Quantum Deep Neural Nets for Spatial-Temporal Problems

Atmospheric behaviour will change substantially with Climate Change and is one of our generation’s most pressing challenges. The world requires accurate estimates of future the impacts from droughts, floods, heat waves, increase hazards, from freezing rain to hurricane to plan, prepare and mitigate. Combatting climate change requires reducing our emissions in a strategic way. Current computers can only approximate these coming changes. Employing quantum computing and machine learning algorithms, we’re developing open-sourced spatiotemporal models and tools that will improve forecasting accuracy of coming events from days to centuries. The developed models are expected to benefit Lakes Environmental in decreasing computational cost and expanding its weather forecasting market share in Canada and internationally.

Faculty Supervisor:

Bahram Gharabaghi

Student:

Partner:

Lakes Environmental Software

Discipline:

Earth science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Guelph

Program: