scale feature expression space by concatenating and fusing features from the three paths. Among them, quantum features serve as high-order expression supplements, classical features are responsible for stable backbone expression, and the hybrid path acts as a bridge to naturally fuse the two.Through extensive experimental validation, the WiMi research team discovered that the hybrid quantum-classical Inception structure has multiple outstanding advantages. The quantum path can capture highly complex texture variations and subtle patterns in images, the classical path ensures overall stability and robustness, and the hybrid path enables the model to possess cross-domain expressive capabilities. When combined, the model's performance in image classification tasks surpasses that of ordinary convolutional networks and single-path quantum networks, with particularly significant performance in scenarios with small data scales and subtle category differences. In addition, the high-dimensional nature of quantum circuits allows the model to achieve strong expressive power with fewer parameters, thereby realizing the dual advantages of high performance + low parameter count.WiMi's hybrid quantum-classical Inception neural network is not merely a structural innovation; it represents a future trend: quantum computing will no longer exist as an independent model but will gradually become one of the foundational capabilities of deep learning. By deeply fusing quantum circuits with classical networks in terms of feature domains, information flows, and spatial structures, this model demonstrates a possible operation mode for future intelligent perception systems—quantum and classical parallel collaboration, processing features of different levels and natures in the most appropriate way. In the future, WiMi will continue to explore deeper hybrid structures, more complex quantum feature encoding methods, and deployment methods oriented toward real quantum hardware, promoting hybrid quantum artificial intelligence toward practical applications.About WiMi Hologram CloudWiMi 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 DisclaimerThe 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.



View original content:https://www.prnewswire.com/news-releases/wimi-studies-hybrid-quantum-classical-inception-neural-network-model-for-image-classification-302691534.htmlSOURCE WiMi Hologram Cloud Inc.

Original: WiMi Studies Hybrid Quantum-Classical Inception Neural Network Model for Image Classification

Huge drop under $2.00 on relatively low volume. I'm averaging😁 down as we speak!🤩

WiMi Studies Quantum Hybrid Neural Network Model to Empower Intelligent Image Classification
Beijing, Jan. 15, 2026 (GLOBE NEWSWIRE) -- WiMi Studies Quantum Hybrid Neural Network Model to Empower Intelligent Image Classification

BEIJING, Jan. 15, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, proposed a brand-new Lean Classical-Quantum Hybrid Neural Network (LCQHNN) framework, aimed at achieving maximized learning efficiency with the smallest possible quantum circuit structure. This technology balances implementability and performance superiority in its design, marking a key step for quantum neural networks from theoretical feasibility toward practical deployment.
The core idea of LCQHNN is to center on quantum feature amplification (Quantum Feature Amplification) while combining a classical stability optimization strategy, establishing an efficient information interaction mechanism between the two computing paradigms. The network architecture is divided into two main parts:
Classical Front-End: responsible for preliminary feature extraction and data pre-encoding;
Quantum Back-End: utilizes variational quantum circuits to complete nonlinear mapping and classification decisions.
In this system, the classical part uses lightweight convolutional and fully connected layers as the data preprocessing channel, with their output results embedded into the quantum state space and subjected to feature transformation through parameterized quantum gate operations. This process is equivalent to mapping high-dimensional classical features to a multi-dimensional quantum Hilbert space, thereby forming nonlinear projections in superposition states, enabling the model to capture the essence of complex data distributions with fewer parameters.
In the quantum part, WiMi designed a structure containing only a four-layer variational quantum circuit (4-layer VQC). This circuit consists of parameterized rotation gates, controlled gates, and entanglement operations. Through optimization of the circuit parameters, the relationship between the measurement results of the quantum state output and the target categories gradually converges. Experiments show that a four-layer circuit can achieve performance comparable to or even better than deep VQCs, thereby significantly reducing the resource consumption and error accumulation risk of quantum hardware.
The complete workflow of WiMi's LCQHNN can be summarized into the following key stages:
Data Preprocessing and Classical Encoding: The original image first undergoes lightweight convolutional layers to extract local features, followed by normalization and compression operations to form a medium-dimensional vector representation. Subsequently, these vectors are mapped into input states encoded by quantum amplitudes or phases. For example, amplitude encoding can compress high-dimensional data into a limited number of qubits, allowing classical information to be stored in the quantum state space in an exponential manner.
Quantum State Preparation and Entanglement Structure Construction: After encoding is completed, the system enters the quantum section. WiMi employs controlled rotation gates and CNOT gates to construct entanglement structures, enhancing correlations between different qubits. The introduction of this entanglement pattern not only improves the expressive power of the quantum feature space but also theoretically endows the model with stronger nonlinear discrimination capability. Research results show that an appropriate number of entanglement layers is one of the key determining factors for model performance, and in LCQHNN, the four-layer variational structure design precisely balances performance and implementability.
Parameterized Quantum Evolution and Measurable Readout: Each layer of the quantum circuit contains adjustable parameters ?, which correspond to the angles of rotation gates or phase shift gates. Through multiple evolutions and measurements of the quantum state, the system collects the statistical distribution of measurement results, thereby constructing a loss function that can be used for gradient backpropagation. WiMi adopts an improved gradient estimation method—an efficient training mechanism based on the parameter shift rule—which significantly reduces the number of quantum measurements required for each parameter update, improving overall training speed and stability.
Classical Feedback and Hybrid Optimization: During the optimization process, the backpropagation algorithm of the classical part runs in coordination with the parameter updates of the quantum part. Classical optimizers (such as Adam or L-BFGS) are responsible for adjusting the quantum circuit parameters ? so that the measurement results minimize classification error. This process embodies the core concept of hybrid quantum-classical collaborative optimization: fully leveraging the high-dimensional expressive power of the quantum feature space while building on the stability of classical computation.
Classification Decision and Feature Visualization: The final quantum measurement results are decoded back into the classical domain and used to output the category to which the image belongs. Through characterization analysis, WiMi found that LCQHNN can form distinct feature cluster distributions during training. These clusters correspond to different quantum state distribution regions in quantum space, exhibiting strong inter-class separability.
The success of LCQHNN has laid a solid foundation for constructing a General Quantum Intelligence Framework. In the future, the research team plans to continue expanding in the following directions: extending the model to multimodal learning scenarios to achieve joint quantum feature learning for images, speech, and text; exploring collaborative integration with quantum support vector machines (QSVM) and quantum convolutional networks (QCNN) to build end-to-end quantum deep learning systems; promoting prototype deployment on quantum hardware to verify the model's performance stability in real noisy environments; and combining quantum parallel optimization with federated learning frameworks to construct secure, efficient, and distributed quantum intelligent systems.
The launch of WiMi's Lean Classical-Quantum Hybrid Neural Network (LCQHNN) marks a new stage in which quantum machine learning technology has moved from theoretical exploration toward efficient practical implementation. By achieving outstanding learning performance under limited quantum resources, this technology not only makes breakthrough progress in image classification tasks but also provides a new paradigm for the design of future quantum intelligent systems. WiMi will continue to devote itself to the engineering and industrialization promotion of quantum algorithms, driving quantum artificial intelligence from the laboratory to real-world application scenarios and accelerating humanity's entry into the era of quantum 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

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning
Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

BEIJING, Jan. 05, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the launch of their independently developed new technology: a Quantum Convolutional Neural Network for Multi-Channel Supervised Learning (MC-QCNN). This breakthrough method, for the first time, constructs a fully hardware-adaptable quantum convolution kernel design, enabling quantum models to efficiently process multi-channel data, thereby demonstrating absolute advantages in industries such as image classification, medical imaging, video analysis, and multimodal monitoring.
From a research and development perspective, the core of this technological breakthrough lies not merely in the construction of multi-channel quantum convolution kernels but in the entire systematized design scheme, including convolution kernel structure, qubit layout, channel interaction encoding, weight learnability, interpretability, and hardware constraint adaptation strategies. To enable the technology to be executed on real hardware, WiMi abandoned a large number of impractical deep circuit structures and instead turned to a design philosophy that is closer to the native gate operation characteristics of quantum hardware. The quantum circuit convolution kernel proposed by WiMi uses single-bit rotation gates, controlled parameterized gates, SWAP interleaving structures, weak entanglement layers, and channel interaction gates, thereby forming a convolution operator that can express complex functions while maintaining robustness against quantum decoherence.
Unlike classical convolution kernels that need to slide within pixel neighborhoods, WiMi adopted a quantum-specific encoding method to compress and encode data from multiple channels into the amplitudes, phases, or entanglement structures of quantum states, performing convolution-like processing on them through parameterized quantum gates. Feature fusion between channels no longer relies on linear weighting but directly generates high-dimensional correlations in the quantum state space through gate-level interactions, producing stronger feature combination capabilities than classical convolution. Through training, these parameterized quantum convolution kernels can learn high-order cross-channel features, such as texture-color co-occurrence, time-space joint patterns, multispectral energy distribution correlations, etc., thereby achieving expressive capabilities superior to traditional QCNN.
One of the cores of this technology architecture is the quantum multi-channel convolution operator established by WiMi. This operator uses parameterized rotation gates and controlled gates to construct convolution patterns. By adjusting the rotation angles of the gates and the controlled structures, the convolution kernel can automatically learn the optimal cross-channel feature combination strategy during training. The entire convolution kernel can not only act on single-bit distributions but also act on multi-bit channel structures in a tensor-like manner, enabling the convolution kernel not only to extract local coherence but also to mine high-order relationships from entanglement structures. This mode cannot be directly realized in classical CNNs because the combination of multi-channel features in classical neural networks is usually based on linear superposition, whereas quantum convolution kernels are based on quantum superposition and quantum entanglement, capable of expressing complex multi-channel correlations in an exponential feature space.
After the convolution operation is completed, the feature maps are compressed into more compact quantum states in the quantum system and downsampled by quantum pooling circuits. The pooling circuits have also been redesigned to handle quantum state features from multiple channels. WiMi adopts a learnable quantum pooling mode, reducing quantum state dimensions through controllable measurements or controllable compression operations while preserving key feature information, which avoids the feature destruction problem caused by direct measurements in traditional QCNNs. Experimental results show that the new pooling structure is more stable than traditional QCNN pooling methods and has a higher feature retention rate.
In addition to convolution kernels and pooling circuits, WiMi has also constructed a dedicated hybrid quantum-classical training framework. During the training process, the classical computing module is responsible for loss function calculation, gradient solving, and parameter updating, while the quantum module is responsible for forward propagation and quantum state evolution. WiMi adopts an extended parameter shift rule approach, enabling all parameters in the multi-channel quantum convolution kernel to be effectively trained. To improve training stability, WiMi also introduces quantum noise simulation and gradient clipping mechanisms, ensuring that the model's performance on real quantum hardware does not sharply decline due to noise.
During the training process, the WiMi team observed a highly valuable phenomenon: the model is able to automatically capture nonlinear correlations between multiple channels. Taking RGB images as an example, the quantum convolution kernels learned by the model do not simply perform linear traversal on the R, G, and B channels but instead establish correlations between channels through entanglement layers, enabling the convolution kernel to recognize joint features of color distribution patterns in the quantum state space. This means that the model is not performing convolution separately on the three channels but is learning an overall deep feature in a higher-dimensional space, with expressive power far superior to that of 3×3 or 1×1 convolutions in classical CNNs.
WiMi believes that multi-channel processing capability will become one of the key abilities for quantum neural networks to move toward real-world applications. Although single-channel QCNN has exploratory significance in academia, its limitations make it unable to meet the industry's requirements for complex data. The emergence of MC-QCNN enables quantum deep learning systems to possess the ability to process real-world data for the first time, meaning that quantum AI is no longer just a laboratory concept but is beginning to have the possibility of commercial implementation. It is believed that, with the improvement of quantum hardware performance, this technology will drive quantum machine learning from laboratory research toward a true era of applications.
In the future, WiMi will continue to refine this technology system, including building more efficient quantum convolution kernel structures, developing more robust noise adaptation strategies, extending to three-dimensional convolution and time-series convolution structures, and exploring integration possibilities with model structures such as Transformer, enabling quantum models to process not only multi-channel images but also multimodal speech, video, text, graph structures, and sensor data. Quantum deep learning will no longer be limited to small-scale tasks but will become an important operator in next-generation general AI models. The combination of quantum computing and artificial intelligence will be the core trend in technological development over the next decade. WiMi will continue to dedicate itself to promoting the construction of the quantum AI ecosystem, allowing quantum technology to truly serve industrial needs, social value, and the human future.

