Machine learning for quantum matter V
FOCUS · U39 · ID: 354895
Presentations
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Nicholas Metropolis Award Talk: Enhancing Quantum Simulators with Neural Networks
Invited
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Presenters
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Giacomo Torlai
Simons Foundation, Center for Computational Quantum Physics, Flatiron Institute, Flatiron Institute
Authors
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Giacomo Torlai
Simons Foundation, Center for Computational Quantum Physics, Flatiron Institute, Flatiron Institute
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Topological codes revisited: Hamiltonian learning and topological phase transitions
ORAL
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Presenters
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Eliska Greplova
ETH Zurich
Authors
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Eliska Greplova
ETH Zurich
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Agnes Valenti
ETH Zurich
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Evert Van Nieuwenburg
IQIM, Caltech, Caltech, Physics, California Institute ot Technology
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Gregor Boschung
ETH Zurich
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Frank Schäfer
University of Basel
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Niels Loerch
University of Basel
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Sebastian Huber
ETH Zurich
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Real time evolution with neural network quantum states
ORAL
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Presenters
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Irene Lopez Gutierrez
TU Munich
Authors
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Irene Lopez Gutierrez
TU Munich
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Christian Mendl
TU Munich
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Hunting for Hamiltonians with a General-Purpose Symmetry-to-Hamiltonian Approach
ORAL
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Presenters
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Eli Chertkov
University of Illinois at Urbana-Champaign
Authors
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Eli Chertkov
University of Illinois at Urbana-Champaign
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Benjamin Villalonga
University of Illinois at Urbana-Champaign
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Bryan Clark
University of Illinois at Urbana-Champaign
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Studying inhomogeneous quantum many-body problems using neural networks
ORAL
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Presenters
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Alexander Blania
Max Planck Inst for Sci Light
Authors
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Alexander Blania
Max Planck Inst for Sci Light
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Evert Van Nieuwenburg
IQIM, Caltech, Caltech, Physics, California Institute ot Technology
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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
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Presenters
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Bradley Magnetta
Yale University
Authors
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Bradley Magnetta
Yale University
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Vidvuds Ozolins
Yale University, Applied Physics, Yale University
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Classical Quantum Optimization with Neural Network Quantum States
ORAL
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Presenters
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Joseph Gomes
The University of Iowa
Authors
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Joseph Gomes
The University of Iowa
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Solving frustrated quantum many-particle models with convolutional neural networks
ORAL
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Presenters
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Xiao Liang
Institute for Advanced Study, Tsinghua University
Authors
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Xiao Liang
Institute for Advanced Study, Tsinghua University
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Quantum dynamics in driven spin systems with neural-network quantum states
ORAL
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Presenters
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Damian Hofmann
Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany
Authors
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Damian Hofmann
Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany
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Giuseppe Carleo
Center for Computational Quantum Physics, Flatiron Institute, New York, NY, USA, Flatiron Institute
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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
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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
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Presenters
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Zhong Yuan Lai
Fudan Univ
Authors
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Zhong Yuan Lai
Fudan Univ
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Francisco Costa Meirinhos
Department of Physics, University of Bonn
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Xiaopeng Li
Department of Physics, Fudan University, Fudan Univ
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Unsupervised machine learning for accelerating discoveries from temperature dependent X-ray data
ORAL
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Presenters
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Jordan Venderley
Cornell University
Authors
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Jordan Venderley
Cornell University
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Michael Matty
Physics, Cornell University, Cornell University
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Varsha Kishore
Cornell University
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Geoff Pleiss
Cornell University
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Kilian Weinberger
Cornell University
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Eun-Ah Kim
Cornell University
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Machine learning effective models for quantum systems
ORAL
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Presenters
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Andrew Mitchell
Univ Coll Dublin, Physics, University College Dublin, School of Physics, University College Dublin
Authors
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Andrew Mitchell
Univ Coll Dublin, Physics, University College Dublin, School of Physics, University College Dublin
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Jonas Rigo
Univ Coll Dublin
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