大规模MIMO系统的无约束量子遗传算法

Abdulbasit M. A. Sabaawi, Mohammed R. Almasaoodi, Sara El Gaily, S. Imre
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引用次数: 1

摘要

有许多实际应用程序需要在未排序的数据库中找到极端值。这个数据库可能非常大,以至于没有可用的量子计算机或经典超级计算机可以执行搜索过程。提出了一种新的无约束量子遗传算法(QGA),以提高找到全局解和摆脱局部极小值的概率。该算法利用盲量子计算(BQC)提供的特性,通过将计算委托给量子远程设备来处理此计算问题。以大规模多输入多输出(MIMO)系统为例,演示了所开发的量子遗传方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unconstrained Quantum Genetic Algorithm for Massive MIMO System
There are plenty of real-world applications that require finding extreme value in an unsorted database. This database can be enormously large, such that there is no available quantum computer or classical supercomputer that can execute the search process. We proposed a new unconstrained quantum genetic algorithm (QGA) in order to increase the probability of finding the global solution and escaping from local minima. This algorithm exploits the features provided by blind quantum computation (BQC), which holds the promise to handle this computation issue by delegating computation to quantum remote devices. Massive multiple-input multiple-output (MIMO) systems are used as a toy example for demonstrating the effectiveness of the developed quantum genetic method.
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