<p>/PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi)（&#34;WiMi&#34;或&#34;公司&#34;）是一家领先的全球全息增强现实（&#34;AR&#34;）技术提供商，推出了一种用于图像多分类的混合量子神经网络结构（H-QNN）。该技术有机地将经典卷积神经网络（CNN）的空间特征提取能力与量子神经网络（QNN）的高维非线性映射特征相结合，形成了一种新型混合结构，在多分类场景中具有更强的泛化能力和计算效率。这项技术不仅在理论上系统性地优化了量子 - 经典混合学习系统，而且在实际实验中实现了优于类似算法的分类准确性和稳定性，为量子智能视觉系统奠定了坚实的技术基础。</p>
<p>这种混合量子神经网络（H-QNN）的设计遵循经典负责抽象和量子负责区分的原则。整个系统由三个主要模块组成：特征降维与编码模块、量子状态转换模块和混合决策与迁移学习模块。</p>
<p>首先，特征降维与编码模块基于经典卷积神经网络（CNN）结构，通过多个卷积层和池化层提取图像的低维特征表示。经过 PCA 降维后的特征向量被标准化，然后输入到量子编码电路。在这一阶段，WiMi 采用了一种改进的角度编码方法（Angle Embedding），将实值特征映射到量子状态幅度，并通过多层量子旋转门（Ry, Rz）实现高效编码，从而减少量子门深度并降低编码噪声。</p>
<p>接下来，量子状态转换模块承担高维特征映射和非线性区分的核心任务。该模块包括多个层的量子电路，每层由参数化旋转门和受控纠缠门（CNOT 或 CZ）组成，形成量子状态的非线性耦合和纠缠。为了缓解梯度消失，WiMi 采用了一种可重构的参数共享策略，允许不同的量子层共享一些可训练参数，同时引入混合状态扰动以保持训练过程中的梯度平衡。这种结构设计有效避免了荒原平台现象，使模型在多分类任务中保持稳定收敛。</p>
<p>最后，混合决策与迁移学习模块将量子计算的结果与经典决策层相结合。量子电路输出的测量概率分布被转换为特征向量，并与经典全连接层的输出融合。这个融合向量被输入到 Softmax 层进行最终分类判断。为了进一步增强多分类任务中的泛化性能，WiMi 引入了一种迁移学习机制，将在小样本任务中预训练的量子层参数迁移到新任务，从而减少训练周期并增强模型稳定性。</p>
<p>在实际实施中，该结构支持在仿真环境和硬件量子处理单元（QPU）上运行。仿真环境使用高性能 GPU 集群完成经典模块的训练，而量子模块则在量子模拟器或 FPGA 加速的量子核估计环境中执行，实现经典与量子计算资源的异构协作。</p>
<p>这项技术的核心创新点主要体现在以下几个方面。</p>
<p>首先，在架构设计层面，实现了卷积神经网络（CNN）与量子神经网络（QNN）的深度融合。传统的量子混合模型通常只是将量子部分简单嵌入作为分类头，而本研究提出的 H-QNN 采用了&#34;卷积特征提取—量子映射—混合决策&#34;的三阶段分布式结构，使量子部分不仅承担非线性区分，还能在特征空间层面实现信息重构。</p>
<p>其次，在编码策略层面，WiMi 提出的角度编码与主成分分析（PCA）联合降维方案有效解决了量子编码维度限制问题。通过优化 PCA 的累计方差贡献率，确保输入特征与量子状态幅度之间的映射保持高信息保真度，从而最大化量子信息的利用率。</p>
<p>第三，在训练策略层面，WiMi 引入了迁移学习机制和参数共享结构。传统的量子神经网络在多分类训练中常面临梯度消失和过拟合的风险，而参数共享可以在不同的量子层之间建立平衡的梯度流，迁移学习机制使模型能够在新任务上以更少的训练周期实现快速收敛。此外，WiMi 设计了一种基于量子保真度指标的早停策略，通过监测量子状态演化的稳定性来判断训练是否达到最佳点，从而防止过拟合。</p>
<p>最后，在系统实施层面，它采用了异构计算架构，经典计算部分在 CPU/GPU 平台上运行，而量子部分则在 FPGA 上实现的量子仿真模块中执行。FPGA 模块实现了可重构的执行逻辑，用于参数化量子电路，能够在纳秒级响应时间内完成量子状态更新，从而显著提高系统的整体训练速度。这种混合计算架构在实验中表现出远超纯 CPU 或 GPU 仿真的性能优势。</p>
<p>WiMi 的混合量子神经网络结构的提出标志着量子人工智能研究从理论探索向实际应用迈出了关键一步。它不仅展示了量子计算在机器学习领域的潜在优势，还为当前量子硬件的性能瓶颈提供了一种工程妥协解决方案。通过将可训练的量子层嵌入经典神经网络的基础中，这项技术实现了量子计算资源的高效利用，使量子优势能够体现在实际的视觉任务中。未来，量子智能将不再仅仅是一个理论概念，而是将与深度学习、计算机视觉和边缘计算等领域深度融合，成为推动智能社会发展的重要动力。让量子智能从实验室走向现实世界，让量子技术真正服务于工业升级和人类认知的扩展。</p>
<p><strong>关于 WiMi 全息云</strong></p>
<p>WiMi 全息云公司（NASDAQ: WiMi）专注于全息云服务，主要集中在车载 AR 全息 HUD、3D 全息脉冲激光雷达、头戴式光场全息设备、全息半导体、全息云软件、全息汽车导航、元宇宙全息 AR/VR 设备和元宇宙全息云软件等专业领域。它涵盖了全息 AR 技术的多个方面，包括车载全息 AR 技术、3D 全息脉冲激光雷达技术、全息视觉半导体技术、全息软件开发、全息 AR 虚拟广告技术、全息 AR 虚拟娱乐技术、全息 ARSDK 支付、互动全息虚拟通信、元宇宙全息 AR 技术和元宇宙虚拟云服务。WiMi 是一个综合性的全息云技术解决方案提供商。有关更多信息，请访问 http://ir.wimiar.com。</p>
<p><strong>翻译免责声明</strong></p>
<p>本公告的原始版本是官方授权的唯一具有法律约束力的版本。如果中文翻译与原始版本之间存在任何不一致或意义上的差异，以原始版本为准。WiMi 全息云公司及相关机构和个人对翻译版本不作任何保证，并对因翻译不准确而导致的任何直接或间接损失不承担责任。</p>
<p><strong>投资者咨询，请联系：</strong></p>
<p>WIMI 全息云公司</p>
<p>电子邮件：pr@wimiar.com</p>
<p>ICR, LLC</p>
<p>Robin Yang</p>
<p>电话：+1 (646) 975-9495</p>
<p>电子邮件：wimi@icrinc.com</p>
<p>来源：WiMi 全息云公司</p>

微美全息

