Machine Learning for Quantum Matter I

FOCUS · L39 · ID: 354882

Presentations

  • Classifying Snapshots of the Doped Hubbard Model with Machine Learning

    Invited

    –

    Presenters

    • Annabelle Bohrdt

      Tech Univ Muenchen, Department of Physics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany

    Authors

    • Annabelle Bohrdt

      Tech Univ Muenchen, Department of Physics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany

    • Christie S. Chiu

      Physics Department, Harvard University

    • Geoffrey Ji

      Physics Department, Harvard University

    • Muqing Xu

      Physics Department, Harvard University

    • Daniel Greif

      Physics Department, Harvard University

    • Markus Greiner

      Physics Department, Harvard University, Harvard University

    • Eugene Demler

      Harvard University, Physics Department, Harvard University

    • Fabian Grusdt

      Physics Department, Ludwig-Maximilians-Universität München, Tech Univ Muenchen, Department of Physics, Technical University Munich

    • Michael Knap

      TU Munich, Department of Physics, Technical University of Munich, Technical University of Munich, Tech Univ Muenchen, Department of Physics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany

    View abstract →

  • AI Assisted Discovery in Quantum Gas Microscope Images

    ORAL

    –

    Presenters

    • Ehsan Khatami

      San Jose State University

    Authors

    • Elmer Guardado-Sanchez

      Princeton University

    • Benjamin M Spar

      Princeton University

    • Juan Carrasquilla

      Vector Institute, Vector Institute for Artificial Intelligence

    • Richard Theodore Scalettar

      University of California, Davis, Physics, UC Davis, UC Davis

    • Waseem S Bakr

      Princeton University, Princeton

    • Ehsan Khatami

      San Jose State University

    View abstract →

  • Unsupervised machine learning of topological phase transitions

    ORAL

    –

    Presenters

    • Mathias Scheurer

      Department of Physics, Harvard University, Cambridge, MA 02138, USA, Harvard University, Department of Physics, Harvard University

    Authors

    • Joaquin Rodriguez Nieva

      Stanford University, Department of Physics, Harvard University

    • Mathias Scheurer

      Department of Physics, Harvard University, Cambridge, MA 02138, USA, Harvard University, Department of Physics, Harvard University

    View abstract →

  • Classification of optical quantum states using machine learning

    ORAL

    –

    Presenters

    • Shahnawaz Ahmed

      MC2, Chalmers University of Technology

    Authors

    • Shahnawaz Ahmed

      MC2, Chalmers University of Technology

    • Carlos Sánchez Muñoz

      Physics, Oxford University, Oxford University

    • Franco Nori

      RIKEN, Theoretical Quantum Physics, Riken

    • Anton Frisk Kockum

      Chalmers Univ of Tech, Department of Microtechnology and Nanoscience, Chalmers University of Technology, MC2, Chalmers University of Technology

    View abstract →

  • Machine learning the Mattis glass transformation

    ORAL

    –

    Presenters

    • Daniel Lozano-Gomez

      Department of Physics and Astronomy, University of Waterloo, University of Waterloo

    Authors

    • Daniel Lozano-Gomez

      Department of Physics and Astronomy, University of Waterloo, University of Waterloo

    • Darren Pereira

      University of Waterloo

    • Michel J P Gingras

      Department of Physics and Astronomy, University of Waterloo, University of Waterloo, Department of Physics, University of Waterloo

    View abstract →

  • Augmenting machine learning algorithms with the addition of a physics based intelligence prior

    ORAL

    –

    Presenters

    • Christopher Singh

      Binghamton University, Physics, Binghamton University, Physics, Applied Physics, and Astronomy, Binghamton University

    Authors

    • Christopher Singh

      Binghamton University, Physics, Binghamton University, Physics, Applied Physics, and Astronomy, Binghamton University

    • Matthew Redell

      Binghamton University, Physics, Binghamton University

    • Mohannad Elhamod

      Virginia Tech, Computer Science, Virginia Tech

    • Jie Bu

      Virginia Tech, Computer Science, Virginia Tech

    • Anuj Karpatne

      Virginia Tech, Computer Science, Virginia Tech

    • Wei-Cheng Lee

      Binghamton University, Physics, Binghamton University, Physics, Applied Physics, and Astronomy, Binghamton University

    View abstract →

  • Adversarial machine learning for modeling the distribution of large-scale ultracold atom experiments

    ORAL

    –

    Presenters

    • Corneel Casert

      Department of Physics and Astronomy, Ghent University, Ghent University

    Authors

    • Corneel Casert

      Department of Physics and Astronomy, Ghent University, Ghent University

    • Kyle Mills

      Ontario Tech University

    • Tom Vieijra

      Department of Physics and Astronomy, Ghent University, Ghent University

    • Jan Ryckebusch

      Department of Physics and Astronomy, Ghent University, Ghent University

    • Isaac Tamblyn

      Natl Res Council, National Research Council of Canada

    View abstract →

  • Machine Learning based BCS superconductivity Predictor from Normal State Properties

    ORAL

    –

    Presenters

    • Fei Han

      Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

    Authors

    • Fei Han

      Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

    • Nina Andrejevic

      Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

    • Thanh Nguyen

      Massachusetts Institute of Technology MIT

    • Quynh Nguyen

      Massachusetts Institute of Technology MIT

    • Shreya Parjan

      Wellesley College

    • Mingda Li

      Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

    View abstract →

  • Unlocking quantum critical phenomena with physics guided artificial intelligence

    ORAL

    –

    Presenters

    • Matthew Redell

      Binghamton University, Physics, Binghamton University

    Authors

    • Christopher Singh

      Binghamton University, Physics, Binghamton University, Physics, Applied Physics, and Astronomy, Binghamton University

    • Matthew Redell

      Binghamton University, Physics, Binghamton University

    • Mohannad Elhamod

      Virginia Tech, Computer Science, Virginia Tech

    • Jie Bu

      Virginia Tech, Computer Science, Virginia Tech

    • Wei-Cheng Lee

      Binghamton University, Physics, Binghamton University, Physics, Applied Physics, and Astronomy, Binghamton University

    • Anuj Karpatne

      Virginia Tech, Computer Science, Virginia Tech

    View abstract →