Machine learning for quantum matter V

Presentations

• Nicholas Metropolis Award Talk: Enhancing Quantum Simulators with Neural Networks

  Invited

  March 5, 2020, 4:30 PM – March 5, 2020, 5:06 PM

  Presenters

  • Giacomo Torlai

    Simons Foundation, Center for Computational Quantum Physics, Flatiron Institute, Flatiron Institute

  Authors

  • Giacomo Torlai

    Simons Foundation, Center for Computational Quantum Physics, Flatiron Institute, Flatiron Institute

  View abstract →

• Topological codes revisited: Hamiltonian learning and topological phase transitions

  ORAL

  March 5, 2020, 5:06 PM – March 5, 2020, 5:18 PM

  Presenters

  • Eliska Greplova

    ETH Zurich

  Authors

  • Eliska Greplova

    ETH Zurich

  • Agnes Valenti

    ETH Zurich

  • Evert Van Nieuwenburg

    IQIM, Caltech, Caltech, Physics, California Institute ot Technology

  • Gregor Boschung

    ETH Zurich

  • Frank Schäfer

    University of Basel

  • Niels Loerch

    University of Basel

  • Sebastian Huber

    ETH Zurich

  View abstract →

• Real time evolution with neural network quantum states

  ORAL

  March 5, 2020, 5:18 PM – March 5, 2020, 5:30 PM

  Presenters

  • Irene Lopez Gutierrez

    TU Munich

  Authors

  • Irene Lopez Gutierrez

    TU Munich

  • Christian Mendl

    TU Munich

  View abstract →

• Hunting for Hamiltonians with a General-Purpose Symmetry-to-Hamiltonian Approach

  ORAL

  March 5, 2020, 5:30 PM – March 5, 2020, 5:42 PM

  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

  • Bryan Clark

    University of Illinois at Urbana-Champaign

  View abstract →

• Studying inhomogeneous quantum many-body problems using neural networks

  ORAL

  March 5, 2020, 5:42 PM – March 5, 2020, 5:54 PM

  Presenters

  • Alexander Blania

    Max Planck Inst for Sci Light

  Authors

  • Alexander Blania

    Max Planck Inst for Sci Light

  • Evert Van Nieuwenburg

    IQIM, Caltech, Caltech, Physics, California Institute ot Technology

  • Florian Marquardt

    Max Planck Inst for Sci Light, Max Planck Institute for the Science of Light

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• Calculating Wannier functions via basis pursuit using a machine learned dictionary

  ORAL

  March 5, 2020, 5:54 PM – March 5, 2020, 6:06 PM

  Presenters

  • Bradley Magnetta

    Yale University

  Authors

  • Bradley Magnetta

    Yale University

  • Vidvuds Ozolins

    Yale University, Applied Physics, Yale University

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• Classical Quantum Optimization with Neural Network Quantum States

  ORAL

  March 5, 2020, 6:06 PM – March 5, 2020, 6:18 PM

  Presenters

  • Joseph Gomes

    The University of Iowa

  Authors

  • Joseph Gomes

    The University of Iowa

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• Solving frustrated quantum many-particle models with convolutional neural networks

  ORAL

  March 5, 2020, 6:18 PM – March 5, 2020, 6:30 PM

  Presenters

  • Xiao Liang

    Institute for Advanced Study, Tsinghua University

  Authors

  • Xiao Liang

    Institute for Advanced Study, Tsinghua University

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• Quantum dynamics in driven spin systems with neural-network quantum states

  ORAL

  March 5, 2020, 6:30 PM – March 5, 2020, 6:42 PM

  Presenters

  • Damian Hofmann

    Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany

  Authors

  • Damian Hofmann

    Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany

  • Giuseppe Carleo

    Center for Computational Quantum Physics, Flatiron Institute, New York, NY, USA, Flatiron Institute

  • Angel Rubio

    Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Center for Computational Quantum Physics (CCQ), The Flatiron Institute, Max Planck Institute for Structure and Dynamics of Matter, Department of Physics, Columbia University, New York, New York 10027, USA, Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany, Max Planck Institute for the Structure and Dynamics of Matter, Structure and Dynamics of Matter, Max Planck Institute, Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany, Max Planck Inst Structure & Dynamics of Matter, Max Planck Institue for the Structure and Dynamics of Matter, Theory, Max Planck Institute for the Structure & Dynamics of Matter

  • Michael Sentef

    Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany, Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany, Max Planck Inst Structure & Dynamics of Matter, Max Planck Institute for the Structure and Dynamics of Matter

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• Study of phi-4 theories with deep learning methods

  ORAL

  March 5, 2020, 6:42 PM – March 5, 2020, 6:54 PM

  Presenters

  • Zhong Yuan Lai

    Fudan Univ

  Authors

  • Zhong Yuan Lai

    Fudan Univ

  • Francisco Costa Meirinhos

    Department of Physics, University of Bonn

  • Xiaopeng Li

    Department of Physics, Fudan University, Fudan Univ

  View abstract →

• Unsupervised machine learning for accelerating discoveries from temperature dependent X-ray data

  ORAL

  March 5, 2020, 6:54 PM – March 5, 2020, 7:06 PM

  Presenters

  • Jordan Venderley

    Cornell University

  Authors

  • Jordan Venderley

    Cornell University

  • Michael Matty

    Physics, Cornell University, Cornell University

  • Varsha Kishore

    Cornell University

  • Geoff Pleiss

    Cornell University

  • Kilian Weinberger

    Cornell University

  • Eun-Ah Kim

    Cornell University

  View abstract →

• Machine learning effective models for quantum systems

  ORAL

  March 5, 2020, 7:06 PM – March 5, 2020, 7:18 PM

  Presenters

  • Andrew Mitchell

    Univ Coll Dublin, Physics, University College Dublin, School of Physics, University College Dublin

  Authors

  • Andrew Mitchell

    Univ Coll Dublin, Physics, University College Dublin, School of Physics, University College Dublin

  • Jonas Rigo

    Univ Coll Dublin

  View abstract →