WiMi Hologram Cloud Inc. (NASDAQ:Â WiMi) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced an in-depth study of the multidimensional pooling optimization technique in variational quantum algorithms. By introducing the Quantum Haar Transform (QHT) and quantum partial measurement, they provided a novel solution for multidimensional data pooling. The Haar transform is a classical signal processing technique used for data compression and feature extraction. The Quantum Haar Transform (QHT) is its extension within the quantum computing framework, which leverages the superposition and entanglement properties of quantum states to efficiently transform multidimensional data.
Through QHT, multidimensional data is mapped to a quantum state space, where each qubit represents a dimension or feature of the data. This mapping not only preserves the global structure of the data but also enhances the expression of local features. After the Quantum Haar Transform, quantum partial measurement techniques can selectively extract key information from the quantum state, enabling the pooling operation for multidimensional data. Unlike traditional pooling methods that directly discard part of the data, quantum partial measurement leverages the probabilistic nature of quantum states to retain the most important feature information in probabilistic form, according to predefined pooling strategies (such as max pooling, average pooling, etc.). This process not only reduces the data dimensionality but also preserves the locality and key features of the data, providing high-quality input for subsequent quantum classification or regression tasks.
Variational Quantum Algorithms (VQA) are hybrid algorithms that combine quantum computing and classical optimization. By using parameterized quantum circuits and optimization techniques such as gradient descent, VQAs iteratively adjust quantum states to minimize a given loss function. In multidimensional pooling optimization, VQA is used to optimize parameters, ensuring that the pooling operation can accurately capture key features of the data while maintaining computational efficiency and accuracy. Through an iterative optimization process, VQA continually adjusts the parameters of the quantum circuit so that the quantum state transformation and measurement process can maximally preserve the locality and feature structure of the data. Moreover, VQAs can directly perform pooling operations on multidimensional data without the need to reduce the data to one dimension, effectively retaining the locality and structural information of the data. The superposition and entanglement properties of quantum states enable more rich representations of multidimensional data in quantum space, helping to extract finer and more complex features. The utilization of quantum parallelism and entanglement allows VQA to significantly accelerate computation when handling large-scale multidimensional data, improving the efficiency of model training and inference. The VQA framework is highly scalable and can accommodate various types of multidimensional data processing needs, ranging from one-dimensional audio data to two-dimensional image data and even three-dimensional hyperspectral data. By adjusting the parameters and structure of the quantum circuit, VQA can be flexibly applied to different dimensional data processing tasks.
The multidimensional pooling optimization technology under the Variational Quantum Algorithm framework researched by WiMi provides a new solution for quantum machine learning in handling complex multidimensional data tasks. It not only overcomes the limitations of traditional pooling methods when dealing with high-dimensional data but also fully leverages the unique advantages of quantum computing. As quantum computing technology continues to develop and mature, the multidimensional pooling optimization technology under the VQA framework is expected to demonstrate its enormous application potential and value in more fields. In the future, with improvements in quantum hardware and algorithm optimization, this technology is expected to provide strong support for building more efficient and accurate quantum machine learning models.
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