Orders of magnitude runtime reduction in quantum error mitigation

Quantum error mitigation (QEM) infers noiseless expectation values by combining outcomes from intentionally modified, noisy variants of a target quantum circuit. Unlike quantum error correction, QEM requires no additional hardware resources and is therefore routinely employed in experiments on contemporary quantum processors. A central limitation of QEM is its substantial sampling overhead, which necessitates long execution times where device noise may drift, potentially compromising the reliability of standard mitigation protocols. QEM strategies based on agnostic noise amplification (ANA) are intrinsically resilient to such noise variations, but their sampling cost remains a major practical bottleneck. Here we introduce a mitigation framework that combines virtual noise scaling with a layered mitigation architecture, yielding orders of magnitude reduction in runtime overhead compared to conventional zero-noise extrapolation post-processing. The proposed approach is compatible with dynamic circuits and can be seamlessly integrated with error detection and quantum error correction schemes. In addition, it naturally extends to ANA-based mitigation of mid-circuit measurements and preparation errors. We validate our post-processing approach by applying it to previously reported experimental data, where we observe a substantial improvement in mitigation efficiency and accuracy.