MicroCloud Hologram develops quantum-assisted data clustering technology

MicroCloud Hologram (HOLO) 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 neural network with the powerful capabilities of quantum computing, enabling efficient data clustering in an unsupervised manner. The Self-Organizing Feature Map 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.