Detecting and tracking drift in quantum information processors

Timothy Proctor (), Melissa Revelle, Erik Nielsen, Kenneth Rudinger, Daniel Lobser, Peter Maunz, Robin Blume-Kohout and Kevin Young
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Timothy Proctor: Quantum Performance Laboratory, Sandia National Laboratories
Melissa Revelle: Sandia National Laboratories
Erik Nielsen: Quantum Performance Laboratory, Sandia National Laboratories
Kenneth Rudinger: Quantum Performance Laboratory, Sandia National Laboratories
Daniel Lobser: Sandia National Laboratories
Peter Maunz: Sandia National Laboratories
Robin Blume-Kohout: Quantum Performance Laboratory, Sandia National Laboratories
Kevin Young: Quantum Performance Laboratory, Sandia National Laboratories

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-19074-4

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