WiMi Explores Multi-Dimensional Pooling Optimization Technology under the Variational Quantum Algorithm Framework

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BEIJING, June 8, 2026 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, is exploring multi-dimensional pooling optimization technology under the variational quantum algorithm framework, proposing an innovative solution that integrates the Quantum Haar Transform (QHT) with quantum partial measurement, and constructing a quantum pooling mechanism that possesses both local feature preservation capability and dimension compression efficiency. From a technical principle perspective, the Haar transform, as a core technology in the field of classical signal processing, is widely used in data compression and feature extraction. As its quantized extension, QHT maps high-dimensional classical data to the quantum state space through parameterized quantum gate groups, achieving a breakthrough improvement in computational efficiency over the classical Haar transform. In this mapping process, each qubit corresponds to one feature dimension of the data, and the superposition coefficients of the quantum state encode the feature intensity information. At the same time, correlations between feature dimensions are constructed through quantum entanglement, which not only fully preserves the global structural information of the data but also reinforces the correlations of local features through the local action domain constraints of quantum gates, effectively solving the problem of exponentially increasing computational complexity that the classical Haar transform faces in high-dimensional data processing. After QHT completes the data mapping, quantum partial measurement technology undertakes the core function of multi-dimensional data pooling. Its core logic differs from the crude dimensionality reduction mode of traditional pooling that directly discards redundant data, instead utilizing the probabilistic characteristics of quantum states combined with preset pooling strategies to selectively extract key feature information from quantum states in probabilistic form.As the core driver of the entire optimization scheme, VQA constructs a hybrid optimization framework by integrating quantum computing and classical optimization technologies. Its core architecture consists of a parameterized quantum circuit (PQC) and a classical optimizer. By iteratively adjusting the paramete...

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