Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.13909 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912039380713472 |
|---|---|
| author | Skavysh, Vladimir Priazhkina, Sofia Guala, Diego Bromley, Thomas R. |
| author_facet | Skavysh, Vladimir Priazhkina, Sofia Guala, Diego Bromley, Thomas R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13909 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning Skavysh, Vladimir Priazhkina, Sofia Guala, Diego Bromley, Thomas R. Quantum Physics 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. |
| title | Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2409.13909 |