Deep learning is a modern approach to realize artificial intelligence. Many frameworks exist to implement the machine learning task; however, performance is limited by computing resources. Using a quantum computer to accelerate training is a promising approach. The variational quantum circuit (VQC) has gained a great deal of attention because it can be run on near-term quantum computers. In this paper, we establish a new framework that merges traditional machine learning tasks with the VQC. Users can implement a trainable quantum operation into a neural network. This framework enables the training of a quantum-classical hybrid task and may lead to a new area of quantum machine learning.
@article{arxiv.1901.09133,
title = {VQNet: Library for a Quantum-Classical Hybrid Neural Network},
author = {Zhao-Yun Chen and Cheng Xue and Si-Ming Chen and Guo-Ping Guo},
journal= {arXiv preprint arXiv:1901.09133},
year = {2019}
}