MicroCloud Hologram Inc. Develops Neural Network-Based Quantum-Assisted Unsupervised Data Clustering Technology
MicroCloud Hologram Inc. (NASDAQ: HOLO) has developed a neural network-based quantum-assisted unsupervised data clustering technology that combines classical self-organizing feature map (SOM) neural networks with quantum computing capabilities. The new Quantum-Assisted Self-Organizing Feature Map (Q-SOM) model addresses traditional computing limitations by leveraging quantum parallelism to process larger data volumes more efficiently.
The technology demonstrates key strengths in computational efficiency, enhanced data processing capability for high-dimensional datasets, improved accuracy through quantum entanglement, and wide applicability across sectors including image processing, natural language processing, and financial data analysis. The hybrid architecture utilizes quantum computing for accelerating data mapping and weight adjustment, while classical computing handles post-processing and final clustering decisions.
MicroCloud Hologram Inc. (NASDAQ: HOLO) ha sviluppato una tecnologia di clustering dati non supervisionato assistita da quantum computing basata su reti neurali, che combina le reti neurali classiche di tipo self-organizing feature map (SOM) con le capacità del calcolo quantistico. Il nuovo modello Quantum-Assisted Self-Organizing Feature Map (Q-SOM) supera i limiti del calcolo tradizionale sfruttando il parallelismo quantistico per elaborare volumi di dati più grandi in modo più efficiente.
Questa tecnologia mostra punti di forza fondamentali nell'efficienza computazionale, nella capacità migliorata di elaborare dati ad alta dimensionalità, nell'aumento della precisione grazie all'entanglement quantistico e nella sua ampia applicabilità in settori come l'elaborazione delle immagini, il trattamento del linguaggio naturale e l'analisi dei dati finanziari. L'architettura ibrida utilizza il calcolo quantistico per accelerare la mappatura dei dati e l'aggiustamento dei pesi, mentre il calcolo classico si occupa del post-processing e delle decisioni finali di clustering.
MicroCloud Hologram Inc. (NASDAQ: HOLO) ha desarrollado una tecnología de agrupamiento de datos no supervisado asistida por computación cuántica basada en redes neuronales, que combina redes neuronales clásicas de tipo self-organizing feature map (SOM) con capacidades de computación cuántica. El nuevo modelo Quantum-Assisted Self-Organizing Feature Map (Q-SOM) supera las limitaciones de la computación tradicional aprovechando el paralelismo cuántico para procesar mayores volúmenes de datos de manera más eficiente.
La tecnología demuestra fortalezas clave en eficiencia computacional, mayor capacidad para procesar conjuntos de datos de alta dimensión, mejor precisión gracias al entrelazamiento cuántico y amplia aplicabilidad en sectores como procesamiento de imágenes, procesamiento de lenguaje natural y análisis de datos financieros. La arquitectura híbrida utiliza la computación cuántica para acelerar el mapeo de datos y el ajuste de pesos, mientras que la computación clásica se encarga del posprocesamiento y las decisiones finales de agrupamiento.
MicroCloud Hologram Inc. (NASDAQ: HOLO)는 고전적인 자기조직화 특징 맵(SOM) 신경망과 양자 컴퓨팅 기능을 결합한 신경망 기반 양자 지원 비지도 데이터 클러스터링 기술을 개발했습니다. 새로운 Quantum-Assisted Self-Organizing Feature Map (Q-SOM) 모델은 양자 병렬성을 활용하여 더 큰 데이터 볼륨을 보다 효율적으로 처리함으로써 전통적인 컴퓨팅 한계를 극복합니다.
이 기술은 계산 효율성, 고차원 데이터셋 처리 능력 향상, 양자 얽힘을 통한 정확도 개선, 이미지 처리, 자연어 처리, 금융 데이터 분석 등 다양한 분야에서의 광범위한 적용 가능성 등 주요 강점을 보여줍니다. 하이브리드 아키텍처는 데이터 매핑 및 가중치 조정을 가속화하기 위해 양자 컴퓨팅을 사용하며, 후처리 및 최종 클러스터링 결정은 고전 컴퓨팅이 담당합니다.
MicroCloud Hologram Inc. (NASDAQ : HOLO) a développé une technologie de regroupement de données non supervisé assistée par ordinateur quantique basée sur des réseaux neuronaux, combinant les réseaux neuronaux classiques de type self-organizing feature map (SOM) avec les capacités du calcul quantique. Le nouveau modèle Quantum-Assisted Self-Organizing Feature Map (Q-SOM) surmonte les limites du calcul traditionnel en exploitant le parallélisme quantique pour traiter des volumes de données plus importants de manière plus efficace.
Cette technologie présente des atouts majeurs en termes d'efficacité de calcul, d'amélioration du traitement des données pour des ensembles de données à haute dimension, de précision accrue grâce à l'intrication quantique, et d'une large applicabilité dans des secteurs tels que le traitement d'images, le traitement du langage naturel et l'analyse de données financières. L'architecture hybride utilise le calcul quantique pour accélérer la cartographie des données et l'ajustement des poids, tandis que le calcul classique prend en charge le post-traitement et les décisions finales de regroupement.
MicroCloud Hologram Inc. (NASDAQ: HOLO) hat eine auf neuronalen Netzen basierende, quantenunterstützte unüberwachte Datencluster-Technologie entwickelt, die klassische Self-Organizing Feature Map (SOM)-Neuronennetze mit Quantencomputing-Fähigkeiten kombiniert. Das neue Quantum-Assisted Self-Organizing Feature Map (Q-SOM)-Modell überwindet traditionelle Rechenbeschränkungen, indem es Quantenparallelität nutzt, um größere Datenmengen effizienter zu verarbeiten.
Die Technologie zeigt wesentliche Stärken in der Recheneffizienz, der verbesserten Datenverarbeitung für hochdimensionale Datensätze, einer erhöhten Genauigkeit durch Quantenverschränkung sowie breite Anwendbarkeit in Bereichen wie Bildverarbeitung, natürlicher Sprachverarbeitung und Finanzdatenanalyse. Die hybride Architektur verwendet Quantencomputing zur Beschleunigung der Datenabbildung und Gewichtsanpassung, während klassische Computer die Nachbearbeitung und endgültige Clusterentscheidungen übernehmen.
- Development of innovative Q-SOM technology combining quantum and classical computing capabilities
- Enhanced computational efficiency and ability to process larger data volumes
- Wide applicability across multiple industries including AI, financial technology, and big data
- Potential competitive advantage in emerging quantum computing market
- Technology is still in development phase with no immediate commercialization timeline
- Dependent on advancement of quantum computing hardware
- No specific revenue or market impact metrics provided
- Faces competition in rapidly evolving quantum computing sector
SHENZHEN, China, May 16, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the development of a neural network-based quantum-assisted unsupervised data clustering technology, utilizing a hybrid quantum-classical algorithm framework. This framework integrates the classical self-organizing feature map (SOM) neural network with the powerful capabilities of quantum computing, enabling efficient data clustering in an unsupervised manner.
The Self-Organizing Feature Map (SOM) is an unsupervised learning neural network model widely used in fields such as data clustering, dimensionality reduction, and data visualization. Its core concept involves mapping high-dimensional data from the input space to a low-dimensional topological space through a competitive learning algorithm. This process ensures that similar input data points are mapped to adjacent neurons, thereby achieving data clustering.
In classical computing, the SOM algorithm continuously adjusts weight vectors to reasonably group input data within the feature space. However, when dealing with massive datasets, the traditional SOM algorithm faces challenges related to computational complexity and storage demands.
