MicroAlgo Inc (MLGO)

MicroAlgo Inc. Develops a Reconfigurable Simulation Technology of High-Precision, High-Throughput Scalable Quantum AlgorithmsJune 4, 2026 11:55 AM
PR Newswire (US) SHENZHEN, China, June 4, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of an innovative high-precision, high-throughput reconfigurable simulation technology, aimed at providing effective solutions for the research and application of quantum algorithms.The core of quantum computing lies in quantum bits (qubits), which can represent multiple states simultaneously, achieving parallel computing capabilities through quantum phenomena such as superposition and entanglement. However, the practical implementation of quantum computers is still in its early stages, facing numerous technical challenges. Currently, mainstream quantum computers, such as those based on superconducting qubits and ion traps, remain imperfect in terms of the number of qubits and error correction capabilities, making large-scale quantum computing difficult. Therefore, simulating quantum algorithms on classical computing platforms has become an important research approach. Through classical simulation, researchers can gain a deep understanding of the characteristics and performance of quantum algorithms, providing strong support for the development and application of actual quantum computers.Traditional quantum algorithm simulation methods are typically based on the quantum circuit model, simulating the operation of each quantum gate step by step. While this approach is intuitively easy to understand, its computational complexity and resource demands grow exponentially when handling a large number of qubits, resulting in low simulation efficiency, significant hardware resource consumption, and excessively long simulation times. Therefore, developing efficient quantum algorithm simulation technology has become an urgent need.MicroAlgo has proposed a reconfigurable simulation technology for quantum algorithms with high precision and high throughput. This technology is primarily based on two innovative simulation models: the arithmetic operation simplification model and the nuclear operation iteration model. The arithmetic operation simplification model reduces the complexity of quantum state operations by transforming the functionality of quantum circuits into basic arithmetic operations (such as multiplication and accumulation). MicroAlgo represents common quantum gate operations as equivalent arithmetic operations and uses precomputation and lookup table methods to quickly obtain the results of these operations. For complex operations, a dynamic generation approach is adopted, producing intermediate results as needed. This method not only reduces computational complexity but also enhances the computational speed and throughput of the simulation through parallel processing.The nuclear operation iteration model, on the other hand, extracts the key operations of a quantum circuit and focuses on processing changes in quantum states, thereby avoiding the complex process of step-by-step simulation of the entire circuit. MicroAlgo first analyzes the quantum circuit to identify the key operations that have the greatest impact on quantum state evolution, then performs nuclear operation iterations on all input quantum states. This approach not only simplifies the computational process but also significantly improves simulation efficiency. By optimizing the design of the extracted nuclear operations and employing parallel processing methods, the simulation speed and throughput are further enhanced.To fully leverage the advantages of these two simulation models, MicroAlgo has adopted a reconfigurable hardware architecture for the implementation of the simulator. The reconfigurable technology enables flexible allocation and utilization of hardware resources through dynamic hardware configuration adjustments, allowing the simulator to dynamically adjust the allocation of computing units and storage resources based on the requirements of different quantum algorithms, thereby improving the efficiency of hardware resource utilization. Additionally, to ensure the numerical precision of simulation results, MicroAlgo's (NASDAQ: MLGO) simulator supports single-precision floating-point operations. Floating-point operations offer higher numerical precision and computational flexibility, making them suitable for handling complex quantum states and operations. Through a fully pipelined design, the simulator's various computing units can continuously process data without interruption, further enhancing simulation efficiency and throughput.To validate the performance of the simulation models and hardware architecture, MicroAlgo conducted simulation experiments on several classic quantum algorithms, including the Quantum Fourier Transform (QFT) and quantum wavelet transform. The experimental results demonstrate that MicroAlgo's proposed simulation models significantly outperform traditional methods in terms of resource utilization and simulation time. For example, in the simulation experiments of the Quantum Fourier Transform, the arithmetic operation simplification model and the nuclear operation iteration model achieved a more efficient simulation process by reducing computational complexity and focusing on processing key operations, respectively. In the simulation of the quantum wavelet transform, MicroAlgo's simulator, through its fully pipelined design and parallel processing, significantly reduced resource consumption and simulation time, proving its superiority in handling complex quantum algorithms.As quantum computing research continues to advance, quantum algorithms are showing broad application prospects in fields such as scientific computing, cryptography, and materials science. This simulation technology provides strong support for the research and application of quantum algorithms, not only accelerating the development and testing of quantum algorithms but also laying the foundation for the practical application of quantum computers. In the field of scientific computing, quantum algorithms can significantly improve the efficiency of solving complex problems. MicroAlgo's simulation technology enables researchers to efficiently simulate quantum algorithms on classical platforms, accelerating the development and validation of new algorithms and providing more possibilities for scientific computing.MicroAlgo's high-precision, high-throughput reconfigurable simulation technology for quantum algorithms has significant applications in the field of quantum computing cryptography, particularly in breaking traditional encryption algorithms. Through efficient simulation technology, researchers can test and optimize quantum cryptographic algorithms on classical platforms, enhancing the security and practicality of cryptography. Additionally, quantum algorithms hold broad application prospects in materials science, enabling the simulation and optimization of the quantum properties of materials. MicroAlgo's simulation technology can assist researchers in efficiently simulating the quantum behavior of materials on classical platforms, promoting the discovery and application of new materials.MicroAlgo's high-precision, high-throughput reconfigurable simulation technology for quantum algorithms provides innovative solutions for the research and application of quantum computing. By integrating the arithmetic operation simplification and nuclear operation iteration simulation models, combined with reconfigurable technology, single-precision floating-point operations, and a fully pipelined design, the simulator achieves significant optimizations in resource utilization and simulation time. Experimental results demonstrate the feasibility and superiority of the simulator in running and testing various quantum algorithms. In the future, as quantum computing technology continues to advance, MicroAlgo's simulation technology will continue to play a crucial role, providing robust support for the research and practical application of quantum algorithms, and driving the arrival of the quantum computing era.About MicroAlgo Inc.MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.Forward-Looking StatementsThis press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law. View original content:https://www.prnewswire.com/news-releases/microalgo-inc-develops-a-reconfigurable-simulation-technology-of-high-precision-high-throughput-scalable-quantum-algorithms-302791813.htmlSOURCE MicroAlgo Inc. Original: MicroAlgo Inc. Develops a Reconfigurable Simulation Technology of High-Precision, High-Throughput Scalable Quantum Algorithms