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 technol

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

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

WiMi Releases Next-Generation Hybrid Quantum Neural Network Structure Technology, Breaking Through the Bottleneck of Image Multi-Classification
BEIJING, Dec. 22, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, launched a hybrid quantum neural network structure (H-QNN) for image multi-classification. This technology organically integrates the spatial feature extraction capabilities of classical convolutional neural networks (CNN) with the high-dimensional nonlinear mapping features of quantum neural networks (QNN), forming a new type of hybrid structure that possesses stronger generalization ability and computational efficiency in multi-class classification scenarios. This technology not only systematically optimizes the quantum-classical hybrid learning system in theory but also achieves classification accuracy and stability superior to similar algorithms in actual experiments, laying a solid technical foundation for quantum intelligent vision systems.

The design of this hybrid quantum neural network (H-QNN) follows the principle of classical responsible for abstraction and quantum responsible for discrimination. The overall system consists of three main modules: feature dimensionality reduction and encoding module, quantum state transformation module, and hybrid decision and transfer learning module.

First, the feature dimensionality reduction and encoding module is based on the classical convolutional neural network (CNN) structure, extracting low-dimensional feature representations of images through several convolutional layers and pooling layers. The feature vectors after PCA dimensionality reduction are standardized and then input into the quantum encoding circuit. At this stage, WiMi adopts an improved angle encoding method (Angle Embedding) to map real-valued features to quantum state amplitudes, and achieves efficient encoding through multi-layer quantum rotation gates (Ry, Rz), thereby reducing quantum gate depth and lowering encoding noise.

Next, the quantum state transformation module undertakes the core tasks of high-dimensional feature mapping and nonlinear discrimination. This module includes several layers of quantum circuits, with each layer composed of parameterized rotation gates and controlled entanglement gates (CNOT or CZ), forming nonlinear coupling and entanglement of quantum states. To alleviate gradient vanishing, WiMi adopts a reconfigurable parameter sharing strategy, allowing different quantum layers to share some trainable parameters, while introducing mixed state perturbations to maintain gradient balance during the training process. This structural design effectively avoids the barren plateau phenomenon, enabling the model to maintain stable convergence in multi-class tasks.

Finally, the hybrid decision and transfer learning module integrates the results of quantum computing with the classical decision layer. The measurement probability distribution output by the quantum circuit is converted into feature vectors and fused with the output of the classical fully connected layer. This fused vector is input into the Softmax layer for final classification judgment. To further enhance the generalization performance in multi-class tasks, WiMi introduces a transfer learning mechanism, migrating the parameters of quantum layers pre-trained in small-sample tasks to new tasks, thereby reducing the number of training epochs and enhancing model stability.

In actual implementation, this structure supports running on simulation environments and hardware quantum processing units (QPU). The simulation environment uses high-performance GPU clusters to complete training of classical modules, while quantum modules are executed in quantum simulators or FPGA-accelerated quantum kernel estimation environments, achieving heterogeneous collaboration of classical and quantum computing resources.

The core innovation points of this technology are mainly embodied in the following aspects.

First, at the architectural design level, it achieves deep integration of convolutional neural networks (CNN) and quantum neural networks (QNN). Traditional quantum hybrid models usually simply embed the quantum part as a classification head, whereas the H-QNN proposed in this research adopts a three-stage distributed structure of "convolutional feature extraction—quantum mapping—hybrid decision-making", enabling the quantum part not only to undertake nonlinear discrimination but also to achieve information reconstruction at the feature space level.

Second, at the encoding strategy level, the joint dimensionality reduction scheme of angle encoding and principal component analysis (PCA) proposed by WiMi effectively solves the quantum encoding dimension limitation problem. By optimizing the cumulative variance contribution rate of PCA, it ensures that the mapping between input features and quantum state amplitudes maintains high information fidelity, thereby maximizing the utilization rate of quantum information.

