Using quantum algorithms to speed up generative artificial intelligence
New research shows how quantum computation can accelerate classical computing processes, particularly in molecular dynamics.
By Naomi Grosman
Researchers at the University of Waterloo’s Institute for Quantum Computing (IQC) have found that quantum algorithms could speed up generative artificial intelligence (AI) creation and usage.
The paper titled Gibbs Sampling of Continuous Potentials on a Quantum Computer by Pooya Ronagh, IQC member and professor in the Department of Physics and Astronomy, and Arsalan Motamedi, IQC alum and researcher at Canadian quantum computing company Xanadu, explores how quantum algorithms can relieve bottlenecks in generative AI.
The paper was instrumental in securing $412,500 from the National Research Council’s Applied Quantum Computing grant, which will fund further research in this area.
Ronagh says his work focuses on the intersection of quantum science and AI and whether quantum computing can speed up mimicking real-world patterns and phenomena as AI and machine learning scientists have done.
“We found that yes it can — but not for the typical generative AI problems in computer vision and speech. We saw more significant speed ups for the types of problems that have periodic patterns, for example in analyzing molecular dynamics.”
- Pooya Ronagh, IQC member and professor in the Department of Physics and Astronomy,
This research was supported in part by Natural Sciences and Engineering Research Council of Canada’s Discovery grant. It was published in Proceedings of Machine Learning Research in July 2024.