  We investigate the stability of probabilistic error cancellation (PEC)
outcomes in the presence of non-stationary noise, which is an obstacle to
achieving accurate observable estimates. Leveraging Bayesian methods, we design
a strategy to enhance PEC stability and accuracy. Our experiments using a
5-qubit implementation of the Bernstein-Vazirani algorithm and conducted on the
ibm_kolkata device reveal a 42% improvement in accuracy and a 60% enhancement
in stability compared to non-adaptive PEC. These results underscore the
importance of adaptive estimation processes to effectively address
non-stationary noise, vital for advancing PEC utility.
