Core Viewpoint - MicroCloud Hologram Inc. has proposed a new method based on Matrix Product States (MPS) that significantly enhances quantum state preparation accuracy and computational efficiency, achieving improvements by two orders of magnitude [1][12]. Group 1: Technology Overview - The new method utilizes a shallow quantum circuit design primarily composed of nearest-neighbor qubit gates, which enhances feasibility on current noisy quantum devices [2][6]. - The research indicates that approximation accuracy in tensor networks is mainly dependent on bond dimension rather than the number of qubits, facilitating future large-scale adoption [2][12]. - The method optimizes the loading of probability distributions by leveraging mirror symmetry, which reduces entanglement and improves computational efficiency [4][5]. Group 2: Advantages of the New Method - The proposed method achieves a precision improvement of two orders of magnitude compared to existing MPS techniques while significantly reducing computation time [8][12]. - The optimized shallow quantum circuit design minimizes global gate operations, reducing circuit depth and enhancing computational stability [6][7]. - The linear scalability of the method allows it to adapt to larger-scale quantum systems without a corresponding exponential increase in computational complexity [7][10]. Group 3: Applications and Future Directions - The MPS method is particularly suitable for high-dimensional probability distributions, making it ideal for applications in quantum finance and quantum machine learning [10][12]. - Future research will focus on optimizing computational complexity, improving adaptability across different quantum hardware platforms, and exploring additional application areas [13].

Core Insights - MicroCloud Hologram Inc. is developing Continuous Variable Quantum Neural Networks (CV-QNN) technology to enhance quantum artificial intelligence capabilities [1][8] - The CV-QNN technology integrates classical neural network structures within quantum computing frameworks, aiming to improve computational efficiency and expand application areas [8][11] Technology Overview - CV-QNN utilizes affine transformations and nonlinear mappings through continuously parameterized quantum gates, distinguishing itself from discrete variable architectures [2][6] - The architecture employs Gaussian gates for linear operations and non-Gaussian gates for nonlinear activation functions, enabling complex feature representation [3][4] - The layered structure of CV-QNN mirrors multilayer perceptrons in classical networks, allowing for complex nonlinear transformations while maintaining quantum coherence [5] Performance and Applications - CV-QNN leverages quantum superposition and entanglement, potentially offering exponential speedup in processing large-scale data [6] - Applications include efficient image classification, object detection, text generation, and market forecasting, showcasing versatility across various fields [7][8] Challenges and Future Prospects - The technology faces challenges such as quantum hardware stability and optimization of computational resources, which require further research [9][10] - As quantum hardware and software improve, the performance and application scope of CV-QNN are expected to expand significantly [10][11]

Core Viewpoint - MicroCloud Hologram Inc. has proposed an innovative high-dimensional quantum key distribution (QKD) protocol that utilizes quantum Fourier transform, enhancing security and information efficiency in quantum transmission [1][5]. Group 1: Quantum Key Distribution Protocol - The proposed QKD protocol employs a coding framework that ensures secure key distribution through quantum operations, specifically quantum Fourier transform and quantum-controlled NOT gate [1][2]. - The secret information is concealed within the relative phases of high-dimensional entangled states, allowing for secure storage and retrieval of information [2][3]. - High-dimensional quantum states significantly increase information-carrying capacity compared to traditional binary states, breaking the previous limit of 1 bit per particle [3][4]. Group 2: Security and Performance - The QKD protocol demonstrates superior cryptographic performance, surpassing traditional methods that rely on mathematical problems, thus reducing the risk of compromise as computational power increases [4]. - The protocol is based on quantum mechanics principles, such as the no-cloning theorem and Heisenberg's uncertainty principle, which fundamentally prevent information eavesdropping or tampering [4][5]. - The high-dimensional quantum state key allows for the transmission of more keys under the same conditions, addressing the growing demands for information security in the big data era [4][5]. Group 3: Future Implications - The advancements made by MicroCloud in the QKD protocol lay a solid foundation for the future development of quantum key distribution technology, potentially leading the field into a new stage of development [5].

Core Viewpoint - MicroCloud Hologram Inc. has introduced a new Bitcoin blockchain hybrid consensus algorithm aimed at enhancing the performance, scalability, security, and consistency of the Bitcoin Blockchain system [1][4]. Group 1: Hybrid Consensus Algorithm - The hybrid consensus algorithm utilizes a PoS based on the verifiable random function (VRF) protocol to ensure fairness and randomness in the consensus process, thereby preventing power concentration and malicious behavior [2][3]. - This algorithm improves network security and reliability by effectively defending against attacks from malicious nodes and ensuring transaction accuracy and integrity [3][4]. - The algorithm's adaptability allows it to intelligently select the most suitable consensus method based on actual demand and resource conditions, catering to various application scenarios [3][4]. Group 2: Financial Position and Investments - The company currently holds a cash reserve of approximately $303 million and plans to call up to $200 million in funds for investments in Bitcoin, other digital currencies, and related securities [5]. - MicroCloud has reported cumulative investment returns of $8.13 million, reflecting its commitment to maintaining a strong financial position and creating long-term value for shareholders [5]. - The company aims to enhance the diversity and risk resistance of its overall asset portfolio through these investments [5]. Group 3: Holographic Technology Services - MicroCloud provides advanced holographic technology services, including high-precision holographic LiDAR solutions and holographic digital twin technology [6]. - The company has developed a proprietary holographic digital twin technology resource library that captures 3D holographic forms using various advanced technologies [6].

Core Insights - MicroCloud Hologram Inc. has announced the purchase of up to $200 million in Bitcoin and cryptocurrency-related securities derivatives, indicating a strategic move into the cryptocurrency sector [1][2][3] - The company currently holds $303 million in cash reserves, which will be allocated towards derivatives and technology development in areas such as blockchain, quantum computing, and AI-powered AR [1][3] - The total accumulated dividends have reached $8.13 million, reflecting the company's commitment to maintaining a strong financial position and creating long-term value for shareholders [3] Financial Strategy - The investment in Bitcoin and its derivatives is part of HOLO's capital reserve strategy aimed at enhancing financial stability and long-term growth potential through diversified investments [2][3] - The allocated funds will primarily target cryptocurrencies with significant market influence and growth potential, contributing to the diversity and risk resilience of HOLO's overall asset portfolio [3] Technological Advancements - As a technology service provider, HOLO has made significant breakthroughs in quantum system research and large-scale DeepSeek integration, which support its investments in the cryptocurrency sector [4] - The company's innovations in holographic technology services, including high-precision LiDAR solutions and holographic digital twin technology, position it well for future business expansion [6]

Core Insights - MicroCloud Hologram Inc. (HOLO) has made significant advancements in scaling laws for large models, particularly in open-source configurations of 7B and 67B [1] - The company has discovered a new balancing mechanism that optimizes the relationship between model parameters and data volume, allowing for efficient scaling without performance bottlenecks [2][3] Scaling Mechanism - HOLO's new mechanism dynamically adjusts the ratio of model parameters to data volume based on specific model needs and computational limitations, enhancing resource utilization during scaling [2] - This approach addresses traditional scaling issues, leading to improved performance and efficiency across different scales [3] Deepseek LLM Project - The Deepseek LLM project aims to develop a robust open-source language model ecosystem through technological innovation and community collaboration, focusing on model performance, interpretability, security, and sustainable development [4] - HOLO has created a comprehensive dataset for the pre-training phase of Deepseek LLM, enhancing the model's adaptability and generalization capabilities [5] Optimization Techniques - The company has implemented supervised fine-tuning (SFT) and direct preference optimization (DPO) to improve the Deepseek LLM Base model, resulting in enhanced performance on specific tasks and alignment with user expectations [6] Industry Impact - HOLO's breakthroughs in large language model scaling technologies are expected to drive digital transformation across various industries, including intelligent customer service, smart writing, and intelligent translation, significantly improving work efficiency and service quality [7]

Core Viewpoint - MicroCloud Hologram Inc. has announced the optimization of stacked sparse autoencoders through the DeepSeek open-source model, enhancing anomaly detection technology and providing an efficient solution [1]. Data Preprocessing - Data quality is essential for model performance, and HOLO employs normalization to eliminate dimensional influences and improve model training effectiveness [2][3]. - Normalization scales data to a specific range, allowing for better comparison and analysis across different features, which enhances model training efficiency and aligns data with deep learning input requirements [3]. Model Architecture - The stacked sparse autoencoder model consists of multiple layers that extract features at different levels, utilizing the DeepSeek model to adjust sparsity constraints dynamically [4]. - The training approach is greedy and layer-wise, allowing the model to progressively learn complex relationships within the data by training lower layers first [5]. Training Techniques - HOLO incorporates denoising training by adding noise to input data, which helps the model learn robust feature representations for accurate anomaly detection in noisy real-world scenarios [6]. - Dropout is applied during training to reduce overfitting by randomly dropping a subset of neurons, ensuring the model learns general and robust feature representations [7]. Computational Efficiency - The DeepSeek model utilizes a distributed computing framework for parallel execution of training tasks, significantly reducing training time and improving efficiency [8]. - A pretraining and fine-tuning strategy is employed to accelerate model convergence and enhance performance [8]. Company Overview - MicroCloud Hologram Inc. specializes in providing advanced holographic technology services, including high-precision LiDAR solutions and holographic digital twin technology [10].

Core Insights - MicroCloud Hologram Inc. has achieved a significant breakthrough in digital simulated quantum computing through the introduction of the DeepSeek model, enhancing simulation efficiency and providing new insights for quantum algorithm design and optimization [1][10][11] Group 1: Breakthrough and Technology - The DeepSeek model improves the simulation efficiency of quantum computing and addresses the challenges of hardware implementation, making digital simulated quantum computing a vital tool for research and development [1][2][11] - The DeepSeek model utilizes a new neural network architecture called the Quantum Tensor Network Neural Network (QTNNN), which optimizes the simulation and prediction of complex systems [6][12] - The training process of the DeepSeek model consists of pre-training and fine-tuning stages, enhancing the model's accuracy and efficiency for specific quantum systems [7][12] Group 2: Computational Efficiency - The optimized algorithm introduced by HOLO reduces computational resource consumption by over 50% when simulating large-scale quantum systems [8][12] - The accuracy of digital simulations for quantum computing has improved by more than 30%, particularly in complex quantum systems, due to the optimizations made through the DeepSeek model [9][12] Group 3: Implications for Research and Industry - The advancements in digital simulation technology will significantly impact scientific research fields such as quantum chemistry, materials science, and drug development, providing powerful tools for understanding complex quantum systems [13] - The accelerated development of quantum computing will create new opportunities in industries like finance, energy, and artificial intelligence, enhancing financial modeling, energy optimization algorithms, and machine learning models [13]

SHENZHEN, China, Feb. 10, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, has announced a major program aimed at enhancing its practice in the digital currency space. The company plans to call upon up to $200 million to purchase bitcoin or other digital currencies and their related securities derivatives and incorporate these assets into its capital reserve strategy. This initiative not only highlights HOLO's in-depth insight and long-t ...

SHENZHEN, China, Feb. 6, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they have announced a significant technological breakthrough—the integration of the DeepSeek large model API application. This initiative aims to provide users with a more natural and seamless interaction experience, as well as more proactive services. This move marks a solid step forward for MicroCloud Hologram in the field of holographic digital human technology ...