NeurIPS 2020poster220 citations
Recurrent Quantum Neural Networks
Abstract
Recurrent neural networks are the foundation of many sequence-to-sequence models in machine learning, such as machine translation and speech synthesis. With applied quantum computing in its infancy, there already exist quantum machine learning models such as variational quantum eigensolvers which have been used e.g. in the context of energy minimization tasks. Yet, to date, no viable recurrent quantum network has been proposed.
BibTeX
@inproceedings{NEURIPS2020_0ec96be3,
author = {Bausch, Johannes},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {1368--1379},
publisher = {Curran Associates, Inc.},
title = {Recurrent Quantum Neural Networks},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/0ec96be397dd6d3cf2fecb4a2d627c1c-Paper.pdf},
volume = {33},
year = {2020}
}