MicroAlgo Inc. Develops Quantum Encryption Technology Based on Lattice Cryptography, Integrating into LSQb Algorithm's Process of Information Hiding and Transmission to Achieve Anti-Quantum Attack Strategies EnhancementMay 28, 2026 11:40 AM
PR Newswire (US) SHENZHEN, China, May 28, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of quantum encryption technology based on lattice cryptography, integrating into the LSQb algorithm process of information hiding and transmission, achieving enhanced anti-quantum attack strategies. Lattice cryptography possesses high security in the face of quantum attacks, and through this integration, it can provide stronger attack resistance for the LSQb algorithm, ensuring the security of information in complex quantum computing environments.Lattice cryptography is a cryptographic method based on mathematical lattice structures, possessing the unique advantage of maintaining high security in quantum computing environments. By combining lattice cryptography with the LSQb algorithm, MicroAlgo aims to build a more secure and reliable quantum information hiding and transmission system.Quantum Image Preprocessing: Before embedding information into a quantum image, the quantum image undergoes preprocessing. This step includes denoising, enhancement, and format conversion of the image to ensure the accuracy and reliability of subsequent information embedding. Through advanced quantum image processing techniques, key features of the quantum image are extracted, providing strong support for subsequent information hiding and transmission.Secret Information Encoding and Embedding: After preprocessing is completed, the secret information is encoded into a sequence of quantum bits and embedded into the least significant quantum bits of the quantum image using the LSQb algorithm. In this process, the superposition and entanglement properties of quantum bits are fully utilized to achieve efficient information hiding. Meanwhile, to ensure the security of the information, complex encoding and embedding strategies are adopted to prevent information leakage or tampering during transmission.Quantum Encryption Based on Lattice Cryptography: After embedding the information into the quantum image, quantum encryption technology based on lattice cryptography is used to encrypt the quantum image. This step aims to further enhance the security of the information, preventing unauthorized access and leakage. The adopted lattice cryptography algorithm possesses a high degree of complexity and unpredictability, capable of resisting various quantum attack methods. Through this encryption process, the security and reliability of the quantum image during transmission are ensured.Quantum Information Transmission and Decryption: After encryption is completed, the quantum image is transmitted to the target node via a quantum network. During the transmission process, various quantum error correction and redundant encoding techniques are employed to ensure the integrity and reliability of the information. After the target node receives the quantum image, the corresponding decryption algorithm is used to decrypt the quantum image, thereby recovering the original secret information.From the perspective of security, lattice cryptography provides it with strong resistance to quantum attacks. In the face of potential threats from quantum computers, traditional encryption algorithms may be vulnerable, but lattice cryptography, with its complex mathematical structure, can effectively resist attacks and ensure information security. Compared to the standalone LSQb algorithm, the integrated technology achieves a qualitative leap in security.In terms of stability, the error-correcting capability of lattice cryptography makes information transmission more stable. Even when subjected to disturbances such as quantum channel noise, it can ensure the accurate transmission of information. This advantage makes the technology more reliable in practical applications, ensuring secure information transmission in both laboratory environments and complex real-world scenarios.In the field of quantum network security, MicroAlgo's technology can be used to build more secure quantum information hiding and transmission systems. By embedding secret information into quantum images and encrypting them using quantum encryption technology based on lattice cryptography, secure transmission of information in quantum networks can be ensured. This application is of great significance for protecting sensitive information and preventing information leakage.In the future, as quantum computing technology continues to develop and improve, MicroAlgo's technology will integrate with other quantum information technologies to form a more comprehensive quantum information processing system. Such a system will not only have higher security and reliability but also possess stronger computational capabilities and processing speeds.About MicroAlgo Inc.
MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.Forward-Looking Statements
This press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law. View original content:https://www.prnewswire.com/news-releases/microalgo-inc-develops-quantum-encryption-technology-based-on-lattice-cryptography-integrating-into-lsqb-algorithms-process-of-information-hiding-and-transmission-to-achieve-anti-quantum-attack-strategies-enhancement-302784683.htmlSOURCE MicroAlgo Inc. Original: MicroAlgo Inc. Develops Quantum Encryption Technology Based on Lattice Cryptography, Integrating into LSQb Algorithm's Process of Information Hiding and Transmission to Achieve Anti-Quantum Attack Strategies Enhancement

MicroAlgo Inc. Develops Multi-Objective Evolutionary Algorithm to Advance Quantum Circuit InnovationMay 14, 2026 11:40 AM
PR Newswire (US) SHENZHEN, China, May 14, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the proposal of a powerful solution—a multi-objective evolutionary search strategy, which is an innovative automated tool that can assist in designing quantum circuits, thereby bringing breakthroughs to quantum algorithm development.The Multi-Objective Evolutionary Algorithm (MOEA) is a class of optimization algorithms based on evolution, specifically designed to address problems involving multiple conflicting objectives. Its working principle mimics the process of natural selection by randomly generating a set of candidate solutions in the solution space and, through iterative processes across multiple generations, continuously improving the quality of solutions through operations such as crossover, mutation, and selection. Ultimately, this evolutionary process can generate a solution set with higher fitness, i.e., optimal solutions that satisfy multiple objectives.The innovation of the Multi-Objective Evolutionary Algorithm technology developed by MicroAlgo lies in its ability to automatically design quantum circuits from "zero." In other words, this technology does not require a pre-defined specific circuit design but instead gradually constructs quantum circuits capable of achieving the target functionality by combining search and optimization methods with a universal library of quantum circuit components.One of the key features of MicroAlgo's algorithm is its task-universal library. This library contains a large number of different quantum circuit components, whose combinations and parameterization can construct circuits that implement complex functions. This design approach means that developers do not need to manually design circuits; instead, the algorithm automatically searches for the optimal circuit configuration based on the input/output requirements of the task.More importantly, this algorithm is not only capable of designing circuits but also, through its multi-objective characteristics, can balance trade-offs among various performance metrics. For example, during the design process, the algorithm considers not only the accuracy of the quantum circuit but also other critical metrics such as the circuit's width, depth, and the number of gates used. This is particularly important for the current stage of quantum computing hardware development, as first-generation quantum processors are extremely limited in resources (such as the number of gates and qubits), and the algorithm must achieve optimal performance within these limited resources.To validate the effectiveness of the multi-objective evolutionary algorithm, MicroAlgo applied it to the automated design of classic quantum algorithms. Specifically, the Quantum Fourier Transform and Grover's Search Algorithm were selected as test cases. The Quantum Fourier Transform is a widely used transformation in quantum computing, playing a significant role in many algorithms, such as Shor's factorization algorithm. Meanwhile, Grover's Search Algorithm is considered another foundational algorithm in quantum computing, capable of finding target data in an unsorted dataset at a faster speed than classical search algorithms.In these two tests, the multi-objective evolutionary algorithm was able to find circuit structures that meet the input/output mapping requirements of these algorithms by combining components from the quantum circuit component library. After multiple iterations, the algorithm not only discovered textbook-style classic quantum circuit designs but also found alternative structures that achieve the same functionality. This demonstrates that the algorithm has the capability to efficiently design quantum circuits and can provide multiple alternative circuit solutions, offering great flexibility for the optimization of quantum computing algorithms.The technical implementation behind the multi-objective evolutionary algorithm involves several key steps and processes. First, in the initial stage, the algorithm generates a set of random quantum circuits. These circuits are composed of quantum components from the library and include adjustable parameters. Subsequently, the algorithm simulates each quantum circuit and evaluates its performance. The evaluation metrics include the circuit's accuracy, the number of gates used, the circuit's width, and its depth.Next, the algorithm filters and optimizes the circuits based on these metrics. Through crossover operations (similar to genetic recombination in biological evolution), the algorithm "crosses" two high-performing circuits to generate new candidate circuits; through mutation operations, the algorithm randomly modifies certain parts of the circuits to introduce new design possibilities. This process is repeated continuously, with each generation eliminating poorly performing circuits while retaining and optimizing high-performing circuits until the optimal solution is found.The core advantage of the multi-objective evolutionary algorithm lies in its ability to optimize multiple metrics simultaneously. For example, in quantum computing, circuit depth and accuracy are often conflicting objectives: deeper circuits may offer higher accuracy but increase the complexity of execution and hardware requirements. Through this algorithm, developers can find the optimal balance point between these objectives, ensuring that the circuit meets the demands of efficient computation while being implementable under existing hardware conditions.The multi-objective evolutionary algorithm developed by MicroAlgo is not only a significant technical breakthrough but also has the potential to change the development direction of the quantum computing industry in multiple ways.First, the introduction of automated tools greatly reduces the difficulty of quantum algorithm development. Currently, the barrier to quantum computing development is high, typically requiring experts with deep backgrounds in quantum physics, quantum information science, and computer science to design effective quantum algorithms. However, with this multi-objective evolutionary algorithm, developers only need to define the objectives of the computational task, and the algorithm can automatically generate circuit designs that meet the requirements, thereby lowering the technical barriers to quantum algorithm development.Second, this algorithm significantly enhances the efficiency and quality of quantum algorithms. Traditional quantum algorithm design relies on the experience and intuition of experts, whereas this evolutionary algorithm can explore a broader design space, even discovering optimization solutions that humans might not easily find. Especially on resource-constrained quantum hardware, this algorithm can find optimal solutions for different tasks, effectively improving the computational performance of the hardware.Finally, the multi-objective evolutionary algorithm paves the way for future applications of quantum computing. As quantum computing gradually moves from the laboratory to practical applications, automated tools will become increasingly important. The technology developed by MicroAlgo is not only suitable for existing quantum computing tasks but also capable of addressing the more complex application demands of the future. Whether in fields such as chemical simulation, financial risk analysis, or cryptography, the design of quantum algorithms can be significantly enhanced through this evolutionary algorithm.The multi-objective evolutionary algorithm represents a major breakthrough in quantum algorithm development. By combining a task-universal library, automated design, and multi-objective optimization, this algorithm not only simplifies the quantum circuit design process but also improves the efficiency and flexibility of circuits. The introduction of this technology marks a new stage in quantum computing, providing a solid foundation for the widespread application of quantum computers across multiple industries. In the future, as quantum hardware continues to advance, there is reason to believe that this multi-objective evolutionary algorithm will have an even more profound impact in the field of quantum computing and drive the emergence of more breakthrough achievements.About MicroAlgo Inc.MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.Forward-Looking StatementsThis press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.  View original content:https://www.prnewswire.com/news-releases/microalgo-inc-develops-multi-objective-evolutionary-algorithm-to-advance-quantum-circuit-innovation-302772534.htmlSOURCE MicroAlgo Inc. Original: MicroAlgo Inc. Develops Multi-Objective Evolutionary Algorithm to Advance Quantum Circuit Innovation

