Machine Learning Quantum Many-body Models

FOCUS · C18

Presentations

  • Quantum Loop Topography for Machine Learning Transport

    Invited

    –

    Presenters

    • Yi Zhang

      Cornell University, Department of Physics, Cornell University

    Authors

    • Yi Zhang

      Cornell University, Department of Physics, Cornell University

    • Carsten Bauer

      Institute for Theoretical Physics, University of Cologne

    • Peter Broecker

      Institute for Theoretical Physics, University of Cologne

    • Paul Ginsparg

      Department of Physics, Cornell University

    • Simon Trebst

      Institute for Theoretical Physics, University of Cologne, Germany, Institute for Theoretical Physics, University of Cologne, Univ Cologne, University of Cologne

    • Eun-Ah Kim

      Cornell University, Department of Physics, Cornell University

    View abstract →

  • Recent advances in the study of frustrated magnetism with Neural-Network quantum states

    ORAL

    –

    Presenters

    • Giuseppe Carleo

      Center for Computational Quantum Physics, Flatiron Institute, CCQ, Flatiron Institute

    Authors

    • Kenny Choo

      University of Zurich, Physik Institut, University of Zurich

    • Titus Neupert

      University of Zurich, Physics, University of Zurich, Physik Institut, University of Zurich

    • Giuseppe Carleo

      Center for Computational Quantum Physics, Flatiron Institute, CCQ, Flatiron Institute

    View abstract →

  • Symmetries and Many-Body Excitations with Neural-Network Quantum States

    ORAL

    –

    Presenters

    • Kenny Choo

      University of Zurich, Physik Institut, University of Zurich

    Authors

    • Kenny Choo

      University of Zurich, Physik Institut, University of Zurich

    • Giuseppe Carleo

      Center for Computational Quantum Physics, Flatiron Institute, CCQ, Flatiron Institute

    • Nicolas Regnault

      Laboratoire Pierre Aigrain, Ecole normale superieure

    • Titus Neupert

      University of Zurich, Physics, University of Zurich, Physik Institut, University of Zurich

    View abstract →

  • Learning Quantum Models from Symmetries

    ORAL

    –

    Presenters

    • Eli Chertkov

      University of Illinois at Urbana-Champaign

    Authors

    • Eli Chertkov

      University of Illinois at Urbana-Champaign

    • Benjamin Villalonga

      University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champaign - Quantum Artificial Intelligence Lab (QuAIL) @ NASA Ames - USRA Research Institute for Advanced Computer Science (RIACS)

    • Bryan Clark

      University of Illinois at Urbana-Champaign, Physics, University of Illinois at Urbana Champaign, Physics, University of Illinois at Urbana-Champaign

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  • Learning a local Hamiltonian from local measurements

    ORAL

    –

    Presenters

    • Eyal Bairey

      Physics, Technion - Israel Institute of Technology

    Authors

    • Eyal Bairey

      Physics, Technion - Israel Institute of Technology

    • Itai Arad

      Physics, Technion - Israel Institute of Technology

    • Netanel Lindner

      Physics Department, Technion - Israel Institute of Technology, Physics, Technion - Israel Institute of Technology, Technion - Israel Institute of Technology, Physics, Technion – Israel Institute of Technology

    View abstract →

  • Accelerating Density Matrix Renormalization Group Computations with Machine Learning

    ORAL

    –

    Presenters

    • Jacob Marks

      Physics, Stanford University

    Authors

    • Jacob Marks

      Physics, Stanford University

    • Hong-Chen Jiang

      Stanford Institute for Materials and Energy Sciences, SLAC and Stanford University, SIMES, SLAC, and Stanford University, SLAC National Accelerator Laboratory, Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory and Stanford University

    • Thomas Devereaux

      Stanford University, Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, SLAC National Accelerator Laboratory, Physics, Stanford University, SLAC and Stanford University, Institute for Materials and Energy Science, Stanford, SIMES, SLAC National Accelerator Lab, SLAC National Accelerator Laboratory and Stanford University, Stanford Institute for Materials and Energy Sciences, SLAC, Stanford, SIMES, SLAC, and Stanford University, Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory and Stanford University

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  • Self-learning with neural networks in determinant quantum Monte Carlo studies of the Holstein model.

    ORAL

    –

    Presenters

    • Philip Dee

      University of Tennessee

    Authors

    • Shaozhi Li

      Department of Physics and Astronomy, University of Michigan, Physics, University of Michigan

    • Philip Dee

      University of Tennessee

    • Ehsan Khatami

      Department of Physics and Astronomy, San Jose State Unversity, San Jose State University, Physics, San Jose State University

    • Steven Johnston

      Department of Physics and Astronomy, Univ of Tennessee, Knoxville, Department of Physics and Astronomy, University of Tennesse, Physics and Astronomy, University of Tennessee, University of Tennessee, Department of Physics and Astronomy, University of Tennessee, Department of Physics and Astronomy, University of Tennessee, Knoxville

    View abstract →

  • Unsupervised manifold learning of ground state wave functions

    ORAL

    –

    Presenters

    • Michael Matty

      Cornell University

    Authors

    • Michael Matty

      Cornell University

    • Yi Zhang

      Cornell University, Department of Physics, Cornell University

    • Senthil Todadri

      Physics, MIT, Massachusetts Institute of Technology, Physics, Massachusetts Institute of Technology

    • Eun-Ah Kim

      Cornell University, Department of Physics, Cornell University

    View abstract →

  • Machinery representation of physics models via structured self-attention network

    ORAL

    –

    Presenters

    • Junwei Liu

      Hong Kong University of Science and Technology, The Hong Kong University of Science and Technology, Department of Physics, Hong Kong University of Science and Technology

    Authors

    • Junwei Liu

      Hong Kong University of Science and Technology, The Hong Kong University of Science and Technology, Department of Physics, Hong Kong University of Science and Technology

    • Yang Zhang

      Max Planck Institute for Chemical Physics of Solids

    • Yujun zhao

      Hong Kong University of Science and Technology

    View abstract →

  • Neural Network Renormalization Group

    ORAL

    –

    Presenters

    • Shuo-Hui Li

      Institute of Physics, Chinese Academy of Sciences, Institute of Physics

    Authors

    • Shuo-Hui Li

      Institute of Physics, Chinese Academy of Sciences, Institute of Physics

    • Lei Wang

      Institute of Physics, Institute of Physics, Chinese Academy of Sciences, Institute of Physics Chinese Academy of Sciences

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  • Learning density functional theory mappings with extensive deep neural networks and deep convolutional inverse graphics networks

    ORAL

    –

    Presenters

    • Kevin Ryczko

      Department of Physics, University of Ottawa

    Authors

    • Kevin Ryczko

      Department of Physics, University of Ottawa

    • David Strubbe

      University of California, Merced, Department of Physics, University of California, Merced, Physics, University of California, Merced

    • Isaac Tamblyn

      University of Ontario Institute of Technology, University of Ottawa, and National Research Council of Canada, University of Ontario Institute of Technology, National Research Council of Canada, National Research Council of Canada

    View abstract →