<p>北京，2026 年 1 月 2 日（全球新聞通訊社）——WiMi Hologram Cloud Inc.（納斯達克：WiMi）（&#34;WiMi&#34;或&#34;公司&#34;），一家全球領先的全息增強現實（&#34;AR&#34;）技術提供商，發佈了一項突破性成就——基於參數高效量子模塊的混合量子 - 經典深度學習技術 QB-Net（量子瓶頸網絡）。該技術通過將輕量級量子計算模塊嵌入經典 U-Net 深度學習架構，實現了重大突破，瓶頸層的參數數量減少了多達 30 倍，同時保持了與經典 U-Net 相當的性能。這一研發成果不僅展示了混合量子 - 經典人工智能的前沿潛力，還為傳統深度學習架構提供了一種全新的優化範式。</p>
<p>量子計算的核心優勢在於其能夠通過量子比特的疊加態表達高維信息，並在指數維空間中執行線性操作，使其具備超越經典架構的表達和變革能力。然而，在當前階段，量子硬件仍無法支持大規模量子神經網絡或構建完整的量子 U-Net 或量子 Transformer。</p>
<p>因此，WiMi 採取了一條完全不同的路徑：不是構建完全量子化的人工智能模型，而是構建量子增強模塊。</p>
<p>這一概念源於一個關鍵觀察：深度網絡的瓶頸層本質上是高維特徵的高密度表達問題，而量子態自然適合表達極高維的向量空間。</p>
<p>當一個經典網絡需要數萬個參數來完成映射任務時，理論上一個量子態只需幾十個量子比特就可以實現相同甚至更高的表達能力。這意味着，只要經典特徵能夠映射到量子態並通過量子電路進行轉換，就有可能以極低的參數數量實現等效能力。</p>
<p>基於這一理念，WiMi 設計了一個可插拔的量子瓶頸模塊。該模塊以最小的參數數量、結構穩定性、可訓練性以及能夠集成到經典網絡中的能力為核心目標，並已嵌入經典 U-Net，形成 QB-Net。</p>
<p>QB-Net 保留了 U-Net 的整體結構，包括編碼器、上採樣路徑和跳躍連接。然而，在瓶頸層位置，傳統的多個卷積層被替換為量子特徵壓縮 - 轉換 - 重建模塊。該模塊由三個關鍵步驟組成：</p>
<p>第一步是將經典特徵編碼為量子態。編碼模塊使用線性投影或幅度編碼等技術，將經典特徵張量映射為適合進入量子電路的緊湊向量形式。編碼策略的設計遵循兩個主要原則：儘可能最小化量子比特的數量，同時保留特徵的關鍵信息而不丟失。</p>
<p>第二步是通過量子電路進行特徵轉換，這是整個系統的核心環節，也是參數效率的關鍵。傳統的卷積瓶頸層可能包含數十萬甚至數百萬個參數，而量子電路只需幾十到幾百個可調旋轉參數即可實現等效的表達轉換。</p>
<p>WiMi 使用參數化量子電路（PQC），通過層疊構建一個深度可控的量子態變換器。量子電路包括糾纏結構，以確保量子比特之間的信息流動充足，形成比經典線性變換更高維的表示能力。</p>
<p>第三步是將量子態解碼回經典張量。通過經典集成和校正模塊重建從量子測量中獲得的結果，最終返回到經典 U-Net 的解碼路徑。通過量子瓶頸壓縮的特徵保留了表達能力，同時以極低的參數數量完成了高維信息的過濾和抽象。整個過程可以直接嵌入現有模型中，而無需修改 U-Net 架構或改變訓練範式，實現真正的 “即插即用量子增強”。</p>
<p>WiMi 的 QB-Net 的發佈標誌着我們公司在量子人工智能技術道路上的關鍵一步。它不僅證明了量子計算現在可以提供真正的價值，還展示了量子技術與深度學習之間深度融合的巨大潛力。在未來，混合量子 - 經典架構將不再被視為過渡技術，而將成為長期以來人工智能的主流形式之一。</p>
<p>QB-Net 代表了一種全新的思維方式：讓量子計算成為人工智能中最有價值的部分，而不是全部。基於參數高效量子模塊的混合深度學習框架將為全球人工智能產業帶來全新的結構優化範式，併為企業級智能系統提供全新的性能提升路徑。</p>
<p><strong>關於 WiMi Hologram Cloud</strong></p>
<p>WiMi Hologram Cloud Inc.（納斯達克：WiMi）專注於全息雲服務，主要集中在專業領域，如車載增強現實全息抬頭顯示器、3D 全息脈衝激光雷達、頭戴式光場全息設備、全息半導體、全息雲軟件、全息汽車導航、元宇宙全息增強現實/虛擬現實設備以及元宇宙全息雲軟件。它涵蓋了全息增強現實技術的多個方面，包括車載全息增強現實技術、3D 全息脈衝激光雷達技術、全息視覺半導體技術、全息軟件開發、全息增強現實虛擬廣告技術、全息增強現實虛擬娛樂技術、全息增強現實 SDK 支付、互動全息虛擬通信、元宇宙全息增強現實技術和元宇宙虛擬雲服務。WiMi 是一個綜合性的全息雲技術解決方案提供商。有關更多信息，請訪問 http://ir.wimiar.com。</p>
<p><strong>翻譯免責聲明</strong></p>
<p>本公告的原始版本是官方授權的唯一具有法律約束力的版本。如果中文翻譯與原始版本之間存在任何不一致或意義上的差異，以原始版本為準。WiMi Hologram Cloud Inc. 及相關機構和個人對翻譯版本不作任何保證，並對因翻譯不準確而導致的任何直接或間接損失不承擔責任。</p>
<p><strong>投資者諮詢，請聯繫：</strong></p>
<p>WIMI Hologram Cloud Inc.</p>
<p>電子郵件：pr@wimiar.com</p>
<p>ICR, LLC</p>
<p>Robin Yang</p>
<p>電話：+1 (646) 975-9495</p>
