<p xmlns="http://www.w3.org/1999/xhtml"><span xmlns="http://www.w3.org/1999/xhtml" class="legendSpan...

<p xmlns="http://www.w3.org/1999/xhtml"><span xmlns="http://www.w3.org/1999/xhtml" class="legendSpanClass">BEIJING</span>, <span xmlns="http://www.w3.org/1999/xhtml" class="legendSpanClass">Aug. 21, 2026</span> /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ:<a class="ticker" href="https://www.benzinga.com/quote/WIMI">WIMI</a>) (&#34;WiMi&#34; or the &#34;Company&#34;), a leading global Hologram Augmented Reality (&#34;AR&#34;) Technology provider, proposes a cutting-edge quantum machine learning technology oriented toward classical data classification tasks—a quantum convolutional neural network with interaction layers for classical data classification. This technology systematically enhances the overall performance of quantum convolutional neural networks in terms of expressive power, entanglement generation capability, and actual classification performance by introducing a novel interaction layer structure based on three-qubit interactions, marking an important step forward in the structural design of quantum deep learning models toward a new phase driven by multi-body interactions.</p>
<p xmlns="http://www.w3.org/1999/xhtml">From the perspective of technical implementation logic, this quantum convolutional network adopts an overall hybrid quantum-classical architecture design. First, classical data is mapped to the quantum state space through an efficient data encoding strategy, ensuring that as much discriminative information from the original data as possible is preserved under limited qubit resources. For image data, the network employs block partitioning and local mapping approaches to embed pixel information into corresponding quantum subsystems; for one-dimensional data, a combination of structured amplitude encoding and angle encoding is used to achieve a compact representation of data features. After data encoding is completed, the quantum state is fed into the quantum feature extraction module composed of multiple layers of quantum convolutional units and interaction layers.</p><p xmlns="http://www.w3.org/1999/xhtml">In this module, quantum convolution operations and the novel interaction layers are executed alternately. The quantum convolutional layers are responsible for extracting low-order features within local qubit subspaces, with their structural design adhering to hardware-friendly principles to avoid introducing excessively deep or difficult-to-implement quantum gate sequences. The interaction layers serve as the key innovation of the entire network, achieving cross-channel and cross-scale information fusion through three-qubit interactions. This design enables the network to significantly enhance its expressive power for complex patterns while keeping circuit depth under control. The WiMi R&amp;D team systematically studied the impact of this interaction layer on the coverage capability of the quantum state space in theoretical analysis. The results show that after introducing three-body interactions, the set of reachable states in the parameter space of the network is significantly expanded, effectively alleviating the common expressivity limitation problem in traditional quantum neural networks.</p><p xmlns="http://www.w3.org/1999/xhtml">In terms of entanglement capability, WiMi further conducted an in-depth analysis of the proposed network structure from the perspective of quantum information theory. The study shows that the three-qubit interaction layer can generate high-intensity, multi-scale entanglement structures at relatively shallow circuit depths, which is crucial for quantum machine learning models to capture nonlinear correlations in the input data. Compared to network structures that rely solely on two-qubit entanglement gates, the new model exhibits clear advantages across multiple metrics, including entanglement entropy, uniformity of entanglement distribution, and efficiency of entanglement propagation. This characteristic not only enhances the model&#39;s learning capability but also provides strong support for maintaining stable performance under the presence of noise.</p><p xmlns="http://www.w3.org/1999/xhtml">In terms of the training mechanism, this quantum convolutional neural network employs a joint iterative approach between classical optimizers and quantum circuit parameters to complete model learning. The output of the quantum circuit is mapped into classical feature vectors through measurement, which are then evaluated by a classical loss function to provide gradient feedback. Addressing the common issues of gradient vanishing and optimization instability in quantum model training, WiMi systematically optimized the parameter initialization strategy and training procedure, enabling the model to achieve stable convergence in both multi-class and binary classification tasks. These engineering improvements ensure that the technology possesses advantages not only at the theoretical level but also has practical deployability and feasibility.</p><p xmlns="http://www.w3.org/1999/xhtml">This achievement not only demonstrates the real-world potential of quantum machine learning in the field of classical data processing, but also provides a replicable and scalable technical paradigm for the architectural design of next-generation quantum intelligent systems. By systematically introducing multi-body quantum interactions into the structural design of neural networks, WiMi is driving the evolution of quantum algorithms from quantum acceleration tools toward quantum-native intelligent models. This direction is expected to form a synergistic effect with the future development of quantum computing hardware, unleashing even more disruptive computational capabilities.</p><p xmlns="http://www.w3.org/1999/xhtml">WiMi plans to further expand the model scale and application scenarios on the basis of existing technology, including directions such as higher-dimensional image data, complex time series analysis, and cross-modal data fusion. At the same time, it will continue to focus on noise robustness and hardware adaptability issues, promoting the verification and deployment of this quantum convolutional neural network on real quantum devices.</p><p xmlns="http://www.w3.org/1999/xhtml">The technical achievement released by WiMi marks a substantial step forward in quantum machine learning model design, moving from imitating classical structures toward fully leveraging the intrinsic advantages of quantum physics. By deeply integrating multi-qubit interaction mechanisms at the network structure level, this technology provides solid support for performance breakthroughs of quantum convolutional neural networks in practical applications, and injects new momentum into the industrial development of quantum artificial intelligence.</p><p xmlns="http://www.w3.org/1999/xhtml"><b xmlns="http://www.w3.org/1999/xhtml">About WiMi Hologram Cloud</b></p><p xmlns="http://www.w3.org/1999/xhtml">WiMi Hologram Cloud Inc. (NASDAQ:<a class="ticker" href="https://www.benzinga.com/quote/WIMI">WIMI</a>) 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 <a xmlns="http://www.w3.org/1999/xhtml" href="http://ir.wimiar.com/" target="_blank" rel="nofollow">http://ir.wimiar.com</a>.</p><p xmlns="http://www.w3.org/1999/xhtml"><b xmlns="http://www.w3.org/1999/xhtml">Translation Disclaimer</b></p><p xmlns="http://www.w3.org/1999/xhtml">The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.</p>



