Quantum computing for finance: Overview and prospects

# Quantum computing for finance: Overview and prospects > OpenAlex Metadata Hub · https://openalex.org/W2841500134 ## Bibliographic - **DOI:** 10.1016/j.revip.2019.100028 - **Year:** 2019 - **Citations:** 637 - **Open Access:** Yes (hybrid) - **License:** cc-by - **Source:** https://doi.org/10.1016/j.revip.2019.100028 ## Authors - Román Orús - Samuel Mugel - Enrique Lizaso ## 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. ## Keywords Quantum computer, Computer science, Quantum, Quantum annealing, Arbitrage, Monte Carlo method, Financial engineering, Computational finance, Computation, Importance sampling, Finance, Algorithm, Economics, Mathematics, Physics, Quantum mechanics ## Concepts - Quantum computer - Computer science - Quantum - Quantum annealing - Arbitrage - Monte Carlo method - Financial engineering - Computational finance - Computation - Importance sampling - Finance - Algorithm - Economics - Mathematics - Physics - Quantum mechanics - Statistics --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “Quantum computing for finance: Overview and prospects” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): 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. Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.