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

TL;DR

This paper introduces ensemble-learning error mitigation techniques, bootstrap aggregating and adaptive boosting, to improve the accuracy of shallow noisy quantum classifiers on classical and quantum data.

Contribution

It presents novel ensemble-learning protocols specifically designed for error mitigation in variational quantum classifiers, outperforming primitive methods.

Findings

Adaptive boosting outperforms bootstrap aggregating in accuracy.

Significant improvement in classification accuracy for classical and quantum datasets.

Protocols effectively utilize shallow circuits for complex classification tasks.

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. Due to the noisy performance of near-term quantum computers, error mitigation techniques are essential for extracting meaningful data from noisy raw experimental measurements. Here, we propose two ensemble-learning error mitigation 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 error-mitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian, in which we observe a significant improvement in accuracy. Our ensemble-learning methods provide a systematic way of utilising shallow circuits to solve complex classification problems.