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Roger Melko

Portrait de Roger Melko
University of Waterloo - Department of Physics and Astronomy

Area of Research:
Phone: (519) 569-7600 x8560

Research Interests

My group's interests involve strongly-correlated quantum many-body systems, with a focus on emergent phenomena, novel phases and 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, series expansions, machine learning and neural networks.
With these methods, my group explores low-energy physics in quantum magnets, cold atoms in optical lattices, bosonic fluids, and quantum computers. 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, and the relationship of these methods to the fields of machine learning and quantum information science.

Positions Held

  • 2017- Creative Destruction Labs Chief Scientist
  • 2007- Department of Physics and Astronomy, University of Waterloo Professor
  • 2005-2007 Oak Ridge National Laboratory, Tennessee Wigner Fellow

Awards

  • Herzberg Medal Canadian Association of Physicists
  • Young Scientist Prize in Computational Physics, International Union of Pure and Applied Physics (IUPAP), "for his innovative and deep achievements in developing quantum Monte Carlo methods for quantum information theory and condensed matter physics."
  • Early Researcher Award, Ontario Ministry of Research and Innovation

Recent Publications

  • Giacomo Torlai, Roger G. Melko Latent Space Purification via Neural Density Operators Phys. Rev. Lett. 120, 240503 (2018) arXiv: 1801.09684
  • Mohammad H. Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, Roger Melko Quantum Boltzmann Machine Phys. Rev. X 8, 021050 (2018) arXiv: 1601.02036
  • Alan Morningstar, Roger G. Melko Deep Learning the Ising Model Near Criticality J. Machine Learning Research arXiv: 1708.04622
  • Giacomo Torlai, Guglielmo Mazzola, Juan Carrasquilla, Matthias Troyer, Roger Melko, Giuseppe Carleo Many-body quantum state tomography with neural networks Nature Physics 14, 447-450 (2018) arXiv: 1703.05334
  • Matthew J. S. Beach, Anna Golubeva, Roger G. Melko Machine learning vortices at the Kosterlitz-Thouless transition Phys. Rev. B 97, 045207 (2018) arXiv: 1710.09842
  • Na Xu, Claudio Castelnovo, Roger G. Melko, Claudio Chamon, Anders W. Sandvik Dynamic scaling of topological ordering in classical systems Phys. Rev. B 97, 024432 (2018) arXiv: 1711.03557
  • Yi Zhang, Roger G. Melko, Eun-Ah Kim Machine Learning Z2 Quantum Spin Liquids with Quasi-particle Statistics Phys. Rev. B 96, 245119 (2017) arXiv: 1705.01947
  • Pedro Ponte, Roger G. Melko Kernel methods for interpretable machine learning of order parameters Phys. Rev. B 96, 205146 (2017) arXiv: 1704.05848
  • Peter Broecker, Juan Carrasquilla, Roger G. Melko, Simon Trebst Machine learning quantum phases of matter beyond the fermion sign problem Scientific Reports 7, 8823 (2017) arXiv: 1608.07848
  • Juan Carrasquilla, Gang Chen, Roger G. Melko Tripartite entangled plaquette state in a cluster magnet Phys. Rev. B 96, 054405 (2017) arXiv: 1704.03478
  • Kelvin Ch'ng, Juan Carrasquilla, Roger G. Melko, Ehsan Khatami Machine Learning Phases of Strongly Correlated Fermions Phys. Rev. X 7, 031038 (2017) arXiv: 1609.02552
  • Giacomo Torlai, Roger G. Melko A Neural Decoder for Topological Codes Phys. Rev. Lett. 119, 030501 (2017) arXiv: 1610.04238
  • Cubic trihedral corner entanglement for a free scalar Lauren E. Hayward Sierens, Pablo Bueno, Rajiv R. P. Singh, Robert C. Myers, Roger G. Melko Journal-ref: Phys. Rev. B 96, 035117 (2017) arXiv: 1703.03413
  • Entanglement area law in superfluid 4He C. M. Herdman, P.-N. Roy, R. G. Melko, A. Del Maestro Nature Physics 13, 556 (2017) arXiv: 1610.08518
  • William Witczak-Krempa, Lauren E. Hayward Sierens, Roger G. Melko Cornering gapless quantum states via their torus entanglement Phys. Rev. Lett. 118, 077202 (2017) arXiv: 1603.02684
  • Juan Carrasquilla, Roger G. Melko Machine learning phases of matter Nature Physics 13, 431-434 (2017) arXiv: 1605.01735
  • Yuan Wan, Juan Carrasquilla, Roger G. Melko Spinon walk in quantum spin ice Phys. Rev. Lett. 116, 167202 (2016) arXiv: 1510.00979
  • Juan Carrasquilla, Zhihao Hao and Roger G. Melko A two-dimensional spin liquid in quantum kagome ice Nature Communications 6, Article number: 7421 http://arxiv.org/abs/1407.0037
  • Jean-Marie Stéphan, Stephen Inglis, Paul Fendley, Roger G. Melko Geometric mutual information at classical critical points Phys. Rev. Lett. 112, 127204 (2014) arXiv: 1312.3954
  • Lauren E. Hayward, David G. Hawthorn, Roger G. Melko, Subir Sachdev Angular Fluctuations of a Multicomponent Order Describe the Pseudogap of YBa2Cu3O6+x Science 343, 1336 (2014) arXiv: 1309.6639
  • Matthew B. Hastings, Grant H. Watson, Roger G. Melko Self-Correcting Quantum Memories Beyond the Percolation Threshold Phys. Rev. Lett. 112, 070501 (2014) arXiv: 1309.2680
  • Matthew S. Block, Roger G. Melko, Ribhu K. Kaul Fate of CP(N-1) fixed points with q-monopoles Phys. Rev. Lett. 111, 137202 (2013) arXiv: 1307.0519
  • Ribhu K. Kaul, Roger G. Melko, Anders W. Sandvik Bridging lattice-scale physics and continuum field theory with quantum Monte Carlo simulations Annu. Rev. Con. Mat. Phys. 4, 179 (2013) arXiv: 1204.5405
  • Ann B. Kallin, Katharine Hyatt, Rajiv R. P. Singh, Roger G. Melko, Entanglement at a Two-Dimensional Quantum Critical Point: a Numerical Linked Cluster Expansion Study, Phys. Rev. Lett. 110, 135702 (2013), arXiv: 1212.5269
  • Strongly Correlated Systems: Numerical Methods Chapter 7: Stochastic Series Expansion Quantum Monte Carlo By Roger Melko Springer Series in Solid-State Sciences Volume 176, 2013, pp 185-206

