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Dynamic Programming on a Quantum Annealer: Solving the RBC Model

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  • Fernández-Villaverde, Jesús
  • Hull, Isaiah

Abstract

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all states and generating candidate global solutions in milliseconds, irrespective of problem size. Using existing quantum hardware, we achieve an order-of-magnitude speed-up in solving the real business cycle model over benchmarks in the literature. We also provide a detailed introduction to quantum annealing and discuss its potential use for more challenging economic problems.

Suggested Citation

  • Fernández-Villaverde, Jesús & Hull, Isaiah, 2023. "Dynamic Programming on a Quantum Annealer: Solving the RBC Model," CEPR Discussion Papers 18190, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:18190
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    1. 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.
    2. Hugo Benitez-Silva & John Rust & Gunter Hitsch & Giorgio Pauletto & George Hall, 2000. "A Comparison Of Discrete And Parametric Methods For Continuous-State Dynamic Programming Problems," Computing in Economics and Finance 2000 24, Society for Computational Economics.
    3. Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
    4. Andrew Sweeting, 2013. "Dynamic Product Positioning in Differentiated Product Markets: The Effect of Fees for Musical Performance Rights on the Commercial Radio Industry," Econometrica, Econometric Society, vol. 81(5), pages 1763-1803, September.
    5. Aruoba, S. Borağan & Fernández-Villaverde, Jesús, 2015. "A comparison of programming languages in macroeconomics," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 265-273.
    6. Roman Orus & Samuel Mugel & Enrique Lizaso, 2018. "Forecasting financial crashes with quantum computing," Papers 1810.07690, arXiv.org, revised Jun 2019.
    7. Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
    8. Isaiah Hull & Or Sattath & Eleni Diamanti & Göran Wendin, 2024. "Quantum Technology for Economists," Contributions to Economics, Springer, number 978-3-031-50780-9.
    9. Sergio Boixo & Vadim N. Smelyanskiy & Alireza Shabani & Sergei V. Isakov & Mark Dykman & Vasil S. Denchev & Mohammad H. Amin & Anatoly Yu Smirnov & Masoud Mohseni & Hartmut Neven, 2016. "Computational multiqubit tunnelling in programmable quantum annealers," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
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    Cited by:

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    More about this item

    Keywords

    Computational methods;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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