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.

Research Field(s): Data Science

Staff working papers

Improving the Efficiency of Payments Systems Using Quantum Computing

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.

Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning

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.

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Journal publications