# A duplication-free quantum neural network for universal approximation

@inproceedings{Hou2022ADQ, title={A duplication-free quantum neural network for universal approximation}, author={Xiaokai Hou and Guanyu Zhou and Qing Li and Shan Jin and Xiaoting Wang}, year={2022} }

The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its eﬀectiveness. A non-universal neural network could fail in completing the machine learning task. One proposal for universality is to encode the quantum data into identical copies of a tensor product, but this will substantially increase the system size and the circuit complexity. To address this problem, we propose a simple design of a duplication-free…

## 2 Citations

### Ensemble-learning variational shallow-circuit quantum classiﬁers

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Two ensemble-learning classification methods, namely bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets are proposed.

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- Computer Science
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Two ensemble-learning error mitigation methods, namely bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets are proposed.

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