shenzhen, May 16, 2025 (GLOBE NEWSWIRE) -- Shenzhen, May. 16, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of a novel quantum entanglement-based training algorithm — the Entanglement-Assisted Training Algorithm for Supervised Quantum Classifiers. They also introduced a cost function based on Bell inequalities, enabling the simultaneous encoding of errors from multiple training samples. This breakthrough surpasses the capability limits of traditional alg ...

Core Viewpoint - MicroAlgo Inc. has announced the development of the Quantum Information Recursive Optimization (QIRO) algorithm, which aims to enhance combinatorial optimization problems by utilizing quantum computing capabilities [1][7]. Group 1: Algorithm Overview - The QIRO algorithm is designed to tackle complex combinatorial optimization problems by integrating quantum computing and recursive algorithms, leveraging parallel computing and quantum state properties [1][7]. - The algorithm recursively invokes quantum optimization processes, progressively reducing problem size to find optimal solutions [4][7]. Group 2: Technical Process - The first step involves modeling the combinatorial optimization problem by defining the objective function, constraints, and candidate elements [2]. - Quantum states are initialized through quantum gate operations, allowing for simultaneous processing of multiple computational paths [3]. - Quantum measurement is performed at the recursion's boundary conditions to extract optimal or near-optimal solutions [5]. - The extracted solution is verified and optimized by comparing objective function values to identify the best solution [6]. Group 3: Advantages and Applications - The QIRO algorithm demonstrates significant technical advantages, achieving exponential improvements in computational efficiency and stronger global search capabilities compared to traditional algorithms [7]. - It is flexible and can be tailored to meet specific problem requirements, enhancing its effectiveness across various applications [7]. - The algorithm has practical applications in logistics, resource allocation, network planning, and graph theory-related problems, proving its value in real-world scenarios [8]. Group 4: Future Potential - The QIRO algorithm holds immense growth potential as quantum technology advances, improving the quality and accessibility of quantum resources [9][10]. - It may serve as a model for developing additional hybrid quantum-classical algorithms, expanding quantum computing applications across various industries [10]. Group 5: Company Background - MicroAlgo Inc. is dedicated to developing and applying bespoke central processing algorithms, providing comprehensive solutions that enhance customer satisfaction and achieve technical goals [11].

Core Insights - MicroAlgo Inc. is developing a Quantum Convolutional Neural Network (QCNN) architecture that integrates quantum computing with classical convolutional neural networks to enhance computer vision tasks [1][2] Group 1: Quantum Convolutional Neural Network (QCNN) Overview - QCNN combines the parallelism of quantum computing with the feature extraction capabilities of classical convolutional neural networks, utilizing quantum bits (qubits) for information processing [2] - The architecture includes convolution layers, pooling layers, and fully connected layers, which improve computational speed and image recognition accuracy [2][3] Group 2: Data Processing Steps - Data preparation involves collecting, screening, and preprocessing image or video data to ensure quality and compliance [4] - Quantum state encoding maps preprocessed image features onto quantum bits, establishing complex feature associations through quantum properties [5] Group 3: QCNN Processing Mechanism - The quantum convolutional layer uses quantum parallelism to extract features, while the quantum pooling layer reduces dimensions to retain key features [6] - The quantum fully connected layer analyzes reduced features and classifies them based on quantum state correlations [6] Group 4: Applications of QCNN - QCNN has potential applications in autonomous driving for recognizing road signs, vehicles, and pedestrians, thereby enhancing safety [8] - In medical imaging, QCNN can facilitate rapid and accurate diagnoses, assisting in disease treatment planning [8] - The architecture can also improve security surveillance by enabling real-time detection of abnormal behavior [8] - Additional applications include smart manufacturing, aerospace, and smart cities, driving technological upgrades in these sectors [8] Group 5: Company Overview - MicroAlgo Inc. focuses on developing bespoke central processing algorithms and provides comprehensive solutions that integrate these algorithms with software and hardware [9] - The company aims to enhance customer satisfaction, reduce costs, and achieve technical goals through algorithm optimization and efficient data processing [9][10]

SHENZHEN, China, May 8, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced that the quantum image encryption algorithm they developed, based on quantum key images, is an innovative image protection scheme. This algorithm uses a quantum key image to store encryption keys, leveraging quantum entanglement and parallelism to achieve efficient image encryption. The quantum key image is a special type of quantum image prepared using a specific quantum storage method, with ...

SHENZHEN, May 08, 2025 (GLOBE NEWSWIRE) -- MicroAlgo Inc. Develops a Blockchain Storage Optimization Solution Based on the Archimedes Optimization Algorithm (AOA) Shenzhen, May. 08, 2025/––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), announced a focus on addressing the efficiency bottlenecks in blockchain storage by introducing the Archimedes Optimization Algorithm (AOA) into distributed storage architecture. Through intelligent algorithmic restructuring of data storage and node collaborati ...

SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This technology significantly reduces the complexity of parameter updates during training through deep optimization of the core circuit, markedly improving computational efficiency. Compared to other quantum classifiers, this optimized model has lower complexity and incorpora ...

SHENZHEN, China, May 1, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), announced today that their newly developed quantum edge detection algorithm has broken through the limitations of classical methods. This technology optimizes the feature extraction process through quantum circuits, reducing computational complexity from O(N²) to O(N) while maintaining detection accuracy, thereby providing new solutions for real-time image processing and edge intelligence devices.The q ...

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Shenzhen, April 28, 2025 (GLOBE NEWSWIRE) -- Shenzhen, China, April 28, 2025 – MicroAlgo Inc. (NASDAQ: MLGO), (the "Company"), a leading developer and application provider of bespoke central processing algorithms, today announced its financial results for the year ended December 31, 2024. The Company reported total revenues of RMB 541.5 million (USD 75.3 million) and net income of RMB 53.4 million (USD 7.3 million), marking a significant turnaround from the previous year's net loss of RMB 266.2 million and ...

Core Idea - MicroAlgo Inc. has developed the Classical Boosted Quantum Optimization Algorithm (CBQOA), which combines classical and quantum computing to solve constrained optimization problems more efficiently [1][10]. Technology Overview - CBQOA integrates classical optimization methods with quantum computing techniques, allowing for effective solutions to combinatorial optimization problems without altering the cost function [1][10]. - The algorithm first uses classical methods to identify high-quality feasible solutions, which are then refined using quantum computing [3][10]. Classical Optimization Techniques - Efficient classical optimization algorithms such as greedy algorithms, heuristic algorithms, and simulated annealing are employed initially to generate feasible solutions [4][10]. - Specific classical strategies can be tailored to different problems, such as using heuristic algorithms for the Maximum Cut Problem and greedy algorithms for the Maximum Independent Set Problem [5][4]. Quantum Computing Integration - After classical optimization, CBQOA utilizes Continuous-Time Quantum Walk (CTQW) to search the solution space, enhancing the efficiency of the search process [6][10]. - CTQW allows quantum states to propagate within the feasible solution space, reducing ineffective searches and increasing the likelihood of finding the global optimum [7][10]. Practical Applications and Impact - The introduction of CBQOA is expected to advance quantum computing from theoretical research to real-world applications, particularly in industries facing complex optimization challenges [11]. - The algorithm is anticipated to become a core component of next-generation optimization algorithms, fostering interdisciplinary research across fields such as computer science, operations research, and artificial intelligence [11][10]. Company Background - MicroAlgo Inc. specializes in developing bespoke central processing algorithms and offers solutions that enhance customer satisfaction, reduce costs, and improve technical performance [12].