Automatic design of quantum feature maps

被引：41

作者：

Altares-Lopez, Sergio ^{[1
,2
]}

Ribeiro, Angela ^{[1
]}

Garcia-Ripoll, Juan Jose ^{[3
]}

机构：

[1] UPM, CSIC, Ctr Automat & Robot CAR, Consejo Super Invest Cient, Ctra Campo Real Km 0,200, Arganda Del Rey 28500, Spain

[2] Univ Politecn Madrid, Programa Doctorado Automat & Robot, Calle Jose Gutierrez Abascal 2, E-28006 Madrid, Spain

[3] CSIC, Inst Fis Fundamental IFF, Consejo Super Invest Cient, Calle Serrano 113b, Madrid 28006, Spain

来源：

QUANTUM SCIENCE AND TECHNOLOGY | 2021年 / 6卷 / 04期

关键词：

quantum machine learning; genetic algorithms; artificial intelligence; automatic quantum classifier generation; optimization; quantum computing;

D O I：

10.1088/2058-9565/ac1ab1

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We propose a new technique for the automatic generation of optimal ad-hoc ansatze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.

引用

页数：11

共 40 条

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