• Corpus ID: 238531441

# QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks

@article{Qi2021QTNVQCAE,
title={QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks},
author={Jun Qi and Chao-Han Huck Yang and Pin-Yu Chen},
journal={ArXiv},
year={2021},
volume={abs/2110.03861}
}
• Published 6 October 2021
• Computer Science
• ArXiv
The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC). The architecture of QTN is composed of a parametric tensor-train network for feature extraction and a tensor product encoding for…
8 Citations

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