<p>北京，2026 年 1 月 5 日（全球新聞通訊社）——WiMi 發佈下一代量子卷積神經網絡技術，用於多通道監督學習</p>
<p>北京，2026 年 1 月 5 日——WiMi Hologram Cloud Inc.（納斯達克：WiMi）（&#34;WiMi&#34;或&#34;公司&#34;），全球領先的全息增強現實（&#34;AR&#34;）技術提供商，宣佈推出其自主開發的新技術：用於多通道監督學習的量子卷積神經網絡（MC-QCNN）。這一突破性方法首次構建了完全硬件適應的量子卷積核設計，使量子模型能夠高效處理多通道數據，從而在圖像分類、醫學成像、視頻分析和多模態監測等行業展現出絕對優勢。</p>
<p>從研發的角度來看，這一技術突破的核心不僅在於多通道量子卷積核的構建，還在於整個系統化的設計方案，包括卷積核結構、量子比特佈局、通道交互編碼、權重可學習性、可解釋性和硬件約束適應策略。為了使該技術能夠在真實硬件上執行，WiMi 放棄了大量不切實際的深度電路結構，而轉向更接近量子硬件原生門操作特性的設計理念。WiMi 提出的量子電路卷積核使用單比特旋轉門、受控參數化門、SWAP 交錯結構、弱糾纏層和通道交互門，從而形成一個能夠表達複雜函數的卷積算子，同時保持對量子退相干的魯棒性。</p>
<p>與經典卷積核需要在像素鄰域內滑動不同，WiMi 採用了一種量子特定的編碼方法，將來自多個通道的數據壓縮並編碼為量子態的幅度、相位或糾纏結構，通過參數化量子門對其進行類似卷積的處理。通道之間的特徵融合不再依賴於線性加權，而是通過門級交互直接在量子態空間中生成高維相關性，產生比經典卷積更強的特徵組合能力。通過訓練，這些參數化的量子卷積核可以學習高階跨通道特徵，如紋理 - 顏色共現、時空聯合模式、多光譜能量分佈相關性等，從而實現優於傳統 QCNN 的表達能力。</p>
<p>該技術架構的核心之一是 WiMi 建立的量子多通道卷積算子。該算子使用參數化旋轉門和受控門構建卷積模式。通過調整門的旋轉角度和受控結構，卷積核可以在訓練過程中自動學習最佳的跨通道特徵組合策略。整個卷積核不僅可以作用於單比特分佈，還可以以張量的方式作用於多比特通道結構，使卷積核不僅能夠提取局部一致性，還能夠從糾纏結構中挖掘高階關係。這種模式在經典 CNN 中無法直接實現，因為經典神經網絡中多通道特徵的組合通常基於線性疊加，而量子卷積核則基於量子疊加和量子糾纏，能夠在指數特徵空間中表達複雜的多通道相關性。</p>
<p>在卷積操作完成後，特徵圖被壓縮為量子系統中更緊湊的量子態，並通過量子池化電路進行下采樣。池化電路也經過重新設計，以處理來自多個通道的量子態特徵。WiMi 採用可學習的量子池化模式，通過可控測量或可控壓縮操作減少量子態維度，同時保留關鍵特徵信息，從而避免傳統 QCNN 中因直接測量導致的特徵破壞問題。實驗結果表明，新的池化結構比傳統 QCNN 池化方法更穩定，特徵保留率更高。</p>
<p>除了卷積核和池化電路，WiMi 還構建了專門的混合量子 - 經典訓練框架。在訓練過程中，經典計算模塊負責損失函數計算、梯度求解和參數更新，而量子模塊負責前向傳播和量子態演化。WiMi 採用擴展參數偏移規則方法，使多通道量子卷積核中的所有參數都能有效訓練。為了提高訓練穩定性，WiMi 還引入了量子噪聲模擬和梯度裁剪機制，確保模型在真實量子硬件上的性能不會因噪聲而急劇下降。</p>
<p>在訓練過程中，WiMi 團隊觀察到一個非常有價值的現象：模型能夠自動捕捉多個通道之間的非線性關聯。以 RGB 圖像為例，模型學習到的量子卷積核並不是簡單地對 R、G 和 B 通道進行線性遍歷，而是通過糾纏層在通道之間建立關聯，使得卷積核能夠識別量子狀態空間中顏色分佈模式的聯合特徵。這意味着模型並不是在三個通道上分別進行卷積，而是在更高維的空間中學習整體的深層特徵，其表達能力遠遠超過經典卷積神經網絡中的 3×3 或 1×1 卷積。</p>
<p>WiMi 認為，多通道處理能力將成為量子神經網絡向現實應用邁進的關鍵能力之一。儘管單通道 QCNN 在學術界具有探索意義，但其侷限性使其無法滿足行業對複雜數據的要求。MC-QCNN 的出現使量子深度學習系統首次具備處理現實數據的能力，這意味着量子人工智能不再僅僅是實驗室的概念，而是開始具備商業實施的可能性。相信隨着量子硬件性能的提升，這項技術將推動量子機器學習從實驗室研究走向真正的應用時代。</p>
<p>未來，WiMi 將繼續完善這一技術體系，包括構建更高效的量子卷積核結構，開發更強大的噪聲適應策略，擴展到三維卷積和時間序列卷積結構，並探索與 Transformer 等模型結構的融合可能性，使量子模型不僅能夠處理多通道圖像，還能處理多模態語音、視頻、文本、圖形結構和傳感器數據。量子深度學習將不再侷限於小規模任務，而將成為下一代通用人工智能模型中的重要運算符。量子計算與人工智能的結合將是未來十年技術發展的核心趨勢。WiMi 將繼續致力於推動量子人工智能生態系統的建設，使量子技術真正服務於工業需求、社會價值和人類未來。</p>
<p><strong>關於 WiMi 全息雲</strong></p>
