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This research introduces Classically Augmented Zero-Noise Extrapolation, a hybrid method designed to mitigate quantum errors by substituting high-noise extrapolation nodes with classically simulated estimates. The study demonstrates that this approach can achieve exponential variance reduction under optimal shot allocation while managing deterministic simulation bias through rigorous numerical validation on Pauli-propagation simulations.
Classically Augmented Zero-Noise Extrapolation
arXiv:2607.25746v1 Announce Type: new Abstract: We investigate a hybrid quantum-classical approach to quantum error mitigation. We propose Classically Augmented Zero-Noise Extrapolation, a hybrid error-mitigation method in which high-noise Richardson extrapolation nodes are replaced by classically simulated estimates. These classical nodes have negligible sampling variance but introduce deterministic simulation bias. We derive the resulting variance reduction under optimal shot allocation and show that, for linear node spacings and fixed index cutoff, the coefficient-level reduction can be exponential. We validate the prediction numerically using Pauli-propagation simulations and demonstrate a reduction in mean-squared error when the truncation bias is sufficiently small.
Abstract:We investigate a hybrid quantum-classical approach to quantum error mitigation. We propose Classically Augmented Zero-Noise Extrapolation, a hybrid error-mitigation method in which high-noise Richardson extrapolation nodes are replaced by classically simulated estimates. These classical nodes have negligible sampling variance but introduce deterministic simulation bias. We derive the resulting variance reduction under optimal shot allocation and show that, for linear node spacings and fixed index cutoff, the coefficient-level reduction can be exponential. We validate the prediction numerically using Pauli-propagation simulations and demonstrate a reduction in mean-squared error when the truncation bias is sufficiently small.
arXiv:2607.25746 [quant-ph]
(or arXiv:2607.25746v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.25746 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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