Quantum Optimization and Quantum Learning: A Survey

Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.23568-23593
Main Authors: Li, Yangyang, Tian, Mengzhuo, Liu, Guangyuan, Peng, Cheng, Jiao, Licheng
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Clustering algorithms
Evolutionary computation
Interdisciplinary subjects
Logic gates
Machine learning
Optimization
quantum clustering (QC)
Quantum computing
quantum evolutionary algorithm (QEA)
quantum immune clonal algorithm (QICA)
quantum learning
quantum neural network (QNN)
Quantum optimization
quantum particle swarm algorithm (QPSO)
Sociology
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2970105