Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers

Abstract

Classification is one of the main applications of supervised learning. Recent advancement in developing quantum computers has opened a new possibility for machine learning on such machines. However, due to the noisy performance of near-term quantum computers, we desire an approach for solving classification problems with only shallow circuits. Here, we propose two ensemble-learning classification methods, namely bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform errormitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. In addition, its training error decays exponentially with the number of classifiers, leading to a favorable complexity for practical realization. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian.