Automatic design of quantum feature maps

TL;DR

This paper introduces an automated method for designing optimal quantum feature maps for classification tasks using multiobjective genetic algorithms, improving quantum machine learning efficiency.

Contribution

It presents a novel genetic algorithm-based approach to automatically generate and optimize quantum feature maps for QSVM, balancing accuracy and circuit complexity.

Findings

Effective generation of quantum feature maps for non-linear datasets

Demonstrated advantages over classical classifiers

Interpretable quantum circuits produced

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.