Press Release: Wimi Studies Quantum Hybrid Neural Network Model To Empower Intelligent Image Classification

Investing.com -- WiMi Hologram Cloud Inc. (NASDAQ:WIMI) stock surged 11.3% on Monday after the company announced the launch of its independently developed Quantum Convolutional Neural Network for Multi-Channel Supervised Learning (MC-QCNN) technology.

The AR technology provider’s new quantum computing breakthrough is designed to process multi-channel data efficiently, potentially offering advantages in image classification, medical imaging, video analysis, and multimodal monitoring applications.

Unlike traditional approaches, WiMi’s quantum circuit convolution kernel uses single-bit rotation gates, controlled parameterized gates, and other quantum-specific structures to maintain robustness against quantum decoherence. The company claims this allows for stronger feature combination capabilities than classical convolution methods.

The MC-QCNN technology compresses and encodes data from multiple channels into quantum states, performing convolution-like processing through parameterized quantum gates. According to WiMi, this enables the system to learn high-order cross-channel features such as texture-color co-occurrence and multispectral energy distribution correlations.

WiMi has also developed a hybrid quantum-classical training framework where classical computing handles loss function calculation and parameter updating, while the quantum module manages forward propagation and quantum state evolution.

The company believes this multi-channel processing capability represents a significant step toward practical applications for quantum neural networks, moving quantum AI beyond laboratory research toward potential commercial implementation as quantum hardware performance improves.

This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.

Investing.com -- WiMi Hologram Cloud Inc. (NASDAQ:WIMI) stock surged 8.1% on Friday after the company announced a breakthrough in quantum-classical deep learning technology.

The AR technology provider revealed its new QB-Net (Quantum Bottleneck Network), which embeds lightweight quantum computing modules into classical deep learning architecture. According to the company, this innovation reduces the number of parameters in the bottleneck layer by up to 30 times while maintaining comparable performance to traditional methods.

WiMi’s approach differs from attempts to build fully quantized AI models by instead constructing quantum enhancement modules that can be integrated into existing classical networks. The company’s Quantum Bottleneck Module takes advantage of quantum states’ ability to express high-dimensional vector spaces with relatively few qubits.

The QB-Net retains the overall structure of the classical U-Net architecture but replaces traditional convolutional layers at the bottleneck with a quantum feature compression-transformation-reconstruction module. This module encodes classical features into quantum states, transforms them through quantum circuits, and then decodes them back into classical tensors.

WiMi Hologram Cloud positions this development as a significant advancement in hybrid quantum-classical AI, suggesting it provides a new optimization paradigm for traditional deep learning architectures. The company believes this approach demonstrates that quantum computing can deliver practical value in AI applications even at the current stage of quantum hardware development.

This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.