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Quantum Machine Learning
QDNN: DNN with Quantum Neural Network Layers
arXiv
Authors: Chen Zhao, Xiao-Shan Gao
Year
2019
Paper ID
39531
Status
Preprint
Abstract Read
~2 min
Abstract Words
88
Citations
N/A
Abstract
In this paper, we introduce a quantum extension of classical DNN, QDNN. The QDNN consisting of quantum structured layers can uniformly approximate any continuous function and has more representation power than the classical DNN. It still keeps the advantages of the classical DNN such as the non-linear activation, the multi-layer structure, and the efficient backpropagation training algorithm. Moreover, the QDNN can be used on near-term noisy intermediate-scale quantum processors. A numerical experiment for image classification based on quantum DNN is given, where a high accuracy rate is achieved.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2019 reference point for readers tracking recent quantum research.
- In this paper, we introduce a quantum extension of classical DNN, QDNN.
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