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

• URL: http://arxiv.org/abs/2110.03861v1
• Date: Wed, 6 Oct 2021 14:44:51 GMT
• Title: QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
• Authors: Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen
• Abstract summary: We introduce a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC) 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.
• Score: 71.14713348443465
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 encoding. 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.

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