Third, at the training strategy level, WiMi introduces a transfer learning mechanism and parameter sharing structure. Traditional quantum neural networks often face risks of gradient vanishing and overfitting in multi-class classification training, while parameter sharing can establish balanced gradient flow between different quantum layers, and the transfer learning mechanism enables the model to achieve rapid convergence on new tasks with fewer training epochs. In addition, WiMi designs an early stopping strategy based on the quantum Fidelity metric, which determines whether the training has reached the optimal point by monitoring the stability of quantum state evolution, thereby preventing overfitting.

Finally, at the system implementation level, it adopts a heterogeneous computing architecture, running the classical computing part on CPU/GPU platforms, while the quantum part is executed in quantum simulation modules implemented on FPGA. The FPGA module realizes reconfigurable execution logic for parameterized quantum circuits, capable of completing quantum state updates within nanosecond-level response times, thereby significantly improving the overall training speed of the system. This hybrid computing architecture demonstrates performance advantages far exceeding pure CPU or GPU simulations in experiments.

The proposal of WiMi's hybrid quantum neural network structure marks a key step in quantum artificial intelligence research moving from theoretical exploration toward practical applications. It not only demonstrates the potential advantages of quantum computing in the field of machine learning but also provides an engineered compromise solution for the current performance bottlenecks of quantum hardware. By embedding trainable quantum layers into the foundation of classical neural networks, this technology achieves efficient utilization of quantum computing resources, enabling quantum advantages to be embodied in real visual tasks. In the future, quantum intelligence will no longer be merely a theoretical conception but will deeply integrate with fields such as deep learning, computer vision, and edge computing, becoming an important driving force for promoting the development of intelligent society. Let quantum intelligence move from the laboratory to the real world, and let quantum technology truly serve industrial upgrades and the expansion of human cognition.

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

MEGA STRONG UNDERVALUED

WIMI......................................................p/m

WIMI Hologram Cloud (NASDAQ: WIMI) Reports 926% Net Income Growth for First Half of 2025
BEIJING, Sept. 29, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi"or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced its financial results for the first half of 2025, showcasing a dramatic 926% year-over-year increase in net income, rising from RMB 12.3 million in the first half of 2024 to RMB 126.3 million (USD 17.6 million) in the first half of 2025.

As of June 30, 2025, WIMI Hologram Cloud maintained strong liquidity, with cash, cash equivalents, and short-term investments totaling RMB 3.1896 billion (USD 445.6 million), reflecting a 183% year-over-year increase.

sounds very good , WIMI is undervalued

After today's great 6k, the large cash balance, and the massive undervaluation, WIMI looks like it will be back above $5, $6, and so on next week - a truly brilliant Quantum/AI and BITCOIN STOCK. Just read the incredibly strong news from the last 3 months.

https://finance.yahoo.com/sec-filing/WIMI/0001213900-25-091971_1770088/
https://investorshub.advfn.com/stock-market/NASDAQ/wimi-hologram-cloud-WIMI/stock-news/96894157/form-6-k-report-of-foreign-issuer-rules-13a-16
🥳

After today's great 6-k, the large cash position, and the massive undervaluation, 🔥 WIMI 🔥looks like it's going from the red to the green. 🚀🚀🚀🚀
🥳

VERY VERY GREAT 6-K HERE TODAY out ,:

Yes, I agree... Maybe there will be a P/R from the company in AH... I re-entered at $4.51 today...

https://www.sec.gov/Archives/edgar/data/1770088/000121390025091971/ea025714801ex99-1_wimi.htm

Revenues were less than a year ago, but they had a huge gain in investment income...

Basic earnings per share from continuing operations was RMB 10.32 ($1.44) compared to RMB 0.78 ( $.11) a year ago. Diluted earnings per share from continuing operations was RMB 4.54 ($.63) compared to RMB 0.78 ($.11) a year ago...

now great 6-k out

now filling out

Yes, I saw that a few minutes ago... Looks like earnings are now scheduled for the 25th...

WiMi Unveils New Efficient Quantum Random Access Memory Technology
BEIJING, Sept. 23, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the launch of a brand-new, efficient Quantum Random Access Memory (QRAM) technology. The introduction of this technology marks a significant breakthrough in the field of quantum computer storage, and is expected to drive the widespread application and development of quantum computing technology.

