Quantum Machine Learning Training and Beyond

Presentations

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

  ORAL · Invited

  March 7, 2024, 11:30 AM – March 7, 2024, 12:06 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, 12:06 PM – March 7, 2024, 12:18 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, 12:18 PM – March 7, 2024, 12:30 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, 12:30 PM – March 7, 2024, 12:42 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, 12:42 PM – March 7, 2024, 12:54 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

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• Full-stack Quantum Machine Learning in High Energy Physics

  ORAL

  March 7, 2024, 12:54 PM – March 7, 2024, 1:06 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

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

  ORAL

  March 7, 2024, 1:06 PM – March 7, 2024, 1:18 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, 1:18 PM – March 7, 2024, 1:30 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, 1:30 PM – March 7, 2024, 1:42 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, 1:42 PM – March 7, 2024, 1:54 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

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• Optimizing ZX-Diagrams with Deep Reinforcement Learning

  ORAL

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