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A Survey of Quantum Computing for Finance

Author

Listed:
  • Dylan Herman
  • Cody Googin
  • Xiaoyuan Liu
  • Alexey Galda
  • Ilya Safro
  • Yue Sun
  • Marco Pistoia
  • Yuri Alexeev

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.

Suggested Citation

  • Dylan Herman & Cody Googin & Xiaoyuan Liu & Alexey Galda & Ilya Safro & Yue Sun & Marco Pistoia & Yuri Alexeev, 2022. "A Survey of Quantum Computing for Finance," Papers 2201.02773, arXiv.org, revised Jun 2022.
  • Handle: RePEc:arx:papers:2201.02773
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    File URL: http://arxiv.org/pdf/2201.02773
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    as
    1. J. M. Pino & J. M. Dreiling & C. Figgatt & J. P. Gaebler & S. A. Moses & M. S. Allman & C. H. Baldwin & M. Foss-Feig & D. Hayes & K. Mayer & C. Ryan-Anderson & B. Neyenhuis, 2021. "Demonstration of the trapped-ion quantum CCD computer architecture," Nature, Nature, vol. 592(7853), pages 209-213, April.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Frank Arute & Kunal Arya & Ryan Babbush & Dave Bacon & Joseph C. Bardin & Rami Barends & Rupak Biswas & Sergio Boixo & Fernando G. S. L. Brandao & David A. Buell & Brian Burkett & Yu Chen & Zijun Chen, 2019. "Quantum supremacy using a programmable superconducting processor," Nature, Nature, vol. 574(7779), pages 505-510, October.
    4. Shleifer, Andrei & Vishny, Robert W, 1997. "The Limits of Arbitrage," Journal of Finance, American Finance Association, vol. 52(1), pages 35-55, March.
    5. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    7. Alberto Peruzzo & Jarrod McClean & Peter Shadbolt & Man-Hong Yung & Xiao-Qi Zhou & Peter J. Love & Alán Aspuru-Guzik & Jeremy L. O’Brien, 2014. "A variational eigenvalue solver on a photonic quantum processor," Nature Communications, Nature, vol. 5(1), pages 1-7, September.
    8. Daniel J. Bernstein & Tanja Lange, 2017. "Post-quantum cryptography," Nature, Nature, vol. 549(7671), pages 188-194, September.
    9. Hao Tang & Anurag Pal & Lu-Feng Qiao & Tian-Yu Wang & Jun Gao & Xian-Min Jin, 2020. "Quantum Computation for Pricing the Collateralized Debt Obligations," Papers 2008.04110, arXiv.org, revised Apr 2021.
    10. Matthew Elliott & Benjamin Golub & Matthew O. Jackson, 2014. "Financial Networks and Contagion," American Economic Review, American Economic Association, vol. 104(10), pages 3115-3153, October.
    11. Rene M. Stulz, 2010. "Credit Default Swaps and the Credit Crisis," Journal of Economic Perspectives, American Economic Association, vol. 24(1), pages 73-92, Winter.
    12. Gary Kochenberger & Jin-Kao Hao & Fred Glover & Mark Lewis & Zhipeng Lü & Haibo Wang & Yang Wang, 2014. "The unconstrained binary quadratic programming problem: a survey," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 58-81, July.
    13. Vojtěch Havlíček & Antonio D. Córcoles & Kristan Temme & Aram W. Harrow & Abhinav Kandala & Jerry M. Chow & Jay M. Gambetta, 2019. "Supervised learning with quantum-enhanced feature spaces," Nature, Nature, vol. 567(7747), pages 209-212, March.
    14. Brett Hemenway & Sanjeev Khanna, 2015. "Sensitivity and Computational Complexity in Financial Networks," Papers 1503.07676, arXiv.org, revised Oct 2016.
    15. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    16. Hemenway, Brett & Khanna, Sanjeev, 2016. "Sensitivity and computational complexity in financial networks," Algorithmic Finance, IOS Press, vol. 5(3-4), pages 95-110.
    17. Shouvanik Chakrabarti & Rajiv Krishnakumar & Guglielmo Mazzola & Nikitas Stamatopoulos & Stefan Woerner & William J. Zeng, 2020. "A Threshold for Quantum Advantage in Derivative Pricing," Papers 2012.03819, arXiv.org, revised May 2021.