Seminars

  • Seminar: Entanglement entropy of corners in interacting quantum field theories in Workshop: Entanglement in Quantum Systems Galileo Galilei Institute for Theoretical Physics, Italy
  • Reconstructing Quantum Wavefunctions with Stochastic Neural Networks Workshop on Machine Learning Quantum Materials University of Maryland/NIST
  • Machine Learning for Quantum Physics Creative Destruction Lab, Toronto
  • Machine Learning for Quantum Many-body Physics International Workshop, organizer and speaker
  • Seminar: CIFAR Quantum Materials Meeting Montreal
  • Colloquium: Machine Learning the Many-Body Problem Ohio State University
  • Research at the Perimeter Institute Quantum Intelligence Lab Simons Flatiron CCQ Workshop
  • Colloquium: Machine Learning the Many-Body Problem University of Victoria
  • March Meeting Invited talk Modelling Many-Body Physics with Restricted Boltzmann Machines Los Angeles, CA
  • Colloquium: Machine Learning the Many-Body Problem TRIUMF
  • Renyi entropies in theory, numerics, and experiment MagLab Winter School The National High Magnetic Field Laboratory
  • Colloquium: Machine Learning the Many-Body Problem Utah University
  • Colloquium: Machine Learning the Many-Body Problem McMaster University
  • Colloquium: Machine Learning the Many-Body Problem University of California Santa Barbara
  • Machine Learning the Many-Body Problem Los Alamos National Lab
  • Machine Learning the Many-Body Problem Boston University
  • Colloquium: Machine Learning the Many-Body Problem University of Cologne
  • Machine Learning the Many-Body Problem University of Maryland
  • Machine Learning the Many-Body Problem University of Chicago
  • Colloquium: Machine Learning the Many-Body Problem University of Toronto
  • Machine Learning Quantum Physics Seminar: Machine Learning Advances and Applications Fields Institute Toronto
  • Machine Learning Quantum Physics Seminar: Machine Learning Advances and Applications Fields Institute, Toronto
  • Colloquium: Machine Learning the Many-Body Problem University of North Carolina at Chapel Hill
  • Machine Learning for the Many-Body Problem University de Montreal
  • Machine Learning for the Many-Body Problem KITP, Santa Barbara
  • Quantum Computing: State of the Science and the Future Scotiabank Toronto
  • Boulder Summer School for Condensed Matter and Materials Physics Topological Phases of Quantum Matter Boulder CO
  • Entanglement Matters Aspen summer workshop
  • Plenary speaker, Canadian Association of Physicists Congress Ottawa ON
  • Computational Quantum Matter Summer School Sherbrooke
  • Colloquium:The Information Age in Simulations of Condensed Matter Northwestern University
  • From Quantum Field Theories to Numerical Methods NORDITA Sweden
  • Turning the corner on Entanglement Entropy Oxford UK
  • PIRSA:18050000, Roger Melko: Perimeter Institute and University of Waterloo, 2018-05-02, Perimeter Public Lectures
  • Artificial Intelligence and the Complexity Frontier Perimeter Public Lecture
  • PIRSA:18040066, PSI 2017/2018 - Machine Learning for Many Body Physics - Lecture 15, 2018-04-25, PSI 2017/2018 - Machine Learning for Many Body Physics (Hayward Sierens)
  • PIRSA:18040064, PSI 2017/2018 - Machine Learning for Many Body Physics - Lecture 12, 2018-04-23, PSI 2017/2018 - Machine Learning for Many Body Physics (Hayward Sierens)
  • PIRSA:18040063, PSI 2017/2018 - Machine Learning for Many Body Physics - Lecture 11, 2018-04-20, PSI 2017/2018 - Machine Learning for Many Body Physics (Hayward Sierens)
  • PIRSA:18040071, PSI 2017/2018 - Machine Learning for Many Body Physics - Lecture 10, 2018-04-19, PSI 2017/2018 - Machine Learning for Many Body Physics (Hayward Sierens)
  • New PSI course in Machine Learning
  • PIRSA:17030006, 2016/2017 Statistical Mechanics 2 - Roger Melko - Lecture 22, 2017-03-17, 2016/2017 PHYS 705 - Statistical Mechanics 2 - Roger Melko
  • PIRSA:17010047, 2016/2017 Statistical Mechanics 2 - Roger Melko - Lecture 1, 2017-01-04, 2016/2017 PHYS 705 - Statistical Mechanics 2 - Roger Melko
  • PIRSA:16080000, Welcome and Opening Remarks, 2016-08-08, Quantum Machine Learning
  • PIRSA:16010036, PHYS 733 - Quantum Many-Body Physics (W2016) - Roger Melko - Lecture 6, 2016-01-21, PHYS 733 - Quantum Many-Body Physics (W2016) - Roger Melko
  • PIRSA:15020090, Quantum Materials Research is Humanity's Only Hope, 2015-02-27, Universe in 60 Minutes