Technology
WiMi Studies Hybrid Quantum-Classical Learning Architecture for Multi-Class Image Classification
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), is a leading global Hologram Augmented Reality ("AR") Technology provider.
About this update from Wimi Hologram Cloud Inc.
[{"type":"text","content":"BEIJING, Dec. 4, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), is a leading global Hologram Augmented Reality ("AR") Technology provider.","length":209,"tagName":"p"},{"type":"text","content":"On the basis of in-depth research on quantum convolutional neural networks (QCNN), it proposed a new type of hybrid quantum-classical learning technology. This technology, through innovatively recycling discarded qubit state information and joint training with classical fully connected layers, achieves significant performance improvements in multi-class image classification tasks.","length":383,"tagName":"p"},{"type":"text","content":"This achievement not only optimizes the efficiency of quantum networks under the conditions of noisy intermediate-scale quantum (NISQ) devices but also demonstrates the possibility of quantum information reuse, opening up a brand-new development path for hybrid quantum-classical models.","length":287,"tagName":"p"},{"type":"text","content":"Image classification is one of the core applications of artificial intelligence. From face recognition to medical image analysis, deep convolutional neural networks (CNN) have become the mainstream. However, as the model depth increases, its training time and computational energy consumption grow exponentially, with the dependence on hardware computing power becoming increasingly strong. Even under the support of GPU clusters or TPU arrays, model optimization is still constrained by bottlenecks. On the other hand, issues such as data security, privacy protection, and computational energy efficiency are forcing academia and industry to rethink the underlying architecture of intelligent computing.","length":704,"tagName":"p"},{"type":"text","content":"Quantum computing provides a completely new approach. It utilizes quantum superposition and entanglement effects to process information simultaneously in an exponential space, bringing theoretical acceleration advantages for complex pattern recognition tasks. Based on this characteristic, quantum machine learning (QML) is considered the next stage of artificial intelligence development. However, current quantum computers are still in the NISQ stage, with limited qubit numbers and susceptibility to noise interference, making how to achieve stable and scalabl...