BEIJING, June 29, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ:WIMI) (&#34;WiMi&#34; or the &#34;Company&#34;), a leading global Hologram Augmented Reality (&#34;AR&#34;) Technology provider, announced that

<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">June 29, 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, 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.</p>
<p xmlns="http://www.w3.org/1999/xhtml">In the study, WiMi trained and evaluated three different types of neural network models:</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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&#39;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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">About WiMi Hologram Cloud</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>) 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.</p><p xmlns="http://www.w3.org/1999/xhtml">Safe Harbor Statements</p><p xmlns="http://www.w3.org/1999/xhtml">This press release contains &#34;forward-looking statements&#34; within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as &#34;will,&#34; &#34;expects,&#34; &#34;anticipates,&#34; &#34;future,&#34; &#34;intends,&#34; &#34;plans,&#34; &#34;believes,&#34; &#34;estimates,&#34; and similar statements. Statements that are not historical facts, including statements about the Company&#39;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&#39;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 (&#34;SEC&#34;) 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&#39;s goals and strategies; the Company&#39;s future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company&#39;s expectations regarding demand for and market acceptance of its products and services.</p><p xmlns="http://www.w3.org/1999/xhtml">Further information regarding these and other risks is included in the Company&#39;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.</p><p xmlns="http://www.w3.org/1999/xhtml"> </p>



<p xmlns="http://www.w3.org/1999/xhtml" id="PURL"><img xmlns="http://www.w3.org/1999/xhtml" title="Cision" width="12" height="12" alt="Cision" src="https://edge.prnewswire.com/c/img/favicon.png?sn=CN94206&amp;sd=2026-06-29"></img> View original content:<a xmlns="http://www.w3.org/1999/xhtml" id="PRNURL" rel="nofollow" href="https://www.prnewswire.com/news-releases/wimi-explores-the-application-of-neural-networks-in-parameter-optimization-for-dual-field-quantum-key-distribution-302813268.html" target="_blank">https://www.prnewswire.com/news-releases/wimi-explores-the-application-of-neural-networks-in-parameter-optimization-for-dual-field-quantum-key-distribution-302813268.html</a></p><p xmlns="http://www.w3.org/1999/xhtml">SOURCE WiMi Hologram Cloud Inc.</p>

WIMI

WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key Distribution

WiMi advances neural network optimization for TF-QKD with potential quantum security applications

<section class="insight" data-version="1">
  <section data-block="information">
    <h3>Information</h3>
    <p>BEIJING, June 29, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ:WIMI) (&amp;#34;WiMi&amp;#34; or the &amp;#34;Company&amp;#34;), a leading global Hologram Augmented Reality (&amp;#34;AR&amp;#34;) Technology provider, announced that</p>
  </section>

  <section data-block="ai-summary-paragraph">
    <h3>AI Summary</h3>
    <p>WiMi Hologram Cloud disclosed research using neural networks to optimize TF‑QKD parameters, aiming to speed key generation and reduce compute needs. The study tested BPNN, RBFNN and GRNN, with RBFNN and GRNN delivering higher accuracy in high-dimensional spaces. If scalable, this could enable real-time quantum-security hardware integration and potential commercialization over the coming years.</p>
  </section>

  <section data-block="ai-summary-bullets">
    <h3>Key Points</h3>
    <ul><li>WiMi investigates neural networks to optimize TF-QKD parameters.</li><li>Tests include BPNN, RBFNN, GRNN; RBFNN/GRNN show higher accuracy in high-dim spaces.</li><li>NN methods cut computation time by orders of magnitude versus LSA; BPNN fastest.</li><li>Future work includes deeper architectures and real-time adaptation for commercialization.</li></ul>
  </section>

  <section data-block="companies-mentioned">
    <h3>Companies Mentioned</h3>
    <ul><li><strong>WiMi Hologram Cloud Inc. (WIMI)</strong>: Main subject; R&amp;D on TF-QKD parameter optimization with potential future product implications.</li></ul>
  </section>