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WIMI

WiMi's Next-Generation Quantum Convolutional Neural Network Reshapes Classical Data Classification Methods

WiMi Advances Quantum CNN with Three-Body Interactions for Classification

<section class="insight" data-version="1">
  <section data-block="information">
    <h3>Information</h3>
    <p>&lt;p xmlns=&quot;http://www.w3.org/1999/xhtml&quot;&gt;&lt;span xmlns=&quot;http://www.w3.org/1999/xhtml&quot; class=&quot;legendSpan...</p>
  </section>

  <section data-block="ai-summary-paragraph">
    <h3>AI Summary</h3>
    <p>WiMi unveiled a quantum convolutional neural network featuring three-qubit interaction layers designed to expand expressive power and entanglement while staying hardware-friendly. The hybrid quantum-classical framework targets improved classification of classical data and could bolster WiMi&#039;s AI offerings for holographic AR and cross-modal sensing. Near-term impact hinges on deployment on real devices and scalable training.</p>
  </section>

  <section data-block="ai-summary-bullets">
    <h3>Key Points</h3>
    <ul><li>WiMi unveils quantum CNN with three-qubit interaction layers. Hybrid quantum-classical design targets classical data classification.</li><li>Three-body interactions expand state space, boosting expressivity. Study claims wider state-coverage and stable training.</li><li>Plans deployment on real quantum devices and expand data types. Includes higher-dimensional images, time series.</li><li>Walks hand-in-hand with WiMi&#039;s AR/holographic product roadmap. Signals possible hardware-software synergy.</li></ul>
  </section>

  <section data-block="companies-mentioned">
    <h3>Companies Mentioned</h3>
    <ul><li><strong>WiMi Hologram Cloud Inc. (WIMI)</strong>: Primary subject; R&amp;D-focused press release; potential future valuation impact if commercial milestones emerge.</li></ul>
  </section>

  <section data-block="category">
    <h3>Industry News</h3>
    <p>Industry News. The release emphasizes WiMi&#039;s R&amp;D progress in quantum ML, which could influence sentiment and valuation if translated into commercialization or partnerships.</p>
  </section>

  <section data-block="market-moving">
    <h3>Market-Moving</h3>
    <ul><li>PR highlights a potential near-term uptick in WiMi&#039;s stock on tech validation.</li><li>No immediate revenue figures; valuation remains speculative until milestones materialize.</li><li>Possible chatter about partnerships with quantum hardware vendors; seek follow-ups.</li></ul>
  </section>

  <section data-block="key-facts">
    <h3>Key Facts</h3>
    <ul><li>Date: Aug 21, 2026.</li><li>Issuer: WiMi Hologram Cloud Inc., NASDAQ: WIMI.</li><li>New tech: quantum convolutional neural network with three-qubit interaction layers.</li><li>Architecture: hybrid quantum-classical data processing for classification tasks.</li><li>Outlook: plan to scale to higher-dimensional images, time series, and cross-modal fusion; focus on noise robustness and real-device deployment.</li></ul>
  </section>

  <section data-block="trading-thesis">
    <h3>Trading Thesis</h3>
    <p>Positive sentiment on quantum ML; monitor device deployment progress over 6–12 months.</p>
  </section>
</section>


<article>
  <section>
    <p><strong>BEIJING</strong>, Aug. 21, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: <strong>WIMI</strong>) announces a cutting-edge quantum machine learning technology aimed at classical data classification. The company describes a quantum convolutional neural network with interaction layers for classical data classification, designed to enhance expressive power, entanglement generation capability, and actual classification performance through a novel interaction layer structure based on <strong>three-qubit interactions</strong>. This represents a forward step in the structural design of quantum deep learning models toward a new phase driven by multi-body interactions.</p>
  </section>

