Automatic design of quantum feature maps
Quantum Physics
2021-08-27 v1 Artificial Intelligence
Machine Learning
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.
Cite
@article{arxiv.2105.12626,
title = {Automatic design of quantum feature maps},
author = {Sergio Altares-López and Angela Ribeiro and Juan José García-Ripoll},
journal= {arXiv preprint arXiv:2105.12626},
year = {2021}
}