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Quantum Machine Learning

An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection

arXiv

Authors: Tong Dou, Zhenwei Zhou, Kaiwei Wang, Shilu Yan, Wei Cui

Year

2021

Paper ID

63147

Status

Preprint

Abstract Read

~2 min

Abstract Words

110

Citations

N/A

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 K-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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2021 reference point for readers tracking recent quantum research.
Combining the advantages of quantum computing and neural networks, quantum neural networks (QNNs) have gained considerable attention recently.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2107.08171 [2] arXiv https://arxiv.org/abs/2107.08171 [3] Publisher https://arxiv.org/abs/2107.08171

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