<p>WiMi 全息雲公司（NASDAQ: WiMi）專注於全息雲服務，主要集中在車載 AR 全息 HUD、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 AR/VR 設備和元宇宙全息雲軟件等專業領域。它涵蓋了全息 AR 技術的多個方面，包括車載全息 AR 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 AR 虛擬廣告技術、全息 AR 虛擬娛樂技術、全息 ARSDK 支付、互動全息虛擬通信、元宇宙全息 AR 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含義差異，以原始版本為準。WiMi 全息雲公司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 全息雲公司</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>微美全息（WiMi Hologram Cloud Inc.）推出了一種新的量子卷積神經網絡技術，用於多通道監督學習。這項創新技術能夠高效處理多通道數據，增強圖像分類、醫學成像和視頻分析的能力。該設計專注於硬件適應性，並利用量子特定編碼方法來改善特徵融合和高階跨通道特徵的學習。微美的方案包括一個混合量子 - 經典訓練框架，確保在真實量子硬件上的穩定性和性能。該技術旨在捕捉數據中的複雜關聯，超越傳統方法</p>

<p>Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning</p>
<div class="lb-trans"><p>北京，2026 年 1 月 5 日（全球新聞通訊社）——WiMi 發佈下一代量子卷積神經網絡技術，用於多通道監督學習</p>
</div><p>BEIJING, Jan. 05, 2026––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 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.</p>
<div class="lb-trans"><p>北京，2026 年 1 月 5 日——WiMi Hologram Cloud Inc.（納斯達克：WiMi）（&#34;WiMi&#34;或&#34;公司&#34;），全球領先的全息增強現實（&#34;AR&#34;）技術提供商，宣佈推出其自主開發的新技術：用於多通道監督學習的量子卷積神經網絡（MC-QCNN）。這一突破性方法首次構建了完全硬件適應的量子卷積核設計，使量子模型能夠高效處理多通道數據，從而在圖像分類、醫學成像、視頻分析和多模態監測等行業展現出絕對優勢。</p>
</div><p>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.</p>
<div class="lb-trans"><p>從研發的角度來看，這一技術突破的核心不僅在於多通道量子卷積核的構建，還在於整個系統化的設計方案，包括卷積核結構、量子比特佈局、通道交互編碼、權重可學習性、可解釋性和硬件約束適應策略。為了使該技術能夠在真實硬件上執行，WiMi 放棄了大量不切實際的深度電路結構，而轉向更接近量子硬件原生門操作特性的設計理念。WiMi 提出的量子電路卷積核使用單比特旋轉門、受控參數化門、SWAP 交錯結構、弱糾纏層和通道交互門，從而形成一個能夠表達複雜函數的卷積算子，同時保持對量子退相干的魯棒性。</p>
</div><p>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.</p>
<div class="lb-trans"><p>與經典卷積核需要在像素鄰域內滑動不同，WiMi 採用了一種量子特定的編碼方法，將來自多個通道的數據壓縮並編碼為量子態的幅度、相位或糾纏結構，通過參數化量子門對其進行類似卷積的處理。通道之間的特徵融合不再依賴於線性加權，而是通過門級交互直接在量子態空間中生成高維相關性，產生比經典卷積更強的特徵組合能力。通過訓練，這些參數化的量子卷積核可以學習高階跨通道特徵，如紋理 - 顏色共現、時空聯合模式、多光譜能量分佈相關性等，從而實現優於傳統 QCNN 的表達能力。</p>
</div><p>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.</p>
<div class="lb-trans"><p>該技術架構的核心之一是 WiMi 建立的量子多通道卷積算子。該算子使用參數化旋轉門和受控門構建卷積模式。通過調整門的旋轉角度和受控結構，卷積核可以在訓練過程中自動學習最佳的跨通道特徵組合策略。整個卷積核不僅可以作用於單比特分佈，還可以以張量的方式作用於多比特通道結構，使卷積核不僅能夠提取局部一致性，還能夠從糾纏結構中挖掘高階關係。這種模式在經典 CNN 中無法直接實現，因為經典神經網絡中多通道特徵的組合通常基於線性疊加，而量子卷積核則基於量子疊加和量子糾纏，能夠在指數特徵空間中表達複雜的多通道相關性。</p>
</div><p>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.</p>
