Roger Melko

Roger Melko profile picture
University of Waterloo
Areas of research:
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, and modern machine learning methods. With these techniques, my group explores low-energy physics in quantum magnets, cold atoms in optical lattices, bosonic fluids, and quantum computers. I am particularly interesting in studying microscopic models that display interesting quantum behavior in the bulk, such as superconducting, spin liquid, topological, or error-correcting 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.
  • 2017- Creative Destruction Labs Scientific Lead
  • 2017- Vector Institute for Artificial Intelligence, Toronto Affiliate Faculty
  • 2007- Department of Physics and Astronomy, University of Waterloo Professor
  • 2005-2007 Oak Ridge National Laboratory, Tennessee Wigner Fellow
  • 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
  • Stewart Morawetz, Isaac J. S. De Vlugt, Juan Carrasquilla, Roger G. Melko U(1) symmetric recurrent neural networks for quantum state reconstruction Phys. Rev. A 104, 012401 (2021)
  • Francesco Bova, Avi Goldfarb and Roger G. Melko Commercial applications of quantum computing EPJ Quantum Technology volume 8, Article number: 2 (2021)
  • David Yevick, Roger Melko The Accuracy of Restricted Boltzmann Machine Models of Ising Systems Computer Physics Communications, 258, 107518 (2021)
  • Giacomo Torlai, Juan Carrasquilla, Matthew T. Fishman, Roger G. Melko, Matthew P. A. Fisher Wavefunction positivization via automatic differentiation Phys. Rev. Research 2, 032060(R) (2020)
  • Sebastian J. Wetzel, Roger G. Melko, Joseph Scott, Maysum Panju, Vijay Ganesh Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks Phys. Rev. Research 2, 033499 (2020)
  • Isaac J. S. De Vlugt, Dmitri Iouchtchenko, Ejaaz Merali, Pierre-Nicholas Roy, Roger G. Melko Reconstructing quantum molecular rotor ground states Phys. Rev. B 102, 035108 (2020) arXiv: 2003.14273
  • Mohamed Hibat-Allah, Martin Ganahl, Lauren E. Hayward, Roger G. Melko, Juan Carrasquilla Recurrent Neural Network Wave Functions Phys. Rev. Research 2, 023358 (2020) arXiv: 2002.02973
  • Giacomo Torlai, Roger G. Melko Machine learning quantum states in the NISQ era Annual Review of Condensed Matter Physics 11:325-344 (2020) arXiv: 1905.04312
  • Yi Hong Teoh, Marina Drygala, Roger G. Melko, Rajibul Islam Machine learning design of a trapped-ion quantum spin simulator Quantum Science and Technology, Volume 5, Number 2 (2020) arXiv: 1910.02496
  • Giacomo Torlai, Brian Timar, Evert P. L. van Nieuwenburg, Harry Levine, Ahmed Omran, Alexander Keesling, Hannes Bernien, Markus Greiner, Vladan Vuletic, Mikhail D. Lukin, Roger G. Melko, Manuel Endres Integrating Neural Networks with a Quantum Simulator for State Reconstruction Phys. Rev. Lett. 123, 230504 (2019) arXiv: 1904.08441
  • Dan Sehayek, Anna Golubeva, Michael S. Albergo, Bohdan Kulchytskyy, Giacomo Torlai, Roger G. Melko The learnability scaling of quantum states: restricted Boltzmann machines Phys. Rev. B 100, 195125 (2019) arXiv: 1908.07532
  • Matthew J. S. Beach, Roger G. Melko, Tarun Grover, Timothy H. Hsieh Making Trotters Sprint: A Variational Imaginary Time Ansatz for Quantum Many-body Systems Phys. Rev. B 100, 094434 (2019) arXiv: 1904.00019
  • Bohdan Kulchytskyy, Lauren E. Hayward Sierens, Roger G. Melko Universal divergence of the Rényi entropy of a thinly sliced torus at the Ising fixed point Phys. Rev. B 100, 045139 arXiv: 1904.08955
