Implementable Simple Quantum Genetic Algorithm

EasyChair Preprint 6092

Abstract

Quantum machine learning (QML) is a relatively recent field of research in which the areas of Quantum Computing (QC) and Machine Learning (ML) are merged in different ways and at different levels. In this paper, we propose a quantum genetic algorithm (QGA) that is a direct translation of the classical genetic algorithm (GA). Compared to other existing works, our proposal allows a simpler and more direct implementation, and therefore with less hardware requirements. QGA is compared with its classical counterpart through a function optimization benchmark, showing that both algorithms are equivalent. The results suggest future work of exploring similar algorithms and strategies in the search of quantum advantages.

Keyphrases: Genetic Algorithms, Optimization, quantum algorithms, quantum computing

@booklet{EasyChair:6092,
  author    = {Mikel Garcia de Andoin and Javier Echanobe},
  title     = {Implementable Simple Quantum Genetic Algorithm},
  howpublished = {EasyChair Preprint 6092},
  year      = {EasyChair, 2021}}