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

57 Pages Posted: 2 Sep 2022 Publication Status: Published

See all articles by Vladimir Skavysh

Vladimir Skavysh

Government of Canada - Bank of Canada

Sofia Priazhkina

Government of Canada - Bank of Canada

Diego Guala

Xanadu

Tom Bromley

Xanadu

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 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 how to benchmark it to classical MC.

Keywords: Monte Carlo, quantum computing, computational methods, Stress Testing, DSGE, Machine Learning

Suggested Citation

Skavysh, Vladimir and Priazhkina, Sofia and Guala, Diego and Bromley, Tom, Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning. Available at SSRN: https://ssrn.com/abstract=4207839 or http://dx.doi.org/10.2139/ssrn.4207839

Vladimir Skavysh

Government of Canada - Bank of Canada ( email )

Sofia Priazhkina (Contact Author)

Government of Canada - Bank of Canada ( email )

234 Wellington Street
Ontario, Ottawa K1A 0G9
Canada

Diego Guala

Xanadu ( email )

Tom Bromley

Xanadu ( email )

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