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Quantum Optimization and Quantum Learning: A Survey
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DOI：10.1109/ACCESS.2020.2970105.png)
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Abstract
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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.
Keywords:
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)
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