<p>/PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi)（&#34;WiMi&#34;或&#34;公司&#34;）是一家領先的全球全息增強現實（&#34;AR&#34;）技術提供商。他們正在探索的基於量子卷積神經網絡（QCNN）的雙判別器量子生成對抗網絡架構，旨在為突破這些技術瓶頸提供創新解決方案。量子生成對抗網絡作為連接量子計算和生成模型的核心環節，通過量子生成器和判別器之間的零和博弈實現分佈學習。其核心優勢在於利用量子比特的疊加完成經典模型在短時間內難以實現的參數優化。然而，在實際訓練過程中，量子電路參數優化的梯度傳播容易受到量子測量噪聲的干擾，導致深度網絡中梯度信息迅速衰減；同時，量子生成器往往趨向於收斂到局部最優解，僅能生成有限的數據模式，顯著降低生成結果的質量和多樣性。</p>
<p>WiMi 創新性地將 QCNN 的強大特徵提取能力與雙判別器架構相結合，構建了一個混合量子 - 經典生成對抗框架。該方案的核心突破在於採用混合量子卷積神經網絡作為判別器核心，完全放棄了傳統 QGAN 中判別器常用的多層線性量子電路結構，而是設計了並行化特徵分析模塊，從根本上增強了識別生成數據分佈缺陷的能力。</p>
<p>QCNN 作為量子計算與深度學習融合的里程碑成果，其核心價值在於將經典卷積操作映射到量子空間，並通過參數化量子電路實現高效特徵提取。WiMi 研究的混合 QCNN 判別器採用了&#34;量子特徵編碼 - 並行特徵提取 - 經典決策輸出&#34;的三層架構：首先，通過量子門序列將輸入圖像的像素信息編碼為量子疊加態，然後使用量子卷積算子構建並行特徵通道，最後通過量子測量將特徵向量輸出到經典全連接層，以輸出真實性判別結果。這種並行化架構設計帶來了雙重技術優勢：一方面，利用 QCNN 的量子糾纏特性，局部特徵通道能夠精確捕捉亞像素級特徵，而全局特徵通道能夠構建整體圖像結構的概率分佈模型，兩者協同為判別器提供微觀細節驗證和宏觀分佈驗證的雙重能力；另一方面，平行結構有效縮短了梯度傳播路徑，並結合粒子羣優化算法用於量子門參數，可以降低梯度消失的風險。</p>
<p>雙判別器協作機制進一步增強了對抗學習的穩定性。在 WiMi 研究的架構中，兩個混合 QCNN 判別器分別關注分佈一致性和特徵真實性兩個維度。通過動態平衡兩個判別器的損失權重，迫使生成器同時優化全局分佈匹配度和局部特徵真實性，有效避免了單一判別器引導下的問題。在量子計算與人工智能協同發展的關鍵時期，量子卷積神經網絡與雙判別器架構的創新融合將破解量子生成對抗網絡的核心技術瓶頸，並有望加速量子生成模型從實驗室走向工業化應用的進程。</p>
<p>隨着量子硬件技術的持續突破和算法理論的不斷深化，WiMi 研究的混合量子 - 經典生成對抗框架不僅為 QGAN 的實用化開闢了新路徑，也為量子人工智能的大規模應用奠定了技術基礎。這種創新架構通過高效提取和並行處理量子特徵，結合雙判別器的協同優化機制，顯著增強了生成模型的穩定性和多樣性，為圖像生成和量子態模擬等複雜任務提供了更可靠的解決方案，推動人工智能技術在量子增強時代邁向新高度。</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，主要集中在汽車增強現實（AR）全息抬頭顯示（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 設備以及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 是一家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>來源：WiMi Hologram Cloud Inc.</p>

