Quantum computing for finance: Overview and prospects

By Roman Orus, Samuel Mugel, Enrique Lizaso. Reviews in Physics, 2018

666 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5f16cd9ad0d9f0dd6b631b46fbe731da92d6b6eb

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In Artificial Intelligence

Citations in the corpus

Quantum risk analysis (2018, methodology): “Problems in finance that make use of machine learning may benefit from quantum machine learning.(10) A quantum computer may also be used to optimize the risk-return of portfolios and sample from the optimal portfolio.”
AI in Finance: Challenges, Techniques, and Opportunities (2021, methodology): “• Modeling financial MTS by quantum-based entropy time series [122], calculating the similarity between a new time series and the existing ones by dynamic time warping methods and modeling the co-evolving momentum and trends between time series.”
Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020, background): “Banks can estimate the risk level of their customers’ loans by credit scoring which can be formulated as classification [67] and/or regression problem based on the rich features of customers, such as, age, salary, historical payment, micro and macro economic indicators and so on (see also [68]).”
Option Pricing using Quantum Computers (2019, background): “Recently developed applications of gate-based quantum computing for use in finance [15] include portfolio optimization [16], the calculation of risk measures [17] and pricing derivatives [18–20].”
Warm-starting quantum optimization (2020, background): “Gate-based quantum computers are expected to aid in solving problems such as quantum chemistry calculations [1–3], machine learning [4, 5], financial simulation [6–13] and combinatorial optimization [14, 15].”
Quantum simulation and computing with Rydberg-interacting qubits (2020, background): “…digital quantum simulation 7,207 , which would potentially open up important applications in high-energy physics 11,207,208 , mathematics 209,210 , quantum chemistry 211–213 , biophysics 153,214–216 and even in some areas of machine learning 217 , ﬁnance 218 and logistics 219,220 , among others.”