WiMi Achieves Breakthrough in Deep Convolutional Neural Network Technology Based on Quantum Parameterized Circuits

About this update from Wimi Hologram Cloud Inc.

BEIJING, May 28, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company") is a leading global Hologram Augmented Reality ("AR") Technology provider. A quantum deep convolutional neural network technology oriented toward image recognition tasks has achieved phased progress. This technology provides a new technical path to address the challenges faced by traditional deep learning in terms of computational complexity, memory consumption, and training efficiency by constructing a quantum deep convolutional neural network model and combining it with a quantum-classical hybrid training mechanism. The proposal of this technology marks a further deepening of the application of quantum machine learning in typical artificial intelligence tasks such as image recognition, and also provides a new research direction for the realization of future large-scale quantum intelligent computing systems. WiMi has proposed a quantum deep convolutional neural network model for image recognition tasks. The model takes quantum parameterized circuits as its core computing structure, performs feature extraction on image data through quantum convolutional layers, and utilizes a quantum classification layer to complete the final recognition task. The overall architecture draws on the hierarchical structure of classical deep convolutional neural networks in terms of design philosophy, while fully leveraging the parallel computing capability of quantum circuits, enabling the model to achieve higher computational efficiency when processing high-dimensional data.At the technical architecture level, the quantum deep convolutional neural network consists of a data encoding module, a quantum convolutional layer module, a quantum feature fusion module, and a quantum classification module. The system first maps classical image data to the quantum state space through the data encoding module. Since quantum computers process quantum state information, it is necessary to convert pixel information into probability amplitudes of qubits through specific encoding strategies. This process is usually achieved through amplitude encoding, angle encoding, or hybrid encoding methods, enabling image data to be effectively processed by quantum circuits.After completing data encoding, the quantum convolutional layer begins to perform feature extraction on the quantum states. Simil...

View stock analysis, news, and events for Wimi Hologram Cloud Inc.