  • Matthew J. S. Beach, Isaac De Vlugt, Anna Golubeva, Patrick Huembeli, Bohdan Kulchytskyy, Xiuzhe Luo, Roger G. Melko, Ejaaz Merali, Giacomo Torlai QuCumber: wavefunction reconstruction with neural networks SciPost Phys. 7, 009 (2019) arXiv: 1812.09329
  • Roger G. Melko, Giuseppe Carleo, Juan Carrasquilla and J. Ignacio Cirac  Restricted Boltzmann machines in quantum physics Nature Physics 15, 887-892 (2019)
  • Grigory Bednik, Lauren E. Hayward Sierens, Minyong Guo, Robert C. Myers, and Roger G. Melko Probing trihedral corner entanglement for Dirac fermions Phys. Rev. B 99, 155153 arXiv: 1810.02831
  • David Poulin, Roger G. Melko, Matthew B. Hastings Self-correction in Wegner's three-dimensional Ising lattice gauge theory Phys. Rev. B 99, 094103 arXiv: 1812.03936
  • Juan Carrasquilla, Giacomo Torlai, Roger G. Melko, Leandro Aolita Reconstructing quantum states with generative models Nature Machine Intelligencevolume 1, pages155-161 arXiv: 1810.10584
  • Stavros Efthymiou, Matthew J. S. Beach, and Roger G. Melko Super-resolving the Ising model with convolutional neural networks Phys. Rev. B 99, 075113 arXiv: 1810.02372
  • 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
  • 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
  • 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
  • Giacomo Torlai, Roger G. Melko Learning Thermodynamics with Boltzmann Machines Phys. Rev. B 94, 165134 (2016) arXiv: 1606.02718
  • Johannes Helmes, Lauren E. Hayward Sierens, Anushya Chandran, William Witczak-Krempa, Roger G. Melko Universal corner entanglement of Dirac fermions and gapless bosons from the continuum to the lattice Phys. Rev. B (in press) arXiv: 1606.03096
  • B. Kulchytskyy, C. M. Herdman, Stephen Inglis, Roger G. Melko Detecting Goldstone Modes with Entanglement Entropy Phys. Rev. B 92, 115146 (2015) http://arxiv.org/abs/1502.01722
  • Ryan V. Mishmash, Iván González, Roger G. Melko, Olexei I. Motrunich, and Matthew P. A. Fisher Continuous Mott transition between a metal and a quantum spin liquid Phys. Rev. B 91, 235140 http://arxiv.org/abs/1403.4258
  • Lauren E. Hayward, Andrew J. Achkar, David G. Hawthorn, Roger G. Melko, Subir Sachdev Diamagnetism and density wave order in the pseudogap regime of YBa2Cu3O6+x Phys. Rev. B 90, 094515 (2014) http://arxiv.org/abs/1406.2694
  • 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
  • Ejaaz Merali, Isaac J. S. De Vlugt, Roger G. Melko Stochastic Series Expansion Quantum Monte Carlo for Rydberg Arrays arXiv: 2107.00766
  • Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn Twin Neural Network Regression is a Semi-Supervised Regression Algorithm arXiv: 2106.06124
  • Stefanie Czischek, Giacomo Torlai, Sayonee Ray, Rajibul Islam, Roger G. Melko Simulating a measurement-induced phase transition for trapped ion circuits arXiv: 2106.03769
  • Kevin Ryczko, Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn Orbital-Free Density Functional Theory with Small Datasets and Deep Learning arXiv: 2104.05408
  • Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema, Roger G. Melko, Juan Carrasquilla Variational Neural Annealing arXiv: 2101.10154
  • Stefanie Czischek, Victor Yon, Marc-Antoine Genest, Marc-Antoine Roux, Sophie Rochette, Julien Camirand Lemyre, Mathieu Moras, Michel Pioro-Ladrière, Dominique Drouin, Yann Beilliard, Roger G. Melko Miniaturizing neural networks for charge state autotuning in quantum dots arXiv: 2101.03181
  • Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko, Isaac Tamblyn Twin Neural Network Regression arXiv: 2012.14873
  • 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
  • Reconstructing quantum states with generative models Max Planck Institute of Quantum Optics, Germany
