Automatic design of quantum feature maps

• URL: http://arxiv.org/abs/2105.12626v1
• Date: Wed, 26 May 2021 15:31:10 GMT
• Title: Automatic design of quantum feature maps
• Authors: Sergio Altares-L\'opez, Angela Ribeiro, Juan Jos\'e Garc\'ia-Ripoll
• Abstract summary: We propose a new technique for the automatic generation of optimal ad-hoc ans"atze for classification by using quantum support vector machine (QSVM) This efficient method is based on NSGA-II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size.
• Score: 0.3867363075280543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new technique for the automatic generation of optimal ad-hoc ans\"atze for classification by using quantum support vector machine (QSVM). This efficient method is based on NSGA-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.

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