Label propagation is an essential semi-supervised learning method based on graphs, which has a broad spectrum of applications in pattern recognition and data mining. This paper proposes a quantum semi-supervised classifier based on label propagation. Considering the difficulty of graph construction, we develop a variational quantum label propagation (VQLP) method. In this method, a locally parameterized quantum circuit is created to reduce the parameters required in the optimization. Furthermore, we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement, which has shallower circuit depth and is more suitable for implementation on near-term quantum devices. We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set, and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier. This work opens a new path to quantum machine learning based on graphs.
Cite
@article{arxiv.2303.07906,
title = {A hybrid quantum-classical classifier based on branching multi-scale entanglement renormalization ansatz},
author = {Yan-Yan Hou and Jian Li and Xiu-Bo Chen and Chong-Qiang Ye},
journal= {arXiv preprint arXiv:2303.07906},
year = {2023}
}