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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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Playback language: English
Abstract
This paper explores the potential of fault-tolerant quantum computing to improve the efficiency of training large machine learning models. The authors demonstrate that for sufficiently dissipative and sparse models, a quantum algorithm can achieve a time complexity of O(T² x polylog(n)), where n is the model size and T is the number of training iterations. This is achieved by adapting quantum algorithms for dissipative differential equations to solve the stochastic gradient descent problem. Numerical experiments on models with parameters ranging from 7 million to 103 million show that quantum enhancement is possible in the early stages of training after model pruning, suggesting a sparse parameter download and re-upload scheme.
Publisher
Nature Communications
Published On
Jan 10, 2024
Authors
Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yunfei Wang, Yuri Alexeev, Jens Eisert, Liang Jiang
Tags
fault-tolerant quantum computing
machine learning models
quantum algorithm
stochastic gradient descent
time complexity
numerical experiments
model pruning

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