Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
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Mixed physical-logical datasets for zero-noise extrapolation reduce estimator variance and physical runtime by orders of magnitude compared to pure logical or pure physical strategies when error correction suppresses noise by a factor of 0.1 or less.
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The finite-shot help-harm boundary of zero-noise extrapolation
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
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Runtime-efficient zero-noise extrapolation from mixed physical and logical data
Mixed physical-logical datasets for zero-noise extrapolation reduce estimator variance and physical runtime by orders of magnitude compared to pure logical or pure physical strategies when error correction suppresses noise by a factor of 0.1 or less.