WiMi Studies Quantum Dilated Convolutional Neural Network Architecture

PR Newswire

BEIJING, Oct. 13, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that active exploration is underway in the field of Quantum Dilated Convolutional Neural Networks (QDCNN) technology. This technology is expected to break through the limitations of traditional convolutional neural networks in handling complex data and high-dimensional problems, bringing technological leaps to various fields such as image recognition, data analysis, and intelligent prediction.

The traditional Convolutional Neural Network (CNN) is a cornerstone in the field of deep learning. Through a combination of convolutional layers, pooling layers, and fully connected layers, it can automatically extract features from large amounts of data. In the convolutional layer, the convolution kernel slides over the input data, performing convolution operations to extract local features. The pooling layer reduces the data dimensions through downsampling, lowering computational load while preserving key information. The fully connected layer integrates the features processed by convolution and pooling, outputting the final classification or prediction results. However, with the explosive growth of data volume and the increasing complexity of problems, traditional CNNs are gradually facing bottlenecks in computational efficiency and feature extraction capabilities.

Quantum computing introduces the concept of quantum bits (qubits). Unlike the binary bits of traditional computers, qubits can exist in multiple superposition states, endowing quantum computers with powerful parallel computing capabilities. The Quantum Dilated Convolutional Neural Network (QDCNN) technology explored by WiMi ingeniously integrates the advantages of quantum computing into the traditional CNN architecture. In QDCNN, certain computational operations are performed by quantum processors. For example, in convolution operations, quantum gate operations are used to perform quantized computations on the convolution kernel and input data, enabling simultaneous processing of multiple data states, which significantly accelerates the feature extraction process. Quantum entanglement properties are also utilized to enhance information transfer and collaborative processing capabilities between different nodes in the network, allowing the network to more efficiently capture complex relationships within the data.

Through dilated convolution technology, the receptive field of the convolution kernel is expanded, enabling the acquisition of broader contextual information without increasing the number of parameters. This is highly effective for processing data with long-distance dependencies, such as natural language text and large-scale images. In Quantum Dilated Convolutional Neural Networks (QDCNN), quantum computing further enhances the effect of dilated convolution. Quantum algorithms can more precisely calculate the weight coefficients in dilated convolution, allowing the network to more accurately model complex features while expanding the receptive field. Traditional CNNs experience exponential growth in computational load when processing large-scale data. In contrast, QDCNN leverages the parallelism of quantum computing to complete convolution operations on massive datasets in a short amount of time.

Quantum Dilated Convolutional Neural Networks not only extract the features that traditional CNNs can obtain but also uncover hidden quantum-level feature information in the data. The superposition and entanglement states of quantum computing enable the network to analyze data from multiple perspectives simultaneously, identifying subtle feature differences that are difficult to detect with traditional methods. Due to quantum computing's ability to explore a larger data feature space, the models built by QDCNN exhibit stronger generalization capabilities. When faced with new, unseen data, QDCNN models can better adapt and predict, reducing the occurrence of overfitting.

Achieving efficient collaboration between quantum computing and classical computing is a major challenge for QDCNN. In the future, WiMi will optimize the data transmission and task scheduling mechanisms between quantum computing and classical computing, rationally allocating computational tasks to allow quantum processors to focus on parts where quantum acceleration is significant, while classical processors handle traditional computational tasks, thereby improving the overall operational efficiency of the system. Additionally, WiMi will reduce algorithm complexity by optimizing algorithm structures, adopting layered designs, and implementing modular programming. At the same time, research into distributed quantum computing technology will enable quantum computing tasks to be distributed across multiple quantum processors for parallel processing, enhancing the scalability of QDCNN and making it capable of meeting the demands of large-scale data processing and complex application scenarios.

With continuous exploration and innovation in Quantum Dilated Convolutional Neural Network technology, it is expected to find wide applications in more fields. For example, in the medical field, Quantum Dilated Convolutional Neural Network technology can be used for molecular structure analysis and disease prediction in drug development, accelerating the process of new drug discovery and improving healthcare standards. In the field of intelligent transportation, it can enable more accurate traffic flow prediction and intelligent driving decisions, enhancing traffic safety and efficiency. In environmental protection, it can analyze large amounts of environmental data to predict climate change trends, providing strong support for formulating environmental policies.

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.

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