    18. Anna, Petrenko, 2016. "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(1), March.
    19. D. Bulger & W. P. Baritompa & G. R. Wood, 2003. "Implementing Pure Adaptive Search with Grover's Quantum Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 116(3), pages 517-529, March.
    20. Seth Lloyd & Silvano Garnerone & Paolo Zanardi, 2016. "Quantum algorithms for topological and geometric analysis of data," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    21. Gili Rosenberg & Poya Haghnegahdar & Phil Goddard & Peter Carr & Kesheng Wu & Marcos L'opez de Prado, 2015. "Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer," Papers 1508.06182, arXiv.org, revised Aug 2016.
    22. Gerard Cornuejols & Marshall L. Fisher & George L. Nemhauser, 1977. "Exceptional Paper--Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms," Management Science, INFORMS, vol. 23(8), pages 789-810, April.
    23. CORNUEJOLS, Gérard & FISHER, Marshall L. & NEMHAUSER, George L., 1977. "Location of bank accounts to optimize float: An analytic study of exact and approximate algorithms," LIDAM Reprints CORE 292, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    24. Duffie, Darrell & Huang, Ming, 1996. "Swap Rates and Credit Quality," Journal of Finance, American Finance Association, vol. 51(3), pages 921-949, July.
    25. Boyle, Phelim P., 1977. "Options: A Monte Carlo approach," Journal of Financial Economics, Elsevier, vol. 4(3), pages 323-338, May.
    26. Bicksler, James & Chen, Andrew H, 1986. "An Economic Analysis of Interest Rate Swaps," Journal of Finance, American Finance Association, vol. 41(3), pages 645-655, July.
    27. Hsin-Yuan Huang & Michael Broughton & Masoud Mohseni & Ryan Babbush & Sergio Boixo & Hartmut Neven & Jarrod R. McClean, 2021. "Power of data in quantum machine learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    28. Ilene Grabel, 2003. "Predicting Financial Crisis in Developing Economies: Astronomy or Astrology?," Eastern Economic Journal, Eastern Economic Association, vol. 29(2), pages 243-258, Spring.
    29. Iordanis Kerenidis & Anupam Prakash & D'aniel Szil'agyi, 2019. "Quantum Algorithms for Portfolio Optimization," Papers 1908.08040, arXiv.org.
    30. Roman Orus & Samuel Mugel & Enrique Lizaso, 2018. "Forecasting financial crashes with quantum computing," Papers 1810.07690, arXiv.org, revised Jun 2019.
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    Cited by:

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    2. Abha Naik & Esra Yeniaras & Gerhard Hellstern & Grishma Prasad & Sanjay Kumar Lalta Prasad Vishwakarma, 2023. "From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance," Papers 2307.01155, arXiv.org.
    3. El Amine Cherrat & Snehal Raj & Iordanis Kerenidis & Abhishek Shekhar & Ben Wood & Jon Dee & Shouvanik Chakrabarti & Richard Chen & Dylan Herman & Shaohan Hu & Pierre Minssen & Ruslan Shaydulin & Yue , 2023. "Quantum Deep Hedging," Papers 2303.16585, arXiv.org, revised Nov 2023.
    4. Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023. "A novel approach for quantum financial simulation and quantum state preparation," Papers 2308.01844, arXiv.org, revised Apr 2024.
    5. Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023. "Preparing random state for quantum financing with quantum walks," Papers 2302.12500, arXiv.org, revised Mar 2023.
    6. Jinge Bao & Patrick Rebentrost, 2022. "Fundamental theorem for quantum asset pricing," Papers 2212.13815, arXiv.org, revised Apr 2023.

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