In classical computer systems, Random Access Memory (RAM) is used for efficient storage and retrieval of data. However, with the widespread application and increasing complexity of quantum computers, traditional RAM can no longer meet the storage demands of quantum computing systems. The new Quantum Random Access Memory (QRAM) is a dedicated memory designed for quantum computing systems, used for efficient storage and retrieval of data in both classical and quantum domains.

The new QRAM offers storage efficiency and capacity far beyond traditional classical RAM, enabling more efficient storage of both classical and quantum information. It adopts a fixed structural design that maximizes storage space utilization while maintaining stability, thereby increasing the overall capacity of the memory. While classical RAM requires memory modules to be expanded according to storage needs, the new QRAM technology, through quantum parallelism, allows storage capacity to dynamically adjust with the complexity of computational tasks, thus avoiding the expansion bottlenecks encountered in classical RAM.

The QRAM technology proposed by WiMi allows for access to any location in the memory with O(1) time complexity. This means that, regardless of the size of the memory, a quantum computer can efficiently access data in storage units in constant time. This feature greatly enhances the overall computational efficiency of quantum computing. In contrast to traditional RAM, where the access time increases as the number of storage units grows, QRAM achieves true constant-time access, breaking through the performance bottleneck in both computation and storage.

The new QRAM can not only store classical data but also store quantum data. While classical RAM can only handle bit data, QRAM can process quantum bit (qubit) information. It can store this data as classical information or directly store quantum states. The fusion of classical and quantum computing has become a trend for future computing, and thus, the demand for memory that can simultaneously store both types of data is particularly urgent. This QRAM technology addresses this critical need.

Another technological breakthrough of QRAM is its ability to support access to both known and unknown quantum states. Unlike classical computing, quantum computing often needs to deal with quantum states that have not yet been measured. The proposed QRAM technology can efficiently handle these quantum states and, through a well-designed architecture, ensure data storage and retrieval without destroying the quantum state superposition. This feature is especially important for computational tasks that require the quantum state to be maintained for extended periods.

WiMi's brand-new, efficient Quantum Random Access Memory (QRAM) utilizes quantum bits (qubits) as its core components. Qubits can exist in multiple superposition states simultaneously, whereas classical bits can only be in a state of 0 or 1. To fully exploit the superposition states of qubits for information storage, the design of QRAM is based on the effective storage and manipulation of quantum states. Specifically, QRAM employs a storage solution based on superconducting quantum interference devices (SQUIDs), which ensures that quantum states maintain their quantum properties during the storage process. Meanwhile, QRAM optimizes the interactions between qubits to ensure that data is not subject to quantum state collapse during storage and retrieval.

In classical RAM, the address decoder is the core module for locating storage units. Similarly, QRAM also requires an efficient quantum address decoder. In WiMi's QRAM technology, the quantum address decoder employs a new parallel address decoding method, which can quickly determine storage locations and perform data retrieval. Through the design of quantum algorithms, the address decoder can decode address information in constant time, which is one of the key technologies enabling QRAM to achieve O(1) access time.

For storing and retrieving classical and quantum data, QRAM employs different mechanisms. Classical data can be directly stored in the fixed states of qubits and retrieved by measurement. In contrast, for quantum data, non-destructive measurement techniques are used to ensure that the quantum superposition state does not collapse during the reading process. This technology uses specific quantum gate operations that allow quantum information to be read without disturbing the quantum state, ensuring the integrity of the quantum state for subsequent quantum computations.

During quantum storage, the system inevitably experiences interference from external noise, leading to quantum decoherence. To address this issue, WiMi's QRAM incorporates a quantum error correction mechanism. Using quantum error-correcting codes, QRAM can detect and correct errors that arise during the storage process in real-time. The introduction of this error-correction mechanism significantly enhances the reliability of quantum storage and ensures the integrity of quantum states.

The introduction of WiMi's new, efficient Quantum Random Access Memory (QRAM) has greatly enhanced the storage capacity and computational efficiency of quantum computing systems. In the future, as quantum computing application scenarios continue to expand, QRAM will become an indispensable key component in quantum computing systems. Large-scale quantum computing requires processing vast amounts of data, which traditional classical memory cannot meet. QRAM, with its efficient storage and retrieval capabilities, will enable quantum computers to handle large volumes of data in a short time, making it ideal for large-scale quantum computing tasks.

