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

Computer Science

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

J. Liu, M. Liu, et al.

This cutting-edge research by Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yunfei Wang, Yuri Alexeev, Jens Eisert, and Liang Jiang delves into the transformative potential of fault-tolerant quantum computing for training large machine learning models. The authors reveal a quantum algorithm that significantly reduces time complexity, demonstrating promising numerical experiments showcasing quantum enhancements in the training process.

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