To address the limitations of classical computing in large-scale data clustering, HOLO has introduced quantum computing into the SOM framework, developing a Quantum-Assisted Self-Organizing Feature Map (Q-SOM) model. In this model, the powerful parallel computing capabilities of quantum computing are leveraged to accelerate the weight adjustment and data point mapping processes in SOM. Through quantum parallelism, it becomes possible to process a larger volume of data in a shorter time, thereby reducing the number of computations and overall time consumption.
HOLO's technology leverages the quantum superposition and quantum entanglement properties of quantum computing, enabling the results of each clustering computation to be processed in parallel across multiple qubits. This quantum parallel computing approach not only significantly enhances computational efficiency but also demonstrates superior computational power compared to classical computing in certain scenarios.
HOLO believes that quantum computing does not entirely replace classical computing but rather works in tandem with it. In this technology, the quantum component is primarily responsible for accelerating the data point mapping and weight adjustment processes within the SOM network, while the classical component handles post-processing of results and the final decision-making for data clustering. This hybrid architecture fully exploits the respective strengths of quantum and classical computing, theoretically enabling more efficient clustering.
By incorporating quantum computing, each iteration of the SOM network can be completed more quickly, significantly reducing the number of computations required during the clustering process. Furthermore, the interference properties and noise tolerance of quantum computing provide additional robustness and reliability to the model.
HOLO’s neural network-based quantum-assisted unsupervised data clustering technology, leveraging the advantages of quantum computing, exhibits significant technical strengths:
Computational Efficiency: Through quantum parallelism, it can significantly reduce the time cost of clustering computations. Particularly when dealing with large-scale data, quantum computing can handle more data points and quickly converge to optimal solutions.
Data Processing Capability: The quantum-assisted algorithm can process higher-dimensional data. Especially when tackling complex high-dimensional datasets, quantum computing accelerates the data mapping process, reducing the complexity of high-dimensional computations.
Accuracy and Stability: Compared to classical methods, quantum computing demonstrates higher accuracy and stability in addressing certain nonlinear and highly complex problems. Through quantum entanglement and superposition effects, it can avoid some of the local optima issues encountered in classical algorithms.
Wide Applicability: This technology is not only suitable for data clustering but can also be extended to various fields such as image processing, natural language processing, and financial data analysis. As quantum computing technology advances, more industry applications will become feasible in the future.
The integration of quantum computing and machine learning marks the advent of next-generation computing technology. By developing quantum-assisted neural network technology, HOLO not only achieves breakthroughs in the field of data clustering but also drives progress across multiple industries. Particularly in areas such as big data, artificial intelligence, and financial technology, the introduction of quantum computing will fundamentally transform data processing methods and provide new solutions for tackling complex problems.
In the future, as quantum computing technology continues to mature, quantum-assisted machine learning algorithms will play an increasingly important role across multiple industries. Especially in fields with extremely high demands for computational speed and precision—such as quantum supremacy experiments, drug discovery, and climate change prediction—the integration of quantum computing and machine learning will unlock unprecedented potential.
HOLO’s breakthrough in neural network-based quantum-assisted unsupervised data clustering technology provides new perspectives for interdisciplinary research in quantum computing and artificial intelligence. With ongoing technological optimization and advancements in quantum computing hardware, quantum computing is poised to achieve practical applications in a broader range of fields, driving technological innovation and societal progress. Through continuous development and application of this technology, HOLO will inject new momentum into global data analysis, decision-making support, and the advancement of artificial intelligence.
About MicroCloud Hologram Inc.
MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud’s holographic technology services include high-precision holographic light detection and ranging (“LiDAR”) solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems (“ADAS”). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud’s holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud’s holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/
Safe Harbor Statement
This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as “may,” “will,” “intend,” “should,” “believe,” “expect,” “anticipate,” “project,” “estimate,” or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company’s expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company’s goals and strategies; the Company’s future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission (“SEC”), including the Company’s most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company’s filings with the SEC, which are available for review at www.sec.gov. The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof.
Contacts
MicroCloud Hologram Inc.
Email: IR@mcvrar.com
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