WiMi Explores Quantum Algorithms for Large-Scale Machine Learning Models
Building upon the construction of sparse neural networks, WiMi further developed a quantum ordinary differential equation (ODE) system corresponding to sparse training. This system requires both sparsity and dissipation conditions to ensure the feasibility of quantum acceleration. Sparsity means fewer interaction terms within the quantum system, which helps reduce the complexity of quantum computing. The dissipation condition ensures that the quantum system can stably evolve toward a certain equilibrium state, facilitating subsequent measurements and parameter extraction. To further enhance the algorithm's computational efficiency and robustness, a quantum Kalman filtering method was employed. This method linearizes the nonlinear equation by transforming the quantum state evolution equation into a linear differential equation, enabling better handling of disturbances such as quantum noise. After solving the quantum system, the state of the quantum system is measured to obtain the final training parameters. These parameters are then used to construct and optimize the classical sparse neural network, thereby improving model performance. The introduction of quantum measurement ensures that the quantum acceleration effect can be practically applied to classical machine learning models, thus achieving an organic integration of quantum and classical computing.
The quantum algorithm for large-scale machine learning models developed by WiMi offers significant technical advantages. By combining sparsity with quantum acceleration, the algorithm notably reduces computational complexity and improves the efficiency and scalability of model training. This makes it possible to achieve rapid training of large-scale machine learning models and helps drive the widespread application of artificial intelligence technologies. Moreover, the application of quantum algorithms will pave new paths for the sustainable development of large-scale machine learning models. Traditional large-scale machine learning model training processes are often associated with massive energy consumption and carbon emissions, while quantum algorithms are expected to reduce energy consumption by lowering computational complexity, thus enabling sustainable development. The construction and solving of the quantum ordinary differential equation system also provides a new framework and methodology for theoretical research in quantum machine learning algorithms. This framework not only helps advance the deep development of the quantum machine learning field but also lays the foundation for the emergence of more innovative algorithms in the future.
With the continuous maturation of quantum hardware and ongoing improvements in quantum algorithm theory, the quantum algorithm for large-scale machine learning models explored by WiMi is expected to demonstrate its revolutionary potential across various fields. For example, in the digital art domain, quantum algorithms can accelerate image and video processing speeds, enhancing the efficiency and quality of digital art creation. In the natural language processing field, quantum algorithms can speed up the training of language models, improving language understanding and generation capabilities, and driving human society toward a more intelligent and efficient future.
About
Safe Harbor Statements
This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the
Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the
View original content:https://www.prnewswire.com/news-releases/wimi-explores-quantum-algorithms-for-large-scale-machine-learning-models-302524261.html
SOURCE