<p>電子郵件：wimi@icrinc.com</p>
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微美全息

<p>WiMi Hologram Cloud Inc. 宣佈在混合量子 - 經典深度學習技術方面取得突破，推出了 QB-Net，該技術將輕量級量子模塊集成到 U-Net 架構中。這一創新將瓶頸層中的參數數量減少了多達 30 倍，同時保持了性能。QB-Net 利用量子態進行高維特徵表達，為深度學習提供了一種新的優化範式。這一發展標誌着量子人工智能技術的重大進步，使混合架構成為未來人工智能的主流</p>

<p>BEIJING, Jan. 02, 2026 (GLOBE NEWSWIRE) -- 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, 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.</p>
<div class="lb-trans"><p>北京，2026 年 1 月 2 日（全球新聞通訊社）——WiMi Hologram Cloud Inc.（納斯達克：WiMi）（&#34;WiMi&#34;或&#34;公司&#34;），一家全球領先的全息增強現實（&#34;AR&#34;）技術提供商，發佈了一項突破性成就——基於參數高效量子模塊的混合量子 - 經典深度學習技術 QB-Net（量子瓶頸網絡）。該技術通過將輕量級量子計算模塊嵌入經典 U-Net 深度學習架構，實現了重大突破，瓶頸層的參數數量減少了多達 30 倍，同時保持了與經典 U-Net 相當的性能。這一研發成果不僅展示了混合量子 - 經典人工智能的前沿潛力，還為傳統深度學習架構提供了一種全新的優化範式。</p>
</div><p>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.</p>
<div class="lb-trans"><p>量子計算的核心優勢在於其能夠通過量子比特的疊加態表達高維信息，並在指數維空間中執行線性操作，使其具備超越經典架構的表達和變革能力。然而，在當前階段，量子硬件仍無法支持大規模量子神經網絡或構建完整的量子 U-Net 或量子 Transformer。</p>
</div><p>Therefore, WiMi has taken a completely different path: instead of building fully quantized AI models, it constructs quantum enhancement modules.</p>
<div class="lb-trans"><p>因此，WiMi 採取了一條完全不同的路徑：不是構建完全量子化的人工智能模型，而是構建量子增強模塊。</p>
</div><p>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.</p>
<div class="lb-trans"><p>這一概念源於一個關鍵觀察：深度網絡的瓶頸層本質上是高維特徵的高密度表達問題，而量子態自然適合表達極高維的向量空間。</p>
</div><p>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.</p>
<div class="lb-trans"><p>當一個經典網絡需要數萬個參數來完成映射任務時，理論上一個量子態只需幾十個量子比特就可以實現相同甚至更高的表達能力。這意味着，只要經典特徵能夠映射到量子態並通過量子電路進行轉換，就有可能以極低的參數數量實現等效能力。</p>
</div><p>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.</p>
<div class="lb-trans"><p>基於這一理念，WiMi 設計了一個可插拔的量子瓶頸模塊。該模塊以最小的參數數量、結構穩定性、可訓練性以及能夠集成到經典網絡中的能力為核心目標，並已嵌入經典 U-Net，形成 QB-Net。</p>
</div><p>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:</p>
<div class="lb-trans"><p>QB-Net 保留了 U-Net 的整體結構，包括編碼器、上採樣路徑和跳躍連接。然而，在瓶頸層位置，傳統的多個卷積層被替換為量子特徵壓縮 - 轉換 - 重建模塊。該模塊由三個關鍵步驟組成：</p>
</div><p>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.</p>
<div class="lb-trans"><p>第一步是將經典特徵編碼為量子態。編碼模塊使用線性投影或幅度編碼等技術，將經典特徵張量映射為適合進入量子電路的緊湊向量形式。編碼策略的設計遵循兩個主要原則：儘可能最小化量子比特的數量，同時保留特徵的關鍵信息而不丟失。</p>
</div><p>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.</p>
<div class="lb-trans"><p>第二步是通過量子電路進行特徵轉換，這是整個系統的核心環節，也是參數效率的關鍵。傳統的卷積瓶頸層可能包含數十萬甚至數百萬個參數，而量子電路只需幾十到幾百個可調旋轉參數即可實現等效的表達轉換。</p>
