Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning

Vladimir Skavysh, Sofia Priazhkina, Diego Guala and Thomas Bromley

Staff Working Papers from Bank of Canada

Abstract: Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. Using the quantum Monte Carlo (QMC) algorithm, we are the first to study whether quantum computing can improve the run time of economic applications and challenges in doing so. We identify a large class of economic problems suitable for improvements. Then, we illustrate how to formulate and encode on quantum circuit two applications: (a) a bank stress testing model with credit shocks and fire sales and (b) a dynamic stochastic general equilibrium (DSGE) model solved with deep learning, and further demonstrate potential efficiency gain. We also present a few innovations in the QMC algorithm itself and in how to benchmark it to classical MC.

Keywords: Business fluctuations and cycles; Central bank research; Econometric and statistical methods; Economic models; Financial stability (search for similar items in EconPapers)
Pages: 62 pages
Date: 2022-06
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-dge, nep-ecm and nep-fdg
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:22-29

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