MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the “Company”), a technology service provider, leverages their own technical accumulation in the fields of quantum computing and big data to develop a quantum-empowered big data real-time computing system. Through the deep integration of quantum technology and data processing technology, it achieves a leapfrog improvement in data efficiency, becoming a core support tool for digital transformation in various industries.
The system developed by HOLO has its core logic in integrating the parallelism and entanglement advantages of quantum technology into the entire data processing workflow to achieve rapid mining and precise transformation of data value. In actual operation, HOLO innovatively adopts quantum sensing technology to integrate multi-source heterogeneous data from IoT terminals, enterprise business systems, public service platforms, and others into the self-developed data middleware—this step particularly introduces quantum error correction coding technology, leveraging the rapid identification capability of qubits for data outliers, making the data cleaning efficiency several times higher than traditional methods while ensuring the security of data transmission and storage. Subsequently, the preprocessed data is fed into HOLO’s self-designed quantum-enhanced real-time computing engine, completing complex analysis operations through a quantum parallel computing mode, and finally relying on HOLO’s developed quantum visualization tools to present the results in forms such as interactive charts and dynamic models, helping users efficiently obtain data insights.
As a core achievement of HOLO’s technology layout, this system adopts a self-designed “five-module collaborative architecture”, with each module deeply integrating the quantum technology developed by HOLO, forming a complete closed loop of “data processing-computation analysis-value output”. Specifically, the data preprocessing module is developed by HOLO in collaboration with the quantum technology team, integrating quantum key encryption and data quality assessment functions; the quantum-enhanced real-time computing engine is HOLO’s core technological breakthrough, supporting hybrid programming of quantum and classical circuits; the quantum algorithm library module integrates HOLO’s self-developed quantum machine learning algorithms and optimization algorithms; the quantum visualization module is equipped with HOLO’s original quantum entanglement state simulation tools; and the distributed quantum fault-tolerant architecture module adopts HOLO’s optimized quantum error correction scheme. The five major modules achieve collaborative linkage through HOLO’s self-developed standardized interfaces, providing users with efficient full-process services from data input to result output.
From a technical details perspective, the performance advantages of each module all originate from HOLO’s technological innovations. In the data preprocessing module, HOLO’s self-developed quantum data cleaning algorithm, leveraging quantum superposition states, can simultaneously process anomaly detection for billions of data entries, compressing the traditional hour-level cleaning time to the minute level; at the same time, the module adopts HOLO’s self-designed quantum key distribution protocol to ensure absolute security in the data collection and transmission process. The quantum-enhanced real-time computing engine, as the core of the system, is HOLO’s “quantum-classical heterogeneous architecture” tailored for big data scenarios—the quantum processing unit (QPU) is responsible for executing complex tasks such as large-scale matrix operations and combinatorial optimization, the classical processing unit (CPU) handles logic control and data scheduling, and the two achieve millisecond-level collaboration through HOLO’s developed quantum-classical interface, with computational energy efficiency improved by three orders of magnitude compared to traditional engines. In the quantum algorithm library, HOLO’s self-developed algorithms such as quantum neural networks and quantum support vector machines can extract more hidden features under the same data volume, with model training speed improved by about 40% compared to traditional algorithms. In addition, HOLO’s customized distributed quantum fault-tolerant architecture for the system adopts an optimized surface code quantum error correction technology, significantly reducing the qubit error rate and ensuring stable system operation.
In the future, HOLO plans to further deepen research on quantum-classical hybrid computing, optimize algorithm adaptability in fields such as intelligent manufacturing and the metaverse, while promoting the integration of the system with quantum communication networks to build a more secure and efficient data processing ecosystem. With the continuous iteration of technology, HOLO is expected to play a leading role in the fusion of the digital economy and quantum industry through this system, providing stronger technical support for the transformation and upgrading of various industries.
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