  <section>
    <h2>Technology Overview: Hybrid Quantum-Classical Architecture</h2>
    <p>The quantum convolutional network adopts an overall hybrid quantum-classical architecture design. First, classical data is mapped to the quantum state space through an efficient data encoding strategy, ensuring that as much discriminative information from the original data as possible is preserved under limited qubit resources. For image data, the network employs block partitioning and local mapping approaches to embed pixel information into corresponding quantum subsystems; for one-dimensional data, a combination of structured amplitude encoding and angle encoding is used to achieve a compact representation of data features. After data encoding is completed, the quantum state is fed into the quantum feature extraction module composed of multiple layers of quantum convolutional units and interaction layers.</p>
  </section>

  <section>
    <h2>Key Components: Quantum Convolution and Interaction Layers</h2>
    <p>In this module, quantum convolution operations and the novel interaction layers are executed alternately. The quantum convolutional layers are responsible for extracting low-order features within local qubit subspaces, with their structural design adhering to hardware-friendly principles to avoid introducing excessively deep or difficult-to-implement quantum gate sequences. The <strong>interaction layers</strong> serve as the key innovation of the entire network, achieving cross-channel and cross-scale information fusion through <strong>three-qubit interactions</strong>. This design enables the network to significantly enhance its expressive power for complex patterns while keeping circuit depth under control. The WiMi R&amp;D team systematically studied the impact of this interaction layer on the coverage capability of the quantum state space in theoretical analysis. The results show that after introducing <strong>three-body interactions</strong>, the set of reachable states in the parameter space of the network is significantly expanded, effectively alleviating the common expressivity limitation problem in traditional quantum neural networks.</p>
  </section>

  <section>
    <h2>Entanglement Capabilities and Training Dynamics</h2>
    <p>In terms of entanglement capability, WiMi conducted an in-depth analysis of the proposed network structure from the perspective of quantum information theory. The study shows that the <strong>three-qubit interaction layer</strong> can generate high-intensity, multi-scale entanglement structures at relatively shallow circuit depths, which is crucial for quantum machine learning models to capture nonlinear correlations in the input data. Compared to network structures that rely solely on two-qubit entanglement gates, the new model exhibits clear advantages across multiple metrics, including entanglement entropy, uniformity of entanglement distribution, and efficiency of entanglement propagation. This characteristic not only enhances the model's learning capability but also provides strong support for maintaining stable performance under the presence of noise.</p>
    <p>In terms of the training mechanism, this quantum convolutional neural network employs a joint iterative approach between classical optimizers and quantum circuit parameters to complete model learning. The output of the quantum circuit is mapped into classical feature vectors through measurement, which are then evaluated by a classical loss function to provide gradient feedback. Addressing the common issues of gradient vanishing and optimization instability in quantum model training, WiMi systematically optimized the parameter initialization strategy and training procedure, enabling the model to achieve stable convergence in both multi-class and binary classification tasks. These engineering improvements ensure that the technology possesses advantages not only at the theoretical level but also has practical deployability and feasibility.</p>
  </section>

  <section>
    <h2>Impact, Outlook, and Next Steps</h2>
    <p>This achievement not only demonstrates the real-world potential of quantum machine learning in the field of classical data processing, but also provides a replicable and scalable technical paradigm for the architectural design of next-generation quantum intelligent systems. By systematically introducing multi-body quantum interactions into the structural design of neural networks, WiMi is driving the evolution of quantum algorithms from quantum acceleration tools toward quantum-native intelligent models. This direction is expected to form a synergistic effect with the future development of quantum computing hardware, unleashing even more disruptive computational capabilities.</p>
    <p>WiMi plans to further expand the model scale and application scenarios on the basis of existing technology, including directions such as higher-dimensional image data, complex time series analysis, and cross-modal data fusion. At the same time, it will continue to focus on noise robustness and hardware adaptability issues, promoting the verification and deployment of this quantum convolutional neural network on real quantum devices.</p>
  </section>

  <section>
    <h2>Strategic Significance for WiMi and the Market</h2>
    <p>The technical achievement released by WiMi marks a substantial step forward in quantum machine learning model design, moving from imitating classical structures toward fully leveraging the intrinsic advantages of quantum physics. By deeply integrating multi-qubit interaction mechanisms at the network structure level, this technology provides solid support for performance breakthroughs of quantum convolutional neural networks in practical applications, and injects new momentum into the industrial development of quantum artificial intelligence.</p>
  </section>

  <section>
    <h2>About WiMi Hologram Cloud</h2>
    <p>WiMi Hologram Cloud Inc. (NASDAQ: <a class="ticker" href="https://www.benzinga.com/quote/WIMI">WIMI</a>) 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.</p>
  </section>
</article>

WiMi unveiled a quantum convolutional neural network featuring three-qubit interaction layers designed to expand expressive power and entanglement while staying hardware-friendly. The hybrid quantum-classical framework targets improved classification of classical data and could bolster WiMi's AI offerings for holographic AR and cross-modal sensing. Near-term impact hinges on deployment on real devices and scalable training.

News insight

WiMi's Next-Generation Quantum Convolutional Neural Network Reshapes Classical Data Classification Methods

AI summary

Sentiment rationale

Key facts

Industry News