  • Reconstructing quantum states with generative models Chalmers University of Technology, Sweden
  • Machine Learning and the Complexity of Quantum Simulation 2021 Kavli Foundation Special Symposium, APS March Meeting https://www.aps.org/publications/apsnews/202105/kavli.cfm
  • Reconstructing Quantum States with Generative Models Machine Learning for Quantum Conference 2021 https://www.youtube.com/channel/UCdZR4gy3R3NADi-Q8ZpQyLA
  • Emergence & Dynamics in Quantum Matter KIAS,KAIST,POSTECH,IBS CALDES, Korea
  • Quantum Days Canada Virtual panel discussion
  • Virtual winter school on Strongly Correlated Quantum Matter Abdus Salam ICTP and the Max Planck Institute for the Physics of Complex Systems
  • Reconstructing quantum states with generative models University of Oxford "ML and Physics" seminar series
  • How hard is it to learn a quantum state? University of California, San Diego
  • Designing Quantum Computers with Generative Models McGill, Université de Montréal and Université de Sherbrooke virtual seminar series
  • Artificial Intelligence We Count Project, Inclusive Design Research Centre, OCAD University Toronto, ON
  • Designing quantum computers with generative models AISC Spotlight/aggregate intellect, Toronto Canada
  • Okinawa Institute of Science and Technology, Virtual seminar
  • Machine Learning and the Complexity of Quantum Simulation Learning New Physics w/Machine Learning Virtual Workshop, Emory University
  • Machine Learning the Quantum Many-Body Problem Dartmouth, Physics & Astronomy Virtual Colloquium
  • Machine Learning Quantum Matter Data Virtual Workshop, Flatiron Institute CCQ, NY
  • Machine Learning, Quantum Acceleration and Robust Quantum Systems Workshop, UCLA
  • How hard is it to learn a quantum state? Seminar, Harvard University
  • How hard is it to learn a quantum state? Seminar, Boston University
  • Machine learning the quantum many-body problem Colloquium, Brown University
  • How hard is it to learn a quantum state? Flatiron CCQ "Quantum Café", NY
  • Physics Challenges for Machine Learning and Network Science Workshop, Queen Mary University of London, UK
  • Machine Learning in Condensed Matter Physics Summer School Lecturer San Sebastian, Spain
  • Machine Learning for Quantum Many-body Physics Seminar, Department of Physics Oxford University, UK
  • Dynamics and Disorder in Quantum Many Body Systems Far from Equilibrium Summer School Lecturer Les Houches Summer School
  • Rethinking Quantum Industrialization True North, Waterloo ON https://truenorthwaterloo.com/speakers/roger-melko/
  • Public Lecture: Artificial Intelligence and the Complexity Frontier Calgary Central Library
  • Colloquium: Machine Learning the Many-Body Problem University of Colorado, Boulder
  • Machine Learning for Quantum Many-body Physics International Workshop, organizer KITP, Santa Barbara
  • PIRSA:19070019, Goodbye and Closing Remarks, 2019-07-12, Machine Learning for Quantum Design
  • PIRSA:19070002, Welcome and Opening Remarks, 2019-07-08, Machine Learning for Quantum Design
  • PIRSA:19040008, PSI 2018/2019 - Machine Learning - Lecture 13, 2019-04-10, PSI 2018/2019 - Machine Learning (Hayward Sierens)
  • PIRSA:19040007, PSI 2018/2019 - Machine Learning - Lecture 12, 2019-04-09, PSI 2018/2019 - Machine Learning (Hayward Sierens)
  • PIRSA:19040006, PSI 2018/2019 - Machine Learning - Lecture 11, 2019-04-08, PSI 2018/2019 - Machine Learning (Hayward Sierens)
  • PIRSA:18050000, Roger Melko: Perimeter Institute and University of Waterloo, 2018-05-02, Perimeter Public Lectures
  • 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