Quantum Neural Networks

FOCUS · K73 · ID: 1067462

Presentations

  • Learning in Finitely-Sampled Quantum Systems 1: Expressive Capacity

    ORAL

    –

    Presenters

    • Fangjun Hu

      Princeton University

    Authors

    • Fangjun Hu

      Princeton University

    • Gerasimos M Angelatos

      BBN Technology - Massachusetts, Princeton University

    • Saeed A Khan

      Princeton University

    • Marti Vives

      Q-CTL, Q-CTRL

    • Esin Tureci

      Princeton University

    • Leon Y Bello

      Princeton, Princeton University

    • Graham E Rowlands

      BBN Technology - Massachusetts, Raytheon BBN Technologies

    • Guilhem J Ribeill

      Raytheon BBN, Raytheon BBN Technologies

    • Hakan E Tureci

      Princeton University

    View abstract →

  • Learning in Finitely-Sampled Quantum Systems 2: Applications

    ORAL

    –

    Presenters

    • Gerasimos M Angelatos

      BBN Technology - Massachusetts, Princeton University

    Authors

    • Gerasimos M Angelatos

      BBN Technology - Massachusetts, Princeton University

    • Fangjun Hu

      Princeton University

    • Saeed A Khan

      Princeton University

    • Marti Vives

      Q-CTL, Q-CTRL

    • Esin Tureci

      Princeton University

    • Leon Y Bello

      Princeton, Princeton University

    • Graham E Rowlands

      BBN Technology - Massachusetts, Raytheon BBN Technologies

    • Guilhem J Ribeill

      Raytheon BBN, Raytheon BBN Technologies

    • Hakan E Tureci

      Princeton University

    View abstract →

  • Quantum Persistent Homology for Time Series

    ORAL

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    Publication: Ameneyro, B., Siopsis, G., and Maroulas, V.. Quantum Persistent Homology for Time Series. ACM/IEEE International Workshop on Quantum Computing 2022.

    Presenters

    • Bernardo Ameneyro

      University of Tennessee, Knoxville

    Authors

    • Bernardo Ameneyro

      University of Tennessee, Knoxville

    • George Siopsis

      University of Tennessee

    • Vasileios Maroulas

      University of Tennessee

    View abstract →

  • On training variational quantum circuits

    ORAL

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    Publication: Quantum machine learning
    J Biamonte, P Wittek, N Pancotti, P Rebentrost, N Wiebe, and S Lloyd
    Nature 549, 195–202 (2017) 10.1038/nature23474

    Ion-native variational ansatz for quantum approximate optimization
    D Rabinovich, S Adhikary, E Campos, V Akshay, E Anikin, R Sengupta, O Lakhmanskaya, K Lakhmanskiy, and J Biamonte
    Physical Review A 106, 032418 (2022) 10.1103/PhysRevA.106.032418

    Progress towards analytically optimal angles in quantum approximate optimisation
    D Rabinovich, R Sengupta, E Campos, V Akshay, and J Biamonte
    Mathematics 10, 2601 (2022) 10.3390/math10152601

    Reachability deficits implicit in quantum approximate optimization of graph problems
    V Akshay, H Philathong, I Zacharov, and J Biamonte
    Quantum 5, 532 (2021) 10.22331/q-2021-08-30-532

    Parameter concentrations in quantum approximate optimization
    V Akshay, D Rabinovich, E Campos, and J Biamonte
    (Letter) Physical Review A 104, L010401 (2021) 10.1103/PhysRevA.104.L010401

    Universal variational quantum computation
    J Biamonte
    (Letter) Physical Review A 103, L030401 (2021) 10.1103/PhysRevA.103.L030401

    Quantum machine learning tensor network states
    A Kardashin, A Uvarov, and J Biamonte
    Frontiers in Physics 8, 586374 (2021) 10.3389/fphy.2020.586374

    Variational simulation of Schwinger's Hamiltonian with polarization qubits
    O Borzenkova, G Struchalin, A Kardashin, V Krasnikov, N Skryabin, S Straupe, S Kulik, and J Biamonte
    Applied Physics Letters 118, 144002 (2021) 10.1063/5.0043322

    Abrupt transitions in variational quantum circuit training
    E Campos, A Nasrallah, and J Biamonte
    Physical Review A 103, 032607 (2021) 10.1103/PhysRevA.103.032607

