A hybrid quantum-classical classifier based on branching multi-scale entanglement renormalization ansatz

• URL: http://arxiv.org/abs/2303.07906v1
• Date: Tue, 14 Mar 2023 13:46:45 GMT
• Title: A hybrid quantum-classical classifier based on branching multi-scale entanglement renormalization ansatz
• Authors: Yan-Yan Hou, Jian Li, Xiu-Bo Chen, Chong-Qiang Ye
• Abstract summary: 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.
• Score: 5.548873288570182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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