Data Science, Artificial Intelligence and Machine Learning

Presentations

• Machine Learning for tuning, controlling, and optimizing semiconductor spin qubits

  ORAL · Invited

  March 16, 2022, 3:00 PM – March 16, 2022, 3:36 PM

  Publication: 1. Identifying Pauli spin blockade using deep learning.
  J. Schuff, D.T. Lennon, S. Geyer, D. Craig, F. Fedele, F. Vigneau, L.C. Camenzind, A.V. Kuhlmann, R.J. Warburton, D.M. Zumbühl, D. Sejdinovic, G.A.D. Briggs, N. Ares. Planned Paper (2021).
  2. Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning.
  B. Severin, D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M. J. Carballido, S. Svab, A. V. Kuhlmann, F. R. Braakman, S. Geyer, F. N. M. Froning, H. Moon, M. A. Osborne, D. Sejdinovic, G. Katsaros, D. M. Zumbühl, G. A. D. Briggs, and N. Ares. Preprint, arXiv:2107.12975 (2021).
  3. Deep Reinforcement Learning for Efficient Measurement of Quantum Devices.
  V. Nguyen*, S. B. Orbell*, D.T. Lennon, H. Moon, F. Vigneau, L.C. Camenzind, L. Yu, D.M. Zumbühl,
  G.A.D. Briggs, M. A. Osborne, D. Sejdinovic, and N. Ares. npj Quantum Information 7, 100 (2021).
  4. Quantum device fine-tuning using unsupervised embedding learning.
  N.M. van Esbroeck, D.T. Lennon, H. Moon, V. Nguyen, F. Vigneau, L.C. Camenzind, L. Yu,
  D.M. Zumbühl, G.A.D. Briggs, D. Sejdinovic, and N. Ares. New J. Phys. 22 09503 (2020)
  5. Machine learning enables completely automatic tuning of a quantum device faster than human experts.
  H. Moon*, D.T. Lennon*, J. Kirkpatrick, N.M. van Esbroeck, L.C. Camenzind, Liuqi Yu, F. Vigneau, D.M. Zumbühl, G.A.D. Briggs, M.A Osborne, D. Sejdinovic, E.A. Laird, N. Ares. Nature Communications 11, 4161 (2020)
  6. Efficiently measuring a quantum device using machine learning.
  D. T. Lennon, H. Moon, L. C. Camenzind, Liuqi Yu, D. M. Zumbühl, G. A. D. Briggs, M. A. Osborne, E. A. Laird, N. Ares. npj Quantum Information 5, 79 (2019)

  Presenters

  • Dominic T Lennon

    • University of Oxford

  Authors

  • Leon Camenzind

    • University of Basel

  • Dominic T Lennon

    • University of Oxford

  • Vu Nguyen

    • University of Oxford

  • Brandon Severin

    • University of Oxford

  • Nina M van Esbroeck

    • University of Oxford

  • James Kirkpatrick

    • DeepMind, London, UK

  • Sebastian Orbell

    • University of Oxford

  • Hyungil Moon

    • University of Oxford

  • Jonas Schuff

    • University of Oxford

  • Florian Vigneau

    • University of Oxford

  • Liuqi Yu

    • University of Maryland, College Park
    • University of Basel

  • Simon Geyer

    • University of Basel

  • Andreas V Kuhlmann

    • University of Basel

  • Florian N Froning

    • University of Basel

  • Dino Sejdinovic

    • University of Oxford

  • Michael A Osborne

    • University of Oxford

  • Edward A Laird

    • Lancaster University

  • G. Andrew D Briggs

    • University of Oxford

  • Dominik M Zumbuhl

    • University of Basel

  • Natalia Ares

    • University of Oxford

  View abstract →

• Machine learning with the quantum earthmover's distance

  ORAL · Invited

  March 16, 2022, 3:36 PM – March 16, 2022, 4:12 PM

  Presenters

  • Seth Lloyd

    • Massachusetts Institute of Technology MIT
    • MIT

  Authors

  • Seth Lloyd

    • Massachusetts Institute of Technology MIT
    • MIT

  View abstract →

• Combining machine learning with first principles to model the Curie temperature of magnetic Heusler compounds

  ORAL

  March 16, 2022, 4:12 PM – March 16, 2022, 4:24 PM

  Presenters

  • Parul R Raghuvanshi

    • Indian Institute of Technology Bombay

  Authors

  • Parul R Raghuvanshi

    • Indian Institute of Technology Bombay

  • Krishnaraj Kundavu

    • Indian Institute of Technology, Bombay
    • Indian Institute of Technology Bombay
    • IIT Bombay

  • Bhavana Panwar

    • IIT Bombay

  • Prasun Keshri

    • IIT Bombay

  • Rohit Pathak

    • Indian Institute of Technology Bombay

  • Amrita Bhattacharya

    • Indian Inst of Tech-Bombay

  View abstract →

• Denoising scanning tunneling microscopy images with deep learning

  ORAL

  March 16, 2022, 4:24 PM – March 16, 2022, 4:36 PM

  Presenters

  • Frederic F Joucken

    • Arizona State University

  Authors

  • Frederic F Joucken

    • Arizona State University

  • John L Davenport

    • University of California, Santa Cruz

  • Zhehao Ge

    • Department of Physics, University of California, Santa Cruz
    • University of California, Santa Cruz

  • Eberth Quezada-Lopez

    • University of California, Santa Cruz

  • Takashi Taniguchi

    • National Institute for Materials Science, Tsukuba, Japan
    • National Institute for Materials Science
    • NIMS
    • Kyoto Univ
    • International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Ibaraki 305-0044, Japan.
    • 3 National Institute for Materials Science, Tsukuba, Japan
    • National Institute for Materials Science; 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
    • National Institute of Materials Science, Tsukuba, Japan
    • National Institute of Materials Science
    • Advanced Materials Laboratory, National Institute for Materials Science, 1-1 Namiki, Tsukuba, 305-0044, Japan
    • National Institute for Materials Science (Japan)
    • International Center for Materials Nanoarchitectonics, National Institute for Materials Science
    • International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
    • Kyoto University
    • International Center for Materials Nanoarchitectonics
    • International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan
    • International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Japan
    • International Center for Materials Nanoarchitectonics, National Institute for MaterialsScience, 1-1 Namiki, Tsukuba 305-0044, Japan
    • National Institute for Material Science, Japan
    • National Institute for Material Science
    • National Institute of Material Sciences, Japan
    • NIMS, Tsukuba
    • 2National Institute for Materials Science, Namiki 1-1, Ibaraki 305-0044, Japan.
    • National Institute of Materials Science, Tsukuba, Ibaraki 305-0044, Japan
    • National Institute for Materials Science, Japan
    • International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, 1-1 Namiki Tsukuba, Ibaraki 305-0044, Japan.
    • NIMS, Japan
    • National Institute for Materials Science (NIMS)
    • NIMS. Japan
    • International Center for Material Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan
    • International Center for Material Nanoarchitectonics, National Institute for Materials Science
    • National Institute for Materials Science Tsukuba
    • National Institute for Materials Science, 1-1 Namiki
    • National Institute for Materials Science of Japan
    • National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
    • NIMS - National Institute for Material Science, Japan
    • International Center for Materials Nanoarchitectonics, National Institute for Material Science, Tsukuba, Ibaraki 305-0044, Japan.
    • National Institute for Material Science, Tsukuba
    • National Institute for Materials Science, International Center for Materials Nanoarchitectonics
    • International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
    • National Institute of Material Science
    • National Institute for Materials Science,1-1 Namiki, Tsukuba, 305-0044, Japan

  • Kenji Watanabe

    • National Institute for Materials Science, Tsukuba, Japan
    • National Institute for Materials Science
    • NIMS
    • Research Center for Functional Materials, National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan
    • Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan.
    • Research Center for Functional Materials, National Institute for Materials Science
    • Advanced, Materials Laboratory, NIMS
    • 3 National Institute for Materials Science, Tsukuba, Japan
    • National Institute for Materials Science; 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
    • National Institute of Materials Science, Tsukuba, Japan
    • National Institute of Materials Science
    • Advanced Materials Laboratory, National Institute for Materials Science, 1-1 Namiki, Tsukuba, 305-0044, Japan
    • National Institute for Materials Science (Japan)
    • National Institute for Materials Science, Japan
    • Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
    • Research Center for Functional Materials
    • Research Center for Functional Materials, National Institute for Materials Science, Tsukuba, Japan
    • Research Center for Functional Materials, National Institute for Materials Science, Japan
    • Research Center for Functional Materials, National Institute for Materials Science, 1-1Namiki, Tsukuba 305-0044, Japan
    • National Institute for Material Science, Japan
    • National Institute for Material Science
    • National Institute of Material Sciences, Japan
    • NIMS, Tsukuba
    • 2National Institute for Materials Science, Namiki 1-1, Ibaraki 305-0044, Japan.
    • National Institute of Materials Science, Tsukuba, Ibaraki 305-0044, Japan
    • National Institute for Materials Science Japan
    • NIMS, Japan
    • nims
    • National Institute for Materials Science, Research Center for Functional Materials, Japan
    • National Institute for Materials Science Tsukuba
    • National Institute for Materials Science, 1-1 Namiki
    • National Institute for Materials Science of Japan
    • National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
    • NIMS - National Institute for Material Science, Japan
    • Research Center for Functional Materials, National Institute for Material Science, Tsukuba, Ibaraki, 305-0044, Japan.
    • National Institute for Material Science, Tsukuba
    • National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan
    • National Institute for Materials Science (NIMS)
    • National Institute for Materials Science, Research Center for Functional Materials
    • Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
    • National Institute of Material Science
    • Kyoto Univ
    • National Institute for Materials Science,1-1 Namiki, Tsukuba, 305-0044, Japan

