SHENZHEN, China, Feb. 6, 2026 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, developed a transmission scheme for GHZ states and W states based on Brownian state quantum channels. This scheme establishes an efficient transmission mechanism for multi-particle entangled states by constructing special quantum channels and measurement systems. At the technical implementation level, HOLO uses quantum Fourier transform for quantum state projectio ...

Core Viewpoint - MicroCloud Hologram Inc. has proposed an innovative hardware acceleration technology that converts quantum tensor network algorithms into parallel computing circuits on field programmable gate arrays (FPGA), enabling efficient quantum spin model simulation on classical hardware [1][11]. Technology Development - The company focuses on algorithm-hardware co-design, breaking down tensor network algorithms into computational units that can be directly implemented in hardware, thus creating a high-density parallel scalable architecture using FPGA [5][11]. - HOLO's technology achieves a performance improvement of 1.7 times faster than traditional CPU computations and more than 2 times improved energy efficiency [11]. Implementation Details - A Hierarchical Tensor Contraction Pipeline has been constructed, consisting of three main layers: input and scheduling layer, core computing layer, and output and reduction layer [7][8]. - The core computing layer utilizes multiple MAC Arrays to support tensor contraction operations, achieving pipeline-level parallelism for floating-point operations [8]. Future Plans - The company aims to continue developing hardware implementations for various quantum computing core modules, including quantum variational algorithms and quantum machine learning models, to establish a comprehensive quantum algorithm acceleration ecosystem [12]. Financial Position - MicroCloud Hologram Inc. has cash reserves exceeding 3 billion RMB and plans to invest over 400 million USD in the development of blockchain, quantum computing, and other frontier technologies [13].

Core Insights - MicroCloud Hologram Inc. has launched a scalable quantum Fourier transform simulator technology utilizing multi-FPGA and high-bandwidth memory, marking a significant engineering advancement for future quantum algorithm simulations [1][6] Technology Overview - The new multi-FPGA QFT simulation platform innovatively stores large-scale complex amplitudes of quantum states in high-bandwidth memory, enabling faster read and update speeds compared to traditional DDR memory [2] - The simulator's core processing unit is designed for parallel QFT structure, utilizing FPGA logic for efficient complex multiplication and data processing, which enhances hardware resource utilization [3] - Multi-FPGA scalability allows for the simulation of larger quantum states by distributing computation tasks across multiple FPGA chips, minimizing cross-FPGA communication through an efficient domain decomposition strategy [4] Engineering Challenges - Integrating multi-FPGA with high-bandwidth memory presents challenges in data flow scheduling and communication overhead, which the company addresses through strict synchronization protocols and high-precision computation modules [5] Strategic Importance - The launch signifies the growing role of FPGA in accelerating quantum software development, with future plans to support distributed quantum circuit simulations and rapid hardware accelerators for various quantum applications [6][8] - The multi-FPGA QFT simulator is positioned to serve not only quantum algorithm researchers but also in quantum compiler optimization and industrial application validation, filling a critical gap in the quantum computing ecosystem [7][8] Company Background - MicroCloud Hologram Inc. focuses on holographic technology and quantum computing, with cash reserves exceeding 3 billion RMB and plans to invest over 400 million USD in various frontier technology developments [9]

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 ...

Core Viewpoint - MicroCloud Hologram Inc. has launched a quantum-enhanced deep convolutional neural network image 3D reconstruction technology system, which integrates quantum computing with deep learning to improve the precision and adaptability of 3D model generation [1][8]. Group 1: Technology Overview - The new system consists of six core modules: quantum-optimized dataset preparation, quantum-assisted feature extraction, quantum-enhanced parameter generation, quantum-accelerated 3D reconstruction, quantum-precision model evaluation, and an interactive application interface [2]. - The quantum-optimized dataset preparation module is crucial for ensuring high-quality 3D model data, which is essential for the deep learning algorithm to accurately learn morphological features [3]. - The quantum-assisted feature extraction module utilizes quantum convolutional neural networks to efficiently extract higher-level features from input images, overcoming limitations of traditional algorithms [4]. - The quantum-enhanced parameter generation module maps high-dimensional feature vectors to three-dimensional space, allowing for refined control over model attributes such as shape and size [5]. - The quantum-accelerated 3D reconstruction module generates high-precision 3D models by leveraging quantum computing's parallel processing capabilities [6]. - The quantum-precision model evaluation module optimizes algorithm parameters based on error measurements, enhancing the robustness of the 3D reconstruction model [7]. Group 2: Competitive Advantages - Compared to traditional 3D reconstruction algorithms, the new system offers significant advantages in precision and adaptability, enabling better alignment with actual needs through quantum-accelerated training on large datasets [8]. - The technology has broad application prospects across various fields, including medical diagnostics, robotics, and manufacturing, with potential integration into augmented and virtual reality technologies [9][10]. Group 3: Company Background - MicroCloud Hologram Inc. focuses on holographic technology and has a cash reserve exceeding 3 billion RMB, with plans to invest over 400 million USD in quantum computing and related technologies [11]. - The company aims to become a global leader in quantum holography and quantum computing technology [11].

Group 1 - The core viewpoint of the article highlights the financial performance and market activity of Alliance Hologram (HOLO), indicating a positive trend in both stock price and financial metrics [1] - As of December 17, HOLO opened at $3.136 per share, reflecting a 2.15% increase, with a total market capitalization of $45.6831 million [1] - The company reported total revenue of 160 million RMB for the period ending June 30, 2025, representing a year-on-year growth of 24.04% [1] - The net profit attributable to the parent company reached 23.8 million RMB, showing a significant year-on-year increase of 297.04% [1] Group 2 - Alliance Hologram, formerly known as Golden Path Acquisition Corporation, was established on May 9, 2018, in the Cayman Islands [1] - The company focuses on the research and application of holographic technology, aiming to provide leading holographic technology services to global clients [1] - Additionally, the company offers holographic digital twin technology services and has established a resource library for this technology [1]

Group 1 - The core viewpoint of the article highlights the performance and financial growth of Alliance Hologram (HOLO), which saw a stock price increase of 3.01% on December 16, reaching $3.029 per share with a total market capitalization of $44.1171 million [1] - As of June 30, 2025, Alliance Hologram reported total revenue of 160 million RMB, representing a year-on-year growth of 24.04% [1] - The company's net profit attributable to shareholders reached 23.8 million RMB, showing a significant year-on-year increase of 297.04% [1] Group 2 - Alliance Hologram, formerly known as Golden Path Acquisition Corporation, was established on May 9, 2018, in the Cayman Islands [1] - The company focuses on the research and application of holographic technology, aiming to provide leading holographic technology services to global clients [1] - Additionally, Alliance Hologram offers holographic digital twin technology services and has established a resource library for holographic digital twin technology [1]

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]