Quantum Monte Carlo for economics : stress testing and macroeconomic deep learning / by Vladimir Skavysh, Sofia Priazhkina, Diego Guala and Thomas R. Bromley. : FB3-5/2022-29E-PDF
"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.
Lien permanent pour cette publication :
publications.gc.ca/pub?id=9.913466&sl=1
| Ministère/Organisme | Bank of Canada, issuing body. |
|---|---|
| Titre | Quantum Monte Carlo for economics : stress testing and macroeconomic deep learning / by Vladimir Skavysh, Sofia Priazhkina, Diego Guala and Thomas R. Bromley. |
| Titre de la série | Staff working paper = Document de travail du personnel, 1701-9397 ; 2022-29 |
| Type de publication | Série - Voir l'enregistrement principal |
| Langue | [Anglais] |
| Format | Électronique |
| Document électronique | |
| Note(s) | "Last updated: June 28, 2022." Includes bibliographical references (pages 39-46). |
| Information sur la publication | [Ottawa] : Bank of Canada = Banque du Canada, 2022. ©2022 |
| Auteur / Contributeur | Skavysh, Vladimir, author. |
| Description | 1 online resource (ii, 59 pages). |
| Numéro de catalogue |
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| Descripteurs | Quantum computing. Economic forecasting. Informatique quantique. Prévision économique. |
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