微美全息

<p>WiMi 部署雙判別器量子生成對抗網絡架構，開啓 QGANs 高效訓練的新紀元 | WIMI 股票新聞</p>

<p>, /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) (&#34;WiMi&#34; or the &#34;Company&#34;) is a leading global Hologram Augmented Reality (&#34;AR&#34;) Technology provider. The dual-discriminator quantum generative adversarial network architecture based on quantum convolutional neural network (QCNN) that they are exploring aims to provide innovative solutions for breaking through these technical bottlenecks. Quantum generative adversarial networks, as the core link connecting quantum computing and generative models, achieve distribution learning through the zero-sum game between quantum generators and discriminators. Its core advantage lies in utilizing the superposition of qubits to complete parameter optimization that classical models can hardly achieve in a short time. However, in the actual training process, the gradient propagation for quantum circuit parameter optimization is easily interfered by quantum measurement noise, leading to rapid decay of gradient information in deep networks; at the same time, quantum generators often tend to converge to local optimal solutions, only able to generate limited data patterns, significantly reducing the quality and diversity of generation results.</p>
<div class="lb-trans"><p>/PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi)（&#34;WiMi&#34;或&#34;公司&#34;）是一家領先的全球全息增強現實（&#34;AR&#34;）技術提供商。他們正在探索的基於量子卷積神經網絡（QCNN）的雙判別器量子生成對抗網絡架構，旨在為突破這些技術瓶頸提供創新解決方案。量子生成對抗網絡作為連接量子計算和生成模型的核心環節，通過量子生成器和判別器之間的零和博弈實現分佈學習。其核心優勢在於利用量子比特的疊加完成經典模型在短時間內難以實現的參數優化。然而，在實際訓練過程中，量子電路參數優化的梯度傳播容易受到量子測量噪聲的干擾，導致深度網絡中梯度信息迅速衰減；同時，量子生成器往往趨向於收斂到局部最優解，僅能生成有限的數據模式，顯著降低生成結果的質量和多樣性。</p>
</div><p>WiMi innovatively combines the robust feature extraction capabilities of QCNN with the dual-discriminator architecture to construct a hybrid quantum-classical generative adversarial framework. The core breakthrough of this scheme lies in adopting a hybrid quantum convolutional neural network as the discriminator core, completely abandoning the multi-layer linear quantum circuit structures commonly used by discriminators in traditional QGANs, and instead designing parallelized feature analysis modules to fundamentally enhance the ability to identify defects in the distribution of generated data.</p>
<div class="lb-trans"><p>WiMi 創新性地將 QCNN 的強大特徵提取能力與雙判別器架構相結合，構建了一個混合量子 - 經典生成對抗框架。該方案的核心突破在於採用混合量子卷積神經網絡作為判別器核心，完全放棄了傳統 QGAN 中判別器常用的多層線性量子電路結構，而是設計了並行化特徵分析模塊，從根本上增強了識別生成數據分佈缺陷的能力。</p>
</div><p>QCNN, as a landmark achievement in the fusion of quantum computing and deep learning, has its core value in mapping classical convolution operations to quantum space and achieving efficient feature extraction through parameterized quantum circuits. The hybrid QCNN discriminator researched by WiMi adopts a three-layer architecture of &#34;quantum feature encoding-parallel feature extraction-classical decision output&#34;: first, the pixel information of the input image is encoded into quantum superposition states through quantum gate sequences, then parallel feature channels are constructed using quantum convolution operators, and finally, the feature vectors are exported to a classical fully connected layer through quantum measurement to output the authenticity discrimination result. This parallelized architecture design brings dual technical advantages: on the one hand, leveraging the quantum entanglement characteristics of QCNN, local feature channels can achieve precise capture of sub-pixel-level features, while global feature channels can construct probabilistic distribution models of the overall image structure, with the two collaborating to give the discriminator dual capabilities of microscopic detail validation and macroscopic distribution verification; on the other hand, the parallel structure effectively shortens the gradient propagation path, and combined with particle swarm optimization algorithms for quantum gate parameters, can reduce the risk of gradient vanishing.</p>
