Quantum computing for finance: overview and prospects

TL;DR

This paper reviews how quantum computing techniques, including optimization, machine learning, and amplitude estimation, can enhance financial analysis and decision-making processes, highlighting current methods and future prospects.

Contribution

It provides a comprehensive overview of quantum algorithms applied to finance, emphasizing potential improvements and new directions in quantum-enhanced financial computations.

Findings

Quantum annealers can optimize portfolios and identify arbitrage opportunities.

Quantum amplitude estimation offers speed-ups for Monte Carlo simulations.

Quantum machine learning can improve deep-learning methods in finance.

Abstract

We discuss how quantum computation can be applied to financial problems, providing an overview of current approaches and potential prospects. We review quantum optimization algorithms, and expose how quantum annealers can be used to optimize portfolios, find arbitrage opportunities, and perform credit scoring. We also discuss deep-learning in finance, and suggestions to improve these methods through quantum machine learning. Finally, we consider quantum amplitude estimation, and how it can result in a quantum speed-up for Monte Carlo sampling. This has direct applications to many current financial methods, including pricing of derivatives and risk analysis. Perspectives are also discussed.