Learnability of a hybrid quantum-classical neural network for graph-structured quantum data

• URL: http://arxiv.org/abs/2401.15665v2
• Date: Tue, 14 May 2024 08:07:24 GMT
• Title: Learnability of a hybrid quantum-classical neural network for graph-structured quantum data
• Authors: Yan-Ying Liang, Si-Le Tang, Zhe-Hao Yi, Hao-Zhen Si-Tu, Zhu-Jun Zheng,
• Abstract summary: We build a hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) with graph-structured quantum data. We show that the using of information about graph structures in quantum data can lead to better learning efficiency compared with the state-of-the-art model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classical data with graph structure always exists when dealing with many real-world problems. In parallel, quantum data with graph structure also need to be investigated since they are always produced by common quantum data sources.In this paper, we build a hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) with graph-structured quantum data. Specifically, based on this special graph-structured quantum data, we first find suitable cost functions for Res-HQCNN model to learn semisupervised quantum data with graphs. Then, we present the training algorithm of Res-HQCNN for graph-structured training data in detail. Next, in order to show the learning ability of Res-HQCNN,we perform extensive experiments to show that the using of information about graph structures in quantum data can lead to better learning efficiency compared with the state-of-the-art model. At the same time, we also design comparable experiments to explain that the using of residual block structure can help deeper quantum neural networks learn graph-structured quantum data faster and better.

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