QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
arXiv:2110.03861
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 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.
Preprint. A Non-archival and preliminary venue was presented in NeurIPS 2021, Quantum Tensor Networks in Machine Learning Workshop
References in corpus (15)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- A Quantum Approximate Optimization Algorithm
- Quantum-enhanced machine learning
- Simulating Strongly Correlated Quantum Systems with Tree Tensor Networks
- The Bitter Truth About Quantum Algorithms in the NISQ Era
- Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
- QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
- Compressing Recurrent Neural Network with Tensor Train
- Ultimate tensorization: compressing convolutional and FC layers alike
- Computations in Quantum Tensor Networks
- Tensor-Train Recurrent Neural Networks for Video Classification
- Reinforcement learning for optimization of variational quantum circuit architectures
- Hybrid quantum-classical classifier based on tensor network and variational quantum circuit
- An efficient quantum algorithm for generative machine learning
- Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement