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.
Benjamin, and Ying Li
4 Pith papers cite this work. Polarity classification is still indexing.
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quant-ph 4years
2026 4verdicts
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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.
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
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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Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.
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When Noisy Quantum Order Finding Remains Recoverable for Shor's Algorithm
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.