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

TLDR

The article reviews the applicability, current state, and future potential of quantum computing for financial problems. It introduces quantum computing fundamentals, surveys finance problem classes that are classically hard, and details quantum algorithms for simulation, optimization, and machine learning in financial services. Demonstrations on IBM Quantum back‑ends illustrate potential benefits, and the paper summarizes technical challenges and future prospects.

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.