A Survey of Quantum Computing for Finance

TL;DR

This survey reviews the current state of quantum computing applications in finance, highlighting potential benefits in stochastic modeling, optimization, and machine learning for financial problem-solving.

Contribution

It provides a comprehensive overview of quantum algorithms for finance, emphasizing their potential impact and feasibility on near-term quantum hardware.

Findings

Quantum algorithms can improve derivative pricing and risk modeling.

Feasibility of quantum solutions on near-term hardware is discussed.

Potential for quantum computing to revolutionize financial industry applications.

Abstract

Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems, such as derivative pricing, risk modeling, portfolio optimization, natural language processing, and fraud detection, more efficiently and accurately. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.