insider brief
- WiMi considers a multidimensional pooling optimization approach that combines variational quantum algorithms, quantum Haar transforms, and quantum fractional measurement techniques.
- The proposed framework is designed to preserve local feature information while reducing the data dimensionality of high-dimensional datasets.
- The company says this approach has the potential to support future quantum machine learning applications including images, audio, point clouds, and hyperspectral data.
- Photo by Dynamic Wang from Unsplash.
Press Release — WiMi Hologram Cloud, Inc. (NASDAQ: WiMi) (“WiMi” or the “Company”), a world-leading hologram augmented reality (“AR”) technology provider, is researching multidimensional pooling optimization techniques under a variational quantum algorithm framework, proposing an innovative solution that integrates quantum Haar transform (QHT) and quantum fractional measurements, and building a quantum pooling mechanism. It has both local feature preservation capability and dimensionality reduction efficiency.
From the perspective of technical principle, Haar transform is a core technology in the field of classical signal processing and is widely used for data compression and feature extraction. As its quantized extension, QHT maps high-dimensional classical data onto a quantum state space through a group of parameterized quantum gates, achieving breakthrough improvements 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 states encode feature strength information.
At the same time, quantum entanglement builds correlations between feature dimensions. This not only fully preserves the global structural information of the data, but also enhances the correlation of local features through the local action domain constraints of quantum gates, effectively solving the problem of exponentially increasing computational complexity faced by classical Haar transforms in high-dimensional data processing. Once the QHT completes the data mapping, the quantum partial measurement technology takes over the core function of multidimensional data pooling. Its core logic differs from the crude dimensionality reduction mode of traditional pooling, which directly discards redundant data, and instead exploits the stochastic properties of quantum states in combination with a preset pooling strategy to selectively extract key feature information from quantum states in a probabilistic form.
VQA builds a hybrid optimization framework by integrating quantum computing and classical optimization techniques as the core driver of the entire optimization scheme. Its core architecture consists of a parameterized quantum circuit (PQC) and a classical optimizer. By iteratively adjusting the quantum circuit’s parameters to minimize a preset loss function, the pooling operation can accurately capture key features of high-dimensional data while balancing computational efficiency and accuracy.
In the multidimensional pooling optimization scenario, the core value of VQA is reflected in three aspects. One is to realize direct pooling of multidimensional 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 correlation of data. Second, we exploit the properties of quantum superposition and entanglement to obtain a richer feature representation of multidimensional data in quantum state space, enabling the extraction of fine and complex features that cannot be captured by classical pooling techniques. Third, we leverage quantum parallelism to significantly reduce the computational complexity of high-dimensional data pooling, achieve polynomial-level computational acceleration, and significantly improve the efficiency of model training and inference. Additionally, the VQA framework is highly extensible. By adjusting the parameters and gate structure of the quantum circuit, it can flexibly adapt to the processing needs of various dimensions and types of unstructured data, such as one-dimensional audio, two-dimensional images, three-dimensional point clouds, and hyperspectral data, demonstrating a wide range of potential applications.
The VQA-driven multidimensional pooling optimization technology studied by WiMi breaks the locality-preserving limitations of traditional pooling methods in high-dimensional data processing, maximizes the inherent advantages of quantum computing in feature representation and computational efficiency, and provides important technical support for the practical application of QML in complex multidimensional data tasks.
In the future, it is expected that with the iterative upgrade of quantum hardware and continuous optimization of algorithms, the multidimensional pooling optimization technique using the VQA framework will be put into practical use in more fields.
