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QTN-VQC: An End-to-End Learning Framework for Quantum Neural Networks
Jun Qi · Huck Yang · Pin-Yu Chen

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.

Author Information

Jun Qi (Georgia Institute of Technology)

I am now a Ph.D. student jointly supervised by Prof. Chin-Hui Lee and Prof. Xiaoli Ma in the School of Electrical and Computer Engineering at Georgia Institute of Technology. Previously, I completed a graduate study in Electrical Engineering with an MSEE at the University of Washington, Seattle. Besides, I was a research intern in Deep Learning Technology Center (DLTC) at Microsoft Research (Redmond), and an NLP research intern at Tencent AI Lab (Seattle). My research work focuses on (1) Non-Convex Optimization and Statistical Learning for Understanding Deep Learning Systems; (2) Tensor Decomposition and Applications in Machine Learning; (3) Determinantal Point Process and Submodular Optimization; (4) Multimodal Speech Enhancement and Recognition; (5) Adversarial Security of Reinforcement Learning.

Huck Yang (Georgia Institute of Technology)

I am a 5th-year Ph.D. student at Georgia Tech working on Speech Recognition, Sequence Modeling, and Reinforcement Learning.

Pin-Yu Chen (IBM Research AI)

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