WiMi Hologram Cloud Inc. announced on December 4 2025 that it has built a hybrid quantum‑classical learning architecture designed to boost multi‑class image‑classification accuracy. The system reuses measurement data from discarded qubits and feeds it into classical fully‑connected layers, allowing the model to extract more features from noisy intermediate‑scale quantum (NISQ) devices.
The architecture couples quantum convolutional layers with classical post‑processing, enabling joint optimization of quantum gate angles and classical weights. By recycling discarded qubit information, WiMi reports higher accuracy and lower training energy consumption compared with conventional quantum convolutional neural networks, a key step toward practical quantum‑AI applications in vision, medical imaging, and autonomous driving.
WiMi’s announcement follows a 2024 financial turnaround that saw the company post a net income of RMB 103.3 million (USD 14.4 million) for the year ended December 31, 2024, after a loss in 2023. The company’s R&D spend has risen sharply, with quantum‑AI research now a significant portion of its investment budget, underscoring a strategic pivot beyond its core holographic AR business.
Management has highlighted the hybrid approach as a way to overcome current hardware limitations and to position WiMi as a leader in quantum‑enabled AI solutions. The company sees the new architecture as a foundation for future product offerings that could open new revenue streams and strengthen its competitive differentiation.
While no specific market reaction data is available, the technology places WiMi in a rapidly evolving field of quantum machine learning, potentially giving it a competitive edge in high‑precision image analysis markets that are increasingly demanding AI‑driven solutions.
The announcement signals WiMi’s commitment to investing in cutting‑edge quantum technologies and suggests a broader strategy to diversify its revenue base beyond holographic AR, positioning the company for long‑term growth in the emerging quantum‑AI landscape.
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