Additionally, quantum machine learning is an important application field of quantum computing. In the process of quantum machine learning, QRAM can be used to efficiently store and retrieve training data and model parameters, providing support for quantum algorithms and thereby accelerating the training and inference processes of quantum machine learning. The future quantum internet will require an efficient storage solution to store and transmit quantum information. QRAM technology can not only serve as memory for quantum computers but also act as a relay station in quantum networks, storing and transmitting quantum state data.

WiMi's new, efficient Quantum Random Access Memory (QRAM) technology is undoubtedly a significant technological breakthrough in the field of quantum computing. As this technology matures and is applied, the performance of quantum computers will be greatly enhanced, bringing unprecedented technological transformations and application prospects to human society.

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.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1

http://www.wimiar.com/en/#

When I read your post, I lost my nerve and actually got out because I'm afraid of losing money, as I've already lost far too much this year with Nasdaq stocks (unfortunately, I'm now at a loss and always get out quickly before any news is due to come out, because I simply don't want to lose any more money). I used to always hold on, and that was a huge mistake, and I hope it wasn't a mistake to sell out of fear now... lol (but I spend all day watching my computer and pressing the button. If there's good news, I'll jump in. And then I don't care if I lose five or ten percent and chase after it).


And the annoying thing is that some shareholders sell even if there's no news today. In my experience, sometimes there are deadlines, and then nothing happens on the day of the deadline, and no news comes through, no figures come out, and people are disappointed, and things go downhill because everyone is waiting for the news, and if that happens... I wouldn't know what to do. : "Jump in quickly because no news comes in as expected or is out. Stay and wait until the numbers come in and we know what the company is planning and what the trend looks like........?" Of course I hope that $WIMI will continue to be successful with its Quantum, AI and Bitcoin separation but we can't know that, but this share has a lot of catalysts and this share is active in a lot of areas and we rarely have (find) that and that's why I'm very excited about this share and would like to hold this share for a long time but I can't afford to lose 1000 or 2000 euros again and that's why I'm being so cautious and waiting to see what management says and there hasn't been any interim information and the market and people have gone crazy. If $WIMI said they only have 420 million in cash instead of 480 million in cash, the market would go crazy and let the share fall. I don't want to participate in such illogical reactions in the future, so I'm afraid and prefer to watch every minute on my PC and jump in. I'd rather chase a 10% increase than a 10% loss (but hey, if I had enough money and had tens of thousands of euros to spare and I didn't care if it fell by 30% or 40% and I could just leave the stock alone, then you'd be on the lucky side of this world, but unfortunately I can't afford it).

all only imho

Earnings are due out prior to market open on Monday, September 22nd...

BOOOOM

💥💥💥 READY 🔥🔥🔥🔥

WiMi Has Developed a Scalable Quantum Neural Network (SQNN) Technology Based on Multi-quantum-device Collaborative Computing
BEIJING, Sept. 19, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of a Scalable Quantum Neural Network (SQNN) technology based on multi-quantum-device collaborative computing. This technology utilizes multiple small quantum devices as quantum feature extractors, which extract local features from input data in parallel. The extracted local features are then aggregated into a quantum predictor through classical communication channels to accomplish the final classification task.

This technology aims to overcome the limitations of current quantum computing hardware by enabling multiple small quantum devices to work collaboratively, thereby building an efficient and scalable quantum neural network system. The technology not only achieves classification accuracy comparable to traditional Quantum Neural Networks (QNN) in theory but also introduces a novel approach to optimizing the utilization of quantum computing resources and enhancing data efficiency.

The core architecture of WiMi's SQNN system consists of three main components:

Quantum Feature Extractor: The quantum feature extractor is responsible for extracting local features from input data. Each quantum device can independently perform feature extraction tasks using Variational Quantum Circuits (VQC) to encode and transform input data. Since these devices operate independently, they can flexibly adapt to quantum devices of different sizes. For instance, larger quantum devices can handle more complex data patterns, while smaller quantum devices can process simpler local features.

Classical Communication Channel: In the SQNN framework, quantum feature extractors transmit the extracted local features to a central computing node via a classical communication channel. This communication process is similar to the concept of Federated Learning, where different computing units process data independently, but the final decision-making process relies on the integration of global information.

