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BEIJING - WiMi Hologram Cloud Inc. (NASDAQ:WIMI), a technology company with a market capitalization of $348.64 million and strong financial health according to InvestingPro metrics, announced Thursday it is exploring a quantum machine learning algorithm designed to improve the efficiency of training large-scale machine learning models.
The company’s approach combines classical machine learning algorithms for pre-training with quantum acceleration technology. The process involves constructing sparse neural networks that reduce computational burden while enabling quantum acceleration.
WiMi has developed a quantum ordinary differential equation system that requires both sparsity and dissipation conditions to ensure quantum acceleration feasibility. The company also implemented a quantum Kalman filtering method to enhance computational efficiency and manage quantum noise.
According to the company’s press release, the algorithm aims to reduce computational complexity while improving training efficiency and scalability for large-scale machine learning models. WiMi suggests the approach could potentially lower energy consumption compared to traditional training methods.
The company indicated potential applications for the technology across various fields, including digital art for accelerating image and video processing, and natural language processing to speed up language model training.
WiMi Hologram Cloud describes itself as a holographic cloud technical solution provider focusing on areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, and holographic semiconductor technology.
The announcement contains forward-looking statements about the technology’s potential applications and benefits, with actual results subject to various risks and uncertainties as noted in the company’s SEC filings.
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