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

TL;DR

This paper explores the application of Quantum Monte Carlo algorithms to economic modeling, demonstrating potential computational advantages in stress testing and macroeconomic deep learning.

Contribution

It introduces the use of QMC in economic analysis, formulates quantum circuits for complex economic models, and discusses potential computational benefits over classical methods.

Findings

QMC can potentially reduce computation time for economic models.

Formulated quantum circuits for stress testing and macroeconomic models.

Benchmarking QMC shows promising computational gains.

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.