Towards provably efficient quantum algorithms for large-scale machine-learning models.

ABSTRACT: Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as [Formula: see text], where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine lear

SUBMITTER: Liu J 

PROVIDER: S-EPMC10781664 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature