<p>北京，2026 年 1 月 5 日（全球新闻通讯社）——WiMi 发布下一代量子卷积神经网络技术，用于多通道监督学习</p>
<p>北京，2026 年 1 月 5 日——WiMi Hologram Cloud Inc.（纳斯达克：WiMi）（&#34;WiMi&#34;或&#34;公司&#34;），全球领先的全息增强现实（&#34;AR&#34;）技术提供商，宣布推出其自主开发的新技术：用于多通道监督学习的量子卷积神经网络（MC-QCNN）。这一突破性方法首次构建了完全硬件适应的量子卷积核设计，使量子模型能够高效处理多通道数据，从而在图像分类、医学成像、视频分析和多模态监测等行业展现出绝对优势。</p>
<p>从研发的角度来看，这一技术突破的核心不仅在于多通道量子卷积核的构建，还在于整个系统化的设计方案，包括卷积核结构、量子比特布局、通道交互编码、权重可学习性、可解释性和硬件约束适应策略。为了使该技术能够在真实硬件上执行，WiMi 放弃了大量不切实际的深度电路结构，而转向更接近量子硬件原生门操作特性的设计理念。WiMi 提出的量子电路卷积核使用单比特旋转门、受控参数化门、SWAP 交错结构、弱纠缠层和通道交互门，从而形成一个能够表达复杂函数的卷积算子，同时保持对量子退相干的鲁棒性。</p>
<p>与经典卷积核需要在像素邻域内滑动不同，WiMi 采用了一种量子特定的编码方法，将来自多个通道的数据压缩并编码为量子态的幅度、相位或纠缠结构，通过参数化量子门对其进行类似卷积的处理。通道之间的特征融合不再依赖于线性加权，而是通过门级交互直接在量子态空间中生成高维相关性，产生比经典卷积更强的特征组合能力。通过训练，这些参数化的量子卷积核可以学习高阶跨通道特征，如纹理 - 颜色共现、时空联合模式、多光谱能量分布相关性等，从而实现优于传统 QCNN 的表达能力。</p>
<p>该技术架构的核心之一是 WiMi 建立的量子多通道卷积算子。该算子使用参数化旋转门和受控门构建卷积模式。通过调整门的旋转角度和受控结构，卷积核可以在训练过程中自动学习最佳的跨通道特征组合策略。整个卷积核不仅可以作用于单比特分布，还可以以张量的方式作用于多比特通道结构，使卷积核不仅能够提取局部一致性，还能够从纠缠结构中挖掘高阶关系。这种模式在经典 CNN 中无法直接实现，因为经典神经网络中多通道特征的组合通常基于线性叠加，而量子卷积核则基于量子叠加和量子纠缠，能够在指数特征空间中表达复杂的多通道相关性。</p>
<p>在卷积操作完成后，特征图被压缩为量子系统中更紧凑的量子态，并通过量子池化电路进行下采样。池化电路也经过重新设计，以处理来自多个通道的量子态特征。WiMi 采用可学习的量子池化模式，通过可控测量或可控压缩操作减少量子态维度，同时保留关键特征信息，从而避免传统 QCNN 中因直接测量导致的特征破坏问题。实验结果表明，新的池化结构比传统 QCNN 池化方法更稳定，特征保留率更高。</p>
<p>除了卷积核和池化电路，WiMi 还构建了专门的混合量子 - 经典训练框架。在训练过程中，经典计算模块负责损失函数计算、梯度求解和参数更新，而量子模块负责前向传播和量子态演化。WiMi 采用扩展参数偏移规则方法，使多通道量子卷积核中的所有参数都能有效训练。为了提高训练稳定性，WiMi 还引入了量子噪声模拟和梯度裁剪机制，确保模型在真实量子硬件上的性能不会因噪声而急剧下降。</p>
<p>在训练过程中，WiMi 团队观察到一个非常有价值的现象：模型能够自动捕捉多个通道之间的非线性关联。以 RGB 图像为例，模型学习到的量子卷积核并不是简单地对 R、G 和 B 通道进行线性遍历，而是通过纠缠层在通道之间建立关联，使得卷积核能够识别量子状态空间中颜色分布模式的联合特征。这意味着模型并不是在三个通道上分别进行卷积，而是在更高维的空间中学习整体的深层特征，其表达能力远远超过经典卷积神经网络中的 3×3 或 1×1 卷积。</p>
<p>WiMi 认为，多通道处理能力将成为量子神经网络向现实应用迈进的关键能力之一。尽管单通道 QCNN 在学术界具有探索意义，但其局限性使其无法满足行业对复杂数据的要求。MC-QCNN 的出现使量子深度学习系统首次具备处理现实数据的能力，这意味着量子人工智能不再仅仅是实验室的概念，而是开始具备商业实施的可能性。相信随着量子硬件性能的提升，这项技术将推动量子机器学习从实验室研究走向真正的应用时代。</p>
<p>未来，WiMi 将继续完善这一技术体系，包括构建更高效的量子卷积核结构，开发更强大的噪声适应策略，扩展到三维卷积和时间序列卷积结构，并探索与 Transformer 等模型结构的融合可能性，使量子模型不仅能够处理多通道图像，还能处理多模态语音、视频、文本、图形结构和传感器数据。量子深度学习将不再局限于小规模任务，而将成为下一代通用人工智能模型中的重要运算符。量子计算与人工智能的结合将是未来十年技术发展的核心趋势。WiMi 将继续致力于推动量子人工智能生态系统的建设，使量子技术真正服务于工业需求、社会价值和人类未来。</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>
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微美全息

