Quantum Machine Learning Training and Beyond

Presentations

• On quantum backpropagation, information reuse, and cheating measurement collapse

  ORAL · Invited

  March 7, 2024, 12:30 PM – March 7, 2024, 1:06 PM

  Presenters

  • Amira M Abbas

    University of Amsterdam

  Authors

  • Amira M Abbas

    University of Amsterdam

  View abstract →

• Connecting channel expressiveness to gradient magnitudes and noise induced barren plateaus

  ORAL

  March 7, 2024, 1:06 PM – March 7, 2024, 1:18 PM

  Presenters

  • Matthew Duschenes

    University of Waterloo

  Authors

  • Matthew Duschenes

    University of Waterloo

  • Diego García-Martín

    Los Alamos National Laboratory

  • Martin Larocca

    Los Alamos National Laboratory

  • Zoe P Holmes

    Los Alamos National Laboratory, École Polytechnique Fédérale de Lausanne

  • Marco Cerezo

    Los Alamos National Laboratory

  View abstract →

• Generalization Error in Quantum Machine Learning in the Presence of Sampling Noise

  ORAL

  March 7, 2024, 1:18 PM – March 7, 2024, 1:30 PM

  Publication: [1] F. Hu, et. al., Phys. Rev. X 13, 041020 (2023)

  Presenters

  • Fangjun Hu

    Princeton University

  Authors

  • Fangjun Hu

    Princeton University

  • Xun Gao

    University of Colorado, Boulder, University of Colorado Boulder

  • Hakan E Tureci

    Princeton University

  View abstract →

• Demonstration of a Quantum Machine Learning Algorithm beyond the Coherence Time

  ORAL

  March 7, 2024, 1:30 PM – March 7, 2024, 1:42 PM

  Presenters

  • Hakan E Tureci

    Princeton University

  Authors

  • Hakan E Tureci

    Princeton University

  • Fangjun Hu

    Princeton University

  • Saeed A Khan

    Princeton University

  • Nicholas T Bronn

    IBM TJ Watson Research Center

  • Guilhem J Ribeill

    Raytheon BBN

  • Gerasimos M Angelatos

    Raytheon BBN Technologies

  • Graham E Rowlands

    BBN Technology - Massachusetts

  View abstract →

• Reduction of finite sampling noise in quantum neural networks

  ORAL

  March 7, 2024, 1:42 PM – March 7, 2024, 1:54 PM

  Publication: Kreplin, David A., and Marco Roth. "Reduction of finite sampling noise in quantum neural networks." arXiv preprint arXiv:2306.01639 (2023).
  

  Presenters

  • David Kreplin

    Fraunhofer IPA

  Authors

  • David Kreplin

    Fraunhofer IPA

  • Marco Roth

    Fraunhofer IPA

  View abstract →

• Full-stack Quantum Machine Learning in High Energy Physics

  ORAL

  March 7, 2024, 1:54 PM – March 7, 2024, 2:06 PM

  Publication: https://arxiv.org/abs/2210.10787
  https://arxiv.org/abs/2308.05657
  https://arxiv.org/abs/2303.11346

  Presenters

  • Matteo Robbiati

    CERN

  Authors

  • Matteo Robbiati

    CERN

  View abstract →

• Quantum Inception Score as an Expressivity Measure of the Quantum Generative Models

  ORAL

  March 7, 2024, 2:06 PM – March 7, 2024, 2:18 PM

  Presenters

  • Akira Sone

    University of Massachusetts Boston

  Authors

  • Akira Sone

    University of Massachusetts Boston

  • Naoki Yamamoto

    Keio University

  View abstract →

• Problem-informed Graphical Quantum Generative Learning

  ORAL

  March 7, 2024, 2:18 PM – March 7, 2024, 2:30 PM

  Presenters

  • Bence Bakó

    Wigner Research Center for Physics

  Authors

  • Bence Bakó

    Wigner Research Center for Physics

  • Zsófia Kallus

    Ericsson Research

  • Zoltan Zimboras

    Wigner Research Center for Physics

  View abstract →

• Generative quantum machine learning via denoising diffusion probabilistic models

  ORAL

  March 7, 2024, 2:30 PM – March 7, 2024, 2:42 PM

  Publication: arXiv: 2310.05866

  Presenters

  • Peng Xu

    University of Illinois at Urbana-Champaign

  Authors

  • Bingzhi Zhang

    University of Southern California

  • Peng Xu

    University of Illinois at Urbana-Champaign

  • Xiaohui Chen

    University of Southern California

  • Quntao Zhuang

    University of Southern California

  View abstract →

• Reinforcement Learning-Assisted Shot Assignment for Improved Convergence in Variational Quantum Eigensolver

  ORAL

  March 7, 2024, 2:42 PM – March 7, 2024, 2:54 PM

  Publication: Zhu, Linghua, et al. arXiv:2307.06504 (2023).

  Presenters

  • Linghua Zhu

    University of Washington

  Authors

  • Linghua Zhu

    University of Washington

  • Senwei Liang

    Lawrence Berkeley National Laboratory

  • Chao Yang

    Lawrence Berkeley Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley national lab

  • Xiaosong Li

    University of Washington

  View abstract →

• Optimizing ZX-Diagrams with Deep Reinforcement Learning

  ORAL

  March 7, 2024, 2:54 PM – March 7, 2024, 3:06 PM

  Presenters

  • Maximilian Nägele

    Max Planck Institute for Science of Light

  Authors

  • Maximilian Nägele

    Max Planck Institute for Science of Light

  • Florian Marquardt

    Friedrich-Alexander University Erlangen, Max Planck Institute for the Science of Light, Friedrich-Alexander University Erlangen-, Max Planck Institute for the Science of Light

  View abstract →