In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.
@article{arxiv.2010.01312,
title = {Use Cases of Quantum Optimization for Finance},
author = {Samuel Mugel and Enrique Lizaso and Roman Orus},
journal= {arXiv preprint arXiv:2010.01312},
year = {2020}
}
Comments
10 pages, conference proceedings, to appear in Quantum Computing in Econometrics and Quantum Economics and Related Topics, edited by Songsak Sriboonchitta, Vladik Kreinovich, Woraphon Yamaka