<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，今天宣布他们正在积极探索可扩展量子卷积神经网络（SQCNN）技术。与现有的量子神经网络模型相比，WiMi 开发的可扩展量子卷积神经网络模型表现出更优越的性能，显著提高了分类准确性。</p>
<p>传统的量子神经网络模型在处理复杂的图像分类任务时，常常由于特征提取不完整或不准确而导致分类结果存在偏差。相比之下，可扩展量子卷积神经网络模型通过优化量子比特的利用和独特的网络架构设计，能够更准确地从图像中提取关键特征，从而显著提高分类准确性。此外，在模型泛化方面，可扩展量子卷积神经网络模型能够更好地适应不同数据集的特征，即使面对新数据也能实现准确分类。这一优势使其在实际应用中更加稳定可靠，防止因微小数据变化而导致显著性能下降。在训练效率方面，可扩展量子卷积神经网络模型通过优化量子算法大大减少了训练所需的时间。通过利用先进的算法和高效的量子计算架构，可扩展量子卷积神经网络模型显著提升了应用效率。</p>
<p>在传统的卷积神经网络中，卷积层通过滑动卷积核对图像进行卷积操作，以提取图像的局部特征。在可扩展量子卷积神经网络的量子电路中，类似的功能是依赖于量子门的叠加和纠缠特性来实现的。量子门的叠加使得量子比特能够同时存在于多个状态，相当于同时处理多个特征，显著提高了处理效率。量子比特之间的纠缠建立了更复杂的关联，使得量子电路能够学习图像中更细微和更深层的特征。这一独特设计使得可扩展量子卷积神经网络的量子电路能够更好地学习特征，为后续的分类任务提供了坚实的基础。</p>
<p>特别是在可扩展量子卷积神经网络系统中，多个独立的量子设备可以并行提取特征，这一设计具有高度的创新性和实用性。在传统的机器学习任务中，特征提取通常是顺序进行的，这限制了处理速度和效率。相比之下，可扩展量子卷积神经网络系统中的并行设计允许不同的量子设备同时从图像的不同部分或不同类型的特征中提取特征，类似于多个工人在不同区域同时工作，显著加快了特征提取的速度。此外，这一设计允许灵活使用不同规模的量子设备。在面对不同规模和复杂性的机器学习任务时，可以根据实际需求选择和组合适当规模的量子设备。对于简单的小规模任务，可以使用较小的量子设备以降低成本和计算复杂性；对于复杂的大规模任务，可以组合多个较大规模的量子设备，以满足任务的计算需求，从而实现更大规模的机器学习任务。</p>
<p>WiMi 探索的可扩展量子卷积神经网络不仅在特征提取中实现了并行化和多维性，还通过动态适应量子设备的规模，打破了计算资源与任务复杂性之间的冲突。这一创新不仅显著提高了图像分类的准确性和效率，还在泛化能力与训练成本之间取得了平衡，为自动驾驶和医学图像分析等高实时性、高复杂性场景提供了技术支持。随着量子技术的不断发展，它将推动人工智能迈向更高维度的计算范式。</p>
<p>关于 WiMi Hologram Cloud</p>
<p>WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) 是一家全息云综合技术解决方案提供商，专注于包括全息 AR 汽车 HUD 软件、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、全息汽车导航等专业领域。其服务和全息 AR 技术包括全息 AR 汽车应用、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 广告技术、全息 AR 娱乐技术、全息 ARSDK 支付、互动全息通信及其他全息 AR 技术。</p>
<p>安全港声明</p>
<p>本新闻稿包含 1995 年《私人证券诉讼改革法案》中的 “前瞻性声明”。这些前瞻性声明可以通过诸如 “将”、“预计”、“预期”、“未来”、“打算”、“计划”、“相信”、“估计” 等术语来识别。非历史事实的声明，包括关于公司信念和预期的声明，均为前瞻性声明。除此之外，本新闻稿中的商业前景和管理层的引用以及公司的战略和运营计划均包含前瞻性声明。公司还可能在其定期向美国证券交易委员会（“SEC”）提交的 20-F 和 6-K 表格的报告中、在向股东的年度报告中、在新闻稿和其他书面材料中，以及在其高管、董事或员工向第三方作出的口头声明中作出书面或口头的前瞻性声明。前瞻性声明涉及固有的风险和不确定性。多个因素可能导致实际结果与任何前瞻性声明中包含的结果存在重大差异，包括但不限于以下因素：公司的目标和战略；公司的未来业务发展、财务状况和运营结果；AR 全息行业的预期增长；以及公司对其产品和服务的需求和市场接受度的预期。</p>
<p>有关这些及其他风险的更多信息包含在公司提交给 SEC 的 20-F 表格的年度报告和 6-K 表格的当前报告以及其他文件中。本新闻稿中提供的所有信息均截至本新闻稿的日期。公司不承担更新任何前瞻性声明的义务，除非适用法律要求。</p>
<p>来源：WiMi Hologram Cloud Inc.</p>

微美全息

<p>WiMi Hologram Cloud Inc. 宣布开发一种可扩展量子卷积神经网络（SQCNN），以提高图像分类的准确性和效率。与传统的量子神经网络相比，这种创新模型改善了特征提取和分类性能。它允许使用多个量子设备进行并行特征提取，优化训练时间并适应各种数据特征。SQCNN 旨在支持复杂应用，如自动驾驶和医学图像分析，标志着量子技术和人工智能的重大进步</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, today announced that they are actively exploring Scalable Quantum Convolutional Neural Networks (SQCNN) technology. Compared to existing quantum neural network models, the scalable quantum convolutional neural network model developed by WiMi demonstrates superior performance, significantly improving classification accuracy.</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，今天宣布他们正在积极探索可扩展量子卷积神经网络（SQCNN）技术。与现有的量子神经网络模型相比，WiMi 开发的可扩展量子卷积神经网络模型表现出更优越的性能，显著提高了分类准确性。</p>
</div><p>Traditional quantum neural network models, when handling complex image classification tasks, often suffer from biases in classification results due to incomplete or inaccurate feature extraction. In contrast, the scalable quantum convolutional neural network model, through optimized utilization of qubits and a unique network architecture design, can more accurately extract key features from images, thereby significantly improving classification accuracy. Additionally, in terms of model generalization, the scalable quantum convolutional neural network model can better adapt to the characteristics of different datasets, enabling accurate classification even when faced with new data. This advantage makes it more stable and reliable in practical applications, preventing significant performance degradation due to minor data variations. In terms of training efficiency, the scalable quantum convolutional neural network model greatly reduces the time required for training through optimization of quantum algorithms. By leveraging advanced algorithms and efficient quantum computing architectures, the scalable quantum convolutional neural network model significantly enhances application efficiency.</p>
<div class="lb-trans"><p>传统的量子神经网络模型在处理复杂的图像分类任务时，常常由于特征提取不完整或不准确而导致分类结果存在偏差。相比之下，可扩展量子卷积神经网络模型通过优化量子比特的利用和独特的网络架构设计，能够更准确地从图像中提取关键特征，从而显著提高分类准确性。此外，在模型泛化方面，可扩展量子卷积神经网络模型能够更好地适应不同数据集的特征，即使面对新数据也能实现准确分类。这一优势使其在实际应用中更加稳定可靠，防止因微小数据变化而导致显著性能下降。在训练效率方面，可扩展量子卷积神经网络模型通过优化量子算法大大减少了训练所需的时间。通过利用先进的算法和高效的量子计算架构，可扩展量子卷积神经网络模型显著提升了应用效率。</p>
</div><p>In traditional convolutional neural networks, the convolutional layer performs convolution operations on the image through a sliding convolution kernel to extract local features of the image. In the quantum circuit of the scalable quantum convolutional neural network, similar functionality is achieved by relying on the superposition and entanglement properties of quantum gates. The superposition of quantum gates allows qubits to exist in multiple states simultaneously, which is equivalent to processing multiple features at the same time, significantly improving processing efficiency. The entanglement between qubits establishes more complex correlations, enabling the quantum circuit to learn subtler and deeper features in the image. This unique design allows the quantum circuit of the scalable quantum convolutional neural network to better learn features, providing a solid foundation for subsequent classification tasks.</p>
<div class="lb-trans"><p>在传统的卷积神经网络中，卷积层通过滑动卷积核对图像进行卷积操作，以提取图像的局部特征。在可扩展量子卷积神经网络的量子电路中，类似的功能是依赖于量子门的叠加和纠缠特性来实现的。量子门的叠加使得量子比特能够同时存在于多个状态，相当于同时处理多个特征，显著提高了处理效率。量子比特之间的纠缠建立了更复杂的关联，使得量子电路能够学习图像中更细微和更深层的特征。这一独特设计使得可扩展量子卷积神经网络的量子电路能够更好地学习特征，为后续的分类任务提供了坚实的基础。</p>
</div><p>In particular, in the scalable quantum convolutional neural network system, multiple independent quantum devices can extract features in parallel, a design that is highly innovative and practical. In traditional machine learning tasks, feature extraction is often performed sequentially, which limits processing speed and efficiency. In contrast, the parallel design in the scalable quantum convolutional neural network system allows different quantum devices to simultaneously extract features from different parts of an image or different types of features, akin to multiple workers operating simultaneously in different areas, significantly accelerating the speed of feature extraction. Moreover, this design allows for the flexible use of quantum devices of varying sizes. When facing machine learning tasks of different scales and complexities, quantum devices of appropriate sizes can be selected and combined based on actual needs. For simple, small-scale tasks, smaller quantum devices can be used to reduce costs and computational complexity; for complex, large-scale tasks, multiple larger-scale quantum devices can be combined to meet the computational demands of the task, thereby enabling larger-scale machine learning tasks.</p>
<div class="lb-trans"><p>特别是在可扩展量子卷积神经网络系统中，多个独立的量子设备可以并行提取特征，这一设计具有高度的创新性和实用性。在传统的机器学习任务中，特征提取通常是顺序进行的，这限制了处理速度和效率。相比之下，可扩展量子卷积神经网络系统中的并行设计允许不同的量子设备同时从图像的不同部分或不同类型的特征中提取特征，类似于多个工人在不同区域同时工作，显著加快了特征提取的速度。此外，这一设计允许灵活使用不同规模的量子设备。在面对不同规模和复杂性的机器学习任务时，可以根据实际需求选择和组合适当规模的量子设备。对于简单的小规模任务，可以使用较小的量子设备以降低成本和计算复杂性；对于复杂的大规模任务，可以组合多个较大规模的量子设备，以满足任务的计算需求，从而实现更大规模的机器学习任务。</p>
</div><p>The scalable quantum convolutional neural network explored by WiMi not only achieves parallelization and multidimensionality in feature extraction but also breaks the conflict between computational resources and task complexity through its ability to dynamically adapt to the scale of quantum devices. This innovation not only significantly enhances the accuracy and efficiency of image classification but also strikes a balance between generalization capability and training costs, providing technical support for high-real-time, high-complexity scenarios such as autonomous driving and medical image analysis. With the continuous development of quantum technology, it will propel artificial intelligence toward a higher-dimensional computational paradigm.</p>
<div class="lb-trans"><p>WiMi 探索的可扩展量子卷积神经网络不仅在特征提取中实现了并行化和多维性，还通过动态适应量子设备的规模，打破了计算资源与任务复杂性之间的冲突。这一创新不仅显著提高了图像分类的准确性和效率，还在泛化能力与训练成本之间取得了平衡，为自动驾驶和医学图像分析等高实时性、高复杂性场景提供了技术支持。随着量子技术的不断发展，它将推动人工智能迈向更高维度的计算范式。</p>
</div><p>About WiMi Hologram Cloud</p>
<div class="lb-trans"><p>关于 WiMi Hologram Cloud</p>
</div><p>WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.</p>
<div class="lb-trans"><p>WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) 是一家全息云综合技术解决方案提供商，专注于包括全息 AR 汽车 HUD 软件、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、全息汽车导航等专业领域。其服务和全息 AR 技术包括全息 AR 汽车应用、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 广告技术、全息 AR 娱乐技术、全息 ARSDK 支付、互动全息通信及其他全息 AR 技术。</p>
</div><p>Safe Harbor Statements</p>
<div class="lb-trans"><p>安全港声明</p>
</div><p>This press release contains &#34;forward-looking statements&#34; within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as &#34;will,&#34; &#34;expects,&#34; &#34;anticipates,&#34; &#34;future,&#34; &#34;intends,&#34; &#34;plans,&#34; &#34;believes,&#34; &#34;estimates,&#34; and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission (&#34;SEC&#34;) on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.</p>
<div class="lb-trans"><p>本新闻稿包含 1995 年《私人证券诉讼改革法案》中的 “前瞻性声明”。这些前瞻性声明可以通过诸如 “将”、“预计”、“预期”、“未来”、“打算”、“计划”、“相信”、“估计” 等术语来识别。非历史事实的声明，包括关于公司信念和预期的声明，均为前瞻性声明。除此之外，本新闻稿中的商业前景和管理层的引用以及公司的战略和运营计划均包含前瞻性声明。公司还可能在其定期向美国证券交易委员会（“SEC”）提交的 20-F 和 6-K 表格的报告中、在向股东的年度报告中、在新闻稿和其他书面材料中，以及在其高管、董事或员工向第三方作出的口头声明中作出书面或口头的前瞻性声明。前瞻性声明涉及固有的风险和不确定性。多个因素可能导致实际结果与任何前瞻性声明中包含的结果存在重大差异，包括但不限于以下因素：公司的目标和战略；公司的未来业务发展、财务状况和运营结果；AR 全息行业的预期增长；以及公司对其产品和服务的需求和市场接受度的预期。</p>
</div><p>Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.</p>
<div class="lb-trans"><p>有关这些及其他风险的更多信息包含在公司提交给 SEC 的 20-F 表格的年度报告和 6-K 表格的当前报告以及其他文件中。本新闻稿中提供的所有信息均截至本新闻稿的日期。公司不承担更新任何前瞻性声明的义务，除非适用法律要求。</p>
</div><p>SOURCE WiMi Hologram Cloud Inc.</p>
<div class="lb-trans"><p>来源：WiMi Hologram Cloud Inc.</p>
</div>

微美全息推出可扩展的量子卷积神经网络，以提升图像分类的准确性和效率 | 微美全息股票新闻