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

• URL: http://arxiv.org/abs/2107.08171v1
• Date: Sat, 17 Jul 2021 03:16:59 GMT
• Title: An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection
• Authors: Tong Dou, Zhenwei Zhou, Kaiwei Wang, Shilu Yan, Wei Cui
• Abstract summary: We present a simple unsupervised method for quantum-classical convolutional networks to learn a hierarchy of quantum feature extractors. 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.
• Score: 5.609958919699706
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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