<p>微美全息（WiMi Hologram Cloud Inc.）推出了一种用于图像多分类的混合量子神经网络结构（H-QNN），将经典卷积神经网络（CNN）与量子神经网络相结合。这项技术提高了泛化能力和计算效率，实现了更高的分类准确性和稳定性。它包括特征维度减少、量子态转换和混合决策模块，支持模拟环境和硬件量子处理单元。关键创新包括 CNN 与 QNN 的深度集成、优化的编码策略和先进的训练机制，增强了量子智能视觉系统</p>

<p>, /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, launched a hybrid quantum neural network structure (H-QNN) for image multi-classification. This technology organically integrates the spatial feature extraction capabilities of classical convolutional neural networks (CNN) with the high-dimensional nonlinear mapping features of quantum neural networks (QNN), forming a new type of hybrid structure that possesses stronger generalization ability and computational efficiency in multi-class classification scenarios. This technology not only systematically optimizes the quantum-classical hybrid learning system in theory but also achieves classification accuracy and stability superior to similar algorithms in actual experiments, laying a solid technical foundation for quantum intelligent vision systems.</p>
<div class="lb-trans"><p>/PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi)（&#34;WiMi&#34;或&#34;公司&#34;）是一家领先的全球全息增强现实（&#34;AR&#34;）技术提供商，推出了一种用于图像多分类的混合量子神经网络结构（H-QNN）。该技术有机地将经典卷积神经网络（CNN）的空间特征提取能力与量子神经网络（QNN）的高维非线性映射特征相结合，形成了一种新型混合结构，在多分类场景中具有更强的泛化能力和计算效率。这项技术不仅在理论上系统性地优化了量子 - 经典混合学习系统，而且在实际实验中实现了优于类似算法的分类准确性和稳定性，为量子智能视觉系统奠定了坚实的技术基础。</p>
</div><p>The design of this hybrid quantum neural network (H-QNN) follows the principle of classical responsible for abstraction and quantum responsible for discrimination. The overall system consists of three main modules: feature dimensionality reduction and encoding module, quantum state transformation module, and hybrid decision and transfer learning module.</p>
<div class="lb-trans"><p>这种混合量子神经网络（H-QNN）的设计遵循经典负责抽象和量子负责区分的原则。整个系统由三个主要模块组成：特征降维与编码模块、量子状态转换模块和混合决策与迁移学习模块。</p>
</div><p>First, the feature dimensionality reduction and encoding module is based on the classical convolutional neural network (CNN) structure, extracting low-dimensional feature representations of images through several convolutional layers and pooling layers. The feature vectors after PCA dimensionality reduction are standardized and then input into the quantum encoding circuit. At this stage, WiMi adopts an improved angle encoding method (Angle Embedding) to map real-valued features to quantum state amplitudes, and achieves efficient encoding through multi-layer quantum rotation gates (Ry, Rz), thereby reducing quantum gate depth and lowering encoding noise.</p>
<div class="lb-trans"><p>首先，特征降维与编码模块基于经典卷积神经网络（CNN）结构，通过多个卷积层和池化层提取图像的低维特征表示。经过 PCA 降维后的特征向量被标准化，然后输入到量子编码电路。在这一阶段，WiMi 采用了一种改进的角度编码方法（Angle Embedding），将实值特征映射到量子状态幅度，并通过多层量子旋转门（Ry, Rz）实现高效编码，从而减少量子门深度并降低编码噪声。</p>
</div><p>Next, the quantum state transformation module undertakes the core tasks of high-dimensional feature mapping and nonlinear discrimination. This module includes several layers of quantum circuits, with each layer composed of parameterized rotation gates and controlled entanglement gates (CNOT or CZ), forming nonlinear coupling and entanglement of quantum states. To alleviate gradient vanishing, WiMi adopts a reconfigurable parameter sharing strategy, allowing different quantum layers to share some trainable parameters, while introducing mixed state perturbations to maintain gradient balance during the training process. This structural design effectively avoids the barren plateau phenomenon, enabling the model to maintain stable convergence in multi-class tasks.</p>
<div class="lb-trans"><p>接下来，量子状态转换模块承担高维特征映射和非线性区分的核心任务。该模块包括多个层的量子电路，每层由参数化旋转门和受控纠缠门（CNOT 或 CZ）组成，形成量子状态的非线性耦合和纠缠。为了缓解梯度消失，WiMi 采用了一种可重构的参数共享策略，允许不同的量子层共享一些可训练参数，同时引入混合状态扰动以保持训练过程中的梯度平衡。这种结构设计有效避免了荒原平台现象，使模型在多分类任务中保持稳定收敛。</p>
</div><p>Finally, the hybrid decision and transfer learning module integrates the results of quantum computing with the classical decision layer. The measurement probability distribution output by the quantum circuit is converted into feature vectors and fused with the output of the classical fully connected layer. This fused vector is input into the Softmax layer for final classification judgment. To further enhance the generalization performance in multi-class tasks, WiMi introduces a transfer learning mechanism, migrating the parameters of quantum layers pre-trained in small-sample tasks to new tasks, thereby reducing the number of training epochs and enhancing model stability.</p>
<div class="lb-trans"><p>最后，混合决策与迁移学习模块将量子计算的结果与经典决策层相结合。量子电路输出的测量概率分布被转换为特征向量，并与经典全连接层的输出融合。这个融合向量被输入到 Softmax 层进行最终分类判断。为了进一步增强多分类任务中的泛化性能，WiMi 引入了一种迁移学习机制，将在小样本任务中预训练的量子层参数迁移到新任务，从而减少训练周期并增强模型稳定性。</p>
</div><p>In actual implementation, this structure supports running on simulation environments and hardware quantum processing units (QPU). The simulation environment uses high-performance GPU clusters to complete training of classical modules, while quantum modules are executed in quantum simulators or FPGA-accelerated quantum kernel estimation environments, achieving heterogeneous collaboration of classical and quantum computing resources.</p>
<div class="lb-trans"><p>在实际实施中，该结构支持在仿真环境和硬件量子处理单元（QPU）上运行。仿真环境使用高性能 GPU 集群完成经典模块的训练，而量子模块则在量子模拟器或 FPGA 加速的量子核估计环境中执行，实现经典与量子计算资源的异构协作。</p>
</div><p>The core innovation points of this technology are mainly embodied in the following aspects.</p>
<div class="lb-trans"><p>这项技术的核心创新点主要体现在以下几个方面。</p>
