【摘要】 提出一种基于量子纠缠的联想记忆神经网络(QuEAM)。对比传统的联想记忆网络,QuEAM的 存储容量得到了指数级的增大。学习算法是根据纠缠量度的性质,采用Grover量子迭代算法的基本原理局域放 大量子位(qubit)的概率振幅,相当于传统计算机的按位操作,讨论了这个学习算法下的量子基本原理。最后给 出具体的例子说明了算法的有效性。更多还原

【Abstract】 An approach to constructing an artificial quantum associative memory based on entanglement (QuEAM) is discussed. The QuEAM is an exponential increase in the capacity of the memory when compared to classical associative memories such as the Hopfield network. According to the characteristics of amount of entanglement, the study algorithm based on Grover’s well-known algorithm is locally magnified the probability amplitude for the qubit. The basic principle of entangled states is discussed. Concrete examples illustrating the properties of the proposed model are also presented.更多还原