  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.
