Hamiltonian Learning on Superconducting Qubits using Bayesian Inference

ORAL

Abstract

Bayesian inference uses Bayes’ theorem to update the probability of a hypothesis and as a result can be used to great effect when trying to learn the Hamiltonian of a quantum system. In comparison to traditional techniques for characterisation it has the benefit of providing statistically relevant information about the learning procedure, enabling more efficient data taking and revealing limits of the model provided to produce the data. It can be used to compare how well different models fit measured data and hence diagnose noise sources. We demonstrate this by applying it to a superconductor semiconductor 'gatemon' qubit and use it to learn the parameters of the Hamiltonian.

*We would like to acknowledge Microsoft Project Q, the U.S. Army Research Office, the Swiss National Science Foundation, the Danish National Research Foundation and NCCR QSIT.

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Presenters

  • Natalie Pearson

    • Department of Physics, ETH Zurich
    • Theoretical Physics, ETH Zurich
    • Theoretische Physik, ETH Zürich, Zürich, Switzerland

Authors

  • Lillian Austin

    • Center for Quantum Devices, Niels Bohr Institute, Copenhagen

  • Lucas Casparis

    • Microsoft
    • Niels Bohr Institute, Univ of Copenhagen
    • Niels Bohr Institute
    • Center for Quantum Devices and Microsoft Quantum Lab–Copenhagen, Niels Bohr Institute, University of Copenhagen, 2100 Copenhagen, Denmark
    • Microsoft Quantum Research, Copenhagen

  • Christopher Granade

    • Microsoft Corporation Redmond, WA
    • Microsoft Research, Redmond

  • Albert Hertel

    • Center for Quantum Devices, Station Q Copenhagen, Niels Bohr Institute, University of Copenhagen
    • Center for Quantum Devices, Niels Bohr Institute, Copenhagen

  • Natalie Pearson

    • Department of Physics, ETH Zurich
    • Theoretical Physics, ETH Zurich
    • Theoretische Physik, ETH Zürich, Zürich, Switzerland

  • Karl D Petersson

    • Niels Bohr Institute
    • Center for Quantum Devices, Station Q Copenhagen, Niels Bohr Institute, University of Copenhagen
    • Center for Quantum Devices and Microsoft Quantum Lab–Copenhagen, Niels Bohr Institute, University of Copenhagen, 2100 Copenhagen, Denmark
    • University of Copenhagen
    • Microsoft Corp
    • Microsoft Quantum Research, Copenhagen

  • Nathan Wiebe

    • Microsoft Corporation Redmond, WA
    • Microsoft Research, Redmond