SHENZHEN, China, Jan. 8, 2026 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, launched a brand-new scalable quantum Fourier transform simulator technology based on multi-FPGA and high-bandwidth memory. This breakthrough achievement, by introducing a parallel distributed architecture with multiple FPGAs as well as high-bandwidth memory, lays an engineering foundation for future larger-scale quantum algorithm simulations. The multi-FPGA QFT s ...

Core Viewpoint - MicroCloud Hologram Inc. has introduced a learnable quantum spectral filter technology for hybrid graph neural networks, marking a significant advancement in quantum-classical hybrid graph neural network architecture, which enhances graph signal processing capabilities and paves the way for practical quantum graph machine learning applications [1][12]. Technology Overview - The new technology integrates graph convolution and pooling operations into a quantum computing process, allowing for efficient processing of graph signals through a quantum circuit that performs spectral transformations based on graph structures [2][10]. - The quantum measurement process enables structured nonlinear mapping, addressing complex structural search challenges in classical graph neural networks (GNNs) [3][9]. - The quantum convolution layer can compress a graph of size N into log(N)-dimensional features, significantly reducing computational costs compared to classical methods [4][10]. Mathematical Foundation - The technology is based on the spectral structure of the graph Laplacian operator, which reflects key properties of the graph, such as connectivity and clustering [5][6]. - A mapping between the graph's adjacency matrix and quantum gates allows for the simulation of local adjacency relationships, while hierarchical rotation logic provides multi-scale filtering consistent with graph spectrum decoupling [6][7]. Implementation and Optimization - The training of the quantum circuit utilizes classical-quantum hybrid optimization, enabling the extraction of spectral features from high-dimensional input signals and outputting low-dimensional features for further processing by classical networks [8][10]. - The logarithmic encoding method reduces the number of qubits needed, allowing for efficient representation of the original feature space [7][10]. Industry Implications - The technology addresses the challenges of large-scale graph learning in various domains, such as social media and traffic networks, where classical GNNs struggle with memory and computational demands [9][10]. - Quantum spectral filters present a disruptive solution, as the qubit requirements grow logarithmically with the number of nodes, making them suitable for future quantum-classical GNNs [10][12]. Future Outlook - The introduction of this technology positions MicroCloud Hologram Inc. at the forefront of quantum computing and graph neural networks, establishing a foundation for future hardware development and practical applications in artificial intelligence and physical computing [11][13].

Core Insights - MicroCloud Hologram Inc. has launched the Q-DPC Accelerator, a tool that utilizes quantum-enhanced density peak clustering algorithms to enhance strategy evaluation efficiency [1][4] - The Q-DPC Accelerator features three main functions: strategy set preprocessing, quantum clustering grouping, and intelligent strategy matching, which collectively improve the efficiency and accuracy of strategy evaluations [2][4] Group 1: Q-DPC Accelerator Functions - The strategy set preprocessing stage includes quantum data cleaning, feature extraction, and data conversion, ensuring data accuracy and consistency for effective clustering analysis [2] - The quantum clustering grouping stage employs the quantum-enhanced density peak clustering algorithm to accurately identify clustering structures and reduce evaluation complexity [2][4] - The intelligent strategy matching stage rapidly matches access requests with pre-generated strategy clusters, enhancing both speed and accuracy of the matching process [3][4] Group 2: Application and Impact - The Q-DPC Accelerator is designed to provide enterprises with efficient and precise strategy evaluation solutions, significantly improving operational efficiency in complex strategy set scenarios [4] - The tool has broad application potential across various industries, aiding enterprises in building robust security systems to address increasing security challenges [4] - Continuous advancements in quantum computing technology are expected to further enhance the performance and accuracy of the Q-DPC Accelerator, providing reliable protection for enterprise data security [4][7]

SHENZHEN, China, Dec. 22, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, launched a brand-new FPGA-based quantum computing simulation framework founded on a serial-parallel architecture. This framework adopts an innovative hardware-level data path design and, by redefining the execution mode of quantum gate operations, achieves a linear reduction in resource utilization. This framework not only demonstrates the inherent advantages ...

SHENZHEN, China, Dec. 18, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction technology system. This system first utilizes quantum convolutional neural network to complete the feature extraction of input images, then generates the core parameters of the 3D model through quantum fully connected layers, and finally imports these parameters int ...

本文源自：市场资讯 作者：行情君 资料显示，盟云全息公司 (前Golden Path Acquisition Corporation)是于2018年5月9日在开曼群岛注册成 立。该公司专注于全息技术的研发和应用。一直致力于为全球客户提供领先的全息技术服务。公司还为 客户提供全息数字孪生技术服务,并建立了全息数字孪生技术资源库。 据交易所数据显示，12月17日，盟云全息(HOLO)开盘上涨2.15%，截至22:32，报3.136美元/股，成交 6.0万美元，总市值4568.31万美元。 财务数据显示，截至2025年06月30日，盟云全息收入总额1.6亿人民币，同比增长24.04%；归母净利润 2.38亿人民币，同比增长297.04%。 ...

