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MicroCloud Hologram Inc. Develops End-to-End Quantum Classifier Technology Based on Quantum Kernel Technology
SHENZHEN, China, May 20, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ('HOLO' or the "Company"), a technology service provider, announced the development of a new quantum supervised learning method, with rigorous proof of its quantum speedup capability in end-to-end classification problems. This method not only overcomes the limitations of many current quantum machine learning algorithms but also provides a robust approach, enabling it to maintain efficient and high-precision classification capabilities even under errors introduced by limited sampling statistics. The core of HOLO's end-to-end quantum-accelerated classifier method lies in constructing a classification problem and designing a quantum kernel learning approach that leverages quantum computing for acceleration. In this process, a carefully constructed dataset is proposed, and it is proven that, under the widely accepted assumption that the discrete logarithm problem is computationally difficult, no classical learner can classify this data with inverse polynomial accuracy better than random guessing. The choice of this assumption is critical, as the discrete logarithm problem is a cornerstone of modern cryptography and is considered extremely difficult to solve on classical computers. Thus, if HOLO's quantum method can effectively address this problem and provide classification capabilities significantly superior to classical algorithms, it would formally demonstrate the existence of quantum advantage. Furthermore, to ensure the quantum classifier's feasibility in real quantum computing environments, HOLO designed a series of parameterized unitary quantum circuits and proved their efficient implementation on fault-tolerant quantum computers. These quantum circuits map data samples into a high-dimensional quantum feature space and estimate kernel entries through the inner product of quantum states. Through this process, HOLO's quantum classifier fully exploits the exponential computational power of quantum computing, achieving classification accuracy far surpassing that of classical machine learning methods. The core idea of quantum kernel learning lies in using quantum computers to compute specific kernel functions that classical computers cannot efficiently calculate due to computational complexity. Traditional supervised learning methods, such as support vector machines (SVMs), rely on kernel methods to measure similarity between data points, whereas HOLO's approach achieves this by leveraging the inner product of quantum states. HOLO proposes a parameterized quantum circuit (PQC) that embeds classical data into quantum states and computes the inner product of these states on a quantum computer to estimate quantum kernel function values. This method not only harnesses the immense computational power of quantum computers but also exhibits greater robustness under limited sampling statistics, ensuring the algorithm's stability and scalability. Dataset Construction: HOLO designs a dataset that prevents classical computers from finding effective classification schemes in polynomial time, while quantum computers can efficiently perform classification using quantum kernel methods. The construction of this dataset is based on the hardness of the discrete logarithm problem, which results in exponential time complexity on classical computers. In contrast, quantum computers can leverage techniques like the quantum Fourier transform (QFT) to provide efficient solutions. Quantum Feature Mapping: HOLO employs a parameterized quantum circuit (PQC) for feature mapping of data samples. These circuits are designed to be flexible enough to accommodate various types of input data and can be effectively executed on quantum computers. Specifically, by utilizing the high-dimensional representation capabilities of quantum states, classical data is transformed into quantum states, ensuring that data from different classes are projected as separably as possible in the quantum feature space, thereby enhancing classification feasibility and accuracy. Quantum Kernel Computation and Classification: The key to quantum kernel methods lies in computing the similarity of data points in the quantum feature space, a process that is typically infeasible to perform efficiently on classical computers. However, HOLO's approach leverages quantum computers to directly compute the inner product between quantum states, thereby constructing a quantum kernel matrix that is ultimately used to train classical machine learning models such as support vector machines (SVMs). During the training process, the efficient kernel computation provided by quantum computers significantly reduces computational complexity and achieves quantum speedup. Robustness Enhancement and Error Handling: Due to the fact that existing quantum computers are still in the stage of strong noise interference, special attention has been paid to the error problem introduced by finite sampling address this, HOLO introduces an error correction method that effectively mitigates the impact of random noise in quantum computations, ensuring the stability of the results. Additionally, the method incorporates optimization strategies from variational quantum algorithms (VQAs), enabling the quantum classifier to maintain high classification accuracy even under constrained quantum resources. This research not only demonstrates the feasibility of end-to-end quantum speedup but also provides new directions for future quantum machine learning studies. Currently, many quantum machine learning algorithms rely on strong assumptions or heuristic methods, making it challenging to provide rigorous theoretical guarantees. In contrast, HOLO's research showcases a genuinely viable quantum advantage approach, successfully achieving end-to-end speedup in the context of supervised learning. From an application perspective, this technology can be widely applied in numerous fields requiring efficient classification. For instance, in financial market prediction, where vast amounts of complex market data need to be processed efficiently, HOLO's quantum supervised learning method can leverage the speedup capabilities of quantum computing to achieve faster and more accurate classification and prediction of financial data. Additionally, in the biomedical field, this method can be used for large-scale gene data classification to identify different disease patterns, thereby advancing the development of precision medicine. As quantum computing hardware continues to advance, HOLO's research outcomes are expected to undergo larger-scale validation and application on future fault-tolerant quantum computers. It is foreseeable that, with improvements in quantum computing capabilities, quantum supervised learning methods will play an increasingly significant role in the field of machine learning, providing more efficient solutions for various complex data problems. HOLO has proposed a robust quantum supervised learning method and successfully demonstrated its quantum speedup capabilities in end-to-end classification problems. By constructing specific datasets and utilizing parameterized quantum circuits for quantum feature mapping, it achieves an efficient and robust quantum classifier. Furthermore, HOLO's method effectively mitigates errors introduced by limited sampling statistics, delivering superior classification performance. This research provides critical theoretical foundations for the development of quantum machine learning and further promotes the application of quantum computing in artificial intelligence. Looking ahead, as quantum computing technology continues to break through, this method is expected to demonstrate true quantum advantage in a broader range of practical applications. About MicroCloud Hologram Inc. MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ('LiDAR') solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ('ADAS'). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit Safe Harbor Statement This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as 'may,' 'will,' 'intend,' 'should,' 'believe,' 'expect,' 'anticipate,' 'project,' 'estimate,' or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission ('SEC'), including the Company's most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company's filings with the SEC, which are available for review at The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof. ContactsMicroCloud Hologram IR@ in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
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MicroCloud Hologram Inc. Develops Neural Network-Based Quantum-Assisted Unsupervised Data Clustering Technology
SHENZHEN, China, May 16, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ('HOLO' or the "Company"), a technology service provider, announced the development of a neural network-based quantum-assisted unsupervised data clustering technology, utilizing a hybrid quantum-classical algorithm framework. This framework integrates the classical self-organizing feature map (SOM) neural network with the powerful capabilities of quantum computing, enabling efficient data clustering in an unsupervised manner. The Self-Organizing Feature Map (SOM) is an unsupervised learning neural network model widely used in fields such as data clustering, dimensionality reduction, and data visualization. Its core concept involves mapping high-dimensional data from the input space to a low-dimensional topological space through a competitive learning algorithm. This process ensures that similar input data points are mapped to adjacent neurons, thereby achieving data clustering. In classical computing, the SOM algorithm continuously adjusts weight vectors to reasonably group input data within the feature space. However, when dealing with massive datasets, the traditional SOM algorithm faces challenges related to computational complexity and storage demands. To address the limitations of classical computing in large-scale data clustering, HOLO has introduced quantum computing into the SOM framework, developing a Quantum-Assisted Self-Organizing Feature Map (Q-SOM) model. In this model, the powerful parallel computing capabilities of quantum computing are leveraged to accelerate the weight adjustment and data point mapping processes in SOM. Through quantum parallelism, it becomes possible to process a larger volume of data in a shorter time, thereby reducing the number of computations and overall time consumption. HOLO's technology leverages the quantum superposition and quantum entanglement properties of quantum computing, enabling the results of each clustering computation to be processed in parallel across multiple qubits. This quantum parallel computing approach not only significantly enhances computational efficiency but also demonstrates superior computational power compared to classical computing in certain scenarios. HOLO believes that quantum computing does not entirely replace classical computing but rather works in tandem with it. In this technology, the quantum component is primarily responsible for accelerating the data point mapping and weight adjustment processes within the SOM network, while the classical component handles post-processing of results and the final decision-making for data clustering. This hybrid architecture fully exploits the respective strengths of quantum and classical computing, theoretically enabling more efficient clustering. By incorporating