Hamiltonian Meta-Learning

ORAL

Abstract

Precise calibration of quantum devices is necessary for reliable quantum information processing. Full characterization and tuning a quantum system without making any assumption require resources that scale exponentially with the system size. Here, we assume a model for the noisy evolution of a quantum system, and by using a machine learning technique known as meta-learning to train an optimizer that finds model parameters with less resources than other gradient-based optimization algorithms. The training of our algorithm is done efficiently on smaller systems. However, the learned optimizer is transferable to larger and different systems.

–

Presenters

  • Przemyslaw Bienias

    • University Of Maryland, College Park
    • Joint Quantum Institute and Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park
    • University of Maryland, College Park
    • JQI/QuICS, NIST/University of Maryland, College Park
    • Physics, University of Maryland, College Park

Authors

  • Przemyslaw Bienias

    • University Of Maryland, College Park
    • Joint Quantum Institute and Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park
    • University of Maryland, College Park
    • JQI/QuICS, NIST/University of Maryland, College Park
    • Physics, University of Maryland, College Park

  • Alireza Seif

    • University of Maryland, College Park

  • Mohammad Hafezi

    • University of Maryland, College Park
    • Joint Quantum Institute
    • University of Maryland

  • Paraj Titum

    • APL
    • Applied Phys Lab/JHU
    • Johns Hopkins University Applied Physics Laboratory

  • Norbert M Linke

    • University of Maryland, College Park
    • Physics, University of Maryland

  • Jiehang Zhang

    • NYU