Prnewswire·2026-01-05 15:30Core Viewpoint - MicroCloud Hologram Inc. has introduced a learnable quantum spectral filter technology for hybrid graph neural networks, marking a significant advancement in quantum-classical hybrid graph neural network architecture, which enhances graph signal processing capabilities and paves the way for practical quantum graph machine learning applications [1][12]. Technology Overview - The new technology integrates graph convolution and pooling operations into a quantum computing process, allowing for efficient processing of graph signals through a quantum circuit that performs spectral transformations based on graph structures [2][10]. - The quantum measurement process enables structured nonlinear mapping, addressing complex structural search challenges in classical graph neural networks (GNNs) [3][9]. - The quantum convolution layer can compress a graph of size N into log(N)-dimensional features, significantly reducing computational costs compared to classical methods [4][10]. Mathematical Foundation - The technology is based on the spectral structure of the graph Laplacian operator, which reflects key properties of the graph, such as connectivity and clustering [5][6]. - A mapping between the graph's adjacency matrix and quantum gates allows for the simulation of local adjacency relationships, while hierarchical rotation logic provides multi-scale filtering consistent with graph spectrum decoupling [6][7]. Implementation and Optimization - The training of the quantum circuit utilizes classical-quantum hybrid optimization, enabling the extraction of spectral features from high-dimensional input signals and outputting low-dimensional features for further processing by classical networks [8][10]. - The logarithmic encoding method reduces the number of qubits needed, allowing for efficient representation of the original feature space [7][10]. Industry Implications - The technology addresses the challenges of large-scale graph learning in various domains, such as social media and traffic networks, where classical GNNs struggle with memory and computational demands [9][10]. - Quantum spectral filters present a disruptive solution, as the qubit requirements grow logarithmically with the number of nodes, making them suitable for future quantum-classical GNNs [10][12]. Future Outlook - The introduction of this technology positions MicroCloud Hologram Inc. at the forefront of quantum computing and graph neural networks, establishing a foundation for future hardware development and practical applications in artificial intelligence and physical computing [11][13].