Predicting the Temporal Variability of Error Rates in Superconducting Quantum Processors: Mitigating Error Accuracy Loss in Queue-Based Quantum Computing Platforms [Focus: Quantum Software and its Engineering]

Multiple layers of the quantum system full-stack require data on temporal variations in error rates of physical elements. This article proposes using five machine learning and time-series models to predict error rate variability for two-qubit gates based on calibration data.

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Bibliographic Details
Published inIEEE software Vol. 42; no. 5; pp. 90 - 97
Main Authors Rodriguez-Soriano, Laura, Garcia-Herrero, Francisco, Almudever, Carmen G.
Format Journal Article
LanguageEnglish
Published Los Alamitos IEEE 01.09.2025
IEEE Computer Society
Subjects
Accuracy
Calibration
Computational modeling
Error analysis
Errors
Integrated circuit modeling
Logic gates
Machine learning
Noise measurement
Predictive models
Program processors
Quantum computing
Quantum theory
Qubit
Qubits (quantum computing)
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Summary:Multiple layers of the quantum system full-stack require data on temporal variations in error rates of physical elements. This article proposes using five machine learning and time-series models to predict error rate variability for two-qubit gates based on calibration data.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0740-7459
1937-4194
DOI:10.1109/MS.2025.3572982