Quantum Computing for Finance: State of the Art and Future Prospects

TL;DR

This paper reviews the current state and future potential of quantum computing in finance, highlighting promising algorithms, practical demonstrations, and technical challenges in applying quantum methods to financial problems.

Contribution

It provides a comprehensive survey of quantum algorithms for finance, including practical demonstrations and discusses future prospects and challenges in the field.

Findings

Quantum algorithms show promise for financial simulation and optimization.

Demonstrations on IBM Quantum back-ends validate algorithm feasibility.

Quantum computing could significantly impact financial problem-solving.

Abstract

This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.