quantum computing, each iteration of the SOM network can be completed more quickly, significantly reducing the number of computations required during the clustering process. Furthermore, the interference properties and noise tolerance of quantum computing provide additional robustness and reliability to the model. HOLO's neural network-based quantum-assisted unsupervised data clustering technology, leveraging the advantages of quantum computing, exhibits significant technical strengths: Computational Efficiency: Through quantum parallelism, it can significantly reduce the time cost of clustering computations. Particularly when dealing with large-scale data, quantum computing can handle more data points and quickly converge to optimal solutions. Data Processing Capability: The quantum-assisted algorithm can process higher-dimensional data. Especially when tackling complex high-dimensional datasets, quantum computing accelerates the data mapping process, reducing the complexity of high-dimensional computations. Accuracy and Stability: Compared to classical methods, quantum computing demonstrates higher accuracy and stability in addressing certain nonlinear and highly complex problems. Through quantum entanglement and superposition effects, it can avoid some of the local optima issues encountered in classical algorithms. Wide Applicability: This technology is not only suitable for data clustering but can also be extended to various fields such as image processing, natural language processing, and financial data analysis. As quantum computing technology advances, more industry applications will become feasible in the future. The integration of quantum computing and machine learning marks the advent of next-generation computing technology. By developing quantum-assisted neural network technology, HOLO not only achieves breakthroughs in the field of data clustering but also drives progress across multiple industries. Particularly in areas such as big data, artificial intelligence, and financial technology, the introduction of quantum computing will fundamentally transform data processing methods and provide new solutions for tackling complex problems. In the future, as quantum computing technology continues to mature, quantum-assisted machine learning algorithms will play an increasingly important role across multiple industries. Especially in fields with extremely high demands for computational speed and precision—such as quantum supremacy experiments, drug discovery, and climate change prediction—the integration of quantum computing and machine learning will unlock unprecedented potential. HOLO's breakthrough in neural network-based quantum-assisted unsupervised data clustering technology provides new perspectives for interdisciplinary research in quantum computing and artificial intelligence. With ongoing technological optimization and advancements in quantum computing hardware, quantum computing is poised to achieve practical applications in a broader range of fields, driving technological innovation and societal progress. Through continuous development and application of this technology, HOLO will inject new momentum into global data analysis, decision-making support, and the advancement of artificial intelligence. About MicroCloud Hologram Inc. MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ('LiDAR') solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ('ADAS'). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit Safe Harbor Statement This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as 'may,' 'will,' 'intend,' 'should,' 'believe,' 'expect,' 'anticipate,' 'project,' 'estimate,' or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission ('SEC'), including the Company's most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company's filings with the SEC, which are available for review at The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof. ContactsMicroCloud Hologram IR@
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MicroCloud Hologram Inc. Develops Nonlinear Quantum Optimization Technology Based on Efficient Model Encoding
SHENZHEN, China, May 12, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ('HOLO' or the "Company"), a technology service provider, announced the development of a groundbreaking nonlinear quantum optimization algorithm based on efficient model encoding technology. This algorithm significantly enhances computational efficiency while reducing the consumption of quantum resources. This innovation not only addresses the key bottlenecks of current quantum optimization methods but also demonstrates remarkable performance advantages in practical applications, paving the way for the industrial adoption of quantum computing. Traditional quantum optimization algorithms primarily rely on the Variational Quantum Algorithm (VQA) framework, where the depth of quantum circuits is often high, making the demand for computational resources difficult to meet. However, HOLO's efficient model encoding technology overcomes this limitation through two key innovations: multi-basis graph encoding and the application of nonlinear activation functions. The multi-basis graph encoding method is a novel quantum encoding strategy that effectively represents high-dimensional optimization problems with a limited number of qubits. In HOLO's approach, an optimized tensor network structure is employed to map high-dimensional optimization spaces using fewer qubits. This not only reduces the depth of quantum circuits but also improves computational efficiency. On the other hand, the introduction of nonlinear activation functions enables HOLO's optimization method to better address non-convex optimization problems. Traditional variational quantum algorithms are often constrained by the optimization landscape, easily getting trapped in local minima when dealing with complex non-convex problems. In contrast, HOLO's nonlinear activation functions can adaptively adjust the optimization path during training, allowing the algorithm to converge more efficiently to the global optimum. This innovation significantly enhances the algorithm's optimization capabilities, demonstrating greater adaptability in tackling large-scale optimization challenges. In quantum computing, the efficient utilization of computational resources is of paramount importance. HOLO's nonlinear quantum optimization algorithm technology not only achieves a breakthrough in computational performance but also significantly improves resource utilization efficiency. First, compared to existing methods, HOLO's algorithm reduces measurement complexity to a polynomial level. Measurement complexity is a critical metric in quantum computing, directly impacting the execution time and accuracy of computational tasks. Traditional quantum optimization methods typically require a large number of repeated measurements, whereas HOLO's algorithm optimizes measurement strategies, significantly reducing the number of measurements while maintaining computational accuracy. This leads to a notable improvement in overall computational efficiency. Second, HOLO's algorithm doubles computational speed while halving the demand for quantum resources. This breakthrough stems from HOLO's optimized quantum circuit architecture. Compared to traditional approaches, HOLO's shallow circuit design can complete computational tasks in less time while reducing the need for qubits and quantum gate operations. In other words, this algorithm technology not only runs faster but also imposes lower hardware requirements, making it more feasible for implementation on current quantum computers. In experiments, HOLO employed an efficient simulation strategy based on tensor methods. While traditional quantum computing simulations face exponential scaling issues as the number of qubits increases, our algorithm, with its optimized tensor network structure, enables computations to be completed on a single GPU even with 512 qubits. This experimental result not only validates the efficiency of our algorithm but also further demonstrates its potential for application in large-scale optimization problems. HOLO's nonlinear quantum optimization algorithm has achieved groundbreaking progress in theoretical research while also showcasing broad prospects across multiple real-world application scenarios. In the financial sector, optimization algorithms are widely used in tasks such as portfolio optimization and risk management. HOLO's algorithm can compute optimal investment portfolios in a shorter time and effectively address non-convex optimization challenges arising from market fluctuations. This opens up new possibilities for the application of quantum computing in the financial industry. In logistics and supply chain management, the ability to solve optimization problems directly impacts overall efficiency. HOLO's technology can be applied to tasks such as intelligent scheduling and route planning, helping businesses utilize resources more efficiently, thereby reducing costs and improving service quality. Furthermore, in the fields of artificial intelligence and machine learning, HOLO's algorithm can serve as an efficient optimization tool for training deep learning models. Leveraging the parallel computing capabilities of quantum computing, our algorithm can provide faster convergence speeds during the optimization process, laying the groundwork for future quantum artificial intelligence. HOLO remains committed to advancing the development of quantum computing technology and continuously exploring new optimization methods. In the future, plans are in place to further refine this technology to accommodate larger-scale computational tasks. As quantum computing technology continues to progress, there is every reason to believe that efficient quantum optimization algorithms will play an increasingly vital role. HOLO's research not only offers a new perspective on quantum optimization but also establishes a solid foundation for the industrial application of quantum computing. In the forthcoming era of quantum computing, we will continue to lead technological innovation, contributing even more to global scientific and technological advancement. About MicroCloud Hologram Inc. MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ('LiDAR') solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ('ADAS'). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit Safe Harbor Statement This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as 'may,' 'will,' 'intend,' 'should,' 'believe,' 'expect,' 'anticipate,' 'project,' 'estimate,' or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission ('SEC'), including the Company's most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company's filings with the SEC, which are available for review at The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof. ContactsMicroCloud Hologram IR@
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MicroCloud Hologram Announced an Edge Storage (Computing) System Based on Blockchain Technology
