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 published in the Proceedings of Machine Learning Research.
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,” Ronagh says. “We saw more significant speed ups for the types of problems that have periodic patterns, for example in analyzing molecular dynamics.”
The function of large molecules like proteins depends on how they fold into specific 3D structures, which makes the search and generation of these structures a vital problem in pharmacology. And current state-of-the-art techniques use generative AI to enhance this process.
Ronagh says even though quantum mechanical effects are typically ignored in molecular dynamics simulations, they can benefit from quantum computing solutions thanks to the periodicity of molecular bond angles. Many other examples of problems with such periodic structures exist in condensed matter physics and quantum field theories.
Ronagh says one of the most salient examples of the power of quantum computers is in cryptography. Shor’s algorithm famously uses the periodicity that underlies the factoring problem to break the RSA encryption. However, he clarifies that this is not a practical use case in itself but rather a demonstration of the unique capabilities of quantum algorithms. There is true potential in quantum computing rather than being merely a threat to information security.
“Hacking is a scary implication that drives our urgency for changing our encryption protocols, as well as our curiosity for whether quantum computers are buildable,” he says. “But, instead, we can aspire to simulate molecules better, leading to the development of superior materials and life-saving drugs. This holds the potential of being a very economically valuable application of quantum computers to our daily lives.”
He says exploring applications of quantum computing goes beyond daydreaming about the future impacts of quantum technologies.
“That’s where I think finding useful quantum algorithms is so important. They can tell us more about the types of applications we want to run on the computer we are trying to build, so we can design and optimize the computer architecture more informed, and plan the massive undertaking of building it better,” Ronagh says.
More information:
Arsalan Motamedi and Pooya Ronagh, Gibbs Sampling of Continuous Potentials on a Quantum Computer, Proceedings of Machine Learning Research. proceedings.mlr.press/v235/motamedi24a.html
University of Waterloo
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Quantum algorithms can break generative AI bottlenecks (2024, December 12)
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