    Training saturation in layerwise quantum approximate optimisation
    E Campos, D Rabinovich, V Akshay, and J Biamonte
    (Letter) Physical Review A 104, L030401 (2021) 10.1103/PhysRevA.104.L030401

    On barren plateaus and cost function locality in variational quantum algorithms
    A Uvarov and J Biamonte
    Journal of Physics A: Mathematical and Theoretical 54, 245–301 (2021) 10.1088/1751-8121/abfac7

    Reachability deficits in quantum approximate optimization
    V Akshay, H Philathong, M Morales, and J Biamonte
    Physical Review Letters 124, 090504 (2020) 10.1103/PhysRevLett.124.090504

    On the universality of the quantum approximate optimization algorithm
    M Morales, J Biamonte, and Z Zimborás
    Quantum Information Processing 19, 291 (2020) 10.1007/s11128-020-02748-9

    Variational quantum eigensolver for frustrated quantum systems
    A Uvarov, J Biamonte, and D Yudin
    Physical Review B 102, 075104 (2020) 10.1103/PhysRevB.102.075104

    Machine learning phase transitions with a quantum processor
    A Uvarov, A Kardashin, and J Biamonte
    Physical Review A 102, 012415 (2020) 10.1103/PhysRevA.102.012415

    Variational learning of Grover's quantum search algorithm
    M Morales, T Tlyachev, and J Biamonte
    Physical Review A 98, 062333 (2018) 10.1103/PhysRevA.98.062333

    Presenters

    • Jacob Biamonte

      Beijing Institute of Mathematical Sciences and Applications

    Authors

    • Jacob Biamonte

      Beijing Institute of Mathematical Sciences and Applications

    View abstract →

  • Novel Data Encoding Method for Quantum Machine Learning

    ORAL

    –

    Presenters

    • Kaiwen Gui

      University of Chicago

    Authors

    • Kaiwen Gui

      University of Chicago

    • Alexander M Dalzell

      AWS Center for Quantum Computing

    • Alessandro Achille

      AWS AI Labs

    • Martin Suchara

      Amazon Web Services, Amazon Web Service

    • Frederic T Chong

      University of Chicago, Department of Computer Science, University of Chicago, ColdQuanta Inc.

    View abstract →

  • Signatures of double descent in deep quantum models

    ORAL

    –

    Presenters

    • Aroosa Ijaz

      Univeristy of Waterloo

    Authors

    • Aroosa Ijaz

      Univeristy of Waterloo

    • Jason W Rocks

      Boston University

    • Juan Carrasquilla

      Vector Institute for Artificial Intelligence

    • Evan Peters

      University of Waterloo

    • Marco Cerezo

      Los Alamos National Laboratory

    View abstract →

  • Quantifying Information Flow in Parametrized Quantum Circuits.

    ORAL

    –

    Publication: Information flow in parameterized quantum circuits (arXiv:2207.05149)

    Presenters

    • Lasse B Kristensen

      University of Toronto

    Authors

    • Lasse B Kristensen

      University of Toronto

    • Abhinav Anand

      University of Toronto

    • Felix Frohnert

      University of Copenhagen

    • Sukin Sim

      Harvard, Zapata Computing

    • Alán Aspuru-Guzik

      University of Toronto, University of Toronto, Vector Institute for Artificial Intelligence, Canadian Institute for Advanced Research Lebovic Fellow

    View abstract →

  • Demystifying a generative Quantum Machine Learning model using Information scrambling and Imaginary components of out-of-time correlators.

    ORAL

    –

    Publication: Imaginary components of out-of-time correlators and information scrambling for navigating the learning landscape of a quantum machine learning model. arXiv:2208.13384 [quant-ph]

    Presenters

    • Vinit K Singh

      Purdue University

    Authors

    • Manas Sajjan

      Purdue University

    • Vinit K Singh

      Purdue University

    • Sabre Kais

      Purdue University

    View abstract →

  • Evaluating the performance of sigmoid quantum perceptrons in quantum neural networks

    ORAL

    –

    Publication: arXiv:2208.06198

    Presenters

    • Samuel A Wilkinson

      Friedrich-Alexander University Erlangen-Nuremberg

    Authors

    • Samuel A Wilkinson

      Friedrich-Alexander University Erlangen-Nuremberg

    • Michael J Hartmann

      Friedrich-Alexander University Erlangen-Nuremberg, Friedrich-Alexander University Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg

    View abstract →