<p>微美全息（WiMi Hologram Cloud Inc.）推出了一种新的量子卷积神经网络技术，用于多通道监督学习。这项创新技术能够高效处理多通道数据，增强图像分类、医学成像和视频分析的能力。该设计专注于硬件适应性，并利用量子特定编码方法来改善特征融合和高阶跨通道特征的学习。微美的方案包括一个混合量子 - 经典训练框架，确保在真实量子硬件上的稳定性和性能。该技术旨在捕捉数据中的复杂关联，超越传统方法</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（全球新闻通讯社）——WiMi 发布下一代量子卷积神经网络技术，用于多通道监督学习</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.（纳斯达克：WiMi）（&#34;WiMi&#34;或&#34;公司&#34;），全球领先的全息增强现实（&#34;AR&#34;）技术提供商，宣布推出其自主开发的新技术：用于多通道监督学习的量子卷积神经网络（MC-QCNN）。这一突破性方法首次构建了完全硬件适应的量子卷积核设计，使量子模型能够高效处理多通道数据，从而在图像分类、医学成像、视频分析和多模态监测等行业展现出绝对优势。</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>从研发的角度来看，这一技术突破的核心不仅在于多通道量子卷积核的构建，还在于整个系统化的设计方案，包括卷积核结构、量子比特布局、通道交互编码、权重可学习性、可解释性和硬件约束适应策略。为了使该技术能够在真实硬件上执行，WiMi 放弃了大量不切实际的深度电路结构，而转向更接近量子硬件原生门操作特性的设计理念。WiMi 提出的量子电路卷积核使用单比特旋转门、受控参数化门、SWAP 交错结构、弱纠缠层和通道交互门，从而形成一个能够表达复杂函数的卷积算子，同时保持对量子退相干的鲁棒性。</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>与经典卷积核需要在像素邻域内滑动不同，WiMi 采用了一种量子特定的编码方法，将来自多个通道的数据压缩并编码为量子态的幅度、相位或纠缠结构，通过参数化量子门对其进行类似卷积的处理。通道之间的特征融合不再依赖于线性加权，而是通过门级交互直接在量子态空间中生成高维相关性，产生比经典卷积更强的特征组合能力。通过训练，这些参数化的量子卷积核可以学习高阶跨通道特征，如纹理 - 颜色共现、时空联合模式、多光谱能量分布相关性等，从而实现优于传统 QCNN 的表达能力。</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>该技术架构的核心之一是 WiMi 建立的量子多通道卷积算子。该算子使用参数化旋转门和受控门构建卷积模式。通过调整门的旋转角度和受控结构，卷积核可以在训练过程中自动学习最佳的跨通道特征组合策略。整个卷积核不仅可以作用于单比特分布，还可以以张量的方式作用于多比特通道结构，使卷积核不仅能够提取局部一致性，还能够从纠缠结构中挖掘高阶关系。这种模式在经典 CNN 中无法直接实现，因为经典神经网络中多通道特征的组合通常基于线性叠加，而量子卷积核则基于量子叠加和量子纠缠，能够在指数特征空间中表达复杂的多通道相关性。</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>在卷积操作完成后，特征图被压缩为量子系统中更紧凑的量子态，并通过量子池化电路进行下采样。池化电路也经过重新设计，以处理来自多个通道的量子态特征。WiMi 采用可学习的量子池化模式，通过可控测量或可控压缩操作减少量子态维度，同时保留关键特征信息，从而避免传统 QCNN 中因直接测量导致的特征破坏问题。实验结果表明，新的池化结构比传统 QCNN 池化方法更稳定，特征保留率更高。</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>除了卷积核和池化电路，WiMi 还构建了专门的混合量子 - 经典训练框架。在训练过程中，经典计算模块负责损失函数计算、梯度求解和参数更新，而量子模块负责前向传播和量子态演化。WiMi 采用扩展参数偏移规则方法，使多通道量子卷积核中的所有参数都能有效训练。为了提高训练稳定性，WiMi 还引入了量子噪声模拟和梯度裁剪机制，确保模型在真实量子硬件上的性能不会因噪声而急剧下降。</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>在训练过程中，WiMi 团队观察到一个非常有价值的现象：模型能够自动捕捉多个通道之间的非线性关联。以 RGB 图像为例，模型学习到的量子卷积核并不是简单地对 R、G 和 B 通道进行线性遍历，而是通过纠缠层在通道之间建立关联，使得卷积核能够识别量子状态空间中颜色分布模式的联合特征。这意味着模型并不是在三个通道上分别进行卷积，而是在更高维的空间中学习整体的深层特征，其表达能力远远超过经典卷积神经网络中的 3×3 或 1×1 卷积。</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 认为，多通道处理能力将成为量子神经网络向现实应用迈进的关键能力之一。尽管单通道 QCNN 在学术界具有探索意义，但其局限性使其无法满足行业对复杂数据的要求。MC-QCNN 的出现使量子深度学习系统首次具备处理现实数据的能力，这意味着量子人工智能不再仅仅是实验室的概念，而是开始具备商业实施的可能性。相信随着量子硬件性能的提升，这项技术将推动量子机器学习从实验室研究走向真正的应用时代。</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>未来，WiMi 将继续完善这一技术体系，包括构建更高效的量子卷积核结构，开发更强大的噪声适应策略，扩展到三维卷积和时间序列卷积结构，并探索与 Transformer 等模型结构的融合可能性，使量子模型不仅能够处理多通道图像，还能处理多模态语音、视频、文本、图形结构和传感器数据。量子深度学习将不再局限于小规模任务，而将成为下一代通用人工智能模型中的重要运算符。量子计算与人工智能的结合将是未来十年技术发展的核心趋势。WiMi 将继续致力于推动量子人工智能生态系统的建设，使量子技术真正服务于工业需求、社会价值和人类未来。</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含义差异，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>
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