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Machine Learning for Quantum Matter IV

FOCUS · S62 · ID: 1100953

Presentations

  • Enhancing Variational Monte Carlo with Neural Network Quantum States

    ORAL · Invited

    –

    Publication: S. Czischek, M.S. Moss, M. Radzihovsky, E. Merali, and R.G. Melko, "Data-enhanced variational Monte Carlo simulations for Rydberg atom arrays", PRB 105, 205108 (2022)

    Presenters

    • Stefanie Czischek

      U of Ottawa, University of Ottawa

    Authors

    • Stefanie Czischek

      U of Ottawa, University of Ottawa

    View abstract →

  • Machine Learning for Optical Scanning Probe Nanoscopy

    ORAL

    –

    Publication: arXiv:2204.09820

    Presenters

    • Suheng Xu

      Columbia University

    Authors

    • Suheng Xu

      Columbia University

    • Xinzhong Chen

      Stony Brook University (SUNY)

    • Sara Shabani

      Columbia University

    • Yueqi Zhao

      UCSD

    • Matthew Fu

      Columbia University

    • Andrew Millis

      Columbia University, Columbia University, Flatiron Institute

    • Michael M Fogler

      University of California, San Diego

    • Abhay N Pasupathy

      Brookhaven National Laboratory & Columbia University, Columbia University

    • Mengkun Liu

      Stony Brook University (SUNY)

    • Dmitri N Basov

      Columbia University, Department of Physics, Columbia University, New York, NY, USA

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  • Invited Talk: Cristian BonatoBayesian inference for quantum sensing and model learning

    ORAL · Invited

    –

    Publication: (1) Muhammad Junaid Arshad, Christiaan Bekker, Ben Haylock, Krzysztof Skrzypczak, Daniel White, Benjamin Griffiths, Joe Gore, Gavin W. Morley, Patrick Salter, Jason Smith, Inbar Zohar, Amit Finkler, Yoann Altmann, Erik M. Gauger, Cristian Bonato, "Online adaptive estimation of decoherence timescales for a single qubit", arXiv:2210.06103 (2022)<br>(2) Inbar Zohar, Yoav Romach, Muhammad Junaid Arshad, Nir Halay, Niv Drucker, Rainer Stöhr, Andrej Denisenko, Yonatan Cohen, Cristian Bonato, Amit Finkler, " Real-time frequency estimation of a qubit without single-shot-readout ", arXiv:2210.05542 (2022)<br>(3) Valentin Gebhart, Raffaele Santagati, Antonio Andrea Gentile, Erik Gauger, David Craig, Natalia Ares, Leonardo Banchi, Florian Marquardt, Luca Pezze', Cristian Bonato, "Learning Quantum Systems", arXiv:2207.00298 (2022)<br>(4) Eleanor Scerri, Erik M. Gauger, Cristian Bonato, "Extending qubit coherence by adaptive quantum environment learning", New Journal of Physics 22, 035002 (2020)<br>(5) Cristian Bonato, Machiel S. Blok, Hossein T. Dinani, Dominic W. Berry, Matthew L. Markham, Daniel J. Twitchen, Ronald Hanson, "Optimized quantum sensing with a single electron spin using real-time adaptive measurements", Nature Nanotechnology 11, 247-252 (2016)

    Presenters

    • Cristian Bonato

      Heriot-Watt University, Bonato, Heriot-Watt University, Edinburgh

    Authors

    • Cristian Bonato

      Heriot-Watt University, Bonato, Heriot-Watt University, Edinburgh

    • Muhammad Junaid Arshad

      Heriot-Watt University

    • Stewart Wallace

      Heriot-Watt University

    • Christiaan Bekker

      Heriot-Watt University

    • Ben Haylock

      Heriot-Watt University

    • Yoann Altmann

      Heriot-Watt University

    • Erik Gauger

      Heriot-Watt University

    View abstract →

  • Towards improving generalization of a neural network by interpretation for topological phases of matter

    ORAL

    –

    Presenters

    • Kacper J Cybinski

      University of Warsaw

    Authors

    • Kacper J Cybinski

      University of Warsaw

    • Marcin Plodzien

      ICFO-The Institute of Photonic Sciences

    • Michal Tomza

      University of Warsaw

    • Maciej A Lewenstein

      ICFO-The Institute of Photonic Sciences

    • Alexandre Dauphin

      ICFO-The Institute of Photonic Sciences

    • Anna Dawid

      Flatiron Institute

    View abstract →

  • Digital Discovery of a Scientific Concept at the Core of Experimental Quantum Optics

    ORAL

    –

    Publication: https://doi.org/10.48550/arXiv.2210.09980<br>https://doi.org/10.48550/arXiv.2210.09980

    Presenters

    • Sören Arlt

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

    Authors

    • Sören Arlt

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

    • Mario Krenn

      Max Planck Institute for the Science of Light

    • Carlos Ruiz Gonzalez

      Max Planck Institute for the Science of Light

    • Mario Krenn

      Max Planck Institute for the Science of Light

    View abstract →

  • From 4D-STEM data to interpretable physics — an unsupervised learning approach to the charge order physics in TaS<sub>2</sub>

    ORAL

    –

    Publication: [1] J. Venderley et al., Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-Ray Diffraction, Proceedings of the National Academy of Sciences 119, e2109665119 (2022).

    Presenters

    • Haining Pan

      Cornell University

    Authors

    • Haining Pan

      Cornell University

    • Krishnanand M Mallayya

      Cornell University

    • James L Hart

      Cornell University

    • Judy J Cha

      Cornell University

    • Eun-Ah Kim

      Cornell University

    View abstract →