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

Autor:	Qi, Jun, Yang, Chao-Han Huck, Chen, Pin-Yu
Rok vydání:	2021
Předmět:	Quantum Physics Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Zdroj:	Quantum Tensor Networks in Machine Learning Workshop, NeurIPS 2021
Druh dokumentu:	Working Paper
Popis:	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. Comment: Preprint. A Non-archival and preliminary venue was presented in NeurIPS 2021, Quantum Tensor Networks in Machine Learning Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.03861 Zobrazit plný text záznamu View this record from Arxiv