Detecting and tracking drift in quantum information processors

Journal Article · 26 October 2020 · Nature Communications

DOI: https://doi.org/10.1038/s41467-020-19074-4 · OSTI ID:1686237

Proctor, Timothy ; Revelle, Melissa; Nielsen, Erik; Rudinger, Kenneth; Lobser, Daniel; Maunz, Peter; Blume-Kohout, Robin; Young, Kevin

Abstract If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA
• Sponsoring Organization: USDOE; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1686237
• Report Number(s): SAND--2020-12460J; 5396; PII: 19074
• Journal Information: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 11; ISSN 2041-1723
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English