量子联想记忆

摘要：

本文将量子计算与经典神经网络理论相结合，提出了一种量子计算学习算法。量子计算使用微观量子级效应来执行计算任务，并且在某些情况下产生的结果比经典计算结果快得多。量子理论的独特特性也可以用于创建具有神经元数量的容量指数的量子联想记忆。本文结合两种量子计算算法来产生这样的量子联想记忆。结果是与传统的联想记忆（如Hopfield网络）相比，记忆容量呈指数级增长。本文涵盖了必要的高层次量子力学和量子计算的想法，并介绍了量子联想记忆。理论分析证明了该存储器的实用性，并指出在不久的将来，一个小的版本应该是物理上可实现的。

英文摘要：

This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce such a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. The paper covers necessary high-level quantum mechanical and quantum computational ideas and introduces a quantum associative memory. Theoretical analysis proves the utility of the memory, and it is noted that a small version should be physically realizable in the near future.

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