Work on artificial general intelligence started in eager in the 1950s. Quantum computing is a more recent idea, with a history of slightly over thirty years. Yet, we still did not achieve strong AI nor we have a scalable quantum computer. Recent trends indicate that the fate of these two seemingly unrelated technologies might be intertwined.
Machine learning, a branch of AI resting on statistical foundations, is making advances in quantum information processing, and in turn, quantum resources are gaining importance in learning theory. This seminar starts with the philosophical and statistical role of sparsity in learning, introduces some necessary concepts of quantum physics, looks at what we can expect from quantum resources in learning theory, and eventually hints at curious security implications, such as blind quantum machine learning.
Peter Wittek is a research fellow exploring the synergies between artificial intelligence, quantum physics, and high-performance computing. His experience ranges from solving problems in theoretical physics to working on practical machine learning applications with startups. He holds an MSc in Mathematics from the Budapest University of Technology and Economics, and a PhD in Computer Science from the National University of Singapore. He is affiliated with ICFO-The Institute of Photonic Sciences, Barcelona, Spain and the University of Borås, Sweden, and he is a scientific advisor to the Creative Destruction Lab at the University of Toronto, Canada.