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MicroCloud Hologram Inc. Releases Learnable Quantum Spectral Filter Technology for Hybrid Graph Neural Networks
MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This achievement proposes a brand-new quantum-classical hybrid graph neural network foundational architecture. By mapping the graph Laplacian operator to a trainable quantum circuit, it enables graph signal processing to gain exponential compression capability and a new computational perspective, representing a
About this update from Microcloud Hologram Inc.
[{"type":"text","content":"SHENZHEN, China, Jan. 5, 2026 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This achievement proposes a brand-new quantum-classical hybrid graph neural network foundational architecture. By mapping the graph Laplacian operator to a trainable quantum circuit, it enables graph signal processing to gain exponential compression capability and a new computational perspective, representing a key step for quantum graph machine learning toward practicalization.","length":634,"tagName":"p"},{"type":"text","content":"HOLO's this technology proposes a quantum spectral filter that fuses graph convolution and pooling operations into a complete quantum computing process. The input signal is loaded into the quantum state using amplitude encoding or probability encoding. The quantum circuit performs spectral transformation based on the graph structure. After passing through learnable rotation gates and controlled gates, the measurement results of the output state naturally form an n-dimensional probability distribution vector, where n = log(N). This property enables the quantum circuit to directly map high-dimensional graph signals to low-dimensional space, achieving a unified function of convolution + pooling.","length":705,"tagName":"p"},{"type":"text","content":"HOLO points out that the quantum measurement process is essentially a structured nonlinear mapping, capable of overcoming the complex structural search problems in classical GNN pooling operations. In quantum circuits, nonlinear behaviors that are difficult to simulate in classical networks are automatically realized through quantum state collapse, making the pooling results both compressive and separable while preserving key spectral features of the graph structure.","length":471,"tagName":"p"},{"type":"text","content":"This means that a graph of size N, after processing through the quantum convolution layer, can immediately obtain log(N)-dimensional compressed features, with computational costs remaining controllable even for large graphs. For a network with one million nodes, classical spectral convolution is almost impossible to run in terms of memory and time, whereas this quantum circuit requires onl...