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.
Exponentially tighter bounds on limitations of quantum error mitigation
4 Pith papers cite this work, alongside 22 external citations. Polarity classification is still indexing.
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Derives MSE bounds for PEC and CDR under finite shots, revealing CDR-dominant windows scaling as 1/(δ₁²p) and a projection theorem for affine CDR bias removal.
Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
citing papers explorer
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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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Finite-shot operating windows for probabilistic error cancellation and Clifford data regression
Derives MSE bounds for PEC and CDR under finite shots, revealing CDR-dominant windows scaling as 1/(δ₁²p) and a projection theorem for affine CDR bias removal.
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Certified Finite-Shot Operating Windows for Virtual Distillation and Symmetry Verification
Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
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Sampling (noisy) quantum circuits through randomized rounding
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].