[论文解读] A Survey of Quantum Computing for Finance

Dylan Herman, Cody Googin+6 more|arXiv (Cornell University)|Jan 8, 2022

Quantum Computing Algorithms and Architecture被引用 95

一句话总结

本综述提供了将量子计算应用于金融的综合概述，聚焦于随机建模、优化与机器学习，并讨论近端可行性与硬件挑战。

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.

研究动机与目标

• Motivate the study of quantum computing applications in finance and identify key financial problems amenable to quantum speedups.
• Summarize core quantum computing concepts, hardware realities, and algorithm families relevant to finance.
• Map financial use cases to quantum algorithms in stochastic modeling, optimization, and machine learning.
• Highlight challenges and opportunities for near-term quantum devices (NISQ) in real-world financial tasks.

提出的方法

• Review and synthesize the state of the art across stochastic modeling, optimization, and ML for finance.
• Explain gate-based and adiabatic quantum computing models and their suitability for different problem classes.
• Discuss foundational quantum algorithms (e.g., QMCI, QPE, QAOA, VQE) and their relevance to finance.
• Correlate financial problems with quantum techniques and hardware constraints across sections 5–7 and 8.

实验结果

研究问题

• RQ1 Which financial problems can benefit from quantum computing, and what are the expected speedups on near-term devices?
• RQ2 How do stochastic modeling, optimization, and machine learning problems in finance map to quantum algorithms?
• RQ3 What are the practical hardware limitations (noise, connectivity, gate speed) affecting quantum finance applications?
• RQ4 What is the current status and feasibility of implementing quantum-finance use cases on actual devices?
• RQ5 What future research directions emerge for achieving quantum advantage in finance?

主要发现

• Quantum computing offers potential quadratic speedups for Monte Carlo-based risk metrics and QMCI in finance.
• Finance problems in stochastic modeling, optimization, and ML can be formulated for near-term quantum devices, though hardware limitations remain significant.
• A broad set of financial use cases—derivative pricing, risk modeling, portfolio optimization, and ML-driven tasks—can be addressed by quantum algorithms in principle.
• The survey contrasts gate-based and adiabatic models and discusses error mitigation versus fault-tolerance for practical deployments.
• Near-term quantum hardware (NISQ) may provide partial advantages; robust quantum advantage requires scalable, low-noise devices.
• The work surveys hardware implementations and experimental demonstrations of quantum-risk analysis and portfolio optimization on real devices.