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arXiv · 2105.12626

Automatic design of quantum feature maps

Sergio Altares-López·Angela Ribeiro·Juan José García-Ripoll
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Abstract

We propose a new technique for the automatic generation of optimal ad-hoc ansätze 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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Sergio Altares-López, Angela Ribeiro, Juan José García-Ripoll. 2021-05-26. Automatic design of quantum feature maps. https://doi.org/10.1088/2058-9565%2Fac1ab1

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