Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11128-019-2460-x | Quantum Information Processing |
Keywords | Field | DocType |
Active learning, Quantum machine learning, Modified Grover’s algorithm | Quantum,Text mining,Active learning,Quantum machine learning,Quantum mechanics,Image retrieval,Quadratic equation,Artificial intelligence,Classifier (linguistics),Machine learning,Speedup,Physics | Journal |
Volume | Issue | ISSN |
18 | 11 | 1570-0755 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhimin He | 1 | 3 | 2.51 |
| Lvzhou Li | 2 | 165 | 15.36 |
| Shenggen Zheng | 3 | 83 | 8.77 |
| Xiangfu Zou | 4 | 47 | 5.64 |
| Haozhen Situ | 5 | 43 | 10.96 |