MicroAlgo Inc. Develops Quantum Architecture Search (QAS) Technology to Enhance VQA Robustness and Trainability, Optimizing the Potential of Quantum Computing DevicesMay 8, 2026 8:40 AM
PR Newswire (US) SHENZHEN, China, May 8, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of an innovative technology—Quantum Architecture Search (QAS), aimed at automatically optimizing the architecture of quantum circuits to enhance the robustness and trainability of VQA, maximizing the potential of quantum computing devices.In the traditional VQA framework, the design of quantum circuit architectures is typically performed manually or based on certain predefined standard architectures. However, the noise and errors in quantum computers are extremely severe in medium-scale devices, making circuit design a critical factor affecting VQA performance. More complex circuit architectures may enhance expressive power but simultaneously introduce more noise and errors, leading to difficulties in the training process or even complete failure.To balance the expressive power of circuit architectures and the impact of noise, MicroAlgo has proposed a Quantum Architecture Search (QAS) method. QAS optimizes VQA performance by automatically searching for quantum circuit architectures, mitigating the impact of noise on training, and finding a near-optimal circuit structure. This method not only helps improve the robustness of quantum algorithms in noisy environments but also significantly enhances their performance in practical tasks.The core idea of MicroAlgo QAS is to systematically search the architecture space of quantum circuits to find the circuit structure most suitable for a specific task. Unlike traditional design, QAS adopts an intelligent optimization approach, automatically exploring the space of circuit architectures to maximize the trainability and robustness of VQA.The design of quantum circuit architectures is not merely a matter of arranging quantum gates; it involves multiple levels of optimization, such as the selection of quantum gates, the connectivity of qubits, and the interaction patterns between qubits. QAS first defines a circuit architecture space that encompasses all possible quantum circuit configurations, including the types, order, and connection patterns of quantum gates.To effectively search the circuit architecture space, QAS introduces advanced optimization methods such as reinforcement learning and genetic algorithms. First, QAS uses a reinforcement learning model to evaluate the performance of VQA under different architectures by simulating the training process. Through this approach, QAS can select the optimal solution from millions of possible circuit architectures.Additionally, noise in quantum computing is one of the key factors limiting VQA performance. During the architecture search process, QAS specifically incorporates a noise modeling mechanism, which predicts the performance of different circuit architectures under noisy conditions by simulating the training process in a noisy environment. Through this modeling, QAS can automatically identify which architectures are most robust under specific noise conditions, thereby ensuring that VQA performance is not excessively affected by noise.In each round of optimization in quantum architecture search, MicroAlgo QAS not only considers changes in architecture design but also incorporates classical optimization algorithms such as gradient descent to ensure that the selected architecture can be efficiently trained for a given learning task. Through multiple iterations, QAS gradually converges to a quantum circuit architecture that both enhances expressive power and effectively mitigates the impact of noise. Furthermore, plateau phenomenon is another major challenge in VQA training. During the training process, optimization may encounter "barren plateau" regions, leading to local optima that make further improvements difficult. MicroAlgo QAS, through designing appropriate architectures and optimization strategies, can effectively avoid getting trapped in such barren plateaus, thereby improving the trainability and global optimization capability of VQA.MicroAlgo QAS, by optimizing quantum circuit architectures, can significantly enhance the robustness of VQA in various noisy environments. By automatically searching for suitable circuit designs, QAS avoids the manual selection of unsuitable architectures, thereby enabling VQA to operate more effectively on actual quantum computers.The optimization of quantum circuits is not merely about reducing the number of quantum gates; it is more about finding an architecture that can converge quickly and avoid getting trapped in local optima. Through intelligent search mechanisms and noise modeling, QAS enables VQA to complete training in a shorter time and ultimately find the global optimal solution.Another advantage of MicroAlgo QAS is its broad adaptability. Whether used for quantum machine learning, quantum optimization problems, or quantum simulation tasks, QAS can adjust circuit architectures based on the requirements of different tasks, providing customized solutions. This makes QAS a highly flexible and practical tool in the field of quantum computing. MicroAlgo QAS is not only capable of running on current quantum devices but also possesses strong scalability. By optimizing circuit architectures, QAS can achieve more efficient operation on resource-constrained quantum computers, thereby making quantum computing more practical.In multiple experimental validations, QAS has significantly outperformed traditional VQA approaches with manually designed circuit architectures. In standard quantum machine learning tasks, our QAS method has achieved remarkable results in reducing noise impact, improving training convergence speed, and mitigating the plateau effect. Compared to traditional methods, QAS has improved training speed by over 40% and enhanced robustness in noisy environments by 30%. Furthermore, in quantum optimization problems, QAS has similarly demonstrated powerful performance.The launch of MicroAlgo's QAS technology marks a significant advancement in the application of Variational Quantum Algorithms (VQA). Through automated quantum circuit architecture search, QAS not only addresses issues such as noise, training efficiency, and the plateau effect but also significantly enhances the performance of VQA on real quantum computers. As quantum computing hardware continues to advance, QAS will become one of the core technologies in quantum algorithm development.In the future, QAS can be applied not only to multiple fields such as quantum machine learning, quantum optimization, and quantum chemistry but also integrated with other advanced quantum computing technologies, such as quantum error correction and quantum communication, further promoting the popularization and application of quantum computing. We look forward to MicroAlgo's QAS laying a solid foundation for the commercial application of quantum computing, bringing more efficient and precise quantum solutions to various industries.About MicroAlgo Inc.
MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.Forward-Looking Statements
This press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.  View original content:https://www.prnewswire.com/news-releases/microalgo-inc-develops-quantum-architecture-search-qas-technology-to-enhance-vqa-robustness-and-trainability-optimizing-the-potential-of-quantum-computing-devices-302766899.htmlSOURCE MicroAlgo Inc. Original: MicroAlgo Inc. Develops Quantum Architecture Search (QAS) Technology to Enhance VQA Robustness and Trainability, Optimizing the Potential of Quantum Computing Devices