SHENZHEN, China, May 13, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO) ("HOLO" or the "Company"), a Hologram Digital Twins Technology provider, today announced an edge storage (computing) system based on blockchain technology to realize the value of data circulation. Edge storage is a distributed storage that stores data closer to the source of data generation, which reduces data transmission delays and network congestion. Blockchain technology, on the other hand, provides decentralization, immutability and security to ensure data integrity. The system will utilize the decentralized and tamper-proof features of blockchain to establish a trusted data-sharing platform. Edge devices can store their data on the blockchain and realize data access and exchange through smart contracts. At the same time, the consensus mechanism and encryption algorithm of the blockchain ensure data security and privacy protection. In the system, each edge device has a unique identification that is used to distinguish and verify the identity of the device. When a device wants to share data, it can publish the data to the blockchain and specify the access rights and exchange rules through a smart contract. Other devices can query and access the data through the blockchain, as well as pay the corresponding fees through smart contracts. In this way, data can be easily shared and exchanged between edge devices, realizing the value of data circulation. HOLO realized the value of data circulation by combining edge storage with blockchain technology. Edge storage can store data closer to the source of data generation, allowing data to be accessed and shared more quickly. At the same time, blockchain technology can ensure that data is secure and trustworthy, preventing it from being tampered with or forged. In this way, the process of circulating data can be more efficient and secure. Suppose an IoT device generates a large amount of sensor data that needs to be used by different applications and users. The traditional way is to store the data in the cloud and then transmit it over the network to where it is needed. However, due to the large amount of data and network latency, the transmission process can be slow, affecting the real-time availability and usability of the data. The edge storage (computing) system based on blockchain technology can solve this problem. Devices can store data in edge nodes closer to them, which can reduce the delay of data transmission. At the same time, the security and integrity of data can be ensured through the encryption and authentication mechanisms of blockchain technology. In this way, other applications and users can access the data faster and can trust the authenticity of the data. The edge storage (computing) system architecture based on blockchain technology includes edge nodes, blockchain networks, smart contracts and data storage. Edge nodes: Edge nodes are devices distributed at the edge of the network, such as sensors, IoT devices, and mobile devices. These nodes, by connecting to the blockchain network, can distribute data storage and computation tasks to the edge devices, enabling distributed storage and computation. Blockchain network: A blockchain network is a decentralized network consisting of a set of edge nodes. Each node has a full copy that stores all transaction records and smart contracts. Through consensus algorithms, nodes can agree and ensure data security and reliability. Smart contract: Smart contracts are programmable codes that execute on the blockchain. They define rules for storage and computation and can automatically enforce these rules. Smart contracts can be used to realize data circulation and value exchange, and ensure the security and reliability of data. Data storage: Data storage in blockchain edge storage (computing) can be divided into two parts. One part is the transaction records and smart contracts stored on the blockchain, and the other part is the data stored on the edge nodes. By storing data on edge nodes, the latency and bandwidth consumption of data transmission can be reduced and the privacy and security of data can be improved. These components collaborate to enable distributed storage and computation of data, as well as data circulation and value exchange. HOLO's blockchain-based edge storage and edge computing system uses smart contracts to manage data storage and computation. By using blockchain's decentralized and untamperable features, the security and trustworthiness of data can be ensured. About MicroCloud Hologram Hologram Inc. (NASDAQ:HOLO) engages in the research and development, and application of holographic technology. MicroCloud Hologram provides its holographic technology services to its customers worldwide. MicroCloud Hologram also provides holographic digital twin technology services and has a proprietary holographic digital twin technology resource library. MicroCloud holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. MicroCloud Hologram technology services include holographic light detection and ranging (LiDAR) solutions based on holographic technology, holographic LiDAR point cloud algorithms architecture design, technical holographic imaging solutions, holographic LiDAR sensor chip design, and holographic vehicle intelligent vision technology to service customers that provide holographic advanced driver assistance systems (ADAS). Safe Harbor Statements This press release contains 'forward-looking statements' within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as 'will,' 'expects,' 'anticipates,' 'future,' 'intends,' 'plans,' 'believes,' 'estimates' 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, as well as 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 U.S. Securities and Exchange Commission ('SEC') 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. A number of 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. Further information regarding these and other risks is included in the Company's annual report on Form 20-F and 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, and the Company does not undertake any obligation to update any forward-looking statement, except as required under applicable laws. Contacts MicroCloud Hologram
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MicroCloud Hologram Inc. Announces Progress in Quantum-Enhanced Imaging Based on Nonlocal Effects
