Melko Group

Computational quantum many-body research

Computing the world of quantum matter.

Our research interests involve strongly-correlated many-body systems, with a focus on emergent phenomena, ground state phases, phase transitions, quantum criticality, and entanglement. We emphasize computational methods as a theoretical technique, in particular the development of state-of-the-art algorithms for the study of strongly-interacting systems. Our work has employed Monte Carlo simulations, Density Matrix Renormalization Group, and related methods to explore the low-temperature physics of classical and quantum magnetic materials, cold atoms in optical lattices, bosonic fluids and low-dimensional systems. I am particularly involved in studying microscopic models that display interesting quantum behavior in the bulk, such as superconducting, spin liquid, topological, superfluid or supersolid phases. We are also interested in broader ideas in computational physics, the development of efficient algorithms for simulating quantum mechanical systems on classical computers, including machine learning, and the relationship of these methods to the field of quantum information science.

News and links.

Quantum Machine Learning

Intelligent Machines are Teaching Themselves Quantum Physics

The most complex problem in physics could be solved by machines with brains

AI physicists: The machines cracking the quantum code

Three Ways Physics Could Help Save Humanity

Waterloo and Harvard physicists' eureka moment

University of Waterloo prof finds way to split virtual electron

Associate Professor, University of Waterloo, Canada Research Chair in Computational Quantum Many-Body Physics, Associate Faculty Member at the Perimeter Institute for Theoretical Physics and Affiliate with the Institute for Quantum Computing

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