Automatic design of quantum feature maps
This work addresses the challenge of efficient quantum circuit design for machine learning, though it appears incremental as it builds on existing quantum support vector machine methods.
The authors tackled the problem of designing quantum feature maps for classification by proposing an automatic technique using genetic algorithms to optimize accuracy and circuit size, demonstrating its validity on a non-linear dataset and comparing it with classical classifiers.
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.