Adaptive mitigation of time-varying quantum noise

Understand

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.