A Survey of Quantum Computing for Finance
Comprehensive survey on quantum computing applications in finance, covering algorithms, stochastic modeling, optimization, and machine learning.
What it examines
This survey explores the potential of quantum computing in finance, focusing on stochastic modeling, optimization, and machine learning. It aims to demonstrate how quantum algorithms can solve financial problems like derivative pricing, risk modeling, and portfolio optimization more efficiently and accurately than classical methods.
What it concludes
The research suggests that quantum computing can revolutionize finance by solving complex problems more efficiently. Potential applications include enhanced risk analysis, optimized portfolios, and accurate derivative pricing. Future research should focus on overcoming current hardware limitations and exploring more financial use-cases.
Evidence objects
The research suggests that quantum computing can revolutionize finance by solving complex problems more efficiently. Potential applications include enhanced risk analysis, optimized portfolios, and accurate derivative pricing. Future research should focus on overcoming current hardware limitations and exploring more financial use-cases.
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Raw abstract and provenance
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 comprehen… ▽ More 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. △ Less
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