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Quantum Machine Learning for Applications

ORAL · MAR-B34 · ID: 3108334

Presentations

  • Uncovering Quantum Many-body Scars with Quantum Machine Learning

    ORAL

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    Publication: arXiv:2409.07405

    Presenters

    • Jiajin Feng

      University of Southern California

    Authors

    • Jiajin Feng

      University of Southern California

    • Bingzhi Zhang

      University of Southern California

    • Quntao Zhuang

      University of Southern California

    • Zhicheng Yang

      Peking University

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  • Quantum Machine Learning for Multi-Asset Price Prediction

    ORAL

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    Presenters

    • Hannes Leipold

      Fujitsu Research of America

    Authors

    • Sharan Mourya Bathala

      University of Illinois Urbana Champaign

    • Hannes Leipold

      Fujitsu Research of America

    • Bibhas Adhikari

      Fujitsu Research of America, Inc, Fujitsu Research of America

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  • Exploring Synthesis–Structure Relationships in Epitaxially–Grown Semiconductors with Quantum and Classical Learning Algorithms

    ORAL

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    Publication: Messecar, A. S., Durbin, S. M., & Makin, R. A. (under review). Machine Learning Based Investigation of Optimal Synthesis Parameters for Epitaxially Grown III–Nitride Semiconductors. Materials Science in Semiconductor Processing.<br><br>Messecar, Andrew S.; Durbin, Steven M.; and Makin, Robert A., "Quantum & Classical Machine Learning Studies of Semiconductor Crystal Epitaxy" (2024). Waldo Library Student Exhibits. 13. https://scholarworks.wmich.edu/student_exhibits/13<br><br>Messecar, A.S., Durbin, S.M. & Makin, R.A. Quantum and classical machine learning investigation of synthesis–structure relationships in epitaxially grown wide band gap semiconductors. MRS Communications 14, 660–666 (2024). https://doi.org/10.1557/s43579-024-00590-z<br><br>Messecar, A. S., Durbin, S. M., & Makin, R. A. (2024, March 22), Quantum and Classical Supervised Learning Study of Epitaxially–Grown ZnO Surface Morphology. Paper presented at 2024 ASEE North Central Section Conference, Kalamazoo, Michigan. https://peer.asee.org/45633

    Presenters

    • Andrew S Messecar

      College of Engineering and Applied Sciences, Western Michigan University, Kalamazoo, MI 49008 USA

    Authors

    • Andrew S Messecar

      College of Engineering and Applied Sciences, Western Michigan University, Kalamazoo, MI 49008 USA

    • Steven M Durbin

      College of Engineering, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA

    • Robert A Makin

      College of Engineering and Applied Sciences, Western Michigan University, Kalamazoo, MI 49008, USA

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  • Evaluation and optimization of neurological data sets in quantum feature maps and comparison with conventional classical methods

    ORAL

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    Presenters

    • David Castillo Salazar

      Centro de Investigación de Ciencias Humanas y de la Educación (CICHE), Universidad Tecnológica Indoamérica, 2035 Bolívar Street, Ambato, 180103, Tungurahua

    Authors

    • Saravana Prakash Thirumuruganandham

      SMARTCO, SMARTCO, Director of Software Development, Catalina Aldaz N34-131 y Portugal, Edificio La Suiza 6to Piso, Quito codigo postal-170504

    • David Castillo Salazar

      Centro de Investigación de Ciencias Humanas y de la Educación (CICHE), Universidad Tecnológica Indoamérica, 2035 Bolívar Street, Ambato, 180103, Tungurahua

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  • Quantum generative machine learning for quantum state tomography

    ORAL

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    Presenters

    • Jinghong Yang

      University of Maryland, College Park

    Authors

    • Jinghong Yang

      University of Maryland, College Park

    • shabnam jabeen

      University of Maryland, College Park

    • Dmytro Kurdydyk

      Davidson college

    • Aadi Palnitkar

      University of Maryland, College Park

    • Mihir Talati

      University of Maryland, College Park

    • Sriman Selvakumaran

      University of Maryland, College Park

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  • Structured Models for Quantum Generative Learning

    ORAL

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    Presenters

    • Bence Bakó

      HUN-REN Wigner Research Centre for Physics

    Authors

    • Bence Bakó

      HUN-REN Wigner Research Centre for Physics

    • Zoltán Kolarovszki

      Wigner Research Centre for Physics

    • Zoltan Zimboras

      Wigner Research Center for Physics, Algorithmiq

    View abstract →