Regime-Aware Adaptive Quantum Error Mitigation for NISQ Devices via Machine Learning

Quantum computing on Noisy Intermediate-ScaleQuantum (NISQ) devices is fundamentally limited by noise, ne-cessitating effective error mitigation techniques. Existing methodssuch as Zero Noise Extrapolation (ZNE) including linear andRichardson extrapolation variants exhibit varying performanceacross circuits and noise regimes, with no single method univer-sally optimal. In this work, we propose a regime-aware adaptiveframework that leverages machine learning to dynamically selectthe most effective mitigation strategy for a given circuit and noisecondition. Two XGBoost regressors independently predict theexpected improvement of Linear ZNE and Richardson ZNE, andthe method with the higher predicted score is selected at runtime.The selector achieves 64.3% exact accuracy, representing an 11.9percentage point improvement over the strongest fixed-methodbaseline. The adaptive selector outperforms Richardson ZNEon all four benchmark circuits and matches or exceeds bothbaselines on structured, deeper circuits. These results highlightthe importance of data-driven, regime-aware mitigation selectionfor improving the reliability of near-term quantum computations.