Quantum monte carlo for economics: Stress testing and macroeconomic deep learning

Vladimir Skavysh, Sofia Priazhkina, Diego Guala and Thomas R. Bromley

Journal of Economic Dynamics and Control, 2023, vol. 153, issue C

Abstract: Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and challenges in doing so. We provide a detailed introduction to quantum computing and especially the QMC algorithm. Then, we illustrate how to formulate and encode into quantum circuits (a) a bank stress testing model with credit shocks and fire sales, (b) a neoclassical investment model solved with deep learning, and (c) a realistic macro model solved with deep neural networks. We discuss potential computational gains of QMC versus classical computing systems and present a few innovations in benchmarking QMC.

Keywords: Monte Carlo; Quantum computing; Computational methods; Stress testing; DSGE; Machine learning; Deep learning (search for similar items in EconPapers)
JEL-codes: C C1 C15 C6 C61 C63 C68 C7 E E1 E13 G G1 G17 G2 G21 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:153:y:2023:i:c:s0165188923000866

DOI: 10.1016/j.jedc.2023.104680

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