QTN-VQC: an end-to-end learning framework for quantum neural networks

Abstract

This work focuses on investigating an end-to-end learning approach for quantum neural networks (QNN) on noisy intermediate-scale quantum devices. The proposed model combines a quantum tensor network (QTN) with a variational quantum circuit (VQC), resulting in a QTN-VQC architecture. This architecture integrates a QTN with a horizontal or vertical structure related to the implementation of quantum circuits for a tensor-train network. The study provides theoretical insights into the quantum advantages of the end-to-end learning pipeline based on QTN-VQC from two perspectives. The first perspective refers to the theoretical understanding of QTN-VQC with upper bounds on the empirical error, examining its learnability and generalization powers; The second perspective focuses on using the QTN-VQC architecture to alleviate the Barren Plateau problem in the training stage. Our experimental simulation on CPU/GPUs is performed on a handwritten digit classification dataset to corroborate our proposed methods in this work.

Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems

“…These algorithms leverage quantum entanglement to address fundamental machine learning challenges, such as function approximation and classification [34,35]. More studies about variational quantum algorithms and circuits can be found here [36]. This innovation has facilitated the development of hybrid quantum-classical algorithms, adaptable for use on existing Noisy Intermediate-Scale Quantum (NISQ) devices.…”