Hierarchical quantum classifiers

TL;DR

This paper explores hierarchical quantum classifiers, demonstrating their improved accuracy and robustness in classifying classical and quantum data, including deployment on real quantum hardware.

Contribution

It introduces more expressive hierarchical quantum circuits that outperform simpler ones and can classify highly entangled quantum states, with practical implementation on IBM quantum hardware.

Findings

More expressive circuits achieve better accuracy.

Classifiers are robust to noise.

Successful deployment on ibmqx4 quantum computer.

Abstract

Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there is no known efficient classical method. We compare performance for several different parameterizations on two classical machine learning datasets, Iris and MNIST, and on a synthetic dataset of quantum states. Finally, we demonstrate that performance is robust to noise and deploy an Iris dataset classifier on the ibmqx4 quantum computer.