Quantum speedup for pool-based active learning

He, Zhimin1; Li, Lvzhou2; Zheng, Shenggen3,4; Zou, Xiangfu3,5; Situ, Haozhen6

2019-11

10.1007/s11128-019-2460-x

Quantum Information Processing   影响因子和分区

1570-0755

1573-1332

Active learning aims to select the most informative samples to train an accurate classifier with minimum cost of labeling. It is widely used in many machine learning systems, where there are a large amount of unlabeled data, but it is difficult or expensive to obtain their labels due to the involvement of human efforts. However, active learning is time-consuming, particularly for the applications those have a great number of unlabeled samples, such as image retrieval, text mining and speech recognition. Thus, it is crucial to speed up the active learning algorithm. In this paper, we propose a quantum version of active learning algorithm, which converts a classical active learning to its quantum counterpart. We focus on the pool-based active learning, which is one of the most popular branches of active learning. The proposed quantum active learning algorithm can achieve quadratic speedup over the classical pool-based active learning.

Active learning Quantum machine learning Modified Grover's algorithm

SCI ; EI

英语

其他

Fundamental Research Funds for the Central Universities[17lgzd29]

Physics

Quantum Science & Technology ; Physics, Multidisciplinary ; Physics, Mathematical

WOS:000488351400001

SPRINGER

20194207546798

Character recognition ; Lakes ; Machine learning ; Speech recognition

Speech:751.5

Web of Science

1.Foshan Univ, Sch Elect & Informat Engn, Foshan 528000, Peoples R China
2.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Guangdong, Peoples R China
3.Peng Cheng Lab, Ctr Quantum Comp, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
5.Wuyi Univ, Sch Math & Computat Sci, Jiangmen 529020, Peoples R China
6.South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Guangdong, Peoples R China

He, Zhimin,Li, Lvzhou,Zheng, Shenggen,et al. Quantum speedup for pool-based active learning[J]. Quantum Information Processing,2019,18(11).

He, Zhimin,Li, Lvzhou,Zheng, Shenggen,Zou, Xiangfu,&Situ, Haozhen.(2019).Quantum speedup for pool-based active learning.Quantum Information Processing,18(11).

He, Zhimin,et al."Quantum speedup for pool-based active learning".Quantum Information Processing 18.11(2019).