Rapid tune-up of quantum gates using dynamical decoupling

ORAL

Abstract

Coherent errors in quantum gate operations build up quadratically faster than incoherent errors, severely limiting algorithmic fidelity. These errors result from miscalibrations and nonidealities of the drive Hamiltonian used to implement the gate. Likewise, incoherent errors can manifest very differently from the common model of a uniform depolarizing channel. Full analysis of coherent and incoherent errors typically requires measurements that are both resource-intensive and difficult to analyze, making it difficult to rapidly calibrate gates. In this talk we present Deterministic Benchmarking, a protocol to rapidly characterize single-qubit gate errors by utilizing simple dynamical decoupling sequences. This protocol rapidly measures rotation, phase, and leakage error, and simultaneously characterizes incoherent errors from dephasing and finite-temperature relaxation under the relevant conditions. Using just 4 measurements, all fit by a single functional form, we can extract all parameters required to reconstruct the evolution of the system. We show that the dynamics of the system can be modeled theoretically using a simple Lindbladian master equation using these parameters, including the effects of finite-temperature decoherence and performance under various dynamical decoupling sequences.

*Devices were fabricated and provided by the Superconducting Qubits at Lincoln Laboratory (SQUILL) Foundry at MIT Lincoln Laboratory, with funding from the Laboratory for Physical Sciences (LPS) Qubit Collaboratory. This research was supported by the NSF, the Quantum Leap Big Idea under Grant No. OMA-1936388, the MURI Grant No. W911NF-22-S-0007, and the IARPA under Cooperative Agreement No. W911NF-23-2-0216.

Publication: https://arxiv.org/abs/2407.09942

Presenters

  • Daria Kowsari

    • University of Southern California

Authors

  • Daria Kowsari

    • University of Southern California
  • Vinay Tripathi

    • Univ of Southern California
  • Kumar Saurav

    • University of Southern California
  • Haimeng Zhang

    • University of Southern California
  • Eli M Levenson-Falk

    • University of Southern California
  • Daniel A Lidar

    • University of Southern California