<div class="lb-trans"><p>在卷積操作完成後，特徵圖被壓縮為量子系統中更緊湊的量子態，並通過量子池化電路進行下采樣。池化電路也經過重新設計，以處理來自多個通道的量子態特徵。WiMi 採用可學習的量子池化模式，通過可控測量或可控壓縮操作減少量子態維度，同時保留關鍵特徵信息，從而避免傳統 QCNN 中因直接測量導致的特徵破壞問題。實驗結果表明，新的池化結構比傳統 QCNN 池化方法更穩定，特徵保留率更高。</p>
</div><p>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.</p>
<div class="lb-trans"><p>除了卷積核和池化電路，WiMi 還構建了專門的混合量子 - 經典訓練框架。在訓練過程中，經典計算模塊負責損失函數計算、梯度求解和參數更新，而量子模塊負責前向傳播和量子態演化。WiMi 採用擴展參數偏移規則方法，使多通道量子卷積核中的所有參數都能有效訓練。為了提高訓練穩定性，WiMi 還引入了量子噪聲模擬和梯度裁剪機制，確保模型在真實量子硬件上的性能不會因噪聲而急劇下降。</p>
</div><p>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.</p>
<div class="lb-trans"><p>在訓練過程中，WiMi 團隊觀察到一個非常有價值的現象：模型能夠自動捕捉多個通道之間的非線性關聯。以 RGB 圖像為例，模型學習到的量子卷積核並不是簡單地對 R、G 和 B 通道進行線性遍歷，而是通過糾纏層在通道之間建立關聯，使得卷積核能夠識別量子狀態空間中顏色分佈模式的聯合特徵。這意味着模型並不是在三個通道上分別進行卷積，而是在更高維的空間中學習整體的深層特徵，其表達能力遠遠超過經典卷積神經網絡中的 3×3 或 1×1 卷積。</p>
</div><p>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.</p>
<div class="lb-trans"><p>WiMi 認為，多通道處理能力將成為量子神經網絡向現實應用邁進的關鍵能力之一。儘管單通道 QCNN 在學術界具有探索意義，但其侷限性使其無法滿足行業對複雜數據的要求。MC-QCNN 的出現使量子深度學習系統首次具備處理現實數據的能力，這意味着量子人工智能不再僅僅是實驗室的概念，而是開始具備商業實施的可能性。相信隨着量子硬件性能的提升，這項技術將推動量子機器學習從實驗室研究走向真正的應用時代。</p>
</div><p>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.</p>
<div class="lb-trans"><p>未來，WiMi 將繼續完善這一技術體系，包括構建更高效的量子卷積核結構，開發更強大的噪聲適應策略，擴展到三維卷積和時間序列卷積結構，並探索與 Transformer 等模型結構的融合可能性，使量子模型不僅能夠處理多通道圖像，還能處理多模態語音、視頻、文本、圖形結構和傳感器數據。量子深度學習將不再侷限於小規模任務，而將成為下一代通用人工智能模型中的重要運算符。量子計算與人工智能的結合將是未來十年技術發展的核心趨勢。WiMi 將繼續致力於推動量子人工智能生態系統的建設，使量子技術真正服務於工業需求、社會價值和人類未來。</p>
</div><p><strong>About WiMi Hologram Cloud</strong></p>
<div class="lb-trans"><p><strong>關於 WiMi 全息雲</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 全息雲公司（NASDAQ: WiMi）專注於全息雲服務，主要集中在車載 AR 全息 HUD、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 AR/VR 設備和元宇宙全息雲軟件等專業領域。它涵蓋了全息 AR 技術的多個方面，包括車載全息 AR 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 AR 虛擬廣告技術、全息 AR 虛擬娛樂技術、全息 ARSDK 支付、互動全息虛擬通信、元宇宙全息 AR 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含義差異，以原始版本為準。WiMi 全息雲公司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 全息雲公司</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>
</div><p>Tel: +1 (646) 975-9495</p>
<div class="lb-trans"><p>電話：+1 (646) 975-9495</p>
</div><p>Email: wimi@icrinc.com</p>
<div class="lb-trans"><p>電子郵件：wimi@icrinc.com</p>
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