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SHENZHEN, China, March 13, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that their research team has used quantum neural networks to classify and extract features from data in databases, focusing the search space on subsets that are more likely to contain the target. By leveraging feature extraction and pattern recognition techniques in quantum machine learning, they preprocess and filter the features of the database in advance, narrowing down the search range. Grover's algorithm is then applied for precise searching, thereby improving search efficiency.
Quantum Neural Networks, an emerging technology that combines the principles of quantum mechanics with the architecture of artificial neural networks, are capable of running complex learning algorithms on quantum bits, enabling high-speed data processing and optimization analysis. By simulating the neural network structure of the human brain and integrating quantum superposition and entanglement states, they achieve nonlinear data mapping and advanced abstraction, significantly enhancing the efficiency of pattern recognition and classification.
MicroAlgo's Quantum Neural Network-based intelligent search system follows a sophisticated process framework, ensuring effective data filtering and efficient processing.
Data Preprocessing: Using advanced quantum pattern recognition technology, the raw data is initially filtered to remove irrelevant information, extract core features, and form a dataset that is easy to index.
Feature Extraction: Leveraging the deep learning capabilities of quantum neural networks, the system automatically uncovers hidden correlations within the data, constructing multi-level feature representations to lay the foundation for subsequent searches.
Subset Focusing: Based on the preliminary feature analysis, the search space is finely segmented to identify potential subsets where the target is likely to be, significantly reducing unnecessary computations.
Applying Grover's Algorithm: For the preselected subsets, Grover's algorithm is directly employed, utilizing its quantum parallel search advantages to quickly locate the target and achieve efficient retrieval.
Result Feedback and Optimization: For each search result, the system automatically evaluates its effectiveness, optimizes the search strategy, and iteratively improves the quantum neural network model, continuously enhancing both accuracy and efficiency.
Thanks to the quantum parallel processing mechanism, the quantum neural network intelligent search developed by MicroAlgo outperforms conventional algorithms by a significant margin, especially in the context of big data, where the performance gap becomes even more pronounced. With the support of deep learning technologies, the system has a more profound understanding of the data, enabling it to identify targets with greater accuracy and avoid missed detections or false positives. The combination of quantum neural networks and Grover's algorithm enhances adaptability, making the search system capable of self-learning, automatically adjusting search strategies with data changes, and maintaining long-term effectiveness.