SHENZHEN, China, May 22, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ('HOLO' or the "Company"), a technology service provider, they announced significant progress in the field of quantum-enhanced imaging based on nonlocal effects. This achievement has not only been validated in a laboratory setting but has also demonstrated advantages over traditional imaging in practical technical implementations. Compared to conventional phase imaging systems, HOLO leverages quantum-enhanced holographic LiDAR based on time-frequency entanglement, achieving a signal-to-noise ratio (SNR) of 40dB. The signal-to-noise ratio is widely applied in fields such as biology and communication technology, and in terms of imaging quality, it translates to clear, noise-free visuals. By utilizing the temporal correlation of photon pairs and integrating specialized scanning and optical collection components, HOLO enables imaging of non-reflective targets in noisy environments. In LiDAR and other imaging applications, quantum illumination (QI) is regarded as a solution to address environmental noise. In theory, QI offers significant improvements compared to detection using coherent states. However, regardless of the methods employed so far, the experimental results of QI have not met theoretical expectations. For both QI and traditional coherent detection, the states used must maintain a stable phase, but in practice, achieving phase-locking of interacting waves is extremely challenging. HOLO successfully distinguishes targets from background noise in holographic LiDAR by leveraging quantum temporal correlations. By rotating measurements between time and frequency domains, it amplifies the uncertainty in the probe-reference time while preserving the same level of correlation. This makes it possible to fully exploit the probe-reference correlation to differentiate between the target and background noise. Uncorrelated noise far exceeds the detector's uncertainty range and can subsequently be filtered out within an appropriate time window, eliminating noise that no longer overlaps with the signal. Through this approach, the signal-to-noise ratio can be improved by up to 40dB compared to phase-insensitive traditional target detection using the same probe power. This method not only retains the ease of implementation characteristic of target detection schemes but also increases the tolerable noise power before detector saturation occurs. HOLO first generates non-classical time-correlated photon pairs through femtosecond-pumped spontaneous parametric down-conversion (SPDC). Among these, probe photons are emitted into the environment, while reference photons are stored locally. During the process of traveling to the target and returning, probe photons experience losses, reducing the expected number of photons in the probe beam. Environmental noise couples into the probe path during propagation. If the noise shares the same spectral/temporal distribution as the probe photons, applying anomalous dispersion to the probe/noise photons broadens their temporal distribution, decreasing the probability of detecting these photons within a finite time window. Simultaneously, an equal amount of normal dispersion is applied to the reference photons, also broadening their temporal distribution. Coincidence measurements are then performed on both paths. Due to the quantum correlation between the probe and reference photons, the dispersion effects cancel each other out, and the coincidence measurement results are as if the photons were unaffected by dispersion. The nonlocal dispersion cancellation enabled by entangled photons eliminates the impact of dispersion. In contrast, noise and reference photons exhibit only classical correlation, and the dispersion effect causes the coincidence peak to broaden. By selecting an appropriate time window, the probability of false coincidences between noise photons and reference photons can be reduced, while the probability of true coincidences between probe and reference photons remains largely unchanged, thereby achieving higher precision. To enable the 3D holographic imaging functionality of holographic LiDAR, HOLO has designed a quantum holographic LiDAR device based on nonlocal effects, intended for use in conjunction with superconducting nanowire detectors coupled to single-mode fibers (SMF). Probe photons from the SPDC source are collimated onto a pair of galvanometer mirrors, which direct the probe photons toward the target object. A negative meniscus lens is employed to minimize angular deviation. In addition to the time delay between probe and reference photons, the constant speed of the probe photons allows for the resolution of the target's depth phase information, enabling 3D holographic imaging. HOLO's quantum holographic LiDAR technology, based on nonlocal effects, effectively enhances the signal-to-noise ratio (SNR) of holographic LiDAR. A higher SNR corresponds to lower background noise, which in turn improves the performance and recognition capabilities of holographic LiDAR, making its applications more efficient and widespread. About MicroCloud Hologram Inc. MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ('LiDAR') solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ('ADAS'). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit Safe Harbor Statement This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as 'may,' 'will,' 'intend,' 'should,' 'believe,' 'expect,' 'anticipate,' 'project,' 'estimate,' or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission ('SEC'), including the Company's most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company's filings with the SEC, which are available for review at The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof. ContactsMicroCloud Hologram IR@
