Technology
WiMi Achieves Coexistence of Lightweight Design and High Performance by Efficiently Embedding Quantum Modules into U-Net
BEIJING, Jan. 02, 2026 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, released a breakthrough achievement—a hybrid quantum-classical deep learning technology based on parameter-efficient quantum modules, QB-Net (Quantum Bottleneck Network). This technology achieves a major breakthrough by embedding lightweight quantum computing modules into the classical U-Net deep learning architectur
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[{"type":"text","content":"BEIJING, Jan. 02, 2026 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, released a breakthrough achievement—a hybrid quantum-classical deep learning technology based on parameter-efficient quantum modules, QB-Net (Quantum Bottleneck Network). This technology achieves a major breakthrough by embedding lightweight quantum computing modules into the classical U-Net deep learning architecture, reducing the number of parameters in the bottleneck layer by up to 30 times while maintaining performance comparable to that of the classical U-Net. This research and development outcome not only demonstrates the cutting-edge potential of hybrid quantum-classical artificial intelligence but also provides a brand-new optimization paradigm for traditional deep learning architectures.The core advantage of quantum computing lies in its ability to express high-dimensional information through the superposition states of qubits and perform linear operations in exponentially dimensional spaces, endowing it with expressive and transformative capabilities that surpass classical architectures. However, at the current stage, quantum hardware is still unable to support large-scale quantum neural networks or construct complete quantum U-Net or quantum Transformer.Therefore, WiMi has taken a completely different path: instead of building fully quantized AI models, it constructs quantum enhancement modules.This concept stems from a key observation: the bottleneck layer of deep networks is essentially a problem of high-density expression of high-dimensional features, while quantum states are naturally suited to express extremely high-dimensional vector spaces.When a classical network requires tens of thousands of parameters to accomplish a mapping task, a single quantum state can theoretically achieve the same or even higher expressive power with only a few dozen qubits. This means that as long as classical features can be mapped into quantum states and transformed through quantum circuits, it is possible to achieve equivalent capabilities with extremely low parameter counts.Based on this idea, WiMi designed a pluggable Quantum Bottleneck Module. This module takes minimal parameter count, structural stability, trainability...