Adaptive mitigation of time-varying quantum noise

TL;DR

This paper introduces a Bayesian inference-based adaptive method to learn and mitigate non-stationary quantum noise, significantly improving the accuracy of quantum computations under time-varying error conditions.

Contribution

It presents a novel adaptive algorithm using Bayesian inference and Dirichlet distribution to dynamically mitigate quantum noise, enhancing probabilistic error cancellation performance.

Findings

Bayesian PEC outperforms non-adaptive methods by 4.5x in Hellinger distance.

Dynamic noise characterization improves quantum program accuracy.

The approach effectively mitigates temporal variations in quantum noise.

Abstract

Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference-based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions. Our study emphasizes the need for dynamic inference of critical channel parameters to improve program accuracy. We use the Dirichlet distribution to model the stochasticity of the Pauli channel. This allows us to perform Bayesian inference, which can improve the performance of probabilistic error cancellation (PEC) under time-varying noise. Our work demonstrates the importance of characterizing and mitigating temporal variations in quantum noise, which is crucial for developing more accurate and reliable quantum technologies. Our results show that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x when measured using Hellinger distance from the ideal distribution.