Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265223 
Year of Publication: 
2022
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2022-29
Publisher: 
Bank of Canada, Ottawa
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.
Subjects: 
Business fluctuations and cycles
Central bank research
Econometric and statisticalmethods
Economic models
Financial stability
JEL: 
C
C15
C6
C7
C61
C63
C68
G
G21
G17
E
E13
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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