据交易所数据显示，12月16日，盟云全息(HOLO)盘中上涨3.01%，截至22:38，报3.029美元/股，成交 16.11万美元，总市值4411.71万美元。 财务数据显示，截至2025年06月30日，盟云全息收入总额1.6亿人民币，同比增长24.04%；归母净利润 2.38亿人民币，同比增长297.04%。 本文源自：市场资讯 作者：行情君 资料显示，盟云全息公司 (前Golden Path Acquisition Corporation)是于2018年5月9日在开曼群岛注册成 立。该公司专注于全息技术的研发和应用。一直致力于为全球客户提供领先的全息技术服务。公司还为 客户提供全息数字孪生技术服务,并建立了全息数字孪生技术资源库。 ...

Core Insights - MicroCloud Hologram Inc. has developed a quantum-driven 3D intelligent model that integrates quantum computing and artificial intelligence for high-precision 3D modeling and image processing [1][7] - The model features a quantum-optimized distributed architecture, allowing for flexible expansion and upgrading of subsystems, enhancing stability and scalability [2][7] - The company plans to invest over 400 million USD from its cash reserves exceeding 3 billion RMB into various frontier technology fields, including blockchain and quantum computing [8] Technology and Architecture - The model consists of six major subsystems, each utilizing quantum technology for performance upgrades, including data acquisition, model training, autonomous generation, data management, visualization, and system security [3][4][5][6] - The quantum-enhanced data acquisition subsystem improves data accuracy and stability through quantum data preprocessing and encryption [3] - The quantum-accelerated model training subsystem employs quantum deep learning algorithms for precise data feature extraction and model optimization [4] Advantages and Market Position - Compared to traditional systems, the new model offers efficient processing of massive data, high-quality 3D model generation, and reduced manual intervention, thus improving work efficiency [7] - The architecture supports rapid and stable system expansion while ensuring data security and privacy through multiple layers of quantum security technology [7] - MicroCloud Hologram Inc. aims to become a global leader in quantum holography and quantum computing technology [8]

Core Insights - MicroCloud Hologram Inc. has introduced the "quantum degree" concept, establishing a new theoretical framework for quantum synchronization research [1][2] - The company's research is grounded in a deep understanding of quantum systems, highlighting the differences between quantum and classical systems [2] - The proposed technical framework aims to overcome the limitations of classical synchronization theory, providing a reliable tool for quantum synchronization studies [2] Theoretical Foundation - The research is based on quantum information technology, using synchronized non-commuting observables as a measurement standard for quantumness [2] - The framework maintains compatibility with existing synchronization methods while accurately capturing quantum systems' unique properties [2] Technical Implementation - The experimental platform consists of two weakly interacting cavity qubit systems coupled through boson excitation exchange [3] - The focus is on the synchronization characteristics of Pauli operator expectation values, validating the quantum degree through precise measurements [3] Technical Applications - The quantum degree concept has broad application prospects in quantum computers, quantum sensors, and quantum communication systems [4] - It provides a new technical path for synchronization control in superconducting quantum circuit systems, enhancing measurement precision and application scope [4] Company Overview - MicroCloud Hologram Inc. specializes in holographic technology services, including holographic LiDAR solutions and digital twin technology [5] - The company has cash reserves exceeding 3 billion RMB and plans to invest over 400 million USD in various frontier technology fields, including blockchain and quantum computing [5] - The goal is to become a global leader in quantum holography and quantum computing technology [5]

Core Viewpoint - MicroCloud Hologram Inc. has launched a multi-class classification method based on Quantum Convolutional Neural Network (QCNN) using hybrid quantum-classical learning, showcasing the potential of quantum computing in artificial intelligence and addressing limitations of traditional computing architectures [1][12]. Technology Development - The new technology addresses bottlenecks in classical neural networks, particularly in computing power, energy consumption, and model complexity, as data scales and classification categories expand [2][10]. - The core of the technology is a multi-class classification model that integrates quantum convolutional neural networks with a hybrid quantum-classical optimization framework, utilizing the TensorFlow Quantum platform [3][9]. Model Design - HOLO introduced a quantum perceptron model that leverages quantum state evolution and measurement, enhancing feature extraction through quantum gates, which allows for high-dimensional feature mappings [4][9]. - The model optimizes circuit complexity and improves expressive power by reducing redundant gate operations and enhancing entanglement structures [4][9]. Training Mechanism - The hybrid quantum-classical learning mechanism significantly improves training efficiency and model convergence speed by combining quantum state encoding with classical optimization algorithms [5][12]. - The training process involves quantum circuits encoding input samples, producing measurement results that are normalized and used to update quantum circuit parameters iteratively [5][8]. Experimental Results - Experimental results indicate that HOLO's quantum convolutional neural network achieves accuracy comparable to classical convolutional networks in four-class classification tasks, validating the feasibility of quantum neural networks for practical applications [6][12]. Industry Context - The technology is positioned to address challenges in various fields such as computer vision, medical image analysis, and natural language processing, where traditional deep learning methods face limitations due to high energy consumption and long training times [10][12]. - HOLO's quantum convolutional neural network method aims to reduce computational complexity and leverage future advancements in quantum hardware for significant breakthroughs in computing power [10][11]. Future Prospects - The technology lays the groundwork for broader applications of quantum machine learning, with expectations for expansion into large-scale image recognition and real-time video processing as quantum hardware advances [11][12]. - HOLO plans to further optimize quantum circuits and explore multi-layer quantum convolutional networks combined with deep residual structures in future research [11].