An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection
Quantum Physics
2021-07-20 v1
Abstract
Combining the advantages of quantum computing and neural networks, quantum neural networks (QNNs) have gained considerable attention recently. However, because of the lack of quantum resource, it is costly to train QNNs. In this work, we presented a simple unsupervised method for quantum-classical convolutional networks to learn a hierarchy of quantum feature extractors. Each level of the resulting feature extractors consist of multiple quanvolution filters, followed by a pooling layer. The main contribution of the proposed approach is to use the -means clustering to maximize the difference of quantum properties in quantum circuit ansatz. One experiment on the bearing fault detection task shows the effectiveness of the proposed method.
Cite
@article{arxiv.2107.08171,
title = {An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection},
author = {Tong Dou and Zhenwei Zhou and Kaiwei Wang and Shilu Yan and Wei Cui},
journal= {arXiv preprint arXiv:2107.08171},
year = {2021}
}