量子神经计算和量子遗传算法的理论分析和应用

经过比较研究发现，在量子计算与神经网络和遗传算法之间，不论在计算思想上还是模型表达上，都存在着许多相似之处，这些相似性启发人们去研究基于量子理论的神经网络和遗传算法模型，一方面探索神经网络和遗传算法在量子系统上的实现方法，另一方面研究量子理论启发下的新的神经网络与遗传算法模型。本文总结了本课题组近年来在量子计算与神经网络和遗传算法相结合领域的研究工作，包括量子系统实现神经计算的理论分析，量子神经网络物理模型的研究，基于量子概率表达的量子遗传算法及其应用研究等，并对今后的发展提出了展望。

Theoretical analysis and applications of quantum neural networks and quantum genetic algorithms

There exist many similarities, in both fundamental principle and model expression, between quantum computation and Neural Networks (NNS)/Genetic Algorithms (GAS). These similarities give the inspiration to the studies of the models of NNs and GAs based on the quantum principles. The efforts are to explore the approaches to the implementation of NNs and GAs on quantum systems, and to develop new models inspired by quantum principles and concepts. This article reviews the work of this research group on the combination of quantum computation and NNs/GAs, including theoretical analysis of physical realizability of neural computing, physical model of quantum NN, quantum GA based on quantum probability representation and its applications, and gives a perspective of future development in this research area.