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MicroCloud Hologram Inc. Launches Q-DPC Accelerator: Quantum-Empowered Density Peak Clustering's Strategy Evaluation Performance Leap Solution
MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, launched Q-DPC Accelerator, which is an innovative tool that relies on the quantum-enhanced density peak clustering algorithm to improve strategy evaluation efficiency. The quantum-reinforced density peak clustering strategy set grouping method proposed by HOLO accurately identifies the clustering structure in the strategy set through quantum computing technology, significantly reducing the complex
About this update from Microcloud Hologram Inc.
[{"type":"text","content":"SHENZHEN, China, Jan. 2, 2026 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, launched Q-DPC Accelerator, which is an innovative tool that relies on the quantum-enhanced density peak clustering algorithm to improve strategy evaluation efficiency. The quantum-reinforced density peak clustering strategy set grouping method proposed by HOLO accurately identifies the clustering structure in the strategy set through quantum computing technology, significantly reducing the complexity of strategy evaluation. The overall architecture and operational process of Q-DPC Accelerator cover core links such as quantum-assisted data preprocessing, quantum density peak clustering, quantum-accelerated strategy matching, and performance evaluation.","length":830,"tagName":"p"},{"type":"text","content":"As a strategy evaluation tool relying on the quantum-enhanced density peak clustering algorithm, HOLO's Q-DPC Accelerator possesses three core functions: strategy set preprocessing, quantum clustering grouping, and intelligent strategy matching. The collaborative operation of these functions can significantly improve the efficiency and accuracy of strategy evaluation. Among them, the strategy set preprocessing stage completes data preparation work before evaluation, through steps such as quantum data cleaning, quantum feature extraction, and quantum data conversion, making the strategy data more adapted to density peak clustering analysis. The data cleaning stage removes redundant, incomplete, or erroneous strategy information, ensuring the accuracy and consistency of the data and avoiding impacts on evaluation results; the feature extraction stage extracts key features such as user roles, resource types, and operation permissions from the strategy set, providing support for subsequent clustering operations; the data conversion stage converts the strategy set into vector, matrix, and other data formats suitable for processing by the quantum density peak clustering algorithm. The quantum clustering grouping stage uses the quantum-enhanced density peak clustering algorithm to group the strategy set, evaluating the density and distance relationships between strategies through quantum computing, precisely identifying the clustering structure, determining peak ...