  <section data-block="category">
    <h3>Industry News</h3>
    <p>Category: Industry News. The release centers on R&amp;D progress in quantum-security optimization, aligning with tech infrastructure trends and potential future product opportunities from WiMi.</p>
  </section>

  <section data-block="market-moving">
    <h3>Market-Moving</h3>
    <ul><li>Neural-network–driven TF-QKD optimization could enable real-time quantum key distribution.</li><li>No immediate revenue figures disclosed; near-term price impact likely limited.</li><li>Possible partnerships with quantum hardware firms could emerge in 12–24 months.</li><li>Broader quantum-security demand may improve investor sentiment toward WiMi.</li></ul>
  </section>

  <section data-block="key-facts">
    <h3>Key Facts</h3>
    <ul><li>Date of release: June 29, 2026.</li><li>Company: WiMi Hologram Cloud Inc. (NASDAQ: WIMI).</li><li>Neural networks tested: BPNN, RBFNN, GRNN.</li><li>RBFNN and GRNN showed higher accuracy in high-dimensional spaces.</li><li>Computation time reduced by orders of magnitude versus LSA.</li></ul>
  </section>

  <section data-block="trading-thesis">
    <h3>Trading Thesis</h3>
    <p>Longer-term bullish: commercialization potential may materialize within 12–24 months if pilots succeed.</p>
  </section>
</section>


<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">June 29, 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, 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.</p>
<p xmlns="http://www.w3.org/1999/xhtml">In the study, WiMi trained and evaluated three different types of neural network models:</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">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&#39;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.</p><p xmlns="http://www.w3.org/1999/xhtml">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.</p><p xmlns="http://www.w3.org/1999/xhtml">About WiMi Hologram Cloud</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>) 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.</p><p xmlns="http://www.w3.org/1999/xhtml">Safe Harbor Statements</p><p xmlns="http://www.w3.org/1999/xhtml">This press release contains &#34;forward-looking statements&#34; within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as &#34;will,&#34; &#34;expects,&#34; &#34;anticipates,&#34; &#34;future,&#34; &#34;intends,&#34; &#34;plans,&#34; &#34;believes,&#34; &#34;estimates,&#34; and similar statements. Statements that are not historical facts, including statements about the Company&#39;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&#39;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 (&#34;SEC&#34;) 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&#39;s goals and strategies; the Company&#39;s future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company&#39;s expectations regarding demand for and market acceptance of its products and services.</p><p xmlns="http://www.w3.org/1999/xhtml">Further information regarding these and other risks is included in the Company&#39;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.</p><p xmlns="http://www.w3.org/1999/xhtml"> </p>



<p xmlns="http://www.w3.org/1999/xhtml" id="PURL"><img xmlns="http://www.w3.org/1999/xhtml" title="Cision" width="12" height="12" alt="Cision" src="https://edge.prnewswire.com/c/img/favicon.png?sn=CN94206&amp;sd=2026-06-29"></img> View original content:<a xmlns="http://www.w3.org/1999/xhtml" id="PRNURL" rel="nofollow" href="https://www.prnewswire.com/news-releases/wimi-explores-the-application-of-neural-networks-in-parameter-optimization-for-dual-field-quantum-key-distribution-302813268.html" target="_blank">https://www.prnewswire.com/news-releases/wimi-explores-the-application-of-neural-networks-in-parameter-optimization-for-dual-field-quantum-key-distribution-302813268.html</a></p><p xmlns="http://www.w3.org/1999/xhtml">SOURCE WiMi Hologram Cloud Inc.</p>

WiMi Hologram Cloud disclosed research using neural networks to optimize TF‑QKD parameters, aiming to speed key generation and reduce compute needs. The study tested BPNN, RBFNN and GRNN, with RBFNN and GRNN delivering higher accuracy in high-dimensional spaces. If scalable, this could enable real-time quantum-security hardware integration and potential commercialization over the coming years.

WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key Distribution

AI Summary

Sentiment Rationale

Trading Thesis

Market-Moving

Key Facts

Companies Mentioned

Industry News

Related News