Quantum computing for finance

TL;DR

This paper reviews the current state of quantum computing in finance, highlighting potential advantages, limitations, and challenges in applying quantum techniques to stochastic modeling, optimization, and machine learning.

Contribution

It provides a comprehensive overview tailored for physicists, connecting classical financial methods with emerging quantum approaches and discussing future research directions.

Findings

Quantum computing could revolutionize financial modeling and optimization.

Current quantum techniques face significant practical and theoretical challenges.

Physicists can play a key role in addressing quantum computing challenges in finance.

Abstract

Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present 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. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle.