Quantum Optimization and Quantum Learning: A Survey

被引:63
|
作者
Li, Yangyang [1 ]
Tian, Mengzhuo [1 ]
Liu, Guangyuan [1 ]
Peng, Cheng [1 ]
Mao, Licheng [1 ]
机构
[1] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China
来源
IEEE ACCESS | 2020年 / 8卷 / 08期
基金
中国国家自然科学基金;
关键词
Quantum optimization; quantum learning; quantum evolutionary algorithm (QEA); quantum particle swarm algorithm (QPSO); quantum immune clonal algorithm (QICA); quantum neural network (QNN); quantum clustering (QC); PARTICLE SWARM OPTIMIZATION; INSPIRED EVOLUTIONARY ALGORITHM; COMMUNITY STRUCTURE; GENETIC ALGORITHM; NEURAL-NETWORK; IMMUNE CLONE; SELECTION; MODEL; SYSTEM;
D O I
10.1109/ACCESS.2020.2970105
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:23568 / 23593
页数:26
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