Quantum annealing for semi-supervised learning
| Autor: | Wen Zhang, Cheng Zhou, Yu-Lin Zheng, Wei Geng |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
FOS: Physical sciences General Physics and Astronomy 02 engineering and technology Semi-supervised learning Machine learning computer.software_genre 01 natural sciences 0103 physical sciences Classifier (linguistics) 010306 general physics Quantum Quantum Physics business.industry Quantum annealing Combinatorial optimization problem 021001 nanoscience & nanotechnology Quantum technology ComputingMethodologies_PATTERNRECOGNITION Labeled data Graph (abstract data type) Artificial intelligence Quantum Physics (quant-ph) 0210 nano-technology business computer |
| Zdroj: | Chinese Physics B. 30:040306 |
| ISSN: | 1674-1056 |
| DOI: | 10.1088/1674-1056/abe298 |
| Popis: | Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical counterparts. Semi-supervised learning is a machine learning technique that makes use of both labeled and unlabeled data for training, which enables a good classifier with only a small amount of labeled data. In this paper, we propose and theoretically analyze a graph-based semi-supervised learning method with the aid of the quantum annealing technique, which efficiently utilizes the quantum resources while maintaining good accuracy. We illustrate two classification examples, suggesting the feasibility of this method even with a small portion (30%) of labeled data involved. |
| Databáze: | OpenAIRE |
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