# Detecting and tracking drift in quantum information processors

## TL;DR

This paper introduces a spectral analysis method to detect and localize time-dependent errors in quantum information processors, improving stability and characterization accuracy.

## Contribution

The paper presents a fast, simple, and statistically sound spectral analysis technique applicable to various quantum characterization protocols to identify and mitigate drift.

## Key findings

- Successfully detected and localized drift in quantum processors.
- Implemented drift control techniques to suppress instability.
- Validated method on simulations and trapped-ion experiments.

## 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.