Improving probabilistic error cancellation in the presence of non-stationary noise

• URL: http://arxiv.org/abs/2404.13269v2
• Date: Tue, 9 Jul 2024 16:37:45 GMT
• Title: Improving probabilistic error cancellation in the presence of non-stationary noise
• Authors: Samudra Dasgupta, Travis S. Humble,
• Abstract summary: We design a strategy to enhance PEC stability and accuracy in the presence of non-stationary noise. 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.
• Score: 0.1227734309612871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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