Vladimir Skavysh is a Data Scientist and a creator of the Bank of Canada’s Quantum Lab for Advanced Analytics. He started his research career as a physicist, specializing in dark energy and neutron interferometry, before switching to data science and artificial intelligence. His research interests are quantum computing, deep learning, and big data.
This paper provides an overview of cryptoasset exchanges. We contrast their design with exchanges in traditional financial markets and discuss emerging regulatory trends and innovations aimed at solving the problems cryptoasset exchanges face.
We build a network formation game of firms with trade flows to study the adoption and usage of a new digital currency as an alternative to correspondent banking.
We develop an algorithm and run it on a hybrid quantum annealing solver to find an ordering of payments that reduces the amount of system liquidity necessary without substantially increasing payment delays.
Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.
Saggu, P., Mineeva, T., Arif, M., Cory, D., Haun, R., Heacock, B., Huber, M., Li, K., Nsofini, J., Sarenac, D., Shahi, C., Skavysh, V., Snow, M., Werner, S., Young, A., Pushin, D., 2016, “Decoupling of a neutron interferometer from temperature gradients.” Review of Scientific Instruments 87 (12), 123507.
Li, K., Arif, M., Cory, D., Haun, R., Heacock, B., Huber, M., Nsofini, J., Pushin, D., Saggu, P., Sarenac, D., Shahi, C., Skavysh, V., Snow, M., and Young, A., 2016, “Neutron limit on the strongly-coupled chameleon field.” Physical Review D 93 (6), 062001.
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