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

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 parameters of the quantum circuit to minimize a preset loss function, it ensures that the pooling operation can accurately capture the key features of high-dimensional data while balancing computational efficiency and precision. In multi-dimensional pooling optimization scenarios, the core value of VQA is reflected in three aspects: first, realizing direct pooling of multi-dimensional data without the need to reduce high-dimensional data to one-dimensional space, fundamentally solving the problem of local feature loss caused by traditional pooling and fully preserving the spatial structure and local correlations of the data; second, leveraging the characteristics of quantum superposition and entanglement to obtain richer feature representations of multi-dimensional data in the quantum state space, enabling the extraction of fine and complex features that classical pooling methods cannot capture; third, relying on quantum parallelism to significantly reduce the computational complexity of high-dimensional data pooling, achieving polynomial-level computational acceleration and substantially improving model training and inference efficiency. In addition, the VQA framework possesses good scalability. By adjusting the parameters and gate structures of the quantum circuit, it can flexibly adapt to the processing needs of unstructured data of different dimensions and types, such as one-dimensional audio, two-dimensional images, three-dimensional point clouds, and hyperspectral data, demonstrating broad application prospects.

The VQA-driven multi-dimensional pooling optimization technology researched by WiMi will break through the locality preservation limitations of traditional pooling methods in high-dimensional data processing, fully unleash the inherent advantages of quantum computing in feature representation and computational efficiency, and provide key technical support for the practical application of QML in complex multi-dimensional data tasks.

In the future, with the iterative upgrading of quantum hardware and the continuous optimization of algorithms, the multi-dimensional pooling optimization technology under the VQA framework is expected to achieve practical application in more fields.

About WiMi Hologram Cloud

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.

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