Use Cases of Quantum Optimization for Finance
Samuel Mugel, Enrique Lizaso, Roman Orus
TL;DR
This paper reviews recent applications of quantum optimization algorithms in finance, focusing on predicting financial crashes and optimizing portfolios using various quantum strategies.
Contribution
It provides a comparative overview of quantum approaches like annealers, gate-based processors, and tensor networks in financial problem-solving.
Findings
Quantum algorithms show promise in financial crash prediction.
Quantum strategies can enhance dynamic portfolio optimization.
Different quantum hardware have unique advantages for finance applications.
Abstract
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.
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