MicroAlgo Inc. Develops Optimal Precise Quantum Query Algorithm Based on Sum-of-Squares Representation Form of Boolean FunctionsApril 30, 2026 11:50 AM
PR Newswire (US)

SHENZHEN, China, April 30, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the proposal of a new approach to solving the Boolean function query problem. This framework starts from the sum-of-squares representation form of Boolean functions and constitutes an entirely new technical framework, aimed at designing optimal exact quantum query algorithms. This technology not only holds theoretical significance but also offers new ideas for practical applications.In quantum computing, the query complexity of Boolean functions directly affects the performance of quantum algorithms. Traditional classical algorithms face limitations in time and space when processing Boolean functions, whereas quantum computing, by leveraging the characteristics of superposition and entanglement, has the potential to significantly improve query efficiency. However, the challenge of designing optimal exact quantum query algorithms for arbitrarily small-input Boolean functions still remains, and there is a lack of general methods.Boolean functions can be represented as a sum of squares of multilinear polynomials, and this property provides an important mathematical foundation for designing quantum algorithms. By performing sum-of-squares representations of Boolean functions and their negations, it is possible to reveal their internal structure, thereby enabling the construction of corresponding quantum query algorithms.MicroAlgo's technical framework consists of three fundamental steps:Step One: Finding the Sum-of-Squares Representations of the Boolean Function and Its Negative FunctionFirst, it is necessary to analyze the target Boolean function and find its sum-of-squares representation. The key to this step lies in identifying the structure of the Boolean function and using the properties of multilinear polynomials to express it in the form of a sum of squares. Through this representation, the characteristics of the Boolean function can be obtained, which facilitates the subsequent construction of the algorithm.In practical operation, algebraic tools and computer algebra systems can be effectively used to achieve this goal. Various algorithms (such as the Lagrange interpolation method) can be used to derive the sum-of-squares representations of the Boolean function and its negation.Step Two: Constructing the Final State of the Optimal Exact Quantum Query AlgorithmAfter obtaining the sum-of-squares representation of the Boolean function, the next step is to construct a quantum state. The goal of this process is to determine a state that is assumed to be the final state of the optimal exact quantum query algorithm. The superposition property of quantum states must be used to explore multiple paths simultaneously during the query process, thereby improving efficiency.The construction of the quantum state involves the initialization of qubits, phase modulation, and gate operations. This process can be implemented using basic quantum gates such as rotation gates and CNOT gates, so that the required quantum state can be realized in the quantum circuit.Step Three: Finding Each Unitary Operator in the Uncertainty AlgorithmFinally, each unitary operator must be found within the uncertainty algorithm. This step is crucial because the selection of unitary operators directly affects the effectiveness of the quantum query. By reasonably selecting and designing unitary operators, efficient quantum querying can be achieved.In this process, it may be necessary to utilize methods such as mathematical optimization and machine learning to find the optimal combination of unitary operators. In addition, for specific Boolean functions, customized algorithms may be required to ensure query efficiency and accuracy.The implementation logic of MicroAlgo's entire technical framework can be summarized as: the use of multilinear polynomials, the construction of quantum states, and the selection of unitary operators. Through the sum-of-squares representation, the properties of Boolean functions can be effectively analyzed, providing a theoretical foundation for the subsequent design of quantum algorithms.The constructed quantum states not only need to meet the basic requirements of querying but must also fully leverage the characteristics of quantum superposition and entanglement to enhance the parallelism of queries. Finally, by carefully selecting and designing unitary operators, efficient querying of Boolean functions can be achieved, thereby maximizing the performance of quantum algorithms.MicroAlgo's development of this technology is based on the sum-of-squares representation of Boolean functions and has successfully designed a technical framework for optimal exact quantum query algorithms, bringing a brand-new perspective and implementation path to the field of quantum computing.Through in-depth analysis of the structure of Boolean functions and with the aid of quantum state construction and precise design of unitary operators, this framework demonstrates outstanding query efficiency and theoretical superiority. The sum-of-squares representation of Boolean functions not only provides a solid mathematical foundation for the design of quantum decision tree algorithms but also effectively reveals the intrinsic relationships between functions, helping us better understand the complexity issues in quantum algorithms. This method, which combines algebraic techniques with quantum physics, offers new research directions for quantum computing and lays the groundwork for the further optimization of exact quantum query algorithms.Although the current technical framework faces challenges in dealing with certain practical problems—for example, the algorithm may be infeasible in specific situations—the algorithmic framework based on sum-of-squares representations has already demonstrated its powerful potential in solving problems with low complexity. This optimization of the quantum query model can significantly reduce the consumption of computational resources while increasing the query speed of the algorithm, thereby further improving the overall performance of quantum computing. This has great application prospects and practicality across multiple fields within quantum information science, including quantum communication, quantum security, and quantum machine learning.As a disruptive technology, quantum computing's potential impact will far exceed the scope of traditional computing. The optimal exact quantum query algorithm technical framework developed by MicroAlgo, though currently focused mainly on the exact querying of Boolean functions, possesses a highly scalable philosophy and methodology. By further exploring more complex Boolean functions and their quantum representations, it is expected that MicroAlgo's technology will be applied to a broader range of fields, including large-scale quantum data processing, complex system optimization, and future AI enhancement. As quantum computing technology continues to evolve and improve, more and more difficult problems will find new solutions through this algorithmic framework.Whether in academia or industry, the potential value of this technical framework is immeasurable. It will drive quantum computing to take a solid step from theoretical research toward practical application and inject continuous new momentum into global scientific and technological innovation.About MicroAlgo Inc.MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.Forward-Looking StatementsThis press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.



View original content:https://www.prnewswire.com/news-releases/microalgo-inc-develops-optimal-precise-quantum-query-algorithm-based-on-sum-of-squares-representation-form-of-boolean-functions-302759043.htmlSOURCE MicroAlgo Inc.

Original: MicroAlgo Inc. Develops Optimal Precise Quantum Query Algorithm Based on Sum-of-Squares Representation Form of Boolean Functions

MicroAlgo Inc. Develops Quantum Algorithm Technology for Feedforward Neural Networks to Drive Neural Network RevolutionApril 24, 2026 8:30 AM
PR Newswire (US)

SHENZHEN, China, April 24, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that they have developed a set of quantum algorithms for feedforward neural networks, breaking through the performance bottlenecks of traditional neural networks in training and evaluation. This innovative quantum algorithm is based on the classic feedforward and backpropagation algorithms, leveraging the powerful computational capabilities of quantum computing to greatly enhance the efficiency of network training and evaluation, and it brings a natural resistance to overfitting.The feedforward neural network is the core architecture of deep learning, widely applied in fields such as image classification, natural language processing, and speech recognition. However, traditional neural network algorithms face challenges such as high computational overhead, high risk of overfitting, and long training times when dealing with large-scale data and complex models. Quantum computing, with its potential for exponential acceleration, provides a brand-new pathway to address these issues.Specifically, quantum computing can significantly reduce computational complexity in training neural networks by efficiently handling large-scale matrix and inner product operations. Meanwhile, the unique data storage and retrieval methods of quantum computing can efficiently manage intermediate values during the training process, greatly improving training efficiency and resource utilization. These characteristics make quantum algorithms an ideal choice for enhancing neural network performance.The quantum algorithm technology developed by MicroAlgo this time is based on the classic feedforward and backpropagation mechanisms, optimizing key computational steps by introducing efficient quantum subroutines.First, the efficient approximation of vector inner products. The key to neural network training lies in weight updates, and weight updates are inseparable from the computation of inner products between vectors. In traditional methods, the complexity of computing inner products grows quadratically with the number of neurons and connections, resulting in low computational efficiency. MicroAlgo's quantum algorithm technology introduces quantum subroutines based on the principles of quantum state superposition and interference, which can robustly approximate vector inner products while significantly reducing computational complexity. Specifically, input vectors are encoded into quantum states, utilizing quantum superposition to process computations across multiple dimensions simultaneously. Subsequently, approximate results are extracted through quantum measurements, with a complexity that is only linearly related to the number of neurons, breaking through the limitations of classical methods.Second, the introduction of quantum random access memory. In neural network training, a large number of intermediate values (such as activation values and error values) need to be stored and quickly retrieved in subsequent stages. Traditional storage methods not only consume significant storage resources but may also lead to inefficient data retrieval. To address this, MicroAlgo's algorithm utilizes quantum random access memory (QRAM) technology to implicitly store intermediate values in quantum states. QRAM allows data to be stored and accessed with logarithmic complexity, making the training process more efficient. Additionally, due to the superposition property of quantum states, QRAM can retrieve multiple values simultaneously in a single access, further accelerating the training process.Furthermore, the natural simulation of regularization effects. Overfitting is a common problem faced by neural networks, typically mitigated by adding regularization terms or using techniques such as random dropout. MicroAlgo's quantum algorithm, due to its unique quantum state characteristics, can naturally mimic the effects of regularization techniques during the training process. For example, there is a certain degree of randomness in quantum measurements, which helps prevent the network from overly relying on specific weights. Additionally, the probabilistic distribution characteristics of quantum computing make weight updates more diverse, thereby enhancing the model's generalization ability.The training time of traditional neural networks typically grows exponentially with the increase in network size, whereas this quantum algorithm reduces the training time complexity to a linear level. This improvement is mainly attributed to: the efficient approximate computation of vector inner products significantly reducing computational overhead; the fast storage and retrieval of QRAM avoiding redundant computations; and the parallel computing capability of quantum superposition states accelerating the processing of batch data.Although quantum algorithms themselves have absolute advantages in certain applications, the principles and logic they propose can also provide new ideas for classical algorithms. For example, by introducing concepts such as approximate inner products and random storage, classical heuristic algorithms with effects similar to quantum algorithms can be designed. Although these algorithms have higher complexity, they still hold practical value in certain specific scenarios.The development of this quantum algorithm by MicroAlgo has opened new prospects for the enterprise application of quantum machine learning. First, in large-scale data processing, such as in the fields of finance and healthcare, the demand for large-scale data processing is growing rapidly. This quantum algorithm, through its efficient inner product computation and data management capabilities, can quickly analyze and process large-scale data, providing support for areas such as financial risk assessment and genomic research.In real-time decision-making systems, such as intelligent transportation and autonomous driving, real-time decision-making systems need to rapidly process large amounts of sensor data and respond accordingly. The efficiency and robustness of this algorithm make it an ideal choice for supporting such systems.Additionally, in the fields of edge computing and the Internet of Things, with the proliferation of IoT devices, edge computing is gradually becoming mainstream. The lightweight design and efficient computational characteristics of this quantum algorithm make it suitable for resource-constrained edge devices, contributing to the construction of an intelligent IoT ecosystem. In the future, this quantum algorithm can also serve as a bridge for the integration of quantum and classical computing, further promoting the popularization of machine learning technologies by optimizing the performance of classical algorithms.Of course, although MicroAlgo's quantum algorithm demonstrates immense potential, its industrial implementation still faces some challenges. For example: the development of quantum computing hardware is still in its early stages, and achieving large-scale quantum computing requires overcoming technical bottlenecks; the compatibility and portability issues of quantum algorithms necessitate the development of solutions adaptable to various quantum hardware platforms; optimization and debugging for specific application scenarios still require extensive research and experimentation.This time, the quantum algorithm developed by MicroAlgo not only marks a significant leap in the performance of feedforward neural networks but also opens a new chapter in the integration of quantum computing and artificial intelligence. Through breakthroughs in computational efficiency, resource utilization, and model generalization capabilities, this algorithm provides new ideas for addressing key challenges in the field of deep learning. In the future, with the continuous improvement of quantum computing hardware and software ecosystems, this technology is expected to drive the implementation of more innovative applications.This breakthrough technology showcases the potential of interdisciplinary collaboration, bringing together the intellectual achievements of quantum computing, machine learning, and optimization algorithms. It not only expands the application boundaries of quantum algorithms but also provides new inspiration for the optimization of traditional algorithms. Particularly in fields such as real-time decision-making, edge computing, and the Internet of Things, its impact will be even more profound. The successful development of MicroAlgo's quantum algorithm is not only a technical achievement but also a prelude to artificial intelligence entering the era of quantum computing. In the future, we look forward to the further development of this technology, bringing unprecedented value to more industries and scenarios.About MicroAlgo Inc.MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.Forward-Looking StatementsThis press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.



View original content:https://www.prnewswire.com/news-releases/microalgo-inc-develops-quantum-algorithm-technology-for-feedforward-neural-networks-to-drive-neural-network-revolution-302752855.htmlSOURCE MicroAlgo Inc.

Original: MicroAlgo Inc. Develops Quantum Algorithm Technology for Feedforward Neural Networks to Drive Neural Network Revolution

on the radar

WiMi Hologram Cloud Inc. Reaffirms Long-Term Strategic Investment in MicroAlgo Inc.
https://www.otcmarkets.com/filing/html?id=18861184&guid=1_b-knj9Q178dth

$15 finally today no resistance 

Try converting 2 billion RMB to USD ..... $320 million cash and ST investments.

$11s no surprise if $15 hits 

$10.80 going for $15 minor very weak resistance otw 

$10.40 and solid break of $10.60 goes to $15

$10 let's get that $15

$15 breakout stil at play when the masters decide 

Getting there that one buyer yesterday of 200,000+ shares was bigger recent buy 

Want to run hold above $9.07 but all looks good patience could be a day or weeks unless MACD flips 

rmb MLGO

geez la weez MLGO

$9.45 + 10% Looks like good go at some point and time MACD squeezing and squeezing 

$MLGO Almost 2 billion in cash https://www.otcmarkets.com/filing/html?id=18766978&guid=YaE-kn8STtf8dth

Wang-chan Wong says hello😁

give me a follow I am pretty bang on with picks , i can help you make money. MLGO

Get them

ya swines just hold a bit 1000 shares and snag 2/3k profit MLGO , my first name is scott too

Took everyone’s money on a cray r/s to 40$ piece of crap criminals

ya huge money maker , gave you a follow Mlgo

Can’t kill this Chinese scam
Its here again

ya me too igc 4985 MLGO

Pump and dump flip time let's do it $9.37

Another stock down the crapper. Pumped by thieves and then the thieves pulled the rug leaving everyone who is not connected with empty pockets.

