Quantum Information and Learning

Presentations

• Abstract Withdrawn

  ORAL · Withdrawn

  March 17, 2025, 8:00 AM – March 17, 2025, 8:12 AM

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• Abstract Withdrawn

  ORAL · Withdrawn

  March 17, 2025, 8:12 AM – March 17, 2025, 8:24 AM

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• Investigation of simple quantum reservoir computers

  ORAL

  March 17, 2025, 8:24 AM – March 17, 2025, 8:36 AM

  Presenters

  • Viola Ni

    • Claremont McKenna College

  Authors

  • Sarah Marzen

    • Scripps, Pitzer & Claremont McKenna College

  • Kevin Setter

    • Scripps and Pitzer College

  • Viola Ni

    • Claremont McKenna College

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• Abstract Withdrawn

  ORAL · Withdrawn

  March 17, 2025, 8:36 AM – March 17, 2025, 8:48 AM

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• Abstract Withdrawn

  ORAL · Withdrawn

  March 17, 2025, 8:48 AM – March 17, 2025, 9:00 AM

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• Learning quantum properties from short-range correlations using multi-task networks

  ORAL

  March 17, 2025, 9:00 AM – March 17, 2025, 9:12 AM

  Publication: arXiv:2310.11807

  Presenters

  • Ya-Dong Wu

    • Shanghai Jiao Tong University
    • Shanghai Jiao Tong Univ

  Authors

  • Ya-Dong Wu

    • Shanghai Jiao Tong University
    • Shanghai Jiao Tong Univ

  • Yan Zhu

    • The University of Hong Kong

  • Yuexuan Wang

    • The University of Hong Kong

  • Giulio Chiribella

    • The University of Hong Kong

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• Using variance of gradients to explain quantum phase recognition algorithms

  ORAL

  March 17, 2025, 9:12 AM – March 17, 2025, 9:24 AM

  Presenters

  • Nathan A McMahon

    • Leiden University

  Authors

  • Nathan A McMahon

    • Leiden University

  • Michael Josef Hartmann

    • Friedrich-Alexander University Erlangen-Nuremberg

  • Petr Zapletal

    • University of Basel

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• Skin effect in quantum neural networks

  ORAL

  March 17, 2025, 9:24 AM – March 17, 2025, 9:36 AM

  Publication: Skin effect in quantum neural networks. arXiv https://arxiv.org/abs/2406.14112 (2024)

  Presenters

  • Antonio Sannia

    • IFISC (CSIC-UIB)

  Authors

  • Antonio Sannia

    • IFISC (CSIC-UIB)

  • Gian Luca Giorgi

    • Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC
    • IFISC (CSIC-UIB)
    • Institute for Cross-Disciplinary Physics and Complex Systems

  • Stefano Longhi

    • Politecnico di Milano

  • Roberta Zambrini

    • Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC
    • IFISC (CSIC-UIB)

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• Entanglement-induced provable and robust quantum learning advantages

  ORAL

  March 17, 2025, 9:36 AM – March 17, 2025, 9:48 AM

  Publication: H. Zhao and D.-L. Deng, "Entanglement-induced provable and robust quantum learning advantages", (2024), arXiv:2410.03094.

  Presenters

  • Haimeng Zhao

    • Caltech

  Authors

  • Haimeng Zhao

    • Caltech

  • Dong-Ling Deng

    • Tsinghua University

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• Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms

  ORAL

  March 17, 2025, 9:48 AM – March 17, 2025, 10:00 AM

  Publication: M. Wanner, L. Lewis, C. Bhattacharyya, D. Dubhashi, A. Gheorghiu. "Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms." Accepted to the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024).

  Presenters

  • Laura Lewis

    • University of Edinburgh

  Authors

  • Laura Lewis

    • University of Edinburgh

  • Marc Wanner

    • Chalmers University of Technology

  • Chiranjib Bhattacharyya

    • Indian Institute of Science

  • Devdatt Dubhashi

    • Chalmers University of Technology

  • Alexandru Gheorghiu

    • Chalmers University of Technology

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• Characterization of entanglement using a quantum generative adversarial network

  ORAL

  March 17, 2025, 10:00 AM – March 17, 2025, 10:12 AM

  Publication: Quantum Generative Adversarial Networks: Generating and Detecting Quantum Product States
  JE Steck, EC Behrman
  arXiv preprint arXiv:2408.12620

  Presenters

  • Elizabeth C Behrman

    • Wichita State University

  Authors

  • Elizabeth C Behrman

    • Wichita State University

  • James E Steck

    • Wichita State University

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• Could we exploit quantum Boltzmann machines for physics-constrained nonlinear regression?

  ORAL

  March 17, 2025, 10:12 AM – March 17, 2025, 10:24 AM

  Presenters

  • David D.W. Ren

    • Quantum Open Source Foundation

  Authors

  • David D.W. Ren

    • Quantum Open Source Foundation

  • Wael Itani

    • New York University (NYU)

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• Learning from quantum states: thermal states, ground states, and novel architectures

  ORAL

  March 17, 2025, 10:24 AM – March 17, 2025, 10:36 AM

  Publication: Two preprints are in preparation and will be ready by the end of the year. Work started from https://doi.org/10.1063/5.0209201

  Presenters

  • Stefano Scali

    • University of Exeter

  Authors

  • Stefano Scali

    • University of Exeter

  • Oleksandr Kyriienko

    • University of Exeter

  • Chukwudubem Umeano

    • University of Exeter

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• Tackling Quantum Sampling Noise in Quantum Machine Learning on Large-scale Quantum Devices

  ORAL

  March 17, 2025, 10:36 AM – March 17, 2025, 10:48 AM

  Publication: [1] F. Hu, G. Angelatos, S. A. Khan, et al., Phys. Rev. X 13, 041020 (2023)
  [2] F. Hu, S. A. Khan, et al., Nat. Commun. 15, 7491 (2024)

  Presenters

  • Fangjun Hu

    • Princeton University

  Authors

  • Fangjun Hu

    • Princeton University

  • Marti Vives

    • Princeton University

  • Theodoros Ilias

    • Princeton University

  • Hakan E Tureci

    • Princeton University

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• Learning optical states with quantum computers

  ORAL

  March 17, 2025, 10:48 AM – March 17, 2025, 11:00 AM

  Publication: John Crossman et al 2024 Quantum Sci. Technol. 9 045005

  Presenters

  • Spencer Douglas Dimitroff

    • Sandia National Laboratories & University of New Mexico

  Authors

  • Spencer Douglas Dimitroff

    • Sandia National Laboratories & University of New Mexico

  • Ashe N Miller

    • Sandia National Laboratories

  • John M Kallaugher

    • Sandia National Laboratories

  • Steve M Young

    • Sandia National Laboratories

  • Mohan Sarovar

    • Sandia National Laboratories

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