Quantum Machine Learning I

FOCUS · E27

Presentations

  • Measurement-based adaptation protocol with quantum reinforcement learning

    ORAL

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    Presenters

    • Lucas Lamata

      University of the Basque Country, Department of Physical Chemistry, University of the Basque Country, Bilbao, Spain

    Authors

    • Lucas Lamata

      University of the Basque Country, Department of Physical Chemistry, University of the Basque Country, Bilbao, Spain

    • Francisco Albarrán-Arriagada

      Universidad de Santiago de Chile

    • Juan Carlos Retamal

      Universidad de Santiago de Chile

    • Enrique Solano

      University of the Basque Country, Department of Physical Chemistry, University of the Basque Country, Bilbao, Spain

    View abstract →

  • Quantum generative adversarial learning in a superconducting quantum circuit

    ORAL

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    Presenters

    • Yuwei Ma

      Tsinghua University

    Authors

    • Hu Ling

      Tsinghua University

    • shuhao wu

      USTC

    • Weizhou Cai

      Tsinghua University

    • Yuwei Ma

      Tsinghua University

    • Xianghao Mu

      Tsinghua University

    • Yuan Xu

      Tsinghua University

    • Haiyan Wang

      Tsinghua University

    • Yipu Song

      IIIS, Tsinghua University, Tsinghua University

    • Dong-Ling Deng

      Institute for Interdisciplinary Information Sciences, Tsinghua University, Tsinghua University, University of Maryland

    • Chang-Ling Zou

      University of Science and Technology of China, Key Laboratory of Quantum Information, University of Science and Technology of China, Yale University, USTC

    • Luyan Sun

      Tsinghua University

    View abstract →

  • Hybrid quantum-classical schemes for generative adversarial learning: HQGANs

    ORAL

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    Presenters

    • Jhonathan Romero

      Harvard University, Zapata Computing

    Authors

    • Jhonathan Romero

      Harvard University, Zapata Computing

    • Alan Aspuru-Guzik

      Zapata Computing, Chemistry and Computer Science, University of Toronto, University of Toronto, Department of Chemistry, and Computer Science, Department of Chemistry and Department of Computer Science, University of Toronto; Vector Institute for Artificial Intelligence, Toronto; Canadian Institute for Advanced Rese, University of Toronto

    View abstract →

  • Quantum Manifold Learning Algorithms for Dimensionality Reduction

    ORAL

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    Presenters

    • Xi He

      University of Electronic Science and Technology of China

    Authors

    • Xi He

      University of Electronic Science and Technology of China

    • Li Sun

      University of Electronic Science and Technology of China

    • Xiaokai Hou

      University of Electronic Science and Technology of China

    • Xiaoting Wang

      University of Electronic Science and Technology of China

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  • Differentiable Quantum Circuits and Generative Modeling

    ORAL

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    Presenters

    • JinGuo Liu

      Institute of Physics, Chinese Academy of Sciences

    Authors

    • JinGuo Liu

      Institute of Physics, Chinese Academy of Sciences

    • Lei Wang

      Institute of Physics, Institute of Physics, Chinese Academy of Sciences, Institute of Physics Chinese Academy of Sciences

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  • Machine-learned QCVV for distinguishing single-qubit noise

    ORAL

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    Presenters

    • Travis Scholten

      T. J. Watson Research Center, IBM

    Authors

    • Travis Scholten

      T. J. Watson Research Center, IBM

    • Yi-Kai Liu

      NIST

    • Kevin Young

      Sandia National Laboratories

    • Robin Blume-Kohout

      Center for Computing Research, Sandia National Laboratories, Sandia National Laboratories

    View abstract →

  • Quantum optical neural networks for next generation quantum information processing

    ORAL

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    Presenters

    • Jonathan Olson

      Zapata Computing

    Authors

    • Gregory R Steinbrecher

      Research Laboratory of Electronics, Massachusetts Institute of Technology

    • Jonathan Olson

      Zapata Computing

    • Dirk R. Englund

      Electrical Engineering and Computer Science, MIT, Massachusetts Institute of Technology, MIT, EECS, MIT, Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Research Laboratory of Electronics, Massachusetts Institute of Technology

    • Jacques Carolan

      Research Laboratory of Electronics, Massachusetts Institute of Technology

    View abstract →