$10.10 flip a few and watxh next level $11.66

MicroAlgo Inc. Announces the Development of Grover-based Quantum Algorithm Technology for Finding Pure Nash Equilibria in Graphical Games
SHENZHEN,China, July 7, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of a Grover-based quantum algorithm designed to find pure Nash equilibria in graphical games. This technology represents not only an important advancement in quantum algorithm research but also provides a fresh perspective on game theory and its applications. The Grover search algorithm is an efficient quantum search algorithm that can find a target element in an unstructured database with a time complexity of the square root of the number of elements. By employing amplitude amplification techniques, it enables the identification of a target item in an unsorted database with square-root complexity, making it significantly more efficient to find solutions in a quantum computing environment. The key to applying the Grover algorithm to find Nash equilibria in games lies in constructing an appropriate oracle operator.

MicroAlgo's algorithm achieves this by transforming the oracle in a given graphical game into a Boolean satisfiability problem. Specifically, we first define the participants, strategies, and payoffs in the game, and then represent them as Boolean variables and logical expressions. In this way, the game's state and the participants' strategies are encoded as quantum states.

The core of constructing the oracle lies in how to map the game's payoff structure into a quantum circuit. We have designed a method that effectively converts these Boolean expressions into quantum gate operations, enabling the logical synthesis of the oracle. This process ensures that the quantum circuit reflects the strategy choices and payoff feedback from the game during execution.

In the implementation process, MicroAlgo made adjustments to the Grover search algorithm to better suit the specific needs of graphical games. Traditional Grover algorithms may face efficiency bottlenecks when dealing with multi-objective or multi-dimensional problems. To address this, we adopted a stepwise iterative approach, gradually narrowing down the range of target states through multiple search rounds to improve search efficiency. In each iteration, the search strategy is adjusted based on the feedback from the oracle, maximizing the amplitude of the target state. This process not only enhances the success rate of finding pure Nash equilibria but also demonstrates greater flexibility and adaptability of the algorithm when handling complex games.

MicroAlgo conducted extensive experiments on random graphical game instances using a quantum simulator to validate the algorithm's effectiveness. By simulating different combinations of participants and strategies, the algorithm's performance was tracked in real-time, and data was collected to analyze its results. The experimental outcomes show that MicroAlgo's Grover-based quantum solution significantly improves the speed and accuracy of finding pure Nash equilibria, especially in complex gaming environments. Compared to traditional methods, this algorithm exhibits a higher success rate and shorter computation time across multiple iterations.

In today's rapidly advancing field of quantum computing, MicroAlgo's Grover-based quantum algorithm provides an innovative solution for finding pure Nash equilibria in graphical games. This breakthrough not only significantly enhances the algorithm's efficiency in complex games but also demonstrates the potential application of quantum technology in game theory research.

With further research and experimentation, it is expected that this technology will play a key role in practical business decision-making, market analysis, and multi-party game scenarios. By combining quantum computing with game theory, MicroAlgo is equipping decision-makers with more powerful tools to navigate increasingly complex competitive environments.

Looking ahead, MicroAlgo will continue to explore and expand the application boundaries of this technology, advancing the deployment and practice of quantum computing across multiple fields. Through close collaboration with academia and industry, it is believed that this technology will have a profound impact on driving scientific progress and fostering business innovation.

About MicroAlgo Inc.

MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to inc

In some $8.80s for a flip looks like beatdown spot after split..

haven't been paying attention , did we make a banana split

Still dilution to the tone of around 13 million dollars.

Pretty measly volume

Well since share holders can not sell till their shares clear, Who do you suppose is selling?

Based on the volume I would say you are wrong. Nice try

Ouch MLGO has already started diluting before anyone can sell.

At the open of business on July 18, 2025, the Company’s Class A ordinary shares will Conduct  Reverse split July 18

…..on the current shares outstanding, the 30-for-1 Share Consolidation will reduce the Company’s total issued and outstanding shares from 373,125,199 (comprising 328,246,938 Class A ordinary shares and 44,878,261 Class B ordinary shares) to approximately 12,437,507 shares (comprising approximately 10,941,565 Class A ordinary shares and 1,495,942 Class B ordinary shares). The final post-consolidation share count is subject to the treatment of fractional shares….. 

Danger danger reverse split coming exit out now...  30 to 1

Rinse repeat add again .66-.69

Getting ready for China small cap pumps as Wall Street always hitting a different sector. Even if a piece a crap company will get pumped after the big early year China dilution dumps

Churn churn .71 before it throws up $1s patience as always no instant gratification 

Gap closed! Time to get size!? $MLGO

.72 add zone churning for .86 and $1.10 next up

$3+ incoming here shortly? Even $10+ doable? $MLGO

Next round will be the big round!? $MLGO









LFG $MLGO! GLTYA & DYOR. ~BEES~