Runtime-efficient zero-noise extrapolation from mixed physical and logical data

• URL: http://arxiv.org/abs/2604.15014v1
• Date: Thu, 16 Apr 2026 13:42:05 GMT
• Title: Runtime-efficient zero-noise extrapolation from mixed physical and logical data
• Abstract summary: Partial quantum error correction and quantum error mitigation are expected to coexist in the pre-fault-tolerant regime.<n>We study zero-noise extrapolation constructed from mixed datasets that contain a small number of error-corrected data points.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Partial quantum error correction and quantum error mitigation are expected to coexist in the pre-fault-tolerant regime, yet the resource advantage of combining them remains insufficiently quantified. We study zero-noise extrapolation constructed from mixed datasets that contain a small number of error-corrected data points together with data obtained without error correction. The low-noise logical points anchor the extrapolation, while the higher-noise physical points enlarge the noise baseline at a much smaller runtime cost. Under a simple model in which error correction suppresses the effective gate error rate from p to $γ$p, we derive the variance of the zero-noise estimator and compare the physical runtime required to reach a target precision. For Richardson extrapolation, the mixed-data strategy reduces variance amplification and can lower the required physical runtime by several orders of magnitude when $γ\leq 0.1$. As a proof of principle, we apply the method to digital quantum simulation of a six-spin transverse-field Ising model and find that mixed physical/logical datasets yield lower-variance zero-noise estimates and outperform extrapolation based only on error-corrected data in the parameter regime studied here. These results identify hybrid error correction and error mitigation as a practical route to resource-efficient quantum computation before full fault tolerance.

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