<div class="lb-trans"><p>QCNN 作為量子計算與深度學習融合的里程碑成果，其核心價值在於將經典卷積操作映射到量子空間，並通過參數化量子電路實現高效特徵提取。WiMi 研究的混合 QCNN 判別器採用了&#34;量子特徵編碼 - 並行特徵提取 - 經典決策輸出&#34;的三層架構：首先，通過量子門序列將輸入圖像的像素信息編碼為量子疊加態，然後使用量子卷積算子構建並行特徵通道，最後通過量子測量將特徵向量輸出到經典全連接層，以輸出真實性判別結果。這種並行化架構設計帶來了雙重技術優勢：一方面，利用 QCNN 的量子糾纏特性，局部特徵通道能夠精確捕捉亞像素級特徵，而全局特徵通道能夠構建整體圖像結構的概率分佈模型，兩者協同為判別器提供微觀細節驗證和宏觀分佈驗證的雙重能力；另一方面，平行結構有效縮短了梯度傳播路徑，並結合粒子羣優化算法用於量子門參數，可以降低梯度消失的風險。</p>
</div><p>The dual-discriminator collaborative mechanism further strengthens the stability of adversarial learning. In the architecture researched by WiMi, the two hybrid QCNN discriminators focus on the two dimensions of distribution consistency and feature authenticity respectively. By dynamically balancing the loss weights of the two discriminators, the generator is forced to optimize both global distribution matching degree and local feature authenticity simultaneously, effectively avoiding problems guided by a single discriminator. In the critical period of collaborative development between quantum computing and artificial intelligence, the innovative fusion of quantum convolutional neural networks and dual-discriminator architecture will crack the core technical bottlenecks of quantum generative adversarial networks, and is expected to accelerate the process of quantum generative models moving from the laboratory to industrialized applications.</p>
<div class="lb-trans"><p>雙判別器協作機制進一步增強了對抗學習的穩定性。在 WiMi 研究的架構中，兩個混合 QCNN 判別器分別關注分佈一致性和特徵真實性兩個維度。通過動態平衡兩個判別器的損失權重，迫使生成器同時優化全局分佈匹配度和局部特徵真實性，有效避免了單一判別器引導下的問題。在量子計算與人工智能協同發展的關鍵時期，量子卷積神經網絡與雙判別器架構的創新融合將破解量子生成對抗網絡的核心技術瓶頸，並有望加速量子生成模型從實驗室走向工業化應用的進程。</p>
</div><p>With the continuous breakthroughs in quantum hardware technology and the continuous deepening of algorithm theory, the hybrid quantum-classical generative adversarial framework researched by WiMi not only opens up new paths for the practicalization of QGANs, but also lays the technical cornerstone for the large-scale application of quantum artificial intelligence. This innovative architecture, through efficient extraction and parallelization processing of quantum features, combined with the collaborative optimization mechanism of dual discriminators, significantly enhances the stability and diversity of generative models, providing more reliable solutions for complex tasks such as image generation and quantum state simulation, pushing artificial intelligence technology to new heights in the quantum-enhanced era.</p>
<div class="lb-trans"><p>隨着量子硬件技術的持續突破和算法理論的不斷深化，WiMi 研究的混合量子 - 經典生成對抗框架不僅為 QGAN 的實用化開闢了新路徑，也為量子人工智能的大規模應用奠定了技術基礎。這種創新架構通過高效提取和並行處理量子特徵，結合雙判別器的協同優化機制，顯著增強了生成模型的穩定性和多樣性，為圖像生成和量子態模擬等複雜任務提供了更可靠的解決方案，推動人工智能技術在量子增強時代邁向新高度。</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，主要集中在汽車增強現實（AR）全息抬頭顯示（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 設備以及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 是一家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>SOURCE WiMi Hologram Cloud Inc.</p>
<div class="lb-trans"><p>來源：WiMi Hologram Cloud Inc.</p>
</div>

WiMi Deploys Dual-Discriminator Quantum Generative Adversarial Network Architecture, Ushering in a New Era of Efficient Training for QGANs | WIMI Stock News

微美全息部署雙鑑別器量子生成對抗網絡架構，開啓 QGAN 高效訓練的新紀元 | 微美全息股票新聞