Technology
WiMi Releases Next-Generation Hybrid Quantum Neural Network Structure Technology, Breaking Through the Bottleneck of Image Multi-Classification
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, launched a hybrid quantum neural network structure (H-QNN) for image multi-classification. This technology organically integrates the spatial feature extraction capabilities of classical convolutional neural networks (CNN) with the high-dimensional nonlinear mapping features of quantum neural networks (QNN), forming a new type of hybrid structure that possess
About this update from Wimi Hologram Cloud Inc.
[{"type":"text","content":"BEIJING, Dec. 22, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, launched a hybrid quantum neural network structure (H-QNN) for image multi-classification. This technology organically integrates the spatial feature extraction capabilities of classical convolutional neural networks (CNN) with the high-dimensional nonlinear mapping features of quantum neural networks (QNN), forming a new type of hybrid structure that possesses stronger generalization ability and computational efficiency in multi-class classification scenarios. This technology not only systematically optimizes the quantum-classical hybrid learning system in theory but also achieves classification accuracy and stability superior to similar algorithms in actual experiments, laying a solid technical foundation for quantum intelligent vision systems.","length":964,"tagName":"p"},{"type":"text","content":"The design of this hybrid quantum neural network (H-QNN) follows the principle of classical responsible for abstraction and quantum responsible for discrimination. The overall system consists of three main modules: feature dimensionality reduction and encoding module, quantum state transformation module, and hybrid decision and transfer learning module.","length":355,"tagName":"p"},{"type":"text","content":"First, the feature dimensionality reduction and encoding module is based on the classical convolutional neural network (CNN) structure, extracting low-dimensional feature representations of images through several convolutional layers and pooling layers. The feature vectors after PCA dimensionality reduction are standardized and then input into the quantum encoding circuit. At this stage, WiMi adopts an improved angle encoding method (Angle Embedding) to map real-valued features to quantum state amplitudes, and achieves efficient encoding through multi-layer quantum rotation gates (Ry, Rz), thereby reducing quantum gate depth and lowering encoding noise.","length":661,"tagName":"p"},{"type":"text","content":"Next, the quantum state transformation module undertakes the core tasks of high-dimensional feature mapping and nonlinear discrimination. This module includes several layers of quantum circuits, with each layer composed of...