Quantum Predictor: The quantum predictor serves as the core computational unit of the entire SQNN system. It receives feature information from multiple quantum feature extractors and performs the final classification decision using quantum circuits. The quantum predictor can employ more complex quantum circuits to optimize classification accuracy and dynamically adjust its computational approach based on the scale of the data.

The technical implementation of WiMi's SQNN involves the following steps: Frist, data Preprocessing and Quantum Encoding: Before entering the quantum system, input data undergoes classical preprocessing operations such as standardization and dimensionality reduction. The data is then mapped to quantum states using encoding methods such as Amplitude Encoding or Angle Encoding. Then, sub-feature Extraction: Each quantum device performs independent feature extraction tasks using Parameterized Quantum Circuits (PQC) to transform features and generate local feature representations. Besides, feature Aggregation and Classification: The output of quantum feature extractors is transmitted to a central node via a classical communication channel. The quantum predictor then aggregates the features and performs the final classification task. Finally, parameter Optimization and Training: SQNN employs Variational Quantum Optimization for training. A classical optimizer, such as gradient descent, is used to adjust the quantum circuit parameters to minimize classification error.

Compared to traditional QNNs, WiMi's SQNN offers the following significant advantages:

Improved Data Utilization: Since SQNN leverages multiple quantum devices for collaborative computing, it can utilize data more efficiently without compromising data integrity due to the qubit limitations of a single device.

Enhanced Computational Scale: By coordinating multiple small quantum devices, SQNN can handle larger-scale computational tasks without relying on a single high-performance quantum computer. This modular approach also makes SQNN more scalable.

Optimized Computing Resources: SQNN allows different types of quantum devices to work together, enabling more flexible resource allocation. For example, when the workload is small, only a subset of quantum feature extractors can be activated, whereas for large-scale computing tasks, more quantum devices can be utilized to improve computational efficiency.

Experiments conducted on multiple benchmark datasets demonstrate that WiMi's SQNN achieves classification accuracy comparable to traditional QNNs at the same scale. Additionally, since SQNN utilizes multiple quantum devices for parallel computing, its training efficiency is significantly improved compared to QNNs that rely on a single quantum device.

Furthermore, experimental results show that as the number of participating quantum devices increases, both the classification accuracy and computation speed of SQNN improve significantly. This indicates that the approach has strong scalability as quantum computing hardware continues to evolve.

Despite the promising experimental results achieved under current hardware conditions, several key challenges remain to be addressed. For example, optimizing the interconnection of quantum devices to enhance efficiency while minimizing communication costs, and further refining SQNN's quantum circuit design to reduce noise interference and improve computational accuracy.

WiMi's Scalable Quantum Neural Network (SQNN) provides an innovative solution for quantum machine learning, enabling multiple small quantum devices to work collaboratively for efficient classification tasks. Experimental results indicate that SQNN offers strong computational performance and scalability, laying a solid foundation for the integration of quantum computing and artificial intelligence. As quantum hardware continues to advance, SQNN is expected to become a crucial component of large-scale quantum machine learning systems, driving revolutionary changes in AI and data science.

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.

Several fantastic news items this week, especially yesterday's news was very good, and we have a lot of cash. I don't know

why this stock, with this imagination and these many catalysts in the

quantum and AI sector, isn't at $40 or $50 yet.

But what isn't yet can still happen,

and I'll hold on and wait, whether it takes until 2026 or 2027, but this stock will go its way and should trade above $50 and more.

all only imho

Two fantastic pieces of news yesterday and today. Here's yesterday's super news, and here's the other very good news from today. A few minutes ago, and the collaboration with mlgo looks fantastic. And sooner or later, it will break out sharply upwards, and the chart looks fantastic too.

https://de.marketscreener.com/boerse-nachrichten/wimi-hologram-cloud-inc-erforscht-kollaborative-quanten-generative-netzwerke-mit-quantum-generative-ce7d58d8d18df025

Here's today's super news,

https://www.marketscreener.com/news/wimi-hologram-cloud-inc-researches-a-quantum-machine-learning-framework-for-enhanced-privacy-protec-ce7d58d9df8cf524