QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
Quantum Physics2022-09-28v3Artificial IntelligenceComputation and LanguageComputer Vision and Pattern RecognitionMachine LearningNeural and Evolutionary Computing
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.
@article{arxiv.2110.03861,
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 preprint arXiv:2110.03861},
year = {2022}
}
Comments
Preprint. A Non-archival and preliminary venue was presented in NeurIPS 2021, Quantum Tensor Networks in Machine Learning Workshop