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

Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen

Keywords: quantum neural networks, variational quantum circuits, end-to-end learning framework, tensor-train network

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.

One-sentence Summary: An end-to-end learning framework for quantum neural network based on our proposed QTN-VQC model