Detecting and tracking drift in quantum information processors

TL;DR

This paper introduces a method to detect and track time-dependent errors in quantum processors, improving their reliability and performance.

Contribution

A new spectral analysis technique is introduced for resolving time dependence in quantum processors.

Findings

The method is fast, simple, and statistically sound for analyzing time-series data from quantum processors.

It successfully detects and localizes instability in trapped-ion qubit experiments.

Drift control techniques were implemented to suppress detected instability.

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.

Time-dependent errors are one of the main obstacles to fully-fledged quantum information processing. Here, the authors develop a general methodology to monitor time-dependent errors, which could be used to make other characterisation protocols time-resolved, and demonstrate it on a trapped-ion qubit.

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