Quantum Machine Learning

点击翻译按钮获取中文摘要

Melko is an Associate Professor at the University of Waterloo, Associate Faculty at the Perimeter Institute for Theoretical Physics, and the Canada Research Chair in Computational Quantum Many Body Physics. He is the recipient of the Canadian Association of Physicists "Herzberg" medal in 2016, and holds affiliations at the Institute for Quantum Computing, and the new Vector Institute for Artificial Intelligence. Melko undertakes research in the complex world of quantum many-body physics, emphasizing advanced computer simulation techniques as a theoretical tool. He proposes to engage a team of postdoctoral fellows and graduate students in the theoretical study of real-world quantum systems, with applications in materials science and near-term quantum computing. The research will involve pioneering developments combining established algorithms for quantum simulation, developed by Melko's group over the last decade, and novel machine learning techniques. Machine learning is a transformative technology driving the current revolution in the information technology sector, from computer vision, to natural language comprehension, to translation, and more. Over the duration of this Discovery Grant, Melko plans to integrate computational quantum many-body physics with advanced machine learning techniques to form a novel, multi-disciplinary field called "quantum machine learning". This program will produce new, powerful techniques for the advancement of computational condensed matter research into the 21st century, and train a new breed of personnel simultaneously adept in theoretical quantum physics, and the theory and practice of modern machine learning. One consequence of this research is the staggering potential for scientific disruption in our computer simulation technology for condensed matter, quantum materials, and near-term quantum devices being built in laboratories today. Another is the potential for knowledge transfer from physics back into the technology sector, including new machine learning algorithms, and the possible use of quantum resources for machine learning. With affiliations in both Waterloo's "quantum valley" as well as Toronto's artificial intelligence hotbed, Melko is positioned to give his students and postdocs an absolutely unique supervisory experience, on research topics with broad potential impact for the scientific and technological development of quantum machine learning in the Toronto-Waterloo Innovation Corridor, as well as Canada as a whole.