Qtnvqc An Endtoend Learning Framework For Quantum Neural Networks
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**arXiv ID:** 2110.03861 **Authors:** Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen **Published:** 2021-10-06T14:44:51Z **Abstract:** 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 ar...
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# QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
**arXiv ID:** 2110.03861
**Authors:** Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen
**Published:** 2021-10-06T14:44:51Z
**Abstract:**
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 quantum embedding. We highlight the QTN for quantum embedding in terms of two perspectives: (1) we theoretically characterize QTN by analyzing its representation power of input features; (2) QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement. Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.
## Skill Description
This skill is generated from the arXiv paper: QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks (2110.03861).
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## References
- [arXiv:2110.03861](http://arxiv.org/abs/2110.03861v3)
