量子搜索及量子智能优化研究进展

摘要

为了提高智能优化算法的收敛速度及优化性能,目前国内外将量子计算机制和传统智能优化相融合.研究和提出了多种量子进化算法及量子群智能优化算法;为了进一步推动该领域的研究进展,系统地介绍了国内外提出的多种量子搜索及量子智能优化算法,其中包括量子搜索、量子衍生进化、量子神经网络三个方面内容;总结出目前改进量子搜索算法的主要机制和量子计算与传统智能计算的主要融合方式,并展望了量子搜索和量子智能优化有待进一步研究和需要解决的问题.%To improve the performance of converging and optimizing for the intelligent optimization algorithms, some quantum evolutionary algorithms and quantum swarm intelligent optimization algorithms are proposed by domestic and foreign scholars with application of integrating quantum computing and traditional intelligence computing. In order to further promote the progress of the research in the field,a lot of quantum search and quantum intelligent optimization algorithms are introduced in detailed, which included quantum search, quantum -inspired evolution, and quantum neural networks. Then it is derived that the main measures of improving quantum search algorithm and the integration way of quantum computing and traditional intelligence computing. The new issues of needing to further address are proposed in quantum search and quantum intelligent optimization.