2021 Medal Winners | francais

The 2021 CAP/DCMMP Brockhouse Medal

is awarded to

Roger Melko

"It's an incredible honour for me to receive this recognition from the Canadian physics community. I am grateful for the support of my many wonderful colleagues and collaborators, particularly the brilliant students and postdocs whose many contributions are acknowledged by this award." winner citation

The Canadian Association of Physicists (CAP) is pleased to announce that the 2021 CAP/DCMMP Brockhouse Medal is awarded to Roger Melko, University of Waterloo / Perimeter Institute, to recognize his work on the theoretical understanding of many-body quantum systems through large-scale computer simulations. The theoretical tools developed by Dr. Melko’s group provide a new perspective on understanding of quantum condensed matter and have proven highly influential in areas such as quantum information, field theory, cold atomic matter, and artificial intelligence. announcement

Roger Melko’s impact on the field of condensed matter physics started early in his career. Along with his master’s advisor, Michel Gingras, Melko pioneered computer simulation studies that predicted new phases of matter, including a low-temperature ordered state of the magnetic “spin ice” materials. During his PhD, Melko and his collaborators advanced quantum Monte Carlo technology, discovering a stable commensurate supersolid phase in a model of lattice bosons. He also helped pioneer conceptual and computer simulation studies of quantum phase transitions, including unconventional deconfined quantum critical points.

One of Melko’s most influential discoveries came in 2010, when he used intuition gleaned from quantum field theory to develop an innovative approach for evaluating entanglement entropies using quantum Monte Carlo simulations. This pioneering method is now broadly used in the theoretical and experimental study of quantum matter. It has had wide application, ranging from real materials in physics and chemistry, to cold atomic matter, to quantum information systems, to interacting quantum field theories relevant for high-energy physics and even quantum gravity.

In 2016, Melko pioneered an entirely new line of research that unifies machine learning and quantum many-body physics. With Perimeter postdoc Juan Carrasquilla, Melko demonstrated the ability of neural networks to “learn” fundamental concepts important for condensed matter and quantum information systems – sparking a torrent of cross-disciplinary literature, prompting new workshops and conferences, and engaging a generation of dynamic young scientists to study computational physics and machine learning.

As a dedicated mentor, Melko’s impact extends well beyond his field. Trainees from Melko’s lab are emerging as young leaders, highly sought-after in both academia and industry. Melko and his group have initiated a paradigm shift for the use of artificial intelligence in quantum physics. They continue to provide innovative computational frameworks for quantum many-body systems and to open new frontiers in the unification of nominator citation

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