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

2021/07/17 by Tong Dou, Dou, Tong, Zhenwei Zhou +7
Computer Science · #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum-Dot Cellular Automata

paper · pdf · doi:10.48550/arxiv.2107.08171

openalex publication_date 2021/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Quantum Approximate Optimization Algorithm
• Quantum Support Vector Machine for Big Data Classification
• Quantum machine learning
• Quantum Computing in the NISQ era and beyond
• Quantum convolutional neural networks

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