Model validation and error attribution for a drifting qubit

TL;DR

This paper presents a method to validate noise models for drifting qubits by analyzing the distribution of randomized benchmarking error rates over time, highlighting the importance of accounting for low-frequency noise in qubit performance metrics.

Contribution

It introduces a statistical approach using the Kolmogorov-Smirnov test to validate noise models based on error rate distributions over time in randomized benchmarking.

Findings

Kolmogorov-Smirnov test can effectively validate noise models.

Error rate distributions reveal low-frequency noise effects.

Caution is needed when attributing errors based on drifting error rates.

Abstract

Qubit performance is often reported in terms of a variety of single-value metrics, each providing a facet of the underlying noise mechanism limiting performance. However, the value of these metrics may drift over long time-scales, and reporting a single number for qubit performance fails to account for the low-frequency noise processes that give rise to this drift. In this work, we demonstrate how we can use the distribution of these values to validate or invalidate candidate noise models. We focus on the case of randomized benchmarking (RB), where typically a single error rate is reported but this error rate can drift over time when multiple passes of RB are performed. We show that using a statistical test as simple as the Kolmogorov-Smirnov statistic on the distribution of RB error rates can be used to rule out noise models, assuming the experiment is performed over a long enough time interval to capture relevant low frequency noise. With confidence in a noise model, we show how care must be exercised when performing error attribution using the distribution of drifting RB error rate.