![]() Enhancing Optimization Through Generative Modeling Beyond the classic examples of text and image generation, tensorized generative models could generate novel solutions to complex optimization problems that abound in industrial settings. ![]() This could have major implications for enterprise applications of generative AI. Not only can tensor networks compress generative models, but the resulting tensorized generative models could also generate higher-quality samples. Generative AI aside, tensorization could also reduce the costs of other complex models, such as Monte Carlo simulations, which can take up a large portion of a finance enterprise’s entire compute budget. This cost reduction could also enable generative AI to be deployed on edge devices, such as phones and voice assistants. Thus, through “tensorization,” tensor networks could have the potential to reduce both the costs and the carbon footprint of generative AI, making generative models more accessible and accelerating their adoption in the enterprise. These capabilities apply to major NN architectures, including transformers, the NN type used in ChatGPT. ![]() Tensor networks could be a useful tool for model compression, with the ability to deliver a speedup and size reduction for large neural networks (NNs). This would allow enterprises to develop capabilities that could be useful once we have fault-tolerant or strongly error-mitigated quantum computers.īut tensor networks could also have more near-term business value for generative AI. Thus, tensor networks could allow users to start developing “quantum applications” on classical hardware that can run on the fault-tolerant quantum computers of the future. Tensor networks could potentially play a critical role in the development of quantum computing: They can simulate quantum circuits on classical hardware today and could be replaced with real quantum circuits in the future. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |