Temperature and Magnetic-Field Dependence of Energy Relaxation in a Fluxonium Qubit

ORAL

Abstract

Noise from material defects at device interfaces is known to limit the coherence of superconducting circuits, yet our understanding of the defect origins and noise mechanisms remains incomplete. The fluxonium qubit, which can be made with transition frequencies as low as several MHz and widely tunable matrix elements, serves as a natural probe of charge- and flux-coupled low-frequency noise. Here we investigate energy relaxation in a low-frequency fluxonium qubit (approximately 50 MHz), whose sensitivity to flux noise and charge noise arising from dielectric loss can be tuned by applied flux [1]. We explore the dependence of T1 on temperature and in-plane magnetic field and offer a comparison of flux-coupled loss models near half-flux. We implement a multi-level decoherence model in our analysis, motivated by the widely tunable matrix elements and transition energies approaching the thermal energy scale in our system. Our results aim to offer insight for fluxonium coherence modeling and inform microscopic theories of intrinsic noise in superconducting circuits.

[1] Ateshian, Lamia, et al., arXiv:2507.01175 (2025).

*This material is based upon work supported in part by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704, and in part by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. L.A. acknowledges support by the NSF Graduate Research Fellowship. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.

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Publication: Ateshian, Lamia, et al., arXiv:2507.01175 (2025).

Presenters

  • Lamia Ateshian

    • Google Quantum AI

Authors

  • Lamia Ateshian

    • Google Quantum AI

  • Kate Azar

    • Massachusetts Institute of Technology
    • MIT

  • Max Hays

    • Massachusetts Institute of Technology

  • David A Rower

    • MIT, Department of Physics
    • MIT, Department of Physics, Google Quantum AI

  • Helin Zhang

    • Massachusetts Institute of Technology

  • Réouven Assouly

    • Massachussets Institute of Technology

  • Leon Ding

    • Atlantic Quantum

  • Michael A Gingras

    • MIT Lincoln Laboratory

  • Hannah M Stickler

    • MIT Lincoln Laboratory

  • Bethany M Niedzielski

    • MIT Lincoln Laboratory

  • Mollie E. Schwartz

    • MIT Lincoln Laboratory

  • Terry Philip Orlando

    • Massachusetts Institute of Technology

  • Joel I-Jan Wang

    • Massachusetts Institute of Technology

  • Simon Gustavsson

    • Atlantic Quantum

  • Jeffrey A Grover

    • Massachusetts Institute of Technology

  • Kyle Serniak

    • MIT Lincoln Laboratory

  • William D Oliver

    • Massachusetts Institute of Technology