Improving Probabilistic Error Cancellation in the Presence of Non-stationary Noise

Samudra Dasgupta; Travis S. Humble

doi:10.1109/TQE.2024.3435757

Improving Probabilistic Error Cancellation in the Presence of Non-stationary Noise

Samudra Dasgupta, Travis S. Humble

Quantum Science Center

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1-15
Number of pages	15
Journal	IEEE Transactions on Quantum Engineering
DOIs	https://doi.org/10.1109/TQE.2024.3435757
State	Accepted/In press - 2024

Keywords

Accuracy
Bayesian inference
Logic gates
Noise
Noise measurement
non-stationary quantum channels
probabilistic error cancellation
Probabilistic logic
Quantum computing
Qubit
Stability

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10.1109/TQE.2024.3435757

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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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AB - 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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Improving Probabilistic Error Cancellation in the Presence of Non-stationary Noise

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