  • Jerome Lagoute

    • Université Pari-Diderot

  • Robert A Kaindl

    • Arizona State University

  View abstract →

• Exploring non-equilibrium systems with normalizing flows

  ORAL

  March 16, 2022, 4:36 PM – March 16, 2022, 4:48 PM

  Presenters

  • Christoph Schönle

    • Max Planck Inst for Sci Light

  Authors

  • Christoph Schönle

    • Max Planck Inst for Sci Light

  • Vittorio Peano

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

  • Florian Marquardt

    • Max Planck Inst for Sci Light
    • Friedrich-Alexander University Erlangen-Nürnberg
    • Friedrich-Alexander University Erlangen-Nürnberg, Max Planck Institute for the Science of Light
    • Friedrich-Alexander University Erlangen-

  View abstract →

• Discovering dynamical symmetry breaking and resonances in nonlinear systems through AI.

  ORAL

  March 16, 2022, 4:48 PM – March 16, 2022, 5:00 PM

  Publication: [1] G. D. Barmparis and G. P. Tsironis, "Discovering nonlinear resonances through physics-informed machine learning," J. Opt. Soc. Am. B, 38, C120-C126 (2021).
  
  [2] G. P. Tsironis, G. D. Barmparis, D. K. Campbell, "Dynamical symmetry breaking through AI: The dimer self-trapping transition", Int. J. Mod. Phys. B, accepted.

  Presenters

  • George P Tsironis

    • Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, P.O. Box 2208, 71003 Heraklion, Greece

  Authors

  • George P Tsironis

    • Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, P.O. Box 2208, 71003 Heraklion, Greece

  • Georgios D Barmparis

    • Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, P.O. Box 2208, 71003 Heraklion, Greece

  View abstract →

• Sample generation for the spin-fermion model using neural networks .

  ORAL

  March 16, 2022, 5:00 PM – March 16, 2022, 5:12 PM

  Presenters

  • Georgios Stratis

    • Northeastern University

  Authors

  • Georgios Stratis

    • Northeastern University

  • Phillip E Weinberg

    • Northeastern University

  • Tales Imbiriba

    • Northeastern University

  • Pau Closas

    • Northeastern University

  • Adrian E Feiguin

    • Northeastern University

  View abstract →

• Deep Bayesian Experimental Design for Quantum Many-Body Systems

  ORAL

  March 16, 2022, 5:12 PM – March 16, 2022, 5:24 PM

  Presenters

  • Leopoldo Sarra

    • Max Planck Inst for Sci Light

  Authors

  • Leopoldo Sarra

    • Max Planck Inst for Sci Light

  • Florian Marquardt

    • Max Planck Inst for Sci Light
    • Friedrich-Alexander University Erlangen-Nürnberg
    • Friedrich-Alexander University Erlangen-Nürnberg, Max Planck Institute for the Science of Light
    • Friedrich-Alexander University Erlangen-

  View abstract →

• Band gap predition of very large number of novel Van der Waals heterostructures using active learing models

  ORAL

  March 16, 2022, 5:24 PM – March 16, 2022, 5:36 PM

  Publication: M. Fronzi*, O. Isayev, D. A. Winkler J. G. Shapter, A. V. Ellis, P. C. Sherrell, N. A. Shepelin, Al. Corletto, and M. J. Ford ``Active learning in Bayesian neural networks for the bandgap predictions of novel Van der Waals heterostructures'' Adv Int Sys 2100080, 1-7 (2021)

  Presenters

  • Marco Fronzi

    • Shibaura Inst of Tech

  Authors

  • Marco Fronzi

    • Shibaura Inst of Tech

  • Michael Ford

    • University of Technology Sydney

  • Dawid Winkler

    • La Trobe University

  • Olexandr Isayev

    • Carnegie Mellon University

  View abstract →

• Identifying Pauli spin blockade using deep learning with scarce experimental data

  ORAL

  March 16, 2022, 5:36 PM – March 16, 2022, 5:48 PM

  Presenters

  • Jonas Schuff

    • University of Oxford

  Authors

  • Jonas Schuff

    • University of Oxford

  • Dominic T Lennon

    • University of Oxford

  • Simon Geyer

    • University of Basel

  • David Craig

    • University of Oxford

  • Leon Camenzind

    • University of Basel

  • Federico Fedele

    • University of Oxford

  • Florian Vigneau

    • University of Oxford

  • Andreas V Kuhlmann

    • University of Basel

  • Richard J Warburton

    • University of Basel

  • Dominik M Zumbuhl

    • University of Basel

  • Dino Sejdinovic

    • University of Oxford

  • G. Andrew D Briggs

    • University of Oxford

  • Natalia Ares

    • University of Oxford

  View abstract →