Characterization of adverse drive-induced transitions in superconducting circuits (part 2/2)

ORAL

Abstract

Readout and gate operations in qubits implemented in quantum superconducting circuits are performed by applying microwave drive tones to the circuit. The simultaneous pursuit of fidelity and speed of these operations by increasing drive strength is limited by unwanted drive-induced state transitions allowed by the nonlinearity.

We experimentally address the origin of these adverse state transitions in a driven transmon by measuring transition probabilities as a function of drive frequency and power. We show that there are three distinct mechanisms for adverse transitions caused by the drive: 1) excitation of intrinsic resonances between computational and non-computational states within the transmon spectrum, 2) drive-activated nonlinear processes involving extrinsic degrees of freedoms such as packaging or transmission line modes, 3) AC Stark shift of qubit frequency into resonance with lossy degrees of freedom in the qubit environment. Our findings provide insights for the improvement of readout and gate operations on superconducting qubits.

In part 2/2, we diagnose adverse drive-induced transitions in a 2D transmon in the regime when the drive frequency vastly exceeds that of the transmon.

*Work supported by:AFOSR, ARO, DARPA, DOE, YINQE

Presenters

  • Pavel Kurilovich

    • Yale University

Authors

  • Pavel Kurilovich

    • Yale University
  • Thomas Connolly

    • Yale University
  • Charlotte Bøttcher

    • Stanford University
    • Yale University
  • Vladislav Kurilovich

    • Google LLC
  • Daniel K Weiss

    • Yale University
  • Andy Z Ding

    • Yale University
  • Vidul R Joshi

    • Yale University
  • Heekun Nho

    • Yale University
  • Spencer Diamond

    • Yale University
  • Wei Dai

    • Yale University
  • Sumeru Hazra

    • Yale University
  • Valla Fatemi

    • Cornell University
  • Luigi Frunzio

    • Yale University
  • Leonid I Glazman

    • Yale University
  • Michel H. Devoret

    • Yale University
    • Google Quantum AI