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008	220715t20222022onc     ob   f|0| 0 eng d
040	|aCaOODSP|beng|erda|cCaOODSP
043	|an-cn---
086	1 |aFB3-5/2022-29E-PDF
100	1 |aSkavysh, Vladimir, |eauthor.
245	10|aQuantum Monte Carlo for economics : |bstress testing and macroeconomic deep learning / |cby Vladimir Skavysh, Sofia Priazhkina, Diego Guala and Thomas R. Bromley.
264	1|a[Ottawa] : |bBank of Canada = Banque du Canada, |c2022.
264	4|c©2022
300	|a1 online resource (ii, 59 pages).
336	|atext|btxt|2rdacontent
337	|acomputer|bc|2rdamedia
338	|aonline resource|bcr|2rdacarrier
490	1 |aStaff working paper = Document de travail du personnel, |x1701-9397 ; |v2022-29
500	|a"Last updated: June 28, 2022."
504	|aIncludes bibliographical references (pages 39-46).
520	|a"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"--Abstract.
650	0|aQuantum computing.
650	0|aEconomic forecasting.
650	6|aInformatique quantique.
650	6|aPrévision économique.
710	2 |aBank of Canada, |eissuing body.
830	#0|aStaff working paper (Bank of Canada)|v2022-29.|w(CaOODSP)9.806221
856	40|qPDF|s1.23 MB|uhttps://publications.gc.ca/collections/collection_2022/banque-bank-canada/FB3-5-2022-29-eng.pdf