Quantum computers are expected to surpass the computational capabilities of classical computers and have transformative impact on numerous industry sectors, particularly finance. Finance is estimated to be one of the first industry sectors to benefit from quantum computing, not only in the medium and long terms, but even in the short term (however, this is subject to overcoming several engineering and algorithmic obstacles).
Our team (Argonne National Lab - JPMorgan Chase - Menten AI - University of Chicago - University of Delaware) 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 nearterm quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance as well as discuss various obstacles to achieve these goals. 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.
Herman, Dylan, Cody Googin, Xiaoyuan Liu, Alexey Galda, Ilya Safro, Yue Sun, Marco Pistoia, and Yuri Alexeev. "A survey of quantum computing for finance." arXiv preprint https://arxiv.org/abs/2201.02773 (2022).
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