</div><p>First, at the architectural design level, it achieves deep integration of convolutional neural networks (CNN) and quantum neural networks (QNN). Traditional quantum hybrid models usually simply embed the quantum part as a classification head, whereas the H-QNN proposed in this research adopts a three-stage distributed structure of &#34;convolutional feature extraction—quantum mapping—hybrid decision-making&#34;, enabling the quantum part not only to undertake nonlinear discrimination but also to achieve information reconstruction at the feature space level.</p>
<div class="lb-trans"><p>首先，在架构设计层面，实现了卷积神经网络（CNN）与量子神经网络（QNN）的深度融合。传统的量子混合模型通常只是将量子部分简单嵌入作为分类头，而本研究提出的 H-QNN 采用了&#34;卷积特征提取—量子映射—混合决策&#34;的三阶段分布式结构，使量子部分不仅承担非线性区分，还能在特征空间层面实现信息重构。</p>
</div><p>Second, at the encoding strategy level, the joint dimensionality reduction scheme of angle encoding and principal component analysis (PCA) proposed by WiMi effectively solves the quantum encoding dimension limitation problem. By optimizing the cumulative variance contribution rate of PCA, it ensures that the mapping between input features and quantum state amplitudes maintains high information fidelity, thereby maximizing the utilization rate of quantum information.</p>
<div class="lb-trans"><p>其次，在编码策略层面，WiMi 提出的角度编码与主成分分析（PCA）联合降维方案有效解决了量子编码维度限制问题。通过优化 PCA 的累计方差贡献率，确保输入特征与量子状态幅度之间的映射保持高信息保真度，从而最大化量子信息的利用率。</p>
</div><p>Third, at the training strategy level, WiMi introduces a transfer learning mechanism and parameter sharing structure. Traditional quantum neural networks often face risks of gradient vanishing and overfitting in multi-class classification training, while parameter sharing can establish balanced gradient flow between different quantum layers, and the transfer learning mechanism enables the model to achieve rapid convergence on new tasks with fewer training epochs. In addition, WiMi designs an early stopping strategy based on the quantum Fidelity metric, which determines whether the training has reached the optimal point by monitoring the stability of quantum state evolution, thereby preventing overfitting.</p>
<div class="lb-trans"><p>第三，在训练策略层面，WiMi 引入了迁移学习机制和参数共享结构。传统的量子神经网络在多分类训练中常面临梯度消失和过拟合的风险，而参数共享可以在不同的量子层之间建立平衡的梯度流，迁移学习机制使模型能够在新任务上以更少的训练周期实现快速收敛。此外，WiMi 设计了一种基于量子保真度指标的早停策略，通过监测量子状态演化的稳定性来判断训练是否达到最佳点，从而防止过拟合。</p>
</div><p>Finally, at the system implementation level, it adopts a heterogeneous computing architecture, running the classical computing part on CPU/GPU platforms, while the quantum part is executed in quantum simulation modules implemented on FPGA. The FPGA module realizes reconfigurable execution logic for parameterized quantum circuits, capable of completing quantum state updates within nanosecond-level response times, thereby significantly improving the overall training speed of the system. This hybrid computing architecture demonstrates performance advantages far exceeding pure CPU or GPU simulations in experiments.</p>
<div class="lb-trans"><p>最后，在系统实施层面，它采用了异构计算架构，经典计算部分在 CPU/GPU 平台上运行，而量子部分则在 FPGA 上实现的量子仿真模块中执行。FPGA 模块实现了可重构的执行逻辑，用于参数化量子电路，能够在纳秒级响应时间内完成量子状态更新，从而显著提高系统的整体训练速度。这种混合计算架构在实验中表现出远超纯 CPU 或 GPU 仿真的性能优势。</p>
</div><p>The proposal of WiMi's hybrid quantum neural network structure marks a key step in quantum artificial intelligence research moving from theoretical exploration toward practical applications. It not only demonstrates the potential advantages of quantum computing in the field of machine learning but also provides an engineered compromise solution for the current performance bottlenecks of quantum hardware. By embedding trainable quantum layers into the foundation of classical neural networks, this technology achieves efficient utilization of quantum computing resources, enabling quantum advantages to be embodied in real visual tasks. In the future, quantum intelligence will no longer be merely a theoretical conception but will deeply integrate with fields such as deep learning, computer vision, and edge computing, becoming an important driving force for promoting the development of intelligent society. Let quantum intelligence move from the laboratory to the real world, and let quantum technology truly serve industrial upgrades and the expansion of human cognition.</p>
<div class="lb-trans"><p>WiMi 的混合量子神经网络结构的提出标志着量子人工智能研究从理论探索向实际应用迈出了关键一步。它不仅展示了量子计算在机器学习领域的潜在优势，还为当前量子硬件的性能瓶颈提供了一种工程妥协解决方案。通过将可训练的量子层嵌入经典神经网络的基础中，这项技术实现了量子计算资源的高效利用，使量子优势能够体现在实际的视觉任务中。未来，量子智能将不再仅仅是一个理论概念，而是将与深度学习、计算机视觉和边缘计算等领域深度融合，成为推动智能社会发展的重要动力。让量子智能从实验室走向现实世界，让量子技术真正服务于工业升级和人类认知的扩展。</p>
</div><p><strong>About WiMi Hologram Cloud</strong></p>
<div class="lb-trans"><p><strong>关于 WiMi 全息云</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 全息云公司（NASDAQ: WiMi）专注于全息云服务，主要集中在车载 AR 全息 HUD、3D 全息脉冲激光雷达、头戴式光场全息设备、全息半导体、全息云软件、全息汽车导航、元宇宙全息 AR/VR 设备和元宇宙全息云软件等专业领域。它涵盖了全息 AR 技术的多个方面，包括车载全息 AR 技术、3D 全息脉冲激光雷达技术、全息视觉半导体技术、全息软件开发、全息 AR 虚拟广告技术、全息 AR 虚拟娱乐技术、全息 ARSDK 支付、互动全息虚拟通信、元宇宙全息 AR 技术和元宇宙虚拟云服务。WiMi 是一个综合性的全息云技术解决方案提供商。有关更多信息，请访问 http://ir.wimiar.com。</p>
</div><p><strong>Translation Disclaimer</strong></p>
<div class="lb-trans"><p><strong>翻译免责声明</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权的唯一具有法律约束力的版本。如果中文翻译与原始版本之间存在任何不一致或意义上的差异，以原始版本为准。WiMi 全息云公司及相关机构和个人对翻译版本不作任何保证，并对因翻译不准确而导致的任何直接或间接损失不承担责任。</p>
</div><p><strong>Investor Inquiries, please contact:</strong></p>
<div class="lb-trans"><p><strong>投资者咨询，请联系：</strong></p>
</div><p>WIMI Hologram Cloud Inc.</p>
<div class="lb-trans"><p>WIMI 全息云公司</p>
</div><p>Email: pr@wimiar.com</p>
<div class="lb-trans"><p>电子邮件：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>电话：+1 (646) 975-9495</p>
</div><p>Email: wimi@icrinc.com</p>
<div class="lb-trans"><p>电子邮件：wimi@icrinc.com</p>
</div><p>SOURCE WiMi Hologram Cloud Inc.</p>
<div class="lb-trans"><p>来源：WiMi 全息云公司</p>
</div>

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