</div><p>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.</p>
<div class="lb-trans"><p>WiMi 使用參數化量子電路（PQC），通過層疊構建一個深度可控的量子態變換器。量子電路包括糾纏結構，以確保量子比特之間的信息流動充足，形成比經典線性變換更高維的表示能力。</p>
</div><p>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”.</p>
<div class="lb-trans"><p>第三步是將量子態解碼回經典張量。通過經典集成和校正模塊重建從量子測量中獲得的結果，最終返回到經典 U-Net 的解碼路徑。通過量子瓶頸壓縮的特徵保留了表達能力，同時以極低的參數數量完成了高維信息的過濾和抽象。整個過程可以直接嵌入現有模型中，而無需修改 U-Net 架構或改變訓練範式，實現真正的 “即插即用量子增強”。</p>
</div><p>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.</p>
<div class="lb-trans"><p>WiMi 的 QB-Net 的發佈標誌着我們公司在量子人工智能技術道路上的關鍵一步。它不僅證明了量子計算現在可以提供真正的價值，還展示了量子技術與深度學習之間深度融合的巨大潛力。在未來，混合量子 - 經典架構將不再被視為過渡技術，而將成為長期以來人工智能的主流形式之一。</p>
</div><p>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.</p>
<div class="lb-trans"><p>QB-Net 代表了一種全新的思維方式：讓量子計算成為人工智能中最有價值的部分，而不是全部。基於參數高效量子模塊的混合深度學習框架將為全球人工智能產業帶來全新的結構優化範式，併為企業級智能系統提供全新的性能提升路徑。</p>
</div><p><strong>About WiMi Hologram Cloud</strong></p>
<div class="lb-trans"><p><strong>關於 WiMi Hologram Cloud</strong></p>
</div><p>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.</p>
<div class="lb-trans"><p>WiMi Hologram Cloud Inc.（納斯達克：WiMi）專注於全息雲服務，主要集中在專業領域，如車載增強現實全息抬頭顯示器、3D 全息脈衝激光雷達、頭戴式光場全息設備、全息半導體、全息雲軟件、全息汽車導航、元宇宙全息增強現實/虛擬現實設備以及元宇宙全息雲軟件。它涵蓋了全息增強現實技術的多個方面，包括車載全息增強現實技術、3D 全息脈衝激光雷達技術、全息視覺半導體技術、全息軟件開發、全息增強現實虛擬廣告技術、全息增強現實虛擬娛樂技術、全息增強現實 SDK 支付、互動全息虛擬通信、元宇宙全息增強現實技術和元宇宙虛擬雲服務。WiMi 是一個綜合性的全息雲技術解決方案提供商。有關更多信息，請訪問 http://ir.wimiar.com。</p>
</div><p><strong>Translation Disclaimer</strong></p>
<div class="lb-trans"><p><strong>翻譯免責聲明</strong></p>
</div><p>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>
<div class="lb-trans"><p>本公告的原始版本是官方授權的唯一具有法律約束力的版本。如果中文翻譯與原始版本之間存在任何不一致或意義上的差異，以原始版本為準。WiMi Hologram Cloud Inc. 及相關機構和個人對翻譯版本不作任何保證，並對因翻譯不準確而導致的任何直接或間接損失不承擔責任。</p>
</div><p><strong>Investor Inquiries, please contact:</strong></p>
<div class="lb-trans"><p><strong>投資者諮詢，請聯繫：</strong></p>
</div><p>WIMI Hologram Cloud Inc.</p>
<div class="lb-trans"><p>WIMI Hologram Cloud Inc.</p>
</div><p>Email: pr@wimiar.com</p>
<div class="lb-trans"><p>電子郵件：pr@wimiar.com</p>
</div><p>ICR, LLC</p>
<div class="lb-trans"><p>ICR, LLC</p>
</div><p>Robin Yang</p>
<div class="lb-trans"><p>Robin Yang</p>
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微美全息通過將量子模塊高效嵌入到 U-Net 中，實現了輕量化設計與高性能